Category Archives: R

Calculating simple portfolio statistics with R

02/26/2023Allgemein, Code, Data Analysis, Programming, R, StatisticsMartin Stoppacher

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# World Bank Data using WDI Package
# path: ~/ownCloud/
# file_name: 
# files_used: 
# files_created

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#

rm(list = ls(all = TRUE))

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# packages

#install.packages("httr")
#install.packages("XML")
#install.packages('WDI')
#install.packages("magrittr")
#install.packages("tidyverse")
#install.packages("quantmod")
#install.packages("PerformanceAnalytics")
#install.packages("tidyquant")

library("httr")
library("XML")
library("magrittr")
library("tidyverse")
library("WDI")
library("quantmod")
library("PerformanceAnalytics")
#library("tidyquant")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#

system("ls")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# using quantmod to collect data

# selecting the date while downloading

MCD <- quantmod::getSymbols("MCD",src = "yahoo", 
                            auto.assign=FALSE, 
                            from = "2007-06-01",
                            to = "2012-07-01" )

head(MCD)
tail(MCD)

# or download the available data and selecting later

MCD <- quantmod::getSymbols("MCD",src = "yahoo", 
                            auto.assign=FALSE )

head(MCD)
tail(MCD)

MCD <- MCD["2007-06-01/2012-07-01"]

head(MCD)
tail(MCD)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# download monthly data 

MCD <- quantmod::getSymbols("MCD",src = "yahoo", 
                            auto.assign=FALSE, 
                            from = "2007-06-01",
                            to = "2012-07-01",
                            periodicity = "monthly" )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# alternative ways

#stock_list <- c( "IBM", "MCD" )
#
#stocks_weekly <- tq_get(stock_list, 
#                        from = start_date, 
#                        to = end_date, 
#                        periodicity = "weekly")
#stocks_weekly 

#install.packages("BatchGetSymbols")
#library(BatchGetSymbols)

#stocks <- BatchGetSymbols( c( "IBM", "MCD" ), 
#                           first.date = "2007-06-01", 
#                           last.date = "2012-07-01", 
#                           freq.data = "monthly", 
#                           how.to.aggregate = 'first')
#stocks

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# for now lets use the example time series

IBM <- read.csv("IBM.csv")
MCD <- read.csv("MCD.csv")

head(IBM)
IBM$Adj.Close
MCD$Adj.Close

data <- data.frame(IBM$Adj.Close,MCD$Adj.Close)
data[,3] <- NA
data[,4] <- NA
data

log( IBM$Adj.Close[2] / IBM$Adj.Close[1] )
# log computes logarithms, by default natural logarithms, 
# log10 computes common (i.e., base 10) logarithms, and 
# log2 computes binary (i.e., base 2) logarithms. 

for(i in 2: length(data[,1]) ){
  data[i,3] <- ( log( data[i,1] / data[i-1,1] ) )
}
data

for(i in 2: length(data[,2]) ){
  data[i,4] <- ( log( data[i,2] / data[i-1,2] ) )
}
data

colnames(data)[3] <- "IBM.Adj.roc"
colnames(data)[4] <- "MCD.Adj.roc"

head( data )
#in comparison: the ROC function from the quantmod package
# computes the standard deviation of the values in x. If na.rm is TRUE 
# then missing values are removed before computation proceeds.
ROC( data$IBM.Adj.Close )
str( as.numeric( data$IBM.Adj.roc[2] ) )
str( as.numeric( ROC( data$IBM.Adj.Close )[2] ) )

as.numeric( ROC( data$IBM.Adj.Close ) ) == as.numeric( data$IBM.Adj.roc )
as.numeric( ROC( data$IBM.Adj.Close )[2] ) == as.numeric( data$IBM.Adj.roc[2] )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# why are they not exactly the same? Starting with the 16th digit the 
# numbers differ.
# the continuous formulation of ROC is : roc <- diff(log(x), n, na.pad = na.pad)
# this gives the small difference compared to what we calculated first
# log( t / (t-1) )

# lets try the same 

diff( log( data$IBM.Adj.Close ) )

ROC( data$IBM.Adj.Close )[2:length(data$IBM.Adj.Close)] == 
  diff( log( data$IBM.Adj.Close ) )

# now it is exactly the same, but for practical reasons such 
# minuscule differences do not really matter. 

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# if we use the first 5 digits the numbers are exactly the same 

print( ROC( data$IBM.Adj.Close )[2], digits = 20 )
print( as.numeric( data$IBM.Adj.roc )[2], digits = 20 )

round( as.numeric( data$IBM.Adj.roc ), 5)
round( as.numeric( ROC( data$IBM.Adj.Close ) ), 5 )

round( as.numeric( data$IBM.Adj.roc ), 5) == 
  round( as.numeric( ROC( data$IBM.Adj.Close ) ), 5 )

#options( digits = 10 )      # Modify global options

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# the data preparation phase is somewhat more intensive if using
# a programming tool like R but in the long run i think
# i pays off by having more accessible data that can be processed in a 
# much faster way especially if it comes to big data applications 

summary( data )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# calculating the mean with a function and out of the data

mean( na.omit( data[,3] ) )
mean( na.omit( data[,4] ) )

sum( na.omit( data[,3] ) ) / length( na.omit( data[,3] ) )
sum( na.omit( data[,4] ) ) / length( na.omit( data[,4] ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# latex formulas

# Mean = \frac{1}{N} \sum_{i=1}^{N} r_i     # population and sample mean

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#

var( na.omit( data[,3] ) )

data[,3] - mean( na.omit( data[,3] ) )               #  demeaning
( data[,3] - mean( na.omit( data[,3] ) ) )^2         #  power of 2
sum( na.omit( ( data[,3] - mean( na.omit( data[,3] ) ) )^2 ) )  # sum with drop NA
sum( na.omit( ( data[,3] - mean( na.omit( data[,3] ) ) )^2 ) ) /
  ( length( na.omit( data[,3] ) ) - 1 )
# NA is also omitted for length and we use the sample variance 
# therefore we divide by (n-1)

var( na.omit( data[,4] ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# latex formulas

# Var.p = \frac{1}{N} \sum_{i=1}^{N} (r_i-\bar{r})^2
# Var.s = \frac{1}{N-1} \sum_{i=1}^{N} (r_i-\bar{r})^2

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#

# stats::  calls the package directly
# this is not really necessary unless there are functions with the
# same name in different packages. Then one might be masked be the other 

# SD IBM

stats::sd( data[,3] , na.rm = TRUE )
sd( data[,3] , na.rm = TRUE )

# the squqre root of the variance gives us the standard deviation 
sqrt( var( na.omit( data[,3] ) ) )
# the long way:
sqrt(
  sum( na.omit( ( data[,3] - mean( na.omit( data[,3] ) ) )^2 ) ) /
    ( length( na.omit( data[,3] ) ) - 1 )
)

# SD MCD

stats::sd( data[,4] , na.rm = TRUE )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# latex formulas standard deviation

#Stdev.p = \sqrt{ \frac{1}{N} \sum_{i=1}^{N} (r_i-\bar{r})^2 }
#Stdev.s = \sqrt{ \frac{1}{N-1} \sum_{i=1}^{N} (r_i-\bar{r})^2 }

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# calculate covariance

cov( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) )
# this gives the sample covariance

# calculation by hand
na.omit( data$IBM.Adj.roc ) - mean( na.omit( data$IBM.Adj.roc ) )
na.omit( data$MCD.Adj.roc ) - mean( na.omit( data$MCD.Adj.roc ) )

( na.omit( data$IBM.Adj.roc ) - mean( na.omit( data$IBM.Adj.roc ) ) )*
( na.omit( data$MCD.Adj.roc ) - mean( na.omit( data$MCD.Adj.roc ) ) )

sum( 
  ( na.omit( data$IBM.Adj.roc ) - mean( na.omit( data$IBM.Adj.roc ) ) )*
  ( na.omit( data$MCD.Adj.roc ) - mean( na.omit( data$MCD.Adj.roc ) ) ) 
  ) / 
  length( na.omit( data$IBM.Adj.roc ) ) * 60/59
  # length( na.omit( data$IBM.Adj.roc ) ) == 60

# Population covariance
#  * (n-1)/n    

cov( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) ) * (60-1)/60 

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# latex formulas Covariances

# Covariance.p = \frac{1}{N} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)(r_{jt}-\bar{r}_j)
# Covariance.s = \frac{1}{N-1} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)(r_{jt}-\bar{r}_j)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# calculating correlation    -    sample correlation 

cor( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) )

cov( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) ) /
 ( sqrt( var( na.omit( data[,3] ) ) ) * sqrt( var( na.omit( data[,4] ) ) ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# latex formulas Correlation

# Correl(i,j) = \frac{Covar.p(i,j)}{Stdev.p(i)*Stdev.p(j)} 

# the full formula 

# Correl(i,j) = \frac{Covariance.p = \frac{1}{N} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)(r_{jt}-\bar{r}_j)}{Stdev.p_{(i)} = \sqrt{ \frac{1}{N} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)^2 }*Stdev.p_{(j)} = \sqrt{ \frac{1}{N} \sum_{t=1}^{N} (r_{jt}-\bar{r}_j)^2 }} 

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# ploting return series 

plot( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc),
      xlab = "IBM",
      ylab = "MCD", frame.plot = FALSE, 
      axes = TRUE, 
      xgap.axis = 4,
      ygap.axis = 4,pch=2)
model <- lm( na.omit(data$IBM.Adj.roc) ~ na.omit(data$MCD.Adj.roc) )
abline(model$coefficients, col="red")
abline( h=0, v=0, lty = 3 )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# calculating portfolio performance 

data[,3:4]

ratio.ibm <- 0.5
ratio.mcd <- (1 - ratio.ibm)

data[,5] <- NA
data

for(i in 2: length(data[,3]) ){
  data[i,5] <- ( data[i,3] * ratio.ibm ) + ( data[i,4] * ratio.mcd )
}
colnames(data)[5] <- "portfolio.performance"
data
head( data )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# portfolio performance values

mean( na.omit( data$portfolio.performance ) )
var( na.omit( data$portfolio.performance ) )
sqrt( var( na.omit( data$portfolio.performance ) ) )
# sd( na.omit( data$portfolio.performance ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# calculating different portfolios

ratios <- seq( 0, 1, by = 0.1 )

result <- NULL

for(i in 1:length(ratios) ) {

ratio.ibm <- ratios[i]
ratio.mcd <- (1 - ratio.ibm)

data[,5] <- NA
data

for(i in 2: length(data[,3]) ){
  data[i,5] <- ( data[i,3] * ratio.ibm ) + ( data[i,4] * ratio.mcd )
}
colnames(data)[5] <- "portfolio.performance"
m <- mean( na.omit( data$portfolio.performance ) )
s <- sqrt( var( na.omit( data$portfolio.performance ) ) )

frame <- data.frame( m, s )
result <- data.frame( rbind( result, frame ) )

}

result

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# ploting values


plot( result$s, result$m,
      type = "b",
      xlab = "PF-SD",
      ylab = "PF-Return", frame.plot = FALSE, 
      axes = TRUE, 
      xgap.axis = 4,
      ygap.axis = 4,pch=2)
#model <- lm( result$m ~ result$s )
#abline(model$coefficients, col="red")

plot( result$s, result$m,
      type = "b",
      xlim = c(0.035,0.06),
      ylim = c(0.0117,0.0122),
      xlab = "PF-SD",
      ylab = "PF-Return", frame.plot = FALSE, 
      axes = TRUE, 
      xgap.axis = 4,
      ygap.axis = 4,
      pch=2)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# calculating more portfolios values including the option for shorting

ratios <- seq( -3, 3, by = 0.1 )

result <- NULL

for(i in 1:length(ratios) ) {
  
  ratio.ibm <- ratios[i]
  ratio.mcd <- (1 - ratio.ibm)
  
  data[,5] <- NA
  data
  
  for(i in 2: length(data[,3]) ){
    data[i,5] <- ( data[i,3] * ratio.ibm ) + ( data[i,4] * ratio.mcd )
  }
  colnames(data)[5] <- "portfolio.performance"
  m <- mean( na.omit( data$portfolio.performance ) )
  s <- sqrt( var( na.omit( data$portfolio.performance ) ) )
  
  frame <- data.frame( m, s, ratio.ibm, ratio.mcd )
  result <- data.frame( rbind( result, frame ) )
  
}

result

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# ploting values


plot( result$s, result$m,
      type = "b",
      xlab = "PF-SD",
      ylab = "PF-Return", frame.plot = FALSE, 
      axes = TRUE, 
      xgap.axis = 4,
      ygap.axis = 4,pch=2)

which( round( result$s,4) == round(min( result$s ),4) )
sep.line <- result[which( round( result$s,4) == round(min( result$s ),4) ),1]
abline( h = sep.line , lty = 3, col = "red")


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# The next step would be to calculate values for n asset portfolio 

min( result$s )
dplyr::filter( result, s == min( result$s ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# latex vector notation

#  x=\begin{bmatrix} x_1 \\ x_2 \\ ... \\ x_N \end{bmatrix}

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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# World Bank Data using WDI Package

# path: ~/ownCloud/

# file_name:

# files_used:

# files_created

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

rm(list = ls(all = TRUE))

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# packages

#install.packages("httr")

#install.packages("XML")

#install.packages('WDI')

#install.packages("magrittr")

#install.packages("tidyverse")

#install.packages("quantmod")

#install.packages("PerformanceAnalytics")

#install.packages("tidyquant")

library("httr")

library("XML")

library("magrittr")

library("tidyverse")

library("WDI")

library("quantmod")

library("PerformanceAnalytics")

#library("tidyquant")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

system("ls")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# using quantmod to collect data

# selecting the date while downloading

MCD <- quantmod::getSymbols("MCD",src = "yahoo",

auto.assign=FALSE,

from = "2007-06-01",

to = "2012-07-01" )

head(MCD)

tail(MCD)

# or download the available data and selecting later

MCD <- quantmod::getSymbols("MCD",src = "yahoo",

auto.assign=FALSE )

head(MCD)

tail(MCD)

MCD <- MCD["2007-06-01/2012-07-01"]

head(MCD)

tail(MCD)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# download monthly data

MCD <- quantmod::getSymbols("MCD",src = "yahoo",

auto.assign=FALSE,

from = "2007-06-01",

to = "2012-07-01",

periodicity = "monthly" )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# alternative ways

#stock_list <- c( "IBM", "MCD" )

#stocks_weekly <- tq_get(stock_list,

# from = start_date,

# to = end_date,

# periodicity = "weekly")

#stocks_weekly

#install.packages("BatchGetSymbols")

#library(BatchGetSymbols)

#stocks <- BatchGetSymbols( c( "IBM", "MCD" ),

# first.date = "2007-06-01",

# last.date = "2012-07-01",

# freq.data = "monthly",

# how.to.aggregate = 'first')

#stocks

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# for now lets use the example time series

IBM <- read.csv("IBM.csv")

MCD <- read.csv("MCD.csv")

head(IBM)

IBM$Adj.Close

MCD$Adj.Close

data <- data.frame(IBM$Adj.Close,MCD$Adj.Close)

data[,3] <- NA

data[,4] <- NA

data

log( IBM$Adj.Close[2] / IBM$Adj.Close[1] )

# log computes logarithms, by default natural logarithms,

# log10 computes common (i.e., base 10) logarithms, and

# log2 computes binary (i.e., base 2) logarithms.

for(i in 2: length(data[,1]) ){

data[i,3] <- ( log( data[i,1] / data[i-1,1] ) )

}

data

for(i in 2: length(data[,2]) ){

data[i,4] <- ( log( data[i,2] / data[i-1,2] ) )

}

data

colnames(data)[3] <- "IBM.Adj.roc"

colnames(data)[4] <- "MCD.Adj.roc"

head( data )

#in comparison: the ROC function from the quantmod package

# computes the standard deviation of the values in x. If na.rm is TRUE

# then missing values are removed before computation proceeds.

ROC( data$IBM.Adj.Close )

str( as.numeric( data$IBM.Adj.roc[2] ) )

str( as.numeric( ROC( data$IBM.Adj.Close )[2] ) )

as.numeric( ROC( data$IBM.Adj.Close ) ) == as.numeric( data$IBM.Adj.roc )

as.numeric( ROC( data$IBM.Adj.Close )[2] ) == as.numeric( data$IBM.Adj.roc[2] )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# why are they not exactly the same? Starting with the 16th digit the

# numbers differ.

# the continuous formulation of ROC is : roc <- diff(log(x), n, na.pad = na.pad)

# this gives the small difference compared to what we calculated first

# log( t / (t-1) )

# lets try the same

diff( log( data$IBM.Adj.Close ) )

ROC( data$IBM.Adj.Close )[2:length(data$IBM.Adj.Close)] ==

diff( log( data$IBM.Adj.Close ) )

# now it is exactly the same, but for practical reasons such

# minuscule differences do not really matter.

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# if we use the first 5 digits the numbers are exactly the same

print( ROC( data$IBM.Adj.Close )[2], digits = 20 )

print( as.numeric( data$IBM.Adj.roc )[2], digits = 20 )

round( as.numeric( data$IBM.Adj.roc ), 5)

round( as.numeric( ROC( data$IBM.Adj.Close ) ), 5 )

round( as.numeric( data$IBM.Adj.roc ), 5) ==

round( as.numeric( ROC( data$IBM.Adj.Close ) ), 5 )

#options( digits = 10 ) # Modify global options

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# the data preparation phase is somewhat more intensive if using

# a programming tool like R but in the long run i think

# i pays off by having more accessible data that can be processed in a

# much faster way especially if it comes to big data applications

summary( data )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# calculating the mean with a function and out of the data

mean( na.omit( data[,3] ) )

mean( na.omit( data[,4] ) )

sum( na.omit( data[,3] ) ) / length( na.omit( data[,3] ) )

sum( na.omit( data[,4] ) ) / length( na.omit( data[,4] ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# latex formulas

# Mean = \frac{1}{N} \sum_{i=1}^{N} r_i # population and sample mean

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

var( na.omit( data[,3] ) )

data[,3] - mean( na.omit( data[,3] ) ) # demeaning

( data[,3] - mean( na.omit( data[,3] ) ) )^2 # power of 2

sum( na.omit( ( data[,3] - mean( na.omit( data[,3] ) ) )^2 ) ) # sum with drop NA

sum( na.omit( ( data[,3] - mean( na.omit( data[,3] ) ) )^2 ) ) /

( length( na.omit( data[,3] ) ) - 1 )

# NA is also omitted for length and we use the sample variance

# therefore we divide by (n-1)

var( na.omit( data[,4] ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# latex formulas

# Var.p = \frac{1}{N} \sum_{i=1}^{N} (r_i-\bar{r})^2

# Var.s = \frac{1}{N-1} \sum_{i=1}^{N} (r_i-\bar{r})^2

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# stats:: calls the package directly

# this is not really necessary unless there are functions with the

# same name in different packages. Then one might be masked be the other

# SD IBM

stats::sd( data[,3] , na.rm = TRUE )

sd( data[,3] , na.rm = TRUE )

# the squqre root of the variance gives us the standard deviation

sqrt( var( na.omit( data[,3] ) ) )

# the long way:

sqrt(

sum( na.omit( ( data[,3] - mean( na.omit( data[,3] ) ) )^2 ) ) /

( length( na.omit( data[,3] ) ) - 1 )

)

# SD MCD

stats::sd( data[,4] , na.rm = TRUE )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# latex formulas standard deviation

#Stdev.p = \sqrt{ \frac{1}{N} \sum_{i=1}^{N} (r_i-\bar{r})^2 }

#Stdev.s = \sqrt{ \frac{1}{N-1} \sum_{i=1}^{N} (r_i-\bar{r})^2 }

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# calculate covariance

cov( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) )

# this gives the sample covariance

# calculation by hand

na.omit( data$IBM.Adj.roc ) - mean( na.omit( data$IBM.Adj.roc ) )

na.omit( data$MCD.Adj.roc ) - mean( na.omit( data$MCD.Adj.roc ) )

( na.omit( data$IBM.Adj.roc ) - mean( na.omit( data$IBM.Adj.roc ) ) )*

( na.omit( data$MCD.Adj.roc ) - mean( na.omit( data$MCD.Adj.roc ) ) )

sum(

( na.omit( data$IBM.Adj.roc ) - mean( na.omit( data$IBM.Adj.roc ) ) )*

( na.omit( data$MCD.Adj.roc ) - mean( na.omit( data$MCD.Adj.roc ) ) )

) /

length( na.omit( data$IBM.Adj.roc ) ) * 60/59

# length( na.omit( data$IBM.Adj.roc ) ) == 60

# Population covariance

# * (n-1)/n

cov( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) ) * (60-1)/60

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# latex formulas Covariances

# Covariance.p = \frac{1}{N} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)(r_{jt}-\bar{r}_j)

# Covariance.s = \frac{1}{N-1} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)(r_{jt}-\bar{r}_j)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# calculating correlation - sample correlation

cor( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) )

cov( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc) ) /

( sqrt( var( na.omit( data[,3] ) ) ) * sqrt( var( na.omit( data[,4] ) ) ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# latex formulas Correlation

# Correl(i,j) = \frac{Covar.p(i,j)}{Stdev.p(i)*Stdev.p(j)}

# the full formula

# Correl(i,j) = \frac{Covariance.p = \frac{1}{N} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)(r_{jt}-\bar{r}_j)}{Stdev.p_{(i)} = \sqrt{ \frac{1}{N} \sum_{t=1}^{N} (r_{it}-\bar{r}_i)^2 }*Stdev.p_{(j)} = \sqrt{ \frac{1}{N} \sum_{t=1}^{N} (r_{jt}-\bar{r}_j)^2 }}

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# ploting return series

plot( na.omit(data$IBM.Adj.roc), na.omit(data$MCD.Adj.roc),

xlab = "IBM",

ylab = "MCD", frame.plot = FALSE,

axes = TRUE,

xgap.axis = 4,

ygap.axis = 4,pch=2)

model <- lm( na.omit(data$IBM.Adj.roc) ~ na.omit(data$MCD.Adj.roc) )

abline(model$coefficients, col="red")

abline( h=0, v=0, lty = 3 )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# calculating portfolio performance

data[,3:4]

ratio.ibm <- 0.5

ratio.mcd <- (1 - ratio.ibm)

data[,5] <- NA

data

for(i in 2: length(data[,3]) ){

data[i,5] <- ( data[i,3] * ratio.ibm ) + ( data[i,4] * ratio.mcd )

}

colnames(data)[5] <- "portfolio.performance"

data

head( data )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# portfolio performance values

mean( na.omit( data$portfolio.performance ) )

var( na.omit( data$portfolio.performance ) )

sqrt( var( na.omit( data$portfolio.performance ) ) )

# sd( na.omit( data$portfolio.performance ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# calculating different portfolios

ratios <- seq( 0, 1, by = 0.1 )

result <- NULL

for(i in 1:length(ratios) ) {

ratio.ibm <- ratios[i]

ratio.mcd <- (1 - ratio.ibm)

data[,5] <- NA

data

for(i in 2: length(data[,3]) ){

data[i,5] <- ( data[i,3] * ratio.ibm ) + ( data[i,4] * ratio.mcd )

}

colnames(data)[5] <- "portfolio.performance"

m <- mean( na.omit( data$portfolio.performance ) )

s <- sqrt( var( na.omit( data$portfolio.performance ) ) )

frame <- data.frame( m, s )

result <- data.frame( rbind( result, frame ) )

}

result

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# ploting values

plot( result$s, result$m,

type = "b",

xlab = "PF-SD",

ylab = "PF-Return", frame.plot = FALSE,

axes = TRUE,

xgap.axis = 4,

ygap.axis = 4,pch=2)

#model <- lm( result$m ~ result$s )

#abline(model$coefficients, col="red")

plot( result$s, result$m,

type = "b",

xlim = c(0.035,0.06),

ylim = c(0.0117,0.0122),

xlab = "PF-SD",

ylab = "PF-Return", frame.plot = FALSE,

axes = TRUE,

xgap.axis = 4,

ygap.axis = 4,

pch=2)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# calculating more portfolios values including the option for shorting

ratios <- seq( -3, 3, by = 0.1 )

result <- NULL

for(i in 1:length(ratios) ) {

ratio.ibm <- ratios[i]

ratio.mcd <- (1 - ratio.ibm)

data[,5] <- NA

data

for(i in 2: length(data[,3]) ){

data[i,5] <- ( data[i,3] * ratio.ibm ) + ( data[i,4] * ratio.mcd )

}

colnames(data)[5] <- "portfolio.performance"

m <- mean( na.omit( data$portfolio.performance ) )

s <- sqrt( var( na.omit( data$portfolio.performance ) ) )

frame <- data.frame( m, s, ratio.ibm, ratio.mcd )

result <- data.frame( rbind( result, frame ) )

}

result

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# ploting values

plot( result$s, result$m,

type = "b",

xlab = "PF-SD",

ylab = "PF-Return", frame.plot = FALSE,

axes = TRUE,

xgap.axis = 4,

ygap.axis = 4,pch=2)

which( round( result$s,4) == round(min( result$s ),4) )

sep.line <- result[which( round( result$s,4) == round(min( result$s ),4) ),1]

abline( h = sep.line , lty = 3, col = "red")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# The next step would be to calculate values for n asset portfolio

min( result$s )

dplyr::filter( result, s == min( result$s ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# latex vector notation

# x=\begin{bmatrix} x_1 \\ x_2 \\ ... \\ x_N \end{bmatrix}

Data examples using World Bank Data

03/03/2021Allgemein, Code, Data Analysis, Programming, R, StatisticsMartin Stoppacher

Data examples using World Bank Data in combination with the ggplot package in R.

Tidyverse methods and functions were used to generate a combined data frame (tibble) for all countries and indicators.

countries.wb.data.map %&gt;% filter( country == c("Afghanistan","Germany","Mexico") ) %&gt;% ggplot( aes( x = `year` , y = `Population, total`, #size = `Population, total`, col = `country` #chape = `Region` ) ) + geom_point( ) + labs( title = "Population" )

1	countries.wb.data.map %>% filter( country == c("Afghanistan","Germany","Mexico") ) %>% ggplot( aes( x = `year` , y = `Population, total`, #size = `Population, total`, col = `country` #chape = `Region` ) ) + geom_point( ) + labs( title = "Population" )

countries.wb.data.map %>%
  filter( year > 2017 ) %>%
  filter( year < 2019 )  %>%
  #filter( country == c("Austria","Germany","France") ) %>%
  ggplot( aes( x = log( `GDP (constant 2010 US$)` ) , 
               y = `Fertility rate, total (births per woman)`,
               size = `Population, total`,
               shape = `IncomeGroup`,
               col = `Region`
               #shape = as.character(`year`)
  ) ) +
  geom_point( ) +
  labs( title = "log( `GDP (constant 2010 US$)` ) vs. Fertility rate, total (births per woman)" )

countries.wb.data.map %>%

filter( year > 2017 ) %>%

filter( year < 2019 ) %>%

#filter( country == c("Austria","Germany","France") ) %>%

ggplot( aes( x = log( `GDP (constant 2010 US$)` ) ,

y = `Fertility rate, total (births per woman)`,

size = `Population, total`,

shape = `IncomeGroup`,

col = `Region`

#shape = as.character(`year`)

) ) +

geom_point( ) +

labs( title = "log( `GDP (constant 2010 US$)` ) vs. Fertility rate, total (births per woman)" )

countries.wb.data.map %&gt;%
  filter( year &gt; 2017 ) %&gt;%
  filter( year &lt; 2019 )  %&gt;%
  #filter( country == c("Austria","Germany","France") ) %&gt;%
  ggplot( aes( x = `GDP per capita (constant 2010 US$)`, 
               y = `Fertility rate, total (births per woman)`,
               #size = `Population, total`,
               shape = `IncomeGroup`,
               col = `Region`
               #shape = as.character(`year`)
  ) ) +
  geom_point( ) +
  labs( title = "GDP per capita (constant 2010 US$) vs Fertility rate, total (births per woman)" ) +
  geom_label_repel( aes( label = ifelse( `IncomeGroup` == "Low income", 
                                         country,
                                         "" ) ), 
                    hjust=0, 
                    #vjust=0.5,
                    #nudge_y       = 7, 
                    nudge_x       = 150000, 
                    #force         = 100,
                    box.padding   = 0.35, 
                    point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y",    # limits movement to x axis
                    #color = "white",
                    arrow = arrow(
                      length = unit(0.03, "npc"), 
                      type = "closed", 
                      ends = "first"
                    ) ) +
  #xlim( - 100, 60000000000000)  +
  #scale_x_continuous( expand = expand_scale(mult = c( 0.4, 0.4 ))) +
  #scale_y_continuous( expand = expand_scale(mult = c( 0.1, 0.1))) + 
  theme_bw()

countries.wb.data.map %>%

filter( year > 2017 ) %>%

filter( year < 2019 ) %>%

#filter( country == c("Austria","Germany","France") ) %>%

ggplot( aes( x = `GDP per capita (constant 2010 US$)`,

y = `Fertility rate, total (births per woman)`,

#size = `Population, total`,

shape = `IncomeGroup`,

col = `Region`

#shape = as.character(`year`)

) ) +

geom_point( ) +

labs( title = "GDP per capita (constant 2010 US$) vs Fertility rate, total (births per woman)" ) +

geom_label_repel( aes( label = ifelse( `IncomeGroup` == "Low income",

country,

"" ) ),

hjust=0,

#vjust=0.5,

#nudge_y = 7,

nudge_x = 150000,

#force = 100,

box.padding = 0.35,

point.padding = 0.2,

segment.color = "lightgray",

direction = "y", # limits movement to x axis

#color = "white",

arrow = arrow(

length = unit(0.03, "npc"),

type = "closed",

ends = "first"

) ) +

#xlim( - 100, 60000000000000) +

#scale_x_continuous( expand = expand_scale(mult = c( 0.4, 0.4 ))) +

#scale_y_continuous( expand = expand_scale(mult = c( 0.1, 0.1))) +

theme_bw()

countries.wb.data.map %>%
  filter( year > 2017 ) %>%
  filter( year < 2019 )  %>%
  #filter( country == c("Austria","Germany","France") ) %>%
  ggplot( aes( x = `GDP per capita (constant 2010 US$)`, 
               y = `Fertility rate, total (births per woman)`,
               #size = `Population, total`,
               #shape = `IncomeGroup`,
               col = `IncomeGroup`
               #shape = as.character(`year`)
  ) ) +
  geom_point( ) + 
  geom_line() +
  geom_smooth() +
  theme_bw() +
  labs( title = "log( GDP per capita (constant 2010 US$) ) - Fertility rate, total (births per woman) " ) +
  xlim( 0, 50000 )

countries.wb.data.map %>%

filter( year > 2017 ) %>%

filter( year < 2019 ) %>%

#filter( country == c("Austria","Germany","France") ) %>%

ggplot( aes( x = `GDP per capita (constant 2010 US$)`,

y = `Fertility rate, total (births per woman)`,

#size = `Population, total`,

#shape = `IncomeGroup`,

col = `IncomeGroup`

#shape = as.character(`year`)

) ) +

geom_point( ) +

geom_line() +

geom_smooth() +

theme_bw() +

labs( title = "log( GDP per capita (constant 2010 US$) ) - Fertility rate, total (births per woman) " ) +

xlim( 0, 50000 )

countries.wb.data.map %>%
  filter( year > 2017 ) %>%
  filter( year < 2019 )  %>%
  #filter( country == c("Austria","Germany","France") ) %>%
  ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ), 
               y = `Fertility rate, total (births per woman)`,
               #size = `Population, total`,
               #shape = `IncomeGroup`,
               col = `IncomeGroup`
               #shape = as.character(`year`)
  ) ) +
  geom_point( ) + 
  geom_line() +
  geom_smooth() +
  theme_bw() +
  labs( title = "log( GDP per capita (constant 2010 US$) ) - Fertility rate, total (births per woman) " )

countries.wb.data.map %>%

filter( year > 2017 ) %>%

filter( year < 2019 ) %>%

#filter( country == c("Austria","Germany","France") ) %>%

ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ),

y = `Fertility rate, total (births per woman)`,

#size = `Population, total`,

#shape = `IncomeGroup`,

col = `IncomeGroup`

#shape = as.character(`year`)

) ) +

geom_point( ) +

geom_line() +

geom_smooth() +

theme_bw() +

labs( title = "log( GDP per capita (constant 2010 US$) ) - Fertility rate, total (births per woman) " )

countries.wb.data.map %>%
  filter( year > 2017 ) %>%
  filter( year < 2019 )  %>%
  #filter( country == c("Austria","Germany","France") ) %>%
  ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ), 
               y = `Fertility rate, total (births per woman)`
               #size = `Population, total`,
               #shape = `IncomeGroup`,
               #col = `Region`
               #shape = as.character(`year`)
  ) ) + 
  geom_line() +
  geom_smooth() +
  labs( title = "GDP per capita (constant 2010 US$) vs Fertility rate, total (births per woman) 2018" ) +
  geom_point( aes( x = log( `GDP per capita (constant 2010 US$)` ), 
                   y = `Fertility rate, total (births per woman)`,
                   #size = `IncomeGroup`,
                   shape = `IncomeGroup`,
                   col = `Region`
                   #shape = as.character(`year`)
  )  ) +
  geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` > 5, 
                                         `country`,
                                         "" ) ), 
                    hjust=0, 
                    #vjust=4,
                    #nudge_y       = 7, 
                    nudge_x       = 11, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    #point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  ) +
  geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` < 1.25, 
                                         `country`,
                                         "" ) ), 
                    #hjust=5, 
                    #vjust=2,
                    #nudge_y       = 0, 
                    nudge_x       = -11, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  )

countries.wb.data.map %>%

filter( year > 2017 ) %>%

filter( year < 2019 ) %>%

#filter( country == c("Austria","Germany","France") ) %>%

ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ),

y = `Fertility rate, total (births per woman)`

#size = `Population, total`,

#shape = `IncomeGroup`,

#col = `Region`

#shape = as.character(`year`)

) ) +

geom_line() +

geom_smooth() +

labs( title = "GDP per capita (constant 2010 US$) vs Fertility rate, total (births per woman) 2018" ) +

geom_point( aes( x = log( `GDP per capita (constant 2010 US$)` ),

y = `Fertility rate, total (births per woman)`,

#size = `IncomeGroup`,

shape = `IncomeGroup`,

col = `Region`

#shape = as.character(`year`)

) ) +

geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` > 5,

`country`,

"" ) ),

hjust=0,

#vjust=4,

#nudge_y = 7,

nudge_x = 11,

#force = 100,

#box.padding = 0.9,

#point.padding = 0.2,

segment.color = "lightgray",

direction = "y" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

) +

geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` < 1.25,

`country`,

"" ) ),

#hjust=5,

#vjust=2,

#nudge_y = 0,

nudge_x = -11,

#force = 100,

#box.padding = 0.9,

point.padding = 0.2,

segment.color = "lightgray",

direction = "y" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

)

countries.wb.data.map %>%
  filter( year > 1980 ) %>%
  filter( year < 1982 )  %>%
  #filter( country == c("Austria","Germany","France") ) %>%
  ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ), 
               y = `Fertility rate, total (births per woman)`
               #size = `Population, total`,
               #shape = `IncomeGroup`,
               #col = `Region`
               #shape = as.character(`year`)
  ) ) + 
  geom_line() +
  geom_smooth() +
  labs( title = "GDP per capita (constant 2010 US$) vs Fertility rate, total (births per woman) 1981" ) +
  geom_point( aes( x = log( `GDP per capita (constant 2010 US$)` ), 
                   y = `Fertility rate, total (births per woman)`,
                   #size = `IncomeGroup`,
                   shape = `IncomeGroup`,
                   col = `Region`
                   #shape = as.character(`year`)
  )  ) +
  geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` > 7.3, 
                                         `country`,
                                         "" ) ), 
                    hjust=0, 
                    #vjust=4,
                    #nudge_y       = 7, 
                    nudge_x       = 11, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    #point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  ) +
  geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` < 1.8, 
                                         `country`,
                                         "" ) ), 
                    #hjust=5, 
                    #vjust=2,
                    #nudge_y       = 0, 
                    nudge_x       = -11, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  )

countries.wb.data.map %>%

filter( year > 1980 ) %>%

filter( year < 1982 ) %>%

#filter( country == c("Austria","Germany","France") ) %>%

ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ),

y = `Fertility rate, total (births per woman)`

#size = `Population, total`,

#shape = `IncomeGroup`,

#col = `Region`

#shape = as.character(`year`)

) ) +

geom_line() +

geom_smooth() +

labs( title = "GDP per capita (constant 2010 US$) vs Fertility rate, total (births per woman) 1981" ) +

geom_point( aes( x = log( `GDP per capita (constant 2010 US$)` ),

y = `Fertility rate, total (births per woman)`,

#size = `IncomeGroup`,

shape = `IncomeGroup`,

col = `Region`

#shape = as.character(`year`)

) ) +

geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` > 7.3,

`country`,

"" ) ),

hjust=0,

#vjust=4,

#nudge_y = 7,

nudge_x = 11,

#force = 100,

#box.padding = 0.9,

#point.padding = 0.2,

segment.color = "lightgray",

direction = "y" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

) +

geom_label_repel( aes( label = ifelse( `Fertility rate, total (births per woman)` < 1.8,

`country`,

"" ) ),

#hjust=5,

#vjust=2,

#nudge_y = 0,

nudge_x = -11,

#force = 100,

#box.padding = 0.9,

point.padding = 0.2,

segment.color = "lightgray",

direction = "y" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

)

countries.wb.data.map %>%
  filter( country == c("Austria","Germany", "France" ) ) %>%
  filter( year > 1960 ) %>%
  filter( year < 2019 ) %>%
  ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ), 
               y = `Fertility rate, total (births per woman)`,
               #size = `Population, total`,
               #shape = `IncomeGroup`,
               col = `country`
               #shape = as.character(`year`)
  ) ) +
  geom_line( ) +
  geom_smooth() +
  geom_point( ) +
  geom_label_repel( aes( label = ifelse( `country` == "France", 
                                         `year`,
                                         "" ) ), 
                    #hjust=3, 
                    #vjust=-4,
                    nudge_y       = 1, 
                    #nudge_x       = 9, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    #point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  ) +
  geom_label_repel( aes( label = ifelse( `country` == "Austria", 
                                         `year`,
                                         "" ) ), 
                    #hjust=3, 
                    #vjust=-3,
                    #nudge_y       = 1, 
                    nudge_x       = -3, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    #point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "x"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  ) +
  geom_label_repel( aes( label = ifelse( `country` == "Germany", 
                                         `year`,
                                         "" ) ), 
                    #hjust=3, 
                    #vjust=2,
                    nudge_y       = -4, 
                    #nudge_x       = 9, 
                    #force         = 100,
                    #box.padding   = 0.9, 
                    #point.padding = 0.2,
                    segment.color = "lightgray",
                    direction     = "y"    # limits movement to x axis
                    #color = "white",
                    #arrow = arrow(
                    #  length = unit(0.03, "npc"), 
                    #  type = "closed", 
                    #  ends = "first" )
  )  +
  theme_bw() +
  labs( title = "log( `GDP per capita (constant 2010 US$)` ) - Fertility rate, total (births per woman) 1961 - 2018" )

countries.wb.data.map %>%

filter( country == c("Austria","Germany", "France" ) ) %>%

filter( year > 1960 ) %>%

filter( year < 2019 ) %>%

ggplot( aes( x = log( `GDP per capita (constant 2010 US$)` ),

y = `Fertility rate, total (births per woman)`,

#size = `Population, total`,

#shape = `IncomeGroup`,

col = `country`

#shape = as.character(`year`)

) ) +

geom_line( ) +

geom_smooth() +

geom_point( ) +

geom_label_repel( aes( label = ifelse( `country` == "France",

`year`,

"" ) ),

#hjust=3,

#vjust=-4,

nudge_y = 1,

#nudge_x = 9,

#force = 100,

#box.padding = 0.9,

#point.padding = 0.2,

segment.color = "lightgray",

direction = "y" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

) +

geom_label_repel( aes( label = ifelse( `country` == "Austria",

`year`,

"" ) ),

#hjust=3,

#vjust=-3,

#nudge_y = 1,

nudge_x = -3,

#force = 100,

#box.padding = 0.9,

#point.padding = 0.2,

segment.color = "lightgray",

direction = "x" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

) +

geom_label_repel( aes( label = ifelse( `country` == "Germany",

`year`,

"" ) ),

#hjust=3,

#vjust=2,

nudge_y = -4,

#nudge_x = 9,

#force = 100,

#box.padding = 0.9,

#point.padding = 0.2,

segment.color = "lightgray",

direction = "y" # limits movement to x axis

#color = "white",

#arrow = arrow(

# length = unit(0.03, "npc"),

# type = "closed",

# ends = "first" )

) +

theme_bw() +

labs( title = "log( `GDP per capita (constant 2010 US$)` ) - Fertility rate, total (births per woman) 1961 - 2018" )

creating jukebox labels

08/22/2020Engineering, R, SoundengineeringMartin Stoppacher

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# jukebox labels

j_list  <- read.csv2('Mappe1.csv', skip = 2)

j_list[ 1, ]

j_list[ , 1 ]
length( j_list[ , 1 ] )

ceiling( length( j_list[ , 1 ] ) / 8 )


g <- 1

for( f in 1:ceiling( length( j_list[ , 1 ] ) / 8 ) ) {

  jpeg( paste( "jukebox_" , f, ".jpg", sep = "") , width = 800, height = 800 )

  #jpeg("test2.jpg", width=800, height=800)

  #par( mar = rep( 1, 4 ), bg = "white")
  #par( mfrow = c( 1, 1 ), bg = "white")

  par( mfrow = c( 4, 2 ), bg = "white")

  print( g )
  
  #for( e in 1:8 ){
  for( e in g:(g+7) ){
  
    plot.new()
    plot.window(xlim = c(0, 800), ylim = c(0, 250))
    #lines(c(0, 800, 800, 0,0), c(0, 0, 250, 250,0), type = "l", lwd = 6, col = "red") # data frame with points to connect

    lines(c(0, 800), c(0, 0), type = "l", lwd = 6, col = "red")
    lines(c(0, 800), c(250, 250), type = "l", lwd = 6, col = "red")

    #lines(c(0, 100), c(131, 131), type = "l", lwd = 4, col = "red") 
    #lines(c(0, 100), c(129, 130), type = "l", lwd = 4, col = "red")
    #lines(c(0, 100), c(129, 129), type = "l", lwd = 4, col = "red")

    for(i in 119:131){ lines(c(0, 100), c(i, i), type = "l", lwd = 4, col = "red") }

    #lines(c(700, 800), c(123, 123), type = "l", lwd = 4, col = "red")
    #lines(c(700, 800), c(121, 121), type = "l", lwd = 4, col = "red")
    #lines(c(700, 800), c(119, 119), type = "l", lwd = 4, col = "red")

    for(i in 119:131){ lines(c(700, 800), c(i, i), type = "l", lwd = 4, col = "red") }

    #lines(c(0, 100, 100, 700,700,800), c(125, 125, 150, 150,125,125), type = "l", lwd = 7, col = "red") # data frame with points to connect
    #lines(c(0, 100, 100, 700,700,800), c(125, 125, 100, 100,125,125), type = "l", lwd = 7, col = "red") # data frame with points to connect

    #lines(c(100, 100, 100, 700,700,700), c(125, 125, 150, 150,125,125), type = "l", lwd = 7, col = "black") # data frame with points to connect
    #lines(c(100, 100, 100, 700,700,700), c(125, 125, 100, 100,125,125), type = "l", lwd = 7, col = "black") # data frame with points to connect

    #lines(c(100, 100, 100, 700,700,700), c(125, 125, 150, 150,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect
    #lines(c(100, 100, 100, 700,700,700), c(125, 125, 100, 100,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect

    lines(c(105, 105, 105, 695,695,695), c(125, 125, 150, 150,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect
    lines(c(105, 105, 105, 695,695,695), c(125, 125, 100, 100,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect

    c <- "black"
    f <- "Times New Roman"
    ce <- 2

    #text(400,125,"Rock",cex = 3, col = c, family=f)
    #text(400,200,"Side A",cex = 3, col = "Black")
    #text(400,50,"Side B",cex = 3, col = "Black")

    text( 400, 200, j_list[ e, 1 ], cex = ce, col = c, family = f )
    text( 400, 125, j_list[ e, 2 ] ,cex = ce, col = c, family = f )
    text( 400, 50, j_list[ e, 3 ], cex = ce, col = c, family = f )

  }

  g <- g + 8
  
  graphics.off()
  #browseURL("test.jpg")
  #browseURL("test2.jpg")
  #browseURL( paste( "jukebox_" , f, ".jpg", sep = "") )

}

# Martin Stoppacher                                                             #
# office@martinstoppacher.com                                                   #
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#################################################################################

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# jukebox labels

j_list <- read.csv2('Mappe1.csv', skip = 2)

j_list[ 1, ]

j_list[ , 1 ]

length( j_list[ , 1 ] )

ceiling( length( j_list[ , 1 ] ) / 8 )

g <- 1

for( f in 1:ceiling( length( j_list[ , 1 ] ) / 8 ) ) {

jpeg( paste( "jukebox_" , f, ".jpg", sep = "") , width = 800, height = 800 )

#jpeg("test2.jpg", width=800, height=800)

#par( mar = rep( 1, 4 ), bg = "white")

#par( mfrow = c( 1, 1 ), bg = "white")

par( mfrow = c( 4, 2 ), bg = "white")

print( g )

#for( e in 1:8 ){

for( e in g:(g+7) ){

plot.new()

plot.window(xlim = c(0, 800), ylim = c(0, 250))

#lines(c(0, 800, 800, 0,0), c(0, 0, 250, 250,0), type = "l", lwd = 6, col = "red") # data frame with points to connect

lines(c(0, 800), c(0, 0), type = "l", lwd = 6, col = "red")

lines(c(0, 800), c(250, 250), type = "l", lwd = 6, col = "red")

#lines(c(0, 100), c(131, 131), type = "l", lwd = 4, col = "red")

#lines(c(0, 100), c(129, 130), type = "l", lwd = 4, col = "red")

#lines(c(0, 100), c(129, 129), type = "l", lwd = 4, col = "red")

for(i in 119:131){ lines(c(0, 100), c(i, i), type = "l", lwd = 4, col = "red") }

#lines(c(700, 800), c(123, 123), type = "l", lwd = 4, col = "red")

#lines(c(700, 800), c(121, 121), type = "l", lwd = 4, col = "red")

#lines(c(700, 800), c(119, 119), type = "l", lwd = 4, col = "red")

for(i in 119:131){ lines(c(700, 800), c(i, i), type = "l", lwd = 4, col = "red") }

#lines(c(0, 100, 100, 700,700,800), c(125, 125, 150, 150,125,125), type = "l", lwd = 7, col = "red") # data frame with points to connect

#lines(c(0, 100, 100, 700,700,800), c(125, 125, 100, 100,125,125), type = "l", lwd = 7, col = "red") # data frame with points to connect

#lines(c(100, 100, 100, 700,700,700), c(125, 125, 150, 150,125,125), type = "l", lwd = 7, col = "black") # data frame with points to connect

#lines(c(100, 100, 100, 700,700,700), c(125, 125, 100, 100,125,125), type = "l", lwd = 7, col = "black") # data frame with points to connect

#lines(c(100, 100, 100, 700,700,700), c(125, 125, 150, 150,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect

#lines(c(100, 100, 100, 700,700,700), c(125, 125, 100, 100,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect

lines(c(105, 105, 105, 695,695,695), c(125, 125, 150, 150,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect

lines(c(105, 105, 105, 695,695,695), c(125, 125, 100, 100,125,125), type = "l", lwd = 3, col = "red") # data frame with points to connect

c <- "black"

f <- "Times New Roman"

ce <- 2

#text(400,125,"Rock",cex = 3, col = c, family=f)

#text(400,200,"Side A",cex = 3, col = "Black")

#text(400,50,"Side B",cex = 3, col = "Black")

text( 400, 200, j_list[ e, 1 ], cex = ce, col = c, family = f )

text( 400, 125, j_list[ e, 2 ] ,cex = ce, col = c, family = f )

text( 400, 50, j_list[ e, 3 ], cex = ce, col = c, family = f )

}

g <- g + 8

graphics.off()

#browseURL("test.jpg")

#browseURL("test2.jpg")

#browseURL( paste( "jukebox_" , f, ".jpg", sep = "") )

}

# Martin Stoppacher #

# office@martinstoppacher.com #

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

#################################################################################

Original:

Created:

Basic R commands – 2 – probability distributions

07/04/2020Allgemein, Programming, R, StatisticsMartin Stoppacher

Basic Commands and Statistics with R – 2 – probability distibutions. R Functions for the most common probability distibutions.

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Basic Commands and Statistics with R - 2 - probability distibutions 
# An overview of the most common probability distibutions 
# eg: # https://en.wikipedia.org/wiki/List_of_probability_distributions
# path: ~/ownCloud/STA_Statistics/Basic_Statistics/
# file_name: statistic_basics2.R
# files_used:

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Probability Plots

# Distribution                    Functions
#
# Normal                          pnorm       qnorm      dnorm      rnorm
# Beta                            pbeta       qbeta      dbeta      rbeta
# Binomial                        pbinom      qbinom     dbinom     rbinom
# Uniform                         punif       qunif      dunif      runif
# Cauchy                          pcauchy     qcauchy    dcauchy    rcauchy
# Chi-Square                      pchisq      qchisq     dchisq     rchisq
# Exponential                     pexp        qexp       dexp       rexp
# F                               pf          qf         df         rf
# Gamma                           pgamma      qgamma     dgamma     rgamma
# Geometric                       pgeom       qgeom      dgeom      rgeom
# Hypergeometric                  phyper      qhyper     dhyper     rhyper
# Logistic                        plogis      qlogis     dlogis     rlogis
# Log Normal                      plnorm      qlnorm     dlnorm     rlnorm
# Negative Binomial               pnbinom     qnbinom    dnbinom    rnbinom
# Poisson                         ppois       qpois      dpois      rpois
# Student t                       pt          qt         dt         rt
# Studentized Range               ptukey      qtukey     dtukey     rtukey
# Weibull                         pweibull    qweibull   dweibull   rweibull
# Wilcoxon Rank Sum Statistic     pwilcox     qwilcox    dwilcox    rwilcox
# Wilcoxon Signed Rank Statistic  psignrank   qsignrank  dsignrank  rsignrank
# ...

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Normal  pnorm, qnorm, dnorm, rnorm

# distribution function
# direct - P ( X < 23.5 )
pn <- pnorm( 23.5, mean = 50, sd = 20 )
str( pn ) 
# num 0.0926

pn <- pnorm( 1:100, mean = 50, sd = 20 )
str( pn )
# num [1:100] 0.0512 0.0548 0.0586 0.0626 0.0668 ...

par( mfrow = c( 1, 2 ) )

hist( pn, main = "normal mean = 50, sd = 20" )
plot( 1:100, pn, main = "normal mean = 50, sd = 20")
abline( h = 0.5, v = 50, col = "red" )

par( mfrow = c( 1, 1 ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Basic Commands and Statistics with R - 2 - probability distibutions

# An overview of the most common probability distibutions

# eg: # https://en.wikipedia.org/wiki/List_of_probability_distributions

# path: ~/ownCloud/STA_Statistics/Basic_Statistics/

# file_name: statistic_basics2.R

# files_used:

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Probability Plots

# Distribution Functions

# Normal pnorm qnorm dnorm rnorm

# Beta pbeta qbeta dbeta rbeta

# Binomial pbinom qbinom dbinom rbinom

# Uniform punif qunif dunif runif

# Cauchy pcauchy qcauchy dcauchy rcauchy

# Chi-Square pchisq qchisq dchisq rchisq

# Exponential pexp qexp dexp rexp

# F pf qf df rf

# Gamma pgamma qgamma dgamma rgamma

# Geometric pgeom qgeom dgeom rgeom

# Hypergeometric phyper qhyper dhyper rhyper

# Logistic plogis qlogis dlogis rlogis

# Log Normal plnorm qlnorm dlnorm rlnorm

# Negative Binomial pnbinom qnbinom dnbinom rnbinom

# Poisson ppois qpois dpois rpois

# Student t pt qt dt rt

# Studentized Range ptukey qtukey dtukey rtukey

# Weibull pweibull qweibull dweibull rweibull

# Wilcoxon Rank Sum Statistic pwilcox qwilcox dwilcox rwilcox

# Wilcoxon Signed Rank Statistic psignrank qsignrank dsignrank rsignrank

# ...

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Normal pnorm, qnorm, dnorm, rnorm

# distribution function

# direct - P ( X < 23.5 )

pn <- pnorm( 23.5, mean = 50, sd = 20 )

str( pn )

# num 0.0926

pn <- pnorm( 1:100, mean = 50, sd = 20 )

str( pn )

# num [1:100] 0.0512 0.0548 0.0586 0.0626 0.0668 ...

par( mfrow = c( 1, 2 ) )

hist( pn, main = "normal mean = 50, sd = 20" )

plot( 1:100, pn, main = "normal mean = 50, sd = 20")

abline( h = 0.5, v = 50, col = "red" )

par( mfrow = c( 1, 1 ) )

# quantile function 
# Inverse - qnorm looks up the p-th quantile of the normal distribution

qn <- qnorm( 0.5, mean = 50, sd = 20 )
str( qn )
# num 50

qn <- qnorm( ( ( 1:100 ) / 100 ), mean = 50, sd = 20 )
str( qn ); qn;
# [1]  3.47  8.93 12.38 14.99 17.10 18.90 20.48 21.90 23.18 24.37 25.47 26.5
#hist(qn,breaks=150)

par( mfrow = c( 1, 2 ) )
hist( qn, main = "normal mean = 50, sd = 20" )
plot( ( ( 1:100 ) / 100 ), qn, main = "normal mean = 50, sd = 20" )
abline( v = 0.5, h = 50, col = "red" )

par( mfrow = c( 1, 1 ) )

# quantile function

# Inverse - qnorm looks up the p-th quantile of the normal distribution

qn <- qnorm( 0.5, mean = 50, sd = 20 )

str( qn )

# num 50

qn <- qnorm( ( ( 1:100 ) / 100 ), mean = 50, sd = 20 )

str( qn ); qn;

# [1] 3.47 8.93 12.38 14.99 17.10 18.90 20.48 21.90 23.18 24.37 25.47 26.5

#hist(qn,breaks=150)

par( mfrow = c( 1, 2 ) )

hist( qn, main = "normal mean = 50, sd = 20" )

plot( ( ( 1:100 ) / 100 ), qn, main = "normal mean = 50, sd = 20" )

abline( v = 0.5, h = 50, col = "red" )

par( mfrow = c( 1, 1 ) )

# invertet plot

par( mfrow = c( 1, 2 ) )

plot( ( ( 1:100 ) / 100 ), qn, main = "normal mean = 50, sd = 20")
abline( h = 150, col = "red" )
plot( qn, ( ( 1:100 ) / 100 ), main = "normal mean = 50, sd = 20")
abline( v = 150, col = "red" )

# invertet plot

par( mfrow = c( 1, 2 ) )

plot( ( ( 1:100 ) / 100 ), qn, main = "normal mean = 50, sd = 20")

abline( h = 150, col = "red" )

plot( qn, ( ( 1:100 ) / 100 ), main = "normal mean = 50, sd = 20")

abline( v = 150, col = "red" )

# Density function

dn <- dnorm( 1:100, mean = 50, sd = 20)
str( dn )
dn

# [1]  0.00099187 0.00111973 0.00126091 0.00141635 0.00158698 0.00177373 
#      0.00197750 0.00219918 0.00243960 0.00269955 0.00297974 0.00328079
# [13] 0.00360324 0.00394751 0.00431387 0.00470245 0.00511325 0.00554604 
#      0.00600045 0.00647588 0.00697153 0.00748637 0.00801917 0.00856843
# [25] 0.00913245 0.00970930 0.01029681 0.01089261 0.01149411 0.01209854 
#      0.01270295 0.01330426 0.01389924 0.01448458 0.01505687 0.01561270
# [37] 0.01614862 0.01666123 0.01714719 0.01760327 0.01802635 0.01841351 
#      0.01876202 0.01906939 0.01933341 0.01955213 0.01972397 0.01984763
# [49] 0.01992220 0.01994711 0.01992220 0.01984763 0.01972397 0.01955213 
#      0.01933341 0.01906939 0.01876202 0.01841351 0.01802635 0.01760327
# [61] 0.01714719 0.01666123 0.01614862 0.01561270 0.01505687 0.01448458 
#      0.01389924 0.01330426 0.01270295 0.01209854 0.01149411 0.01089261
# [73] 0.01029681 0.00970930 0.00913245 0.00856843 0.00801917 0.00748637 
#      0.00697153 0.00647588 0.00600045 0.00554604 0.00511325 0.00470245
# [85] 0.00431387 0.00394751 0.00360324 0.00328079 0.00297974 0.00269955 
#      0.00243960 0.00219918 0.00197750 0.00177373 0.00158698 0.00141635
# [97] 0.00126091 0.00111973 0.00099187 0.00087642

sum( dn )

# [1] 0.98756

par( mfrow = c( 1, 2 ) )
hist( dn, main = "normal mean = 50, sd = 20" )
plot( 1:100, dn, main = "normal mean = 50, sd = 20" )
par( mfrow = c( 1, 1 ) )

# Density function

dn <- dnorm( 1:100, mean = 50, sd = 20)

str( dn )

# [1] 0.00099187 0.00111973 0.00126091 0.00141635 0.00158698 0.00177373

# 0.00197750 0.00219918 0.00243960 0.00269955 0.00297974 0.00328079

# [13] 0.00360324 0.00394751 0.00431387 0.00470245 0.00511325 0.00554604

# 0.00600045 0.00647588 0.00697153 0.00748637 0.00801917 0.00856843

# [25] 0.00913245 0.00970930 0.01029681 0.01089261 0.01149411 0.01209854

# 0.01270295 0.01330426 0.01389924 0.01448458 0.01505687 0.01561270

# [37] 0.01614862 0.01666123 0.01714719 0.01760327 0.01802635 0.01841351

# 0.01876202 0.01906939 0.01933341 0.01955213 0.01972397 0.01984763

# [49] 0.01992220 0.01994711 0.01992220 0.01984763 0.01972397 0.01955213

# 0.01933341 0.01906939 0.01876202 0.01841351 0.01802635 0.01760327

# [61] 0.01714719 0.01666123 0.01614862 0.01561270 0.01505687 0.01448458

# 0.01389924 0.01330426 0.01270295 0.01209854 0.01149411 0.01089261

# [73] 0.01029681 0.00970930 0.00913245 0.00856843 0.00801917 0.00748637

# 0.00697153 0.00647588 0.00600045 0.00554604 0.00511325 0.00470245

# [85] 0.00431387 0.00394751 0.00360324 0.00328079 0.00297974 0.00269955

# 0.00243960 0.00219918 0.00197750 0.00177373 0.00158698 0.00141635

# [97] 0.00126091 0.00111973 0.00099187 0.00087642

sum( dn )

# [1] 0.98756

par( mfrow = c( 1, 2 ) )

hist( dn, main = "normal mean = 50, sd = 20" )

plot( 1:100, dn, main = "normal mean = 50, sd = 20" )

par( mfrow = c( 1, 1 ) )

cumsum(dn)

# [1]  0.00099187 0.00211159 0.00337251 0.00478886 0.00637584 0.00814957 
#      0.01012707 0.01232625 0.01476585 0.01746540 0.02044514 0.02372593
# [13] 0.02732917 0.03127668 0.03559054 0.04029300 0.04540624 0.05095229 
#      0.05695274 0.06342862 0.07040014 0.07788652 0.08590568 0.09447411
# [25] 0.10360657 0.11331587 0.12361268 0.13450529 0.14599940 0.15809794 
#      0.17080089 0.18410515 0.19800440 0.21248897 0.22754584 0.24315854
# [37] 0.25930716 0.27596839 0.29311558 0.31071885 0.32874520 0.34715870 
#      0.36592072 0.38499011 0.40432352 0.42387565 0.44359962 0.46344725
# [49] 0.48336944 0.50331656 0.52323875 0.54308638 0.56281035 0.58236248 
#      0.60169589 0.62076528 0.63952729 0.65794080 0.67596715 0.69357042
# [61] 0.71071761 0.72737884 0.74352746 0.75914015 0.77419702 0.78868160 
#      0.80258085 0.81588511 0.82858806 0.84068660 0.85218071 0.86307331
# [73] 0.87337013 0.88307943 0.89221188 0.90078031 0.90879948 0.91628585 
#      0.92325738 0.92973326 0.93573371 0.94127975 0.94639300 0.95109545
# [85] 0.95540932 0.95935683 0.96296007 0.96624086 0.96922060 0.97192015 
#      0.97435975 0.97655893 0.97853643 0.98031016 0.98189714 0.98331349
# [97] 0.98457440 0.98569413 0.98668600 0.98756241

par( mfrow = c( 1, 2 ) )
plot( 1:100, dn, main = "normal mean=50, sd=20" )
plot( 1:100, cumsum( dn ), main= "normal mean=50, sd=20" ) 

par( mfrow = c( 1,1 ) )

cumsum(dn)

# [1] 0.00099187 0.00211159 0.00337251 0.00478886 0.00637584 0.00814957

# 0.01012707 0.01232625 0.01476585 0.01746540 0.02044514 0.02372593

# [13] 0.02732917 0.03127668 0.03559054 0.04029300 0.04540624 0.05095229

# 0.05695274 0.06342862 0.07040014 0.07788652 0.08590568 0.09447411

# [25] 0.10360657 0.11331587 0.12361268 0.13450529 0.14599940 0.15809794

# 0.17080089 0.18410515 0.19800440 0.21248897 0.22754584 0.24315854

# [37] 0.25930716 0.27596839 0.29311558 0.31071885 0.32874520 0.34715870

# 0.36592072 0.38499011 0.40432352 0.42387565 0.44359962 0.46344725

# [49] 0.48336944 0.50331656 0.52323875 0.54308638 0.56281035 0.58236248

# 0.60169589 0.62076528 0.63952729 0.65794080 0.67596715 0.69357042

# [61] 0.71071761 0.72737884 0.74352746 0.75914015 0.77419702 0.78868160

# 0.80258085 0.81588511 0.82858806 0.84068660 0.85218071 0.86307331

# [73] 0.87337013 0.88307943 0.89221188 0.90078031 0.90879948 0.91628585

# 0.92325738 0.92973326 0.93573371 0.94127975 0.94639300 0.95109545

# [85] 0.95540932 0.95935683 0.96296007 0.96624086 0.96922060 0.97192015

# 0.97435975 0.97655893 0.97853643 0.98031016 0.98189714 0.98331349

# [97] 0.98457440 0.98569413 0.98668600 0.98756241

par( mfrow = c( 1, 2 ) )

plot( 1:100, dn, main = "normal mean=50, sd=20" )

plot( 1:100, cumsum( dn ), main= "normal mean=50, sd=20" )

par( mfrow = c( 1,1 ) )

# Random generations - with specified normal distribution
# random generation for the normal distribution

rn <- rnorm( 100, mean = 50, sd = 20 )
par( mfrow=c( 1, 2 ) )
hist( rn, probability = FALSE, main = "normal mean = 50, sd = 20" )
hist( rn, probability = TRUE, main="normal mean = 50, sd = 20" )
#points( rn, dnorm( rn, mean = 50, sd = 20), col = "red" )
lines( sort( rn ), dnorm( sort( rn ), mean = 50, sd = 20 ), col= "red" )

par ( mfrow = c( 1, 1 ) )

# Random generations - with specified normal distribution

# random generation for the normal distribution

rn <- rnorm( 100, mean = 50, sd = 20 )

par( mfrow=c( 1, 2 ) )

hist( rn, probability = FALSE, main = "normal mean = 50, sd = 20" )

hist( rn, probability = TRUE, main="normal mean = 50, sd = 20" )

#points( rn, dnorm( rn, mean = 50, sd = 20), col = "red" )

lines( sort( rn ), dnorm( sort( rn ), mean = 50, sd = 20 ), col= "red" )

par ( mfrow = c( 1, 1 ) )

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# distributions for the mtcars data dnorm densities
# https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/mtcars.html

attach( mtcars )

hist(mtcars$mpg, probability = TRUE)
lines( sort( mtcars$mpg ), 
       dnorm( sort( mtcars$mpg ) , 
              mean = mean(mtcars$mpg) ,sd = sd(mtcars$mpg) ), col= "red" )

par( mfrow = c( 3, 4 ) )
for( i in 1:length( mtcars[1,] ) ){ 
  hist( mtcars[,i], probability = TRUE, 
        main = paste( "Data: MTcars", colnames( mtcars )[i] ),
        xlab = paste( colnames( mtcars )[i] ) )
  lines( sort( mtcars[,i] ), 
         dnorm( sort( mtcars[,i] ) , 
                mean = mean( mtcars[,i] ) ,sd = sd( mtcars[,i] ) ), col= "red" )
}

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# distributions for the mtcars data dnorm densities

# https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/mtcars.html

attach( mtcars )

hist(mtcars$mpg, probability = TRUE)

lines( sort( mtcars$mpg ),

dnorm( sort( mtcars$mpg ) ,

mean = mean(mtcars$mpg) ,sd = sd(mtcars$mpg) ), col= "red" )

par( mfrow = c( 3, 4 ) )

for( i in 1:length( mtcars[1,] ) ){

hist( mtcars[,i], probability = TRUE,

main = paste( "Data: MTcars", colnames( mtcars )[i] ),

xlab = paste( colnames( mtcars )[i] ) )

lines( sort( mtcars[,i] ),

dnorm( sort( mtcars[,i] ) ,

mean = mean( mtcars[,i] ) ,sd = sd( mtcars[,i] ) ), col= "red" )

}

# Martin Stoppacher                                                             #
# office@martinstoppacher.com                                                   #
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#################################################################################

# Martin Stoppacher #

# office@martinstoppacher.com #

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

#################################################################################

world_population_plots

06/23/2020Data Analysis, R, StatisticsMartin Stoppacher

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

browseURL(“https://data.worldbank.org/”)

browseURL(“https://data.worldbank.org/indicator”)

browseURL(“https://data.worldbank.org/country”)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# plots for all countries

print(countries$countries)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# plots for all countries

print(countries$countries)

##   [1] Afghanistan                    Albania                       
##   [3] Algeria                        American Samoa                
##   [5] Andorra                        Angola                        
##   [7] Antigua and Barbuda            Argentina                     
##   [9] Armenia                        Aruba                         
##  [11] Australia                      Austria                       
##  [13] Azerbaijan                     Bahamas, The                  
##  [15] Bahrain                        Bangladesh                    
##  [17] Barbados                       Belarus                       
##  [19] Belgium                        Belize                        
##  [21] Benin                          Bermuda                       
##  [23] Bhutan                         Bolivia                       
##  [25] Bosnia and Herzegovina         Botswana                      
##  [27] Brazil                         British Virgin Islands        
##  [29] Brunei Darussalam              Bulgaria                      
##  [31] Burkina Faso                   Burundi                       
##  [33] Cabo Verde                     Cambodia                      
##  [35] Cameroon                       Canada                        
##  [37] Cayman Islands                 Central African Republic      
##  [39] Chad                           Channel Islands               
##  [41] Chile                          China                         
##  [43] Colombia                       Comoros                       
##  [45] Congo, Dem. Rep.               Congo, Rep.                   
##  [47] Costa Rica                     Cote d'Ivoire                 
##  [49] Croatia                        Cuba                          
##  [51] Curacao                        Cyprus                        
##  [53] Czech Republic                 Denmark                       
##  [55] Djibouti                       Dominica                      
##  [57] Dominican Republic             Ecuador                       
##  [59] Egypt, Arab Rep.               El Salvador                   
##  [61] Equatorial Guinea              Eritrea                       
##  [63] Estonia                        Eswatini                      
##  [65] Ethiopia                       Faroe Islands                 
##  [67] Fiji                           Finland                       
##  [69] France                         French Polynesia              
##  [71] Gabon                          Gambia, The                   
##  [73] Georgia                        Germany                       
##  [75] Ghana                          Gibraltar                     
##  [77] Greece                         Greenland                     
##  [79] Grenada                        Guam                          
##  [81] Guatemala                      Guinea                        
##  [83] Guinea-Bissau                  Guyana                        
##  [85] Haiti                          Honduras                      
##  [87] Hong Kong SAR, China           Hungary                       
##  [89] Iceland                        India                         
##  [91] Indonesia                      Iran, Islamic Rep.            
##  [93] Iraq                           Ireland                       
##  [95] Isle of Man                    Israel                        
##  [97] Italy                          Jamaica                       
##  [99] Japan                          Jordan                        
## [101] Kazakhstan                     Kenya                         
## [103] Kiribati                       Korea, Dem. People’s Rep.     
## [105] Korea, Rep.                    Kosovo                        
## [107] Kuwait                         Kyrgyz Republic               
## [109] Lao PDR                        Latvia                        
## [111] Lebanon                        Lesotho                       
## [113] Liberia                        Libya                         
## [115] Liechtenstein                  Lithuania                     
## [117] Luxembourg                     Macao SAR, China              
## [119] Madagascar                     Malawi                        
## [121] Malaysia                       Maldives                      
## [123] Mali                           Malta                         
## [125] Marshall Islands               Mauritania                    
## [127] Mauritius                      Mexico                        
## [129] Micronesia, Fed. Sts.          Moldova                       
## [131] Monaco                         Mongolia                      
## [133] Montenegro                     Morocco                       
## [135] Mozambique                     Myanmar                       
## [137] Namibia                        Nauru                         
## [139] Nepal                          Netherlands                   
## [141] New Caledonia                  New Zealand                   
## [143] Nicaragua                      Niger                         
## [145] Nigeria                        North Macedonia               
## [147] Northern Mariana Islands       Norway                        
## [149] Oman                           Pakistan                      
## [151] Palau                          Panama                        
## [153] Papua New Guinea               Paraguay                      
## [155] Peru                           Philippines                   
## [157] Poland                         Portugal                      
## [159] Puerto Rico                    Qatar                         
## [161] Romania                        Russian Federation            
## [163] Rwanda                         Samoa                         
## [165] San Marino                     Sao Tome and Principe         
## [167] Saudi Arabia                   Senegal                       
## [169] Serbia                         Seychelles                    
## [171] Sierra Leone                   Singapore                     
## [173] Sint Maarten (Dutch part)      Slovak Republic               
## [175] Slovenia                       Solomon Islands               
## [177] Somalia                        South Africa                  
## [179] South Sudan                    Spain                         
## [181] Sri Lanka                      St. Kitts and Nevis           
## [183] St. Lucia                      St. Martin (French part)      
## [185] St. Vincent and the Grenadines Sudan                         
## [187] Suriname                       Sweden                        
## [189] Switzerland                    Syrian Arab Republic          
## [191] Tajikistan                     Tanzania                      
## [193] Thailand                       Timor-Leste                   
## [195] Togo                           Tonga                         
## [197] Trinidad and Tobago            Tunisia                       
## [199] Turkey                         Turkmenistan                  
## [201] Turks and Caicos Islands       Tuvalu                        
## [203] Uganda                         Ukraine                       
## [205] United Arab Emirates           United Kingdom                
## [207] United States                  Uruguay                       
## [209] Uzbekistan                     Vanuatu                       
## [211] Venezuela, RB                  Vietnam                       
## [213] Virgin Islands (U.S.)          West Bank and Gaza            
## [215] Yemen, Rep.                    Zambia                        
## [217] Zimbabwe                      
## 217 Levels: Afghanistan Albania Algeria American Samoa Andorra ... Zimbabwe

100

101

102

103

104

105

106

107

108

109

110

## [1] Afghanistan Albania

## [3] Algeria American Samoa

## [5] Andorra Angola

## [7] Antigua and Barbuda Argentina

## [9] Armenia Aruba

## [11] Australia Austria

## [13] Azerbaijan Bahamas, The

## [15] Bahrain Bangladesh

## [17] Barbados Belarus

## [19] Belgium Belize

## [21] Benin Bermuda

## [23] Bhutan Bolivia

## [25] Bosnia and Herzegovina Botswana

## [27] Brazil British Virgin Islands

## [29] Brunei Darussalam Bulgaria

## [31] Burkina Faso Burundi

## [33] Cabo Verde Cambodia

## [35] Cameroon Canada

## [37] Cayman Islands Central African Republic

## [39] Chad Channel Islands

## [41] Chile China

## [43] Colombia Comoros

## [45] Congo, Dem. Rep. Congo, Rep.

## [47] Costa Rica Cote d'Ivoire

## [49] Croatia Cuba

## [51] Curacao Cyprus

## [53] Czech Republic Denmark

## [55] Djibouti Dominica

## [57] Dominican Republic Ecuador

## [59] Egypt, Arab Rep. El Salvador

## [61] Equatorial Guinea Eritrea

## [63] Estonia Eswatini

## [65] Ethiopia Faroe Islands

## [67] Fiji Finland

## [69] France French Polynesia

## [71] Gabon Gambia, The

## [73] Georgia Germany

## [75] Ghana Gibraltar

## [77] Greece Greenland

## [79] Grenada Guam

## [81] Guatemala Guinea

## [83] Guinea-Bissau Guyana

## [85] Haiti Honduras

## [87] Hong Kong SAR, China Hungary

## [89] Iceland India

## [91] Indonesia Iran, Islamic Rep.

## [93] Iraq Ireland

## [95] Isle of Man Israel

## [97] Italy Jamaica

## [99] Japan Jordan

## [101] Kazakhstan Kenya

## [103] Kiribati Korea, Dem. People’s Rep.

## [105] Korea, Rep. Kosovo

## [107] Kuwait Kyrgyz Republic

## [109] Lao PDR Latvia

## [111] Lebanon Lesotho

## [113] Liberia Libya

## [115] Liechtenstein Lithuania

## [117] Luxembourg Macao SAR, China

## [119] Madagascar Malawi

## [121] Malaysia Maldives

## [123] Mali Malta

## [125] Marshall Islands Mauritania

## [127] Mauritius Mexico

## [129] Micronesia, Fed. Sts. Moldova

## [131] Monaco Mongolia

## [133] Montenegro Morocco

## [135] Mozambique Myanmar

## [137] Namibia Nauru

## [139] Nepal Netherlands

## [141] New Caledonia New Zealand

## [143] Nicaragua Niger

## [145] Nigeria North Macedonia

## [147] Northern Mariana Islands Norway

## [149] Oman Pakistan

## [151] Palau Panama

## [153] Papua New Guinea Paraguay

## [155] Peru Philippines

## [157] Poland Portugal

## [159] Puerto Rico Qatar

## [161] Romania Russian Federation

## [163] Rwanda Samoa

## [165] San Marino Sao Tome and Principe

## [167] Saudi Arabia Senegal

## [169] Serbia Seychelles

## [171] Sierra Leone Singapore

## [173] Sint Maarten (Dutch part) Slovak Republic

## [175] Slovenia Solomon Islands

## [177] Somalia South Africa

## [179] South Sudan Spain

## [181] Sri Lanka St. Kitts and Nevis

## [183] St. Lucia St. Martin (French part)

## [185] St. Vincent and the Grenadines Sudan

## [187] Suriname Sweden

## [189] Switzerland Syrian Arab Republic

## [191] Tajikistan Tanzania

## [193] Thailand Timor-Leste

## [195] Togo Tonga

## [197] Trinidad and Tobago Tunisia

## [199] Turkey Turkmenistan

## [201] Turks and Caicos Islands Tuvalu

## [203] Uganda Ukraine

## [205] United Arab Emirates United Kingdom

## [207] United States Uruguay

## [209] Uzbekistan Vanuatu

## [211] Venezuela, RB Vietnam

## [213] Virgin Islands (U.S.) West Bank and Gaza

## [215] Yemen, Rep. Zambia

## [217] Zimbabwe

## 217 Levels: Afghanistan Albania Algeria American Samoa Andorra ... Zimbabwe

for(i in 2:length(indicator.select)){
#for(i in 2:10){

  #print(i)
  #par(mfrow=c(1,1))
  
  plot(ggplot(indicator.select, aes(x = indicator.select[,1], y = indicator.select[,i] )) +
    geom_line() +
    ggtitle(colnames(indicator.select)[i]) + 
    ylab(indicator.name) +
    xlab("year"))

  #Sys.sleep(1)
  #system(paste("open",name))
  #dir()
  
}

for(i in 2:length(indicator.select)){

#for(i in 2:10){

#print(i)

#par(mfrow=c(1,1))

plot(ggplot(indicator.select, aes(x = indicator.select[,1], y = indicator.select[,i] )) +

geom_line() +

ggtitle(colnames(indicator.select)[i]) +

ylab(indicator.name) +

xlab("year"))

#Sys.sleep(1)

#system(paste("open",name))

#dir()

}

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

WB Indicators:

colnames(wbd.countries[[<span class="hljs-number">1</span>]])

1	colnames(wbd.countries[[<span class="hljs-number">1</span>]])

##    [1] "year"                                                                                                                                        
##    [2] "Access to clean fuels and technologies for cooking (% of population)"                                                                        
##    [3] "Access to electricity (% of population)"                                                                                                     
##    [4] "Access to electricity, rural (% of rural population)"                                                                                        
##    [5] "Access to electricity, urban (% of urban population)"                                                                                        
##    [6] "Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)"                             
##    [7] "Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)"                     
##    [8] "Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)"                       
##    [9] "Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)"               
##   [10] "Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)"                
##   [11] "Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)"  
##   [12] "Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)"                
##   [13] "Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)"
##   [14] "Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)"             
##   [15] "Adequacy of social insurance programs (% of total welfare of beneficiary households)"                                                        
##   [16] "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)"                                             
##   [17] "Adequacy of social safety net programs (% of total welfare of beneficiary households)"                                                       
##   [18] "Adjusted net enrollment rate, primary (% of primary school age children)"                                                                    
##   [19] "Adjusted net enrollment rate, primary, female (% of primary school age children)"                                                            
##   [20] "Adjusted net enrollment rate, primary, male (% of primary school age children)"                                                              
##   [21] "Adjusted net national income (constant 2010 US$)"                                                                                            
##   [22] "Adjusted net national income (current US$)"                                                                                                  
##   [23] "Adjusted net national income per capita (constant 2010 US$)"                                                                                 
##   [24] "Adjusted net national income per capita (current US$)"                                                                                       
##   [25] "Adjusted net savings, excluding particulate emission damage (% of GNI)"                                                                      
##   [26] "Adjusted net savings, excluding particulate emission damage (current US$)"                                                                   
##   [27] "Adjusted net savings, including particulate emission damage (% of GNI)"                                                                      
##   [28] "Adjusted net savings, including particulate emission damage (current US$)"                                                                   
##   [29] "Adjusted savings: carbon dioxide damage (% of GNI)"                                                                                          
##   [30] "Adjusted savings: carbon dioxide damage (current US$)"                                                                                       
##   [31] "Adjusted savings: consumption of fixed capital (% of GNI)"                                                                                   
##   [32] "Adjusted savings: consumption of fixed capital (current US$)"                                                                                
##   [33] "Adjusted savings: education expenditure (% of GNI)"                                                                                          
##   [34] "Adjusted savings: education expenditure (current US$)"                                                                                       
##   [35] "Adjusted savings: energy depletion (% of GNI)"                                                                                               
##   [36] "Adjusted savings: energy depletion (current US$)"                                                                                            
##   [37] "Adjusted savings: gross savings (% of GNI)"                                                                                                  
##   [38] "Adjusted savings: mineral depletion (% of GNI)"                                                                                              
##   [39] "Adjusted savings: mineral depletion (current US$)"                                                                                           
##   [40] "Adjusted savings: natural resources depletion (% of GNI)"                                                                                    
##   [41] "Adjusted savings: net forest depletion (% of GNI)"                                                                                           
##   [42] "Adjusted savings: net forest depletion (current US$)"                                                                                        
##   [43] "Adjusted savings: net national savings (% of GNI)"                                                                                           
##   [44] "Adjusted savings: net national savings (current US$)"                                                                                        
##   [45] "Adjusted savings: particulate emission damage (% of GNI)"                                                                                    
##   [46] "Adjusted savings: particulate emission damage (current US$)"                                                                                 
##   [47] "Adolescent fertility rate (births per 1,000 women ages 15-19)"                                                                               
##   [48] "Adolescents out of school (% of lower secondary school age)"                                                                                 
##   [49] "Adolescents out of school, female (% of female lower secondary school age)"                                                                  
##   [50] "Adolescents out of school, male (% of male lower secondary school age)"                                                                      
##   [51] "Adults (ages 15+) and children (ages 0-14) newly infected with HIV"                                                                          
##   [52] "Adults (ages 15+) newly infected with HIV"                                                                                                   
##   [53] "Age dependency ratio (% of working-age population)"                                                                                          
##   [54] "Age dependency ratio, old (% of working-age population)"                                                                                     
##   [55] "Age dependency ratio, young (% of working-age population)"                                                                                   
##   [56] "Agricultural irrigated land (% of total agricultural land)"                                                                                  
##   [57] "Agricultural land (% of land area)"                                                                                                          
##   [58] "Agricultural land (sq. km)"                                                                                                                  
##   [59] "Agricultural machinery, tractors"                                                                                                            
##   [60] "Agricultural machinery, tractors per 100 sq. km of arable land"                                                                              
##   [61] "Agricultural methane emissions (% of total)"                                                                                                 
##   [62] "Agricultural methane emissions (thousand metric tons of CO2 equivalent)"                                                                     
##   [63] "Agricultural nitrous oxide emissions (% of total)"                                                                                           
##   [64] "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)"                                                               
##   [65] "Agricultural raw materials exports (% of merchandise exports)"                                                                               
##   [66] "Agricultural raw materials imports (% of merchandise imports)"                                                                               
##   [67] "Agriculture, forestry, and fishing, value added (% of GDP)"                                                                                  
##   [68] "Agriculture, forestry, and fishing, value added (annual % growth)"                                                                           
##   [69] "Agriculture, forestry, and fishing, value added (constant 2010 US$)"                                                                         
##   [70] "Agriculture, forestry, and fishing, value added (constant LCU)"                                                                              
##   [71] "Agriculture, forestry, and fishing, value added (current LCU)"                                                                               
##   [72] "Agriculture, forestry, and fishing, value added (current US$)"                                                                               
##   [73] "Agriculture, forestry, and fishing, value added per worker (constant 2010 US$)"                                                              
##   [74] "Air transport, freight (million ton-km)"                                                                                                     
##   [75] "Air transport, passengers carried"                                                                                                           
##   [76] "Air transport, registered carrier departures worldwide"                                                                                      
##   [77] "Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal)"                                                               
##   [78] "Annual freshwater withdrawals, domestic (% of total freshwater withdrawal)"                                                                  
##   [79] "Annual freshwater withdrawals, industry (% of total freshwater withdrawal)"                                                                  
##   [80] "Annual freshwater withdrawals, total (% of internal resources)"                                                                              
##   [81] "Annual freshwater withdrawals, total (billion cubic meters)"                                                                                 
##   [82] "Antiretroviral therapy coverage (% of people living with HIV)"                                                                               
##   [83] "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)"                                                             
##   [84] "Aquaculture production (metric tons)"                                                                                                        
##   [85] "Arable land (% of land area)"                                                                                                                
##   [86] "Arable land (hectares per person)"                                                                                                           
##   [87] "Arable land (hectares)"                                                                                                                      
##   [88] "ARI treatment (% of children under 5 taken to a health provider)"                                                                            
##   [89] "Armed forces personnel (% of total labor force)"                                                                                             
##   [90] "Armed forces personnel, total"                                                                                                               
##   [91] "Arms exports (SIPRI trend indicator values)"                                                                                                 
##   [92] "Arms imports (SIPRI trend indicator values)"                                                                                                 
##   [93] "Automated teller machines (ATMs) (per 100,000 adults)"                                                                                       
##   [94] "Average number of visits or required meetings with tax officials (for affected firms)"                                                       
##   [95] "Average precipitation in depth (mm per year)"                                                                                                
##   [96] "Average time to clear exports through customs (days)"                                                                                        
##   [97] "Average transaction cost of sending remittances to a specific country (%)"                                                                   
##   [98] "Average working hours of children, study and work, ages 7-14 (hours per week)"                                                               
##   [99] "Average working hours of children, study and work, female, ages 7-14 (hours per week)"                                                       
##  [100] "Average working hours of children, study and work, male, ages 7-14 (hours per week)"                                                         
##  [101] "Average working hours of children, working only, ages 7-14 (hours per week)"                                                                 
##  [102] "Average working hours of children, working only, female, ages 7-14 (hours per week)"                                                         
##  [103] "Average working hours of children, working only, male, ages 7-14 (hours per week)"                                                           
##  [104] "Bank capital to assets ratio (%)"                                                                                                            
##  [105] "Bank liquid reserves to bank assets ratio (%)"                                                                                               
##  [106] "Bank nonperforming loans to total gross loans (%)"                                                                                           
##  [107] "Battle-related deaths (number of people)"                                                                                                    
##  [108] "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)"                                   
##  [109] "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)"                                     
##  [110] "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)"                                        
##  [111] "Binding coverage, all products (%)"                                                                                                          
##  [112] "Binding coverage, manufactured products (%)"                                                                                                 
##  [113] "Binding coverage, primary products (%)"                                                                                                      
##  [114] "Bird species, threatened"                                                                                                                    
##  [115] "Birth rate, crude (per 1,000 people)"                                                                                                        
##  [116] "Births attended by skilled health staff (% of total)"                                                                                        
##  [117] "Borrowers from commercial banks (per 1,000 adults)"                                                                                          
##  [118] "Bound rate, simple mean, all products (%)"                                                                                                   
##  [119] "Bound rate, simple mean, manufactured products (%)"                                                                                          
##  [120] "Bound rate, simple mean, primary products (%)"                                                                                               
##  [121] "Bribery incidence (% of firms experiencing at least one bribe payment request)"                                                              
##  [122] "Broad money (% of GDP)"                                                                                                                      
##  [123] "Broad money (current LCU)"                                                                                                                   
##  [124] "Broad money growth (annual %)"                                                                                                               
##  [125] "Broad money to total reserves ratio"                                                                                                         
##  [126] "Business extent of disclosure index (0=less disclosure to 10=more disclosure)"                                                               
##  [127] "Capture fisheries production (metric tons)"                                                                                                  
##  [128] "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)"                                       
##  [129] "Cause of death, by injury (% of total)"                                                                                                      
##  [130] "Cause of death, by non-communicable diseases (% of total)"                                                                                   
##  [131] "Cereal production (metric tons)"                                                                                                             
##  [132] "Cereal yield (kg per hectare)"                                                                                                               
##  [133] "Charges for the use of intellectual property, payments (BoP, current US$)"                                                                   
##  [134] "Charges for the use of intellectual property, receipts (BoP, current US$)"                                                                   
##  [135] "Children (0-14) living with HIV"                                                                                                             
##  [136] "Children (ages 0-14) newly infected with HIV"                                                                                                
##  [137] "Children in employment, female (% of female children ages 7-14)"                                                                             
##  [138] "Children in employment, male (% of male children ages 7-14)"                                                                                 
##  [139] "Children in employment, study and work (% of children in employment, ages 7-14)"                                                             
##  [140] "Children in employment, study and work, female (% of female children in employment, ages 7-14)"                                              
##  [141] "Children in employment, study and work, male (% of male children in employment, ages 7-14)"                                                  
##  [142] "Children in employment, total (% of children ages 7-14)"                                                                                     
##  [143] "Children in employment, unpaid family workers (% of children in employment, ages 7-14)"                                                      
##  [144] "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)"                                       
##  [145] "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)"                                           
##  [146] "Children in employment, wage workers (% of children in employment, ages 7-14)"                                                               
##  [147] "Children in employment, wage workers, female (% of female children in employment, ages 7-14)"                                                
##  [148] "Children in employment, wage workers, male (% of male children in employment, ages 7-14)"                                                    
##  [149] "Children in employment, work only (% of children in employment, ages 7-14)"                                                                  
##  [150] "Children in employment, work only, female (% of female children in employment, ages 7-14)"                                                   
##  [151] "Children in employment, work only, male (% of male children in employment, ages 7-14)"                                                       
##  [152] "Children out of school (% of primary school age)"                                                                                            
##  [153] "Children out of school, female (% of female primary school age)"                                                                             
##  [154] "Children out of school, male (% of male primary school age)"                                                                                 
##  [155] "Children out of school, primary"                                                                                                             
##  [156] "Children out of school, primary, female"                                                                                                     
##  [157] "Children out of school, primary, male"                                                                                                       
##  [158] "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)"                                                     
##  [159] "Claims on central government (annual growth as % of broad money)"                                                                            
##  [160] "Claims on central government, etc. (% GDP)"                                                                                                  
##  [161] "Claims on other sectors of the domestic economy (annual growth as % of broad money)"                                                         
##  [162] "Claims on private sector (annual growth as % of broad money)"                                                                                
##  [163] "CO2 emissions (kg per 2010 US$ of GDP)"                                                                                                      
##  [164] "CO2 emissions (kg per 2017 PPP $ of GDP)"                                                                                                    
##  [165] "CO2 emissions (kg per PPP $ of GDP)"                                                                                                         
##  [166] "CO2 emissions (kt)"                                                                                                                          
##  [167] "CO2 emissions (metric tons per capita)"                                                                                                      
##  [168] "CO2 emissions from gaseous fuel consumption (% of total)"                                                                                    
##  [169] "CO2 emissions from gaseous fuel consumption (kt)"                                                                                            
##  [170] "CO2 emissions from liquid fuel consumption (% of total)"                                                                                     
##  [171] "CO2 emissions from liquid fuel consumption (kt)"                                                                                             
##  [172] "CO2 emissions from solid fuel consumption (% of total)"                                                                                      
##  [173] "CO2 emissions from solid fuel consumption (kt)"                                                                                              
##  [174] "Coal rents (% of GDP)"                                                                                                                       
##  [175] "Commercial bank branches (per 100,000 adults)"                                                                                               
##  [176] "Commercial banks and other lending (PPG + PNG) (NFL, current US$)"                                                                           
##  [177] "Commercial service exports (current US$)"                                                                                                    
##  [178] "Commercial service imports (current US$)"                                                                                                    
##  [179] "Communications, computer, etc. (% of service exports, BoP)"                                                                                  
##  [180] "Communications, computer, etc. (% of service imports, BoP)"                                                                                  
##  [181] "Compensation of employees (% of expense)"                                                                                                    
##  [182] "Compensation of employees (current LCU)"                                                                                                     
##  [183] "Completeness of birth registration (%)"                                                                                                      
##  [184] "Completeness of birth registration, female (%)"                                                                                              
##  [185] "Completeness of birth registration, male (%)"                                                                                                
##  [186] "Completeness of birth registration, rural (%)"                                                                                               
##  [187] "Completeness of birth registration, urban (%)"                                                                                               
##  [188] "Compulsory education, duration (years)"                                                                                                      
##  [189] "Computer, communications and other services (% of commercial service exports)"                                                               
##  [190] "Computer, communications and other services (% of commercial service imports)"                                                               
##  [191] "Consumer price index (2010 = 100)"                                                                                                           
##  [192] "Consumption of iodized salt (% of households)"                                                                                               
##  [193] "Contraceptive prevalence, any methods (% of women ages 15-49)"                                                                               
##  [194] "Contraceptive prevalence, modern methods (% of women ages 15-49)"                                                                            
##  [195] "Contributing family workers, female (% of female employment) (modeled ILO estimate)"                                                         
##  [196] "Contributing family workers, male (% of male employment) (modeled ILO estimate)"                                                             
##  [197] "Contributing family workers, total (% of total employment) (modeled ILO estimate)"                                                           
##  [198] "Cost of business start-up procedures (% of GNI per capita)"                                                                                  
##  [199] "Cost of business start-up procedures, female (% of GNI per capita)"                                                                          
##  [200] "Cost of business start-up procedures, male (% of GNI per capita)"                                                                            
##  [201] "Cost to export, border compliance (US$)"                                                                                                     
##  [202] "Cost to export, documentary compliance (US$)"                                                                                                
##  [203] "Cost to import, border compliance (US$)"                                                                                                     
##  [204] "Cost to import, documentary compliance (US$)"                                                                                                
##  [205] "Coverage of social insurance programs (% of population)"                                                                                     
##  [206] "Coverage of social insurance programs in 2nd quintile (% of population)"                                                                     
##  [207] "Coverage of social insurance programs in 3rd quintile (% of population)"                                                                     
##  [208] "Coverage of social insurance programs in 4th quintile (% of population)"                                                                     
##  [209] "Coverage of social insurance programs in poorest quintile (% of population)"                                                                 
##  [210] "Coverage of social insurance programs in richest quintile (% of population)"                                                                 
##  [211] "Coverage of social protection and labor programs (% of population)"                                                                          
##  [212] "Coverage of social safety net programs (% of population)"                                                                                    
##  [213] "Coverage of social safety net programs in 2nd quintile (% of population)"                                                                    
##  [214] "Coverage of social safety net programs in 3rd quintile (% of population)"                                                                    
##  [215] "Coverage of social safety net programs in 4th quintile (% of population)"                                                                    
##  [216] "Coverage of social safety net programs in poorest quintile (% of population)"                                                                
##  [217] "Coverage of social safety net programs in richest quintile (% of population)"                                                                
##  [218] "CPIA building human resources rating (1=low to 6=high)"                                                                                      
##  [219] "CPIA business regulatory environment rating (1=low to 6=high)"                                                                               
##  [220] "CPIA debt policy rating (1=low to 6=high)"                                                                                                   
##  [221] "CPIA economic management cluster average (1=low to 6=high)"                                                                                  
##  [222] "CPIA efficiency of revenue mobilization rating (1=low to 6=high)"                                                                            
##  [223] "CPIA equity of public resource use rating (1=low to 6=high)"                                                                                 
##  [224] "CPIA financial sector rating (1=low to 6=high)"                                                                                              
##  [225] "CPIA fiscal policy rating (1=low to 6=high)"                                                                                                 
##  [226] "CPIA gender equality rating (1=low to 6=high)"                                                                                               
##  [227] "CPIA macroeconomic management rating (1=low to 6=high)"                                                                                      
##  [228] "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)"                                                                 
##  [229] "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)"                                                      
##  [230] "CPIA property rights and rule-based governance rating (1=low to 6=high)"                                                                     
##  [231] "CPIA public sector management and institutions cluster average (1=low to 6=high)"                                                            
##  [232] "CPIA quality of budgetary and financial management rating (1=low to 6=high)"                                                                 
##  [233] "CPIA quality of public administration rating (1=low to 6=high)"                                                                              
##  [234] "CPIA social protection rating (1=low to 6=high)"                                                                                             
##  [235] "CPIA structural policies cluster average (1=low to 6=high)"                                                                                  
##  [236] "CPIA trade rating (1=low to 6=high)"                                                                                                         
##  [237] "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)"                                             
##  [238] "Crop production index (2004-2006 = 100)"                                                                                                     
##  [239] "Current account balance (% of GDP)"                                                                                                          
##  [240] "Current account balance (BoP, current US$)"                                                                                                  
##  [241] "Current education expenditure, primary (% of total expenditure in primary public institutions)"                                              
##  [242] "Current education expenditure, secondary (% of total expenditure in secondary public institutions)"                                          
##  [243] "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)"                                            
##  [244] "Current education expenditure, total (% of total expenditure in public institutions)"                                                        
##  [245] "Current health expenditure (% of GDP)"                                                                                                       
##  [246] "Current health expenditure per capita (current US$)"                                                                                         
##  [247] "Current health expenditure per capita, PPP (current international $)"                                                                        
##  [248] "Customs and other import duties (% of tax revenue)"                                                                                          
##  [249] "Customs and other import duties (current LCU)"                                                                                               
##  [250] "Death rate, crude (per 1,000 people)"                                                                                                        
##  [251] "Debt service (PPG and IMF only, % of exports of goods, services and primary income)"                                                         
##  [252] "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)"                                                      
##  [253] "Debt service on external debt, total (TDS, current US$)"                                                                                     
##  [254] "DEC alternative conversion factor (LCU per US$)"                                                                                             
##  [255] "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)"                                 
##  [256] "Depositors with commercial banks (per 1,000 adults)"                                                                                         
##  [257] "Depth of credit information index (0=low to 8=high)"                                                                                         
##  [258] "Diabetes prevalence (% of population ages 20 to 79)"                                                                                         
##  [259] "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)"                                                 
##  [260] "Diarrhea treatment (% of children under 5 who received ORS packet)"                                                                          
##  [261] "Discrepancy in expenditure estimate of GDP (current LCU)"                                                                                    
##  [262] "Domestic credit to private sector (% of GDP)"                                                                                                
##  [263] "Domestic credit to private sector by banks (% of GDP)"                                                                                       
##  [264] "Domestic general government health expenditure (% of current health expenditure)"                                                            
##  [265] "Domestic general government health expenditure (% of GDP)"                                                                                   
##  [266] "Domestic general government health expenditure (% of general government expenditure)"                                                        
##  [267] "Domestic general government health expenditure per capita (current US$)"                                                                     
##  [268] "Domestic general government health expenditure per capita, PPP (current international $)"                                                    
##  [269] "Domestic private health expenditure (% of current health expenditure)"                                                                       
##  [270] "Domestic private health expenditure per capita (current US$)"                                                                                
##  [271] "Domestic private health expenditure per capita, PPP (current international $)"                                                               
##  [272] "Droughts, floods, extreme temperatures (% of population, average 1990-2009)"                                                                 
##  [273] "Ease of doing business index (1=most business-friendly regulations)"                                                                         
##  [274] "Ease of doing business score (0 = lowest performance to 100 = best performance)"                                                             
##  [275] "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)"                                         
##  [276] "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)"                                           
##  [277] "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)"                                          
##  [278] "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)"                                          
##  [279] "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)"                                            
##  [280] "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)"                                           
##  [281] "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)"                                           
##  [282] "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)"                                             
##  [283] "Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)"                                            
##  [284] "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)"                                         
##  [285] "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)"                                           
##  [286] "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)"                                          
##  [287] "Employers, female (% of female employment) (modeled ILO estimate)"                                                                           
##  [288] "Employers, male (% of male employment) (modeled ILO estimate)"                                                                               
##  [289] "Employers, total (% of total employment) (modeled ILO estimate)"                                                                             
##  [290] "Employment in agriculture (% of total employment) (modeled ILO estimate)"                                                                    
##  [291] "Employment in agriculture, female (% of female employment) (modeled ILO estimate)"                                                           
##  [292] "Employment in agriculture, male (% of male employment) (modeled ILO estimate)"                                                               
##  [293] "Employment in industry (% of total employment) (modeled ILO estimate)"                                                                       
##  [294] "Employment in industry, female (% of female employment) (modeled ILO estimate)"                                                              
##  [295] "Employment in industry, male (% of male employment) (modeled ILO estimate)"                                                                  
##  [296] "Employment in services (% of total employment) (modeled ILO estimate)"                                                                       
##  [297] "Employment in services, female (% of female employment) (modeled ILO estimate)"                                                              
##  [298] "Employment in services, male (% of male employment) (modeled ILO estimate)"                                                                  
##  [299] "Employment to population ratio, 15+, female (%) (modeled ILO estimate)"                                                                      
##  [300] "Employment to population ratio, 15+, female (%) (national estimate)"                                                                         
##  [301] "Employment to population ratio, 15+, male (%) (modeled ILO estimate)"                                                                        
##  [302] "Employment to population ratio, 15+, male (%) (national estimate)"                                                                           
##  [303] "Employment to population ratio, 15+, total (%) (modeled ILO estimate)"                                                                       
##  [304] "Employment to population ratio, 15+, total (%) (national estimate)"                                                                          
##  [305] "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)"                                                               
##  [306] "Employment to population ratio, ages 15-24, female (%) (national estimate)"                                                                  
##  [307] "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)"                                                                 
##  [308] "Employment to population ratio, ages 15-24, male (%) (national estimate)"                                                                    
##  [309] "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)"                                                                
##  [310] "Employment to population ratio, ages 15-24, total (%) (national estimate)"                                                                   
##  [311] "Energy intensity level of primary energy (MJ/$2011 PPP GDP)"                                                                                 
##  [312] "Energy related methane emissions (% of total)"                                                                                               
##  [313] "Exclusive breastfeeding (% of children under 6 months)"                                                                                      
##  [314] "Expenditure on primary education (% of government expenditure on education)"                                                                 
##  [315] "Expenditure on secondary education (% of government expenditure on education)"                                                               
##  [316] "Expenditure on tertiary education (% of government expenditure on education)"                                                                
##  [317] "Expense (% of GDP)"                                                                                                                          
##  [318] "Expense (current LCU)"                                                                                                                       
##  [319] "Export unit value index (2000 = 100)"                                                                                                        
##  [320] "Export value index (2000 = 100)"                                                                                                             
##  [321] "Export volume index (2000 = 100)"                                                                                                            
##  [322] "Exports of goods and services (% of GDP)"                                                                                                    
##  [323] "Exports of goods and services (BoP, current US$)"                                                                                            
##  [324] "Exports of goods and services (constant 2010 US$)"                                                                                           
##  [325] "Exports of goods and services (current LCU)"                                                                                                 
##  [326] "Exports of goods and services (current US$)"                                                                                                 
##  [327] "Exports of goods, services and primary income (BoP, current US$)"                                                                            
##  [328] "External balance on goods and services (% of GDP)"                                                                                           
##  [329] "External balance on goods and services (current LCU)"                                                                                        
##  [330] "External balance on goods and services (current US$)"                                                                                        
##  [331] "External debt stocks (% of GNI)"                                                                                                             
##  [332] "External debt stocks, long-term (DOD, current US$)"                                                                                          
##  [333] "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)"                                                                        
##  [334] "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)"                                                               
##  [335] "External debt stocks, short-term (DOD, current US$)"                                                                                         
##  [336] "External debt stocks, total (DOD, current US$)"                                                                                              
##  [337] "External health expenditure (% of current health expenditure)"                                                                               
##  [338] "External health expenditure per capita (current US$)"                                                                                        
##  [339] "External health expenditure per capita, PPP (current international $)"                                                                       
##  [340] "Female headed households (% of households with a female head)"                                                                               
##  [341] "Female share of employment in senior and middle management (%)"                                                                              
##  [342] "Fertility rate, total (births per woman)"                                                                                                    
##  [343] "Fertilizer consumption (% of fertilizer production)"                                                                                         
##  [344] "Fertilizer consumption (kilograms per hectare of arable land)"                                                                               
##  [345] "Final consumption expenditure (% of GDP)"                                                                                                    
##  [346] "Final consumption expenditure (constant 2010 US$)"                                                                                           
##  [347] "Final consumption expenditure (current LCU)"                                                                                                 
##  [348] "Final consumption expenditure (current US$)"                                                                                                 
##  [349] "Firms competing against unregistered firms (% of firms)"                                                                                     
##  [350] "Firms expected to give gifts in meetings with tax officials (% of firms)"                                                                    
##  [351] "Firms experiencing electrical outages (% of firms)"                                                                                          
##  [352] "Firms experiencing losses due to theft and vandalism (% of firms)"                                                                           
##  [353] "Firms formally registered when operations started (% of firms)"                                                                              
##  [354] "Firms offering formal training (% of firms)"                                                                                                 
##  [355] "Firms that spend on R&amp;D (% of firms)"                                                                                                        
##  [356] "Firms using banks to finance investment (% of firms)"                                                                                        
##  [357] "Firms using banks to finance working capital (% of firms)"                                                                                   
##  [358] "Firms visited or required meetings with tax officials (% of firms)"                                                                          
##  [359] "Firms with female participation in ownership (% of firms)"                                                                                   
##  [360] "Firms with female top manager (% of firms)"                                                                                                  
##  [361] "Fish species, threatened"                                                                                                                    
##  [362] "Fixed broadband subscriptions"                                                                                                               
##  [363] "Fixed broadband subscriptions (per 100 people)"                                                                                              
##  [364] "Fixed telephone subscriptions"                                                                                                               
##  [365] "Fixed telephone subscriptions (per 100 people)"                                                                                              
##  [366] "Food exports (% of merchandise exports)"                                                                                                     
##  [367] "Food imports (% of merchandise imports)"                                                                                                     
##  [368] "Food production index (2004-2006 = 100)"                                                                                                     
##  [369] "Foreign direct investment, net (BoP, current US$)"                                                                                           
##  [370] "Foreign direct investment, net inflows (% of GDP)"                                                                                           
##  [371] "Foreign direct investment, net inflows (BoP, current US$)"                                                                                   
##  [372] "Foreign direct investment, net outflows (% of GDP)"                                                                                          
##  [373] "Foreign direct investment, net outflows (BoP, current US$)"                                                                                  
##  [374] "Forest area (% of land area)"                                                                                                                
##  [375] "Forest area (sq. km)"                                                                                                                        
##  [376] "Forest rents (% of GDP)"                                                                                                                     
##  [377] "Fuel exports (% of merchandise exports)"                                                                                                     
##  [378] "Fuel imports (% of merchandise imports)"                                                                                                     
##  [379] "GDP (constant 2010 US$)"                                                                                                                     
##  [380] "GDP (constant LCU)"                                                                                                                          
##  [381] "GDP (current LCU)"                                                                                                                           
##  [382] "GDP (current US$)"                                                                                                                           
##  [383] "GDP deflator (base year varies by country)"                                                                                                  
##  [384] "GDP deflator: linked series (base year varies by country)"                                                                                   
##  [385] "GDP growth (annual %)"                                                                                                                       
##  [386] "GDP per capita (constant 2010 US$)"                                                                                                          
##  [387] "GDP per capita (constant LCU)"                                                                                                               
##  [388] "GDP per capita (current LCU)"                                                                                                                
##  [389] "GDP per capita (current US$)"                                                                                                                
##  [390] "GDP per capita growth (annual %)"                                                                                                            
##  [391] "GDP per capita, PPP (constant 2017 international $)"                                                                                         
##  [392] "GDP per capita, PPP (current international $)"                                                                                               
##  [393] "GDP per person employed (constant 2017 PPP $)"                                                                                               
##  [394] "GDP, PPP (constant 2017 international $)"                                                                                                    
##  [395] "GDP, PPP (current international $)"                                                                                                          
##  [396] "GDP: linked series (current LCU)"                                                                                                            
##  [397] "General government final consumption expenditure (% of GDP)"                                                                                 
##  [398] "General government final consumption expenditure (constant 2010 US$)"                                                                        
##  [399] "General government final consumption expenditure (current LCU)"                                                                              
##  [400] "General government final consumption expenditure (current US$)"                                                                              
##  [401] "GNI (constant 2010 US$)"                                                                                                                     
##  [402] "GNI (current LCU)"                                                                                                                           
##  [403] "GNI (current US$)"                                                                                                                           
##  [404] "GNI per capita (constant 2010 US$)"                                                                                                          
##  [405] "GNI per capita (current LCU)"                                                                                                                
##  [406] "GNI per capita, Atlas method (current US$)"                                                                                                  
##  [407] "GNI per capita, PPP (constant 2017 international $)"                                                                                         
##  [408] "GNI per capita, PPP (current international $)"                                                                                               
##  [409] "GNI, Atlas method (current US$)"                                                                                                             
##  [410] "GNI, PPP (constant 2017 international $)"                                                                                                    
##  [411] "GNI, PPP (current international $)"                                                                                                          
##  [412] "GNI: linked series (current LCU)"                                                                                                            
##  [413] "Goods and services expense (% of expense)"                                                                                                   
##  [414] "Goods and services expense (current LCU)"                                                                                                    
##  [415] "Goods exports (BoP, current US$)"                                                                                                            
##  [416] "Goods imports (BoP, current US$)"                                                                                                            
##  [417] "Government expenditure on education, total (% of GDP)"                                                                                       
##  [418] "Government expenditure on education, total (% of government expenditure)"                                                                    
##  [419] "Government expenditure per student, primary (% of GDP per capita)"                                                                           
##  [420] "Government expenditure per student, secondary (% of GDP per capita)"                                                                         
##  [421] "Government expenditure per student, tertiary (% of GDP per capita)"                                                                          
##  [422] "Grants and other revenue (% of revenue)"                                                                                                     
##  [423] "Grants and other revenue (current LCU)"                                                                                                      
##  [424] "Grants, excluding technical cooperation (BoP, current US$)"                                                                                  
##  [425] "Gross capital formation (% of GDP)"                                                                                                          
##  [426] "Gross capital formation (constant 2010 US$)"                                                                                                 
##  [427] "Gross capital formation (current LCU)"                                                                                                       
##  [428] "Gross capital formation (current US$)"                                                                                                       
##  [429] "Gross domestic savings (% of GDP)"                                                                                                           
##  [430] "Gross domestic savings (current LCU)"                                                                                                        
##  [431] "Gross domestic savings (current US$)"                                                                                                        
##  [432] "Gross fixed capital formation (% of GDP)"                                                                                                    
##  [433] "Gross fixed capital formation (constant 2010 US$)"                                                                                           
##  [434] "Gross fixed capital formation (current LCU)"                                                                                                 
##  [435] "Gross fixed capital formation (current US$)"                                                                                                 
##  [436] "Gross intake ratio in first grade of primary education, female (% of relevant age group)"                                                    
##  [437] "Gross intake ratio in first grade of primary education, male (% of relevant age group)"                                                      
##  [438] "Gross intake ratio in first grade of primary education, total (% of relevant age group)"                                                     
##  [439] "Gross national expenditure (% of GDP)"                                                                                                       
##  [440] "Gross national expenditure (constant 2010 US$)"                                                                                              
##  [441] "Gross national expenditure (current LCU)"                                                                                                    
##  [442] "Gross national expenditure (current US$)"                                                                                                    
##  [443] "Gross savings (% of GDP)"                                                                                                                    
##  [444] "Gross savings (% of GNI)"                                                                                                                    
##  [445] "Gross savings (current LCU)"                                                                                                                 
##  [446] "Gross savings (current US$)"                                                                                                                 
##  [447] "Gross value added at basic prices (GVA) (constant 2010 US$)"                                                                                 
##  [448] "Gross value added at basic prices (GVA) (constant LCU)"                                                                                      
##  [449] "Gross value added at basic prices (GVA) (current LCU)"                                                                                       
##  [450] "Gross value added at basic prices (GVA) (current US$)"                                                                                       
##  [451] "Hospital beds (per 1,000 people)"                                                                                                            
##  [452] "Households and NPISHs final consumption expenditure (% of GDP)"                                                                              
##  [453] "Households and NPISHs Final consumption expenditure (constant 2010 US$)"                                                                     
##  [454] "Households and NPISHs Final consumption expenditure (current LCU)"                                                                           
##  [455] "Households and NPISHs Final consumption expenditure (current US$)"                                                                           
##  [456] "Households and NPISHs Final consumption expenditure per capita (constant 2010 US$)"                                                          
##  [457] "Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $)"                                                    
##  [458] "Households and NPISHs Final consumption expenditure, PPP (current international $)"                                                          
##  [459] "Households and NPISHs final consumption expenditure: linked series (current LCU)"                                                            
##  [460] "Human capital index (HCI) (scale 0-1)"                                                                                                       
##  [461] "Human capital index (HCI), female (scale 0-1)"                                                                                               
##  [462] "Human capital index (HCI), female, lower bound (scale 0-1)"                                                                                  
##  [463] "Human capital index (HCI), female, upper bound (scale 0-1)"                                                                                  
##  [464] "Human capital index (HCI), lower bound (scale 0-1)"                                                                                          
##  [465] "Human capital index (HCI), male (scale 0-1)"                                                                                                 
##  [466] "Human capital index (HCI), male, lower bound (scale 0-1)"                                                                                    
##  [467] "Human capital index (HCI), male, upper bound (scale 0-1)"                                                                                    
##  [468] "Human capital index (HCI), upper bound (scale 0-1)"                                                                                          
##  [469] "IBRD loans and IDA credits (DOD, current US$)"                                                                                               
##  [470] "ICT goods imports (% total goods imports)"                                                                                                   
##  [471] "ICT service exports (% of service exports, BoP)"                                                                                             
##  [472] "ICT service exports (BoP, current US$)"                                                                                                      
##  [473] "IDA resource allocation index (1=low to 6=high)"                                                                                             
##  [474] "IFC, private nonguaranteed (NFL, current US$)"                                                                                               
##  [475] "IMF repurchases and charges (TDS, current US$)"                                                                                              
##  [476] "Immunization, DPT (% of children ages 12-23 months)"                                                                                         
##  [477] "Immunization, HepB3 (% of one-year-old children)"                                                                                            
##  [478] "Immunization, measles (% of children ages 12-23 months)"                                                                                     
##  [479] "Import unit value index (2000 = 100)"                                                                                                        
##  [480] "Import value index (2000 = 100)"                                                                                                             
##  [481] "Import volume index (2000 = 100)"                                                                                                            
##  [482] "Imports of goods and services (% of GDP)"                                                                                                    
##  [483] "Imports of goods and services (BoP, current US$)"                                                                                            
##  [484] "Imports of goods and services (constant 2010 US$)"                                                                                           
##  [485] "Imports of goods and services (current LCU)"                                                                                                 
##  [486] "Imports of goods and services (current US$)"                                                                                                 
##  [487] "Imports of goods, services and primary income (BoP, current US$)"                                                                            
##  [488] "Incidence of HIV (per 1,000 uninfected population ages 15-49)"                                                                               
##  [489] "Incidence of malaria (per 1,000 population at risk)"                                                                                         
##  [490] "Incidence of tuberculosis (per 100,000 people)"                                                                                              
##  [491] "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)"                 
##  [492] "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)"                               
##  [493] "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)"                 
##  [494] "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)"                               
##  [495] "Individuals using the Internet (% of population)"                                                                                            
##  [496] "Industry (including construction), value added (% of GDP)"                                                                                   
##  [497] "Industry (including construction), value added (annual % growth)"                                                                            
##  [498] "Industry (including construction), value added (constant 2010 US$)"                                                                          
##  [499] "Industry (including construction), value added (constant LCU)"                                                                               
##  [500] "Industry (including construction), value added (current LCU)"                                                                                
##  [501] "Industry (including construction), value added (current US$)"                                                                                
##  [502] "Industry (including construction), value added per worker (constant 2010 US$)"                                                               
##  [503] "Inflation, consumer prices (annual %)"                                                                                                       
##  [504] "Inflation, GDP deflator (annual %)"                                                                                                          
##  [505] "Inflation, GDP deflator: linked series (annual %)"                                                                                           
##  [506] "Informal payments to public officials (% of firms)"                                                                                          
##  [507] "Insurance and financial services (% of commercial service exports)"                                                                          
##  [508] "Insurance and financial services (% of commercial service imports)"                                                                          
##  [509] "Insurance and financial services (% of service exports, BoP)"                                                                                
##  [510] "Insurance and financial services (% of service imports, BoP)"                                                                                
##  [511] "Intentional homicides (per 100,000 people)"                                                                                                  
##  [512] "Interest payments (% of expense)"                                                                                                            
##  [513] "Interest payments (% of revenue)"                                                                                                            
##  [514] "Interest payments (current LCU)"                                                                                                             
##  [515] "Internally displaced persons, new displacement associated with conflict and violence (number of cases)"                                      
##  [516] "Internally displaced persons, new displacement associated with disasters (number of cases)"                                                  
##  [517] "Internally displaced persons, total displaced by conflict and violence (number of people)"                                                   
##  [518] "International migrant stock (% of population)"                                                                                               
##  [519] "International migrant stock, total"                                                                                                          
##  [520] "International tourism, expenditures (% of total imports)"                                                                                    
##  [521] "International tourism, expenditures (current US$)"                                                                                           
##  [522] "International tourism, expenditures for passenger transport items (current US$)"                                                             
##  [523] "International tourism, expenditures for travel items (current US$)"                                                                          
##  [524] "International tourism, receipts (% of total exports)"                                                                                        
##  [525] "International tourism, receipts (current US$)"                                                                                               
##  [526] "International tourism, receipts for passenger transport items (current US$)"                                                                 
##  [527] "International tourism, receipts for travel items (current US$)"                                                                              
##  [528] "Investment in energy with private participation (current US$)"                                                                               
##  [529] "Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)"                                                            
##  [530] "Labor force participation rate for ages 15-24, female (%) (national estimate)"                                                               
##  [531] "Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)"                                                              
##  [532] "Labor force participation rate for ages 15-24, male (%) (national estimate)"                                                                 
##  [533] "Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)"                                                             
##  [534] "Labor force participation rate for ages 15-24, total (%) (national estimate)"                                                                
##  [535] "Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)"                                           
##  [536] "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)"                                             
##  [537] "Labor force participation rate, female (% of female population ages 15+) (national estimate)"                                                
##  [538] "Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)"                                               
##  [539] "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)"                                                 
##  [540] "Labor force participation rate, male (% of male population ages 15+) (national estimate)"                                                    
##  [541] "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)"                                             
##  [542] "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)"                                               
##  [543] "Labor force participation rate, total (% of total population ages 15+) (national estimate)"                                                  
##  [544] "Labor force with advanced education (% of total working-age population with advanced education)"                                             
##  [545] "Labor force with advanced education, female (% of female working-age population with advanced education)"                                    
##  [546] "Labor force with advanced education, male (% of male working-age population with advanced education)"                                        
##  [547] "Labor force with basic education (% of total working-age population with basic education)"                                                   
##  [548] "Labor force with basic education, female (% of female working-age population with basic education)"                                          
##  [549] "Labor force with basic education, male (% of male working-age population with basic education)"                                              
##  [550] "Labor force with intermediate education (% of total working-age population with intermediate education)"                                     
##  [551] "Labor force with intermediate education, female (% of female working-age population with intermediate education)"                            
##  [552] "Labor force with intermediate education, male (% of male working-age population with intermediate education)"                                
##  [553] "Labor force, female (% of total labor force)"                                                                                                
##  [554] "Labor force, total"                                                                                                                          
##  [555] "Labor tax and contributions (% of commercial profits)"                                                                                       
##  [556] "Land area (sq. km)"                                                                                                                          
##  [557] "Land under cereal production (hectares)"                                                                                                     
##  [558] "Lead time to export, median case (days)"                                                                                                     
##  [559] "Lead time to import, median case (days)"                                                                                                     
##  [560] "Lending interest rate (%)"                                                                                                                   
##  [561] "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources"                                              
##  [562] "Life expectancy at birth, female (years)"                                                                                                    
##  [563] "Life expectancy at birth, male (years)"                                                                                                      
##  [564] "Life expectancy at birth, total (years)"                                                                                                     
##  [565] "Lifetime risk of maternal death (%)"                                                                                                         
##  [566] "Lifetime risk of maternal death (1 in: rate varies by country)"                                                                              
##  [567] "Literacy rate, adult female (% of females ages 15 and above)"                                                                                
##  [568] "Literacy rate, adult male (% of males ages 15 and above)"                                                                                    
##  [569] "Literacy rate, adult total (% of people ages 15 and above)"                                                                                  
##  [570] "Literacy rate, youth (ages 15-24), gender parity index (GPI)"                                                                                
##  [571] "Literacy rate, youth female (% of females ages 15-24)"                                                                                       
##  [572] "Literacy rate, youth male (% of males ages 15-24)"                                                                                           
##  [573] "Literacy rate, youth total (% of people ages 15-24)"                                                                                         
##  [574] "Livestock production index (2004-2006 = 100)"                                                                                                
##  [575] "Logistics performance index: Ability to track and trace consignments (1=low to 5=high)"                                                      
##  [576] "Logistics performance index: Competence and quality of logistics services (1=low to 5=high)"                                                 
##  [577] "Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high)"                                             
##  [578] "Logistics performance index: Efficiency of customs clearance process (1=low to 5=high)"                                                      
##  [579] "Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high)"             
##  [580] "Logistics performance index: Overall (1=low to 5=high)"                                                                                      
##  [581] "Logistics performance index: Quality of trade and transport-related infrastructure (1=low to 5=high)"                                        
##  [582] "Losses due to theft and vandalism (% of annual sales of affected firms)"                                                                     
##  [583] "Lower secondary completion rate, female (% of relevant age group)"                                                                           
##  [584] "Lower secondary completion rate, male (% of relevant age group)"                                                                             
##  [585] "Lower secondary completion rate, total (% of relevant age group)"                                                                            
##  [586] "Lower secondary school starting age (years)"                                                                                                 
##  [587] "Mammal species, threatened"                                                                                                                  
##  [588] "Manufactures exports (% of merchandise exports)"                                                                                             
##  [589] "Manufactures imports (% of merchandise imports)"                                                                                             
##  [590] "Manufacturing, value added (% of GDP)"                                                                                                       
##  [591] "Manufacturing, value added (annual % growth)"                                                                                                
##  [592] "Manufacturing, value added (constant 2010 US$)"                                                                                              
##  [593] "Manufacturing, value added (constant LCU)"                                                                                                   
##  [594] "Manufacturing, value added (current LCU)"                                                                                                    
##  [595] "Manufacturing, value added (current US$)"                                                                                                    
##  [596] "Maternal mortality ratio (modeled estimate, per 100,000 live births)"                                                                        
##  [597] "Maternal mortality ratio (national estimate, per 100,000 live births)"                                                                       
##  [598] "Medium and high-tech exports (% manufactured exports)"                                                                                       
##  [599] "Medium and high-tech Industry (including construction) (% manufacturing value added)"                                                        
##  [600] "Merchandise exports (current US$)"                                                                                                           
##  [601] "Merchandise exports by the reporting economy (current US$)"                                                                                  
##  [602] "Merchandise exports by the reporting economy, residual (% of total merchandise exports)"                                                     
##  [603] "Merchandise exports to economies in the Arab World (% of total merchandise exports)"                                                         
##  [604] "Merchandise exports to high-income economies (% of total merchandise exports)"                                                               
##  [605] "Merchandise exports to low- and middle-income economies in East Asia &amp; Pacific (% of total merchandise exports)"                             
##  [606] "Merchandise exports to low- and middle-income economies in Europe &amp; Central Asia (% of total merchandise exports)"                           
##  [607] "Merchandise exports to low- and middle-income economies in Latin America &amp; the Caribbean (% of total merchandise exports)"                   
##  [608] "Merchandise exports to low- and middle-income economies in Middle East &amp; North Africa (% of total merchandise exports)"                      
##  [609] "Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports)"                                      
##  [610] "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)"                              
##  [611] "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)"                                     
##  [612] "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)"                                      
##  [613] "Merchandise imports (current US$)"                                                                                                           
##  [614] "Merchandise imports by the reporting economy (current US$)"                                                                                  
##  [615] "Merchandise imports by the reporting economy, residual (% of total merchandise imports)"                                                     
##  [616] "Merchandise imports from economies in the Arab World (% of total merchandise imports)"                                                       
##  [617] "Merchandise imports from high-income economies (% of total merchandise imports)"                                                             
##  [618] "Merchandise imports from low- and middle-income economies in East Asia &amp; Pacific (% of total merchandise imports)"                           
##  [619] "Merchandise imports from low- and middle-income economies in Europe &amp; Central Asia (% of total merchandise imports)"                         
##  [620] "Merchandise imports from low- and middle-income economies in Latin America &amp; the Caribbean (% of total merchandise imports)"                 
##  [621] "Merchandise imports from low- and middle-income economies in Middle East &amp; North Africa (% of total merchandise imports)"                    
##  [622] "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)"                                    
##  [623] "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)"                            
##  [624] "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)"                                   
##  [625] "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)"                                    
##  [626] "Merchandise trade (% of GDP)"                                                                                                                
##  [627] "Methane emissions (% change from 1990)"                                                                                                      
##  [628] "Methane emissions (kt of CO2 equivalent)"                                                                                                    
##  [629] "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)"                                                                 
##  [630] "Methodology assessment of statistical capacity (scale 0 - 100)"                                                                              
##  [631] "Military expenditure (% of GDP)"                                                                                                             
##  [632] "Military expenditure (% of general government expenditure)"                                                                                  
##  [633] "Military expenditure (current LCU)"                                                                                                          
##  [634] "Military expenditure (current USD)"                                                                                                          
##  [635] "Mineral rents (% of GDP)"                                                                                                                    
##  [636] "Mobile cellular subscriptions"                                                                                                               
##  [637] "Mobile cellular subscriptions (per 100 people)"                                                                                              
##  [638] "Monetary Sector credit to private sector (% GDP)"                                                                                            
##  [639] "Mortality caused by road traffic injury (per 100,000 people)"                                                                                
##  [640] "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)"                                                                
##  [641] "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)"                                                        
##  [642] "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)"                                                          
##  [643] "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)"                                 
##  [644] "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)"                  
##  [645] "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)"                      
##  [646] "Mortality rate attributed to unintentional poisoning (per 100,000 population)"                                                               
##  [647] "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)"                                                
##  [648] "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)"                                                    
##  [649] "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)"                                   
##  [650] "Mortality rate, adult, female (per 1,000 female adults)"                                                                                     
##  [651] "Mortality rate, adult, male (per 1,000 male adults)"                                                                                         
##  [652] "Mortality rate, infant (per 1,000 live births)"                                                                                              
##  [653] "Mortality rate, infant, female (per 1,000 live births)"                                                                                      
##  [654] "Mortality rate, infant, male (per 1,000 live births)"                                                                                        
##  [655] "Mortality rate, neonatal (per 1,000 live births)"                                                                                            
##  [656] "Mortality rate, under-5 (per 1,000 live births)"                                                                                             
##  [657] "Mortality rate, under-5, female (per 1,000 live births)"                                                                                     
##  [658] "Mortality rate, under-5, male (per 1,000 live births)"                                                                                       
##  [659] "Multidimensional poverty headcount ratio (% of total population)"                                                                            
##  [660] "Multidimensional poverty headcount ratio, children (% of population ages 0-17)"                                                              
##  [661] "Multidimensional poverty index (scale 0-1)"                                                                                                  
##  [662] "Multidimensional poverty index, children (population ages 0-17) (scale 0-1)"                                                                 
##  [663] "Multidimensional poverty intensity (average share of deprivations experienced by the poor)"                                                  
##  [664] "Multilateral debt service (% of public and publicly guaranteed debt service)"                                                                
##  [665] "Multilateral debt service (TDS, current US$)"                                                                                                
##  [666] "Natural gas rents (% of GDP)"                                                                                                                
##  [667] "Net acquisition of financial assets (% of GDP)"                                                                                              
##  [668] "Net acquisition of financial assets (current LCU)"                                                                                           
##  [669] "Net barter terms of trade index (2000 = 100)"                                                                                                
##  [670] "Net bilateral aid flows from DAC donors, Australia (current US$)"                                                                            
##  [671] "Net bilateral aid flows from DAC donors, Austria (current US$)"                                                                              
##  [672] "Net bilateral aid flows from DAC donors, Belgium (current US$)"                                                                              
##  [673] "Net bilateral aid flows from DAC donors, Canada (current US$)"                                                                               
##  [674] "Net bilateral aid flows from DAC donors, Czech Republic (current US$)"                                                                       
##  [675] "Net bilateral aid flows from DAC donors, Denmark (current US$)"                                                                              
##  [676] "Net bilateral aid flows from DAC donors, European Union institutions (current US$)"                                                          
##  [677] "Net bilateral aid flows from DAC donors, Finland (current US$)"                                                                              
##  [678] "Net bilateral aid flows from DAC donors, France (current US$)"                                                                               
##  [679] "Net bilateral aid flows from DAC donors, Germany (current US$)"                                                                              
##  [680] "Net bilateral aid flows from DAC donors, Greece (current US$)"                                                                               
##  [681] "Net bilateral aid flows from DAC donors, Hungary (current US$)"                                                                              
##  [682] "Net bilateral aid flows from DAC donors, Iceland (current US$)"                                                                              
##  [683] "Net bilateral aid flows from DAC donors, Ireland (current US$)"                                                                              
##  [684] "Net bilateral aid flows from DAC donors, Italy (current US$)"                                                                                
##  [685] "Net bilateral aid flows from DAC donors, Japan (current US$)"                                                                                
##  [686] "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)"                                                                          
##  [687] "Net bilateral aid flows from DAC donors, Luxembourg (current US$)"                                                                           
##  [688] "Net bilateral aid flows from DAC donors, Netherlands (current US$)"                                                                          
##  [689] "Net bilateral aid flows from DAC donors, New Zealand (current US$)"                                                                          
##  [690] "Net bilateral aid flows from DAC donors, Norway (current US$)"                                                                               
##  [691] "Net bilateral aid flows from DAC donors, Poland (current US$)"                                                                               
##  [692] "Net bilateral aid flows from DAC donors, Portugal (current US$)"                                                                             
##  [693] "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)"                                                                      
##  [694] "Net bilateral aid flows from DAC donors, Slovenia (current US$)"                                                                             
##  [695] "Net bilateral aid flows from DAC donors, Spain (current US$)"                                                                                
##  [696] "Net bilateral aid flows from DAC donors, Sweden (current US$)"                                                                               
##  [697] "Net bilateral aid flows from DAC donors, Switzerland (current US$)"                                                                          
##  [698] "Net bilateral aid flows from DAC donors, Total (current US$)"                                                                                
##  [699] "Net bilateral aid flows from DAC donors, United Kingdom (current US$)"                                                                       
##  [700] "Net bilateral aid flows from DAC donors, United States (current US$)"                                                                        
##  [701] "Net capital account (BoP, current US$)"                                                                                                      
##  [702] "Net domestic credit (current LCU)"                                                                                                           
##  [703] "Net errors and omissions (BoP, current US$)"                                                                                                 
##  [704] "Net financial account (BoP, current US$)"                                                                                                    
##  [705] "Net financial flows, bilateral (NFL, current US$)"                                                                                           
##  [706] "Net financial flows, IBRD (NFL, current US$)"                                                                                                
##  [707] "Net financial flows, IDA (NFL, current US$)"                                                                                                 
##  [708] "Net financial flows, IMF concessional (NFL, current US$)"                                                                                    
##  [709] "Net financial flows, IMF nonconcessional (NFL, current US$)"                                                                                 
##  [710] "Net financial flows, multilateral (NFL, current US$)"                                                                                        
##  [711] "Net financial flows, others (NFL, current US$)"                                                                                              
##  [712] "Net financial flows, RDB concessional (NFL, current US$)"                                                                                    
##  [713] "Net financial flows, RDB nonconcessional (NFL, current US$)"                                                                                 
##  [714] "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)"                                                                  
##  [715] "Net foreign assets (current LCU)"                                                                                                            
##  [716] "Net incurrence of liabilities, total (% of GDP)"                                                                                             
##  [717] "Net incurrence of liabilities, total (current LCU)"                                                                                          
##  [718] "Net intake rate in grade 1 (% of official school-age population)"                                                                            
##  [719] "Net intake rate in grade 1, female (% of official school-age population)"                                                                    
##  [720] "Net intake rate in grade 1, male (% of official school-age population)"                                                                      
##  [721] "Net investment in nonfinancial assets (% of GDP)"                                                                                            
##  [722] "Net investment in nonfinancial assets (current LCU)"                                                                                         
##  [723] "Net lending (+) / net borrowing (-) (% of GDP)"                                                                                              
##  [724] "Net lending (+) / net borrowing (-) (current LCU)"                                                                                           
##  [725] "Net migration"                                                                                                                               
##  [726] "Net ODA received (% of central government expense)"                                                                                          
##  [727] "Net ODA received (% of GNI)"                                                                                                                 
##  [728] "Net ODA received (% of gross capital formation)"                                                                                             
##  [729] "Net ODA received (% of imports of goods, services and primary income)"                                                                       
##  [730] "Net ODA received per capita (current US$)"                                                                                                   
##  [731] "Net official development assistance and official aid received (constant 2015 US$)"                                                           
##  [732] "Net official development assistance and official aid received (current US$)"                                                                 
##  [733] "Net official development assistance received (constant 2015 US$)"                                                                            
##  [734] "Net official development assistance received (current US$)"                                                                                  
##  [735] "Net official flows from UN agencies, FAO (current US$)"                                                                                      
##  [736] "Net official flows from UN agencies, IAEA (current US$)"                                                                                     
##  [737] "Net official flows from UN agencies, IFAD (current US$)"                                                                                     
##  [738] "Net official flows from UN agencies, ILO (current US$)"                                                                                      
##  [739] "Net official flows from UN agencies, UNDP (current US$)"                                                                                     
##  [740] "Net official flows from UN agencies, UNFPA (current US$)"                                                                                    
##  [741] "Net official flows from UN agencies, UNHCR (current US$)"                                                                                    
##  [742] "Net official flows from UN agencies, UNICEF (current US$)"                                                                                   
##  [743] "Net official flows from UN agencies, UNTA (current US$)"                                                                                     
##  [744] "Net official flows from UN agencies, WFP (current US$)"                                                                                      
##  [745] "Net official flows from UN agencies, WHO (current US$)"                                                                                      
##  [746] "Net primary income (BoP, current US$)"                                                                                                       
##  [747] "Net primary income (Net income from abroad) (current LCU)"                                                                                   
##  [748] "Net primary income (Net income from abroad) (current US$)"                                                                                   
##  [749] "Net secondary income (BoP, current US$)"                                                                                                     
##  [750] "Net secondary income (Net current transfers from abroad) (current LCU)"                                                                      
##  [751] "Net secondary income (Net current transfers from abroad) (current US$)"                                                                      
##  [752] "Net trade in goods (BoP, current US$)"                                                                                                       
##  [753] "Net trade in goods and services (BoP, current US$)"                                                                                          
##  [754] "New business density (new registrations per 1,000 people ages 15-64)"                                                                        
##  [755] "New businesses registered (number)"                                                                                                          
##  [756] "Newborns protected against tetanus (%)"                                                                                                      
##  [757] "Nitrous oxide emissions (% change from 1990)"                                                                                                
##  [758] "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)"                                                                            
##  [759] "Nitrous oxide emissions in energy sector (% of total)"                                                                                       
##  [760] "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)"                                                           
##  [761] "Number of deaths ages 5-14 years"                                                                                                            
##  [762] "Number of infant deaths"                                                                                                                     
##  [763] "Number of maternal deaths"                                                                                                                   
##  [764] "Number of neonatal deaths"                                                                                                                   
##  [765] "Number of under-five deaths"                                                                                                                 
##  [766] "Nurses and midwives (per 1,000 people)"                                                                                                      
##  [767] "Official exchange rate (LCU per US$, period average)"                                                                                        
##  [768] "Oil rents (% of GDP)"                                                                                                                        
##  [769] "Ores and metals exports (% of merchandise exports)"                                                                                          
##  [770] "Ores and metals imports (% of merchandise imports)"                                                                                          
##  [771] "Other expense (% of expense)"                                                                                                                
##  [772] "Other expense (current LCU)"                                                                                                                 
##  [773] "Other greenhouse gas emissions (% change from 1990)"                                                                                         
##  [774] "Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)"                                                   
##  [775] "Other taxes (% of revenue)"                                                                                                                  
##  [776] "Other taxes (current LCU)"                                                                                                                   
##  [777] "Other taxes payable by businesses (% of commercial profits)"                                                                                 
##  [778] "Out-of-pocket expenditure (% of current health expenditure)"                                                                                 
##  [779] "Out-of-pocket expenditure per capita (current US$)"                                                                                          
##  [780] "Out-of-pocket expenditure per capita, PPP (current international $)"                                                                         
##  [781] "Over-age students, primary (% of enrollment)"                                                                                                
##  [782] "Over-age students, primary, female (% of female enrollment)"                                                                                 
##  [783] "Over-age students, primary, male (% of male enrollment)"                                                                                     
##  [784] "Part time employment, female (% of total female employment)"                                                                                 
##  [785] "Part time employment, male (% of total male employment)"                                                                                     
##  [786] "Part time employment, total (% of total employment)"                                                                                         
##  [787] "People practicing open defecation (% of population)"                                                                                         
##  [788] "People practicing open defecation, rural (% of rural population)"                                                                            
##  [789] "People practicing open defecation, urban (% of urban population)"                                                                            
##  [790] "People using at least basic drinking water services (% of population)"                                                                       
##  [791] "People using at least basic drinking water services, rural (% of rural population)"                                                          
##  [792] "People using at least basic drinking water services, urban (% of urban population)"                                                          
##  [793] "People using at least basic sanitation services (% of population)"                                                                           
##  [794] "People using at least basic sanitation services, rural (% of rural population)"                                                              
##  [795] "People using at least basic sanitation services, urban (% of urban population)"                                                              
##  [796] "People with basic handwashing facilities including soap and water (% of population)"                                                         
##  [797] "People with basic handwashing facilities including soap and water, rural (% of rural population)"                                            
##  [798] "People with basic handwashing facilities including soap and water, urban (% of urban population)"                                            
##  [799] "Periodicity and timeliness assessment of statistical capacity (scale 0 - 100)"                                                               
##  [800] "Permanent cropland (% of land area)"                                                                                                         
##  [801] "Persistence to grade 5, female (% of cohort)"                                                                                                
##  [802] "Persistence to grade 5, male (% of cohort)"                                                                                                  
##  [803] "Persistence to grade 5, total (% of cohort)"                                                                                                 
##  [804] "Persistence to last grade of primary, female (% of cohort)"                                                                                  
##  [805] "Persistence to last grade of primary, male (% of cohort)"                                                                                    
##  [806] "Persistence to last grade of primary, total (% of cohort)"                                                                                   
##  [807] "Personal remittances, paid (current US$)"                                                                                                    
##  [808] "Personal remittances, received (% of GDP)"                                                                                                   
##  [809] "Personal remittances, received (current US$)"                                                                                                
##  [810] "Personal transfers, receipts (BoP, current US$)"                                                                                             
##  [811] "Physicians (per 1,000 people)"                                                                                                               
##  [812] "Plant species (higher), threatened"                                                                                                          
##  [813] "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)"                                                                      
##  [814] "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)"                                                
##  [815] "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)"                                             
##  [816] "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)"                                             
##  [817] "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)"                                             
##  [818] "PNG, bonds (NFL, current US$)"                                                                                                               
##  [819] "PNG, commercial banks and other creditors (NFL, current US$)"                                                                                
##  [820] "Population ages 0-14 (% of total population)"                                                                                                
##  [821] "Population ages 0-14, female"                                                                                                                
##  [822] "Population ages 0-14, female (% of female population)"                                                                                       
##  [823] "Population ages 0-14, male"                                                                                                                  
##  [824] "Population ages 0-14, male (% of male population)"                                                                                           
##  [825] "Population ages 0-14, total"                                                                                                                 
##  [826] "Population ages 00-04, female (% of female population)"                                                                                      
##  [827] "Population ages 00-04, male (% of male population)"                                                                                          
##  [828] "Population ages 05-09, female (% of female population)"                                                                                      
##  [829] "Population ages 05-09, male (% of male population)"                                                                                          
##  [830] "Population ages 10-14, female (% of female population)"                                                                                      
##  [831] "Population ages 10-14, male (% of male population)"                                                                                          
##  [832] "Population ages 15-19, female (% of female population)"                                                                                      
##  [833] "Population ages 15-19, male (% of male population)"                                                                                          
##  [834] "Population ages 15-64 (% of total population)"                                                                                               
##  [835] "Population ages 15-64, female"                                                                                                               
##  [836] "Population ages 15-64, female (% of female population)"                                                                                      
##  [837] "Population ages 15-64, male"                                                                                                                 
##  [838] "Population ages 15-64, male (% of male population)"                                                                                          
##  [839] "Population ages 15-64, total"                                                                                                                
##  [840] "Population ages 20-24, female (% of female population)"                                                                                      
##  [841] "Population ages 20-24, male (% of male population)"                                                                                          
##  [842] "Population ages 25-29, female (% of female population)"                                                                                      
##  [843] "Population ages 25-29, male (% of male population)"                                                                                          
##  [844] "Population ages 30-34, female (% of female population)"                                                                                      
##  [845] "Population ages 30-34, male (% of male population)"                                                                                          
##  [846] "Population ages 35-39, female (% of female population)"                                                                                      
##  [847] "Population ages 35-39, male (% of male population)"                                                                                          
##  [848] "Population ages 40-44, female (% of female population)"                                                                                      
##  [849] "Population ages 40-44, male (% of male population)"                                                                                          
##  [850] "Population ages 45-49, female (% of female population)"                                                                                      
##  [851] "Population ages 45-49, male (% of male population)"                                                                                          
##  [852] "Population ages 50-54, female (% of female population)"                                                                                      
##  [853] "Population ages 50-54, male (% of male population)"                                                                                          
##  [854] "Population ages 55-59, female (% of female population)"                                                                                      
##  [855] "Population ages 55-59, male (% of male population)"                                                                                          
##  [856] "Population ages 60-64, female (% of female population)"                                                                                      
##  [857] "Population ages 60-64, male (% of male population)"                                                                                          
##  [858] "Population ages 65 and above (% of total population)"                                                                                        
##  [859] "Population ages 65 and above, female"                                                                                                        
##  [860] "Population ages 65 and above, female (% of female population)"                                                                               
##  [861] "Population ages 65 and above, male"                                                                                                          
##  [862] "Population ages 65 and above, male (% of male population)"                                                                                   
##  [863] "Population ages 65 and above, total"                                                                                                         
##  [864] "Population ages 65-69, female (% of female population)"                                                                                      
##  [865] "Population ages 65-69, male (% of male population)"                                                                                          
##  [866] "Population ages 70-74, female (% of female population)"                                                                                      
##  [867] "Population ages 70-74, male (% of male population)"                                                                                          
##  [868] "Population ages 75-79, female (% of female population)"                                                                                      
##  [869] "Population ages 75-79, male (% of male population)"                                                                                          
##  [870] "Population ages 80 and above, female (% of female population)"                                                                               
##  [871] "Population ages 80 and above, male (% of male population)"                                                                                   
##  [872] "Population density (people per sq. km of land area)"                                                                                         
##  [873] "Population growth (annual %)"                                                                                                                
##  [874] "Population in largest city"                                                                                                                  
##  [875] "Population in the largest city (% of urban population)"                                                                                      
##  [876] "Population in urban agglomerations of more than 1 million"                                                                                   
##  [877] "Population in urban agglomerations of more than 1 million (% of total population)"                                                           
##  [878] "Population living in slums (% of urban population)"                                                                                          
##  [879] "Population, female"                                                                                                                          
##  [880] "Population, female (% of total population)"                                                                                                  
##  [881] "Population, male"                                                                                                                            
##  [882] "Population, male (% of total population)"                                                                                                    
##  [883] "Population, total"                                                                                                                           
##  [884] "Portfolio equity, net inflows (BoP, current US$)"                                                                                            
##  [885] "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)"                                                                                  
##  [886] "Portfolio investment, net (BoP, current US$)"                                                                                                
##  [887] "Poverty headcount ratio at national poverty lines (% of population)"                                                                         
##  [888] "Power outages in firms in a typical month (number)"                                                                                          
##  [889] "PPG, bonds (NFL, current US$)"                                                                                                               
##  [890] "PPG, commercial banks (NFL, current US$)"                                                                                                    
##  [891] "PPG, IBRD (DOD, current US$)"                                                                                                                
##  [892] "PPG, IDA (DOD, current US$)"                                                                                                                 
##  [893] "PPG, official creditors (NFL, current US$)"                                                                                                  
##  [894] "PPG, other private creditors (NFL, current US$)"                                                                                             
##  [895] "PPG, private creditors (NFL, current US$)"                                                                                                   
##  [896] "PPP conversion factor, GDP (LCU per international $)"                                                                                        
##  [897] "PPP conversion factor, private consumption (LCU per international $)"                                                                        
##  [898] "Pregnant women receiving prenatal care (%)"                                                                                                  
##  [899] "Preprimary education, duration (years)"                                                                                                      
##  [900] "Presence of peace keepers (number of troops, police, and military observers in mandate)"                                                     
##  [901] "Present value of external debt (% of exports of goods, services and primary income)"                                                         
##  [902] "Present value of external debt (% of GNI)"                                                                                                   
##  [903] "Present value of external debt (current US$)"                                                                                                
##  [904] "Prevalence of anemia among children (% of children under 5)"                                                                                 
##  [905] "Prevalence of anemia among non-pregnant women (% of women ages 15-49)"                                                                       
##  [906] "Prevalence of anemia among pregnant women (%)"                                                                                               
##  [907] "Prevalence of anemia among women of reproductive age (% of women ages 15-49)"                                                                
##  [908] "Prevalence of HIV, female (% ages 15-24)"                                                                                                    
##  [909] "Prevalence of HIV, male (% ages 15-24)"                                                                                                      
##  [910] "Prevalence of HIV, total (% of population ages 15-49)"                                                                                       
##  [911] "Prevalence of moderate or severe food insecurity in the population (%)"                                                                      
##  [912] "Prevalence of overweight, weight for height (% of children under 5)"                                                                         
##  [913] "Prevalence of overweight, weight for height, female (% of children under 5)"                                                                 
##  [914] "Prevalence of overweight, weight for height, male (% of children under 5)"                                                                   
##  [915] "Prevalence of severe food insecurity in the population (%)"                                                                                  
##  [916] "Prevalence of severe wasting, weight for height (% of children under 5)"                                                                     
##  [917] "Prevalence of severe wasting, weight for height, female (% of children under 5)"                                                             
##  [918] "Prevalence of severe wasting, weight for height, male (% of children under 5)"                                                               
##  [919] "Prevalence of stunting, height for age (% of children under 5)"                                                                              
##  [920] "Prevalence of stunting, height for age, female (% of children under 5)"                                                                      
##  [921] "Prevalence of stunting, height for age, male (% of children under 5)"                                                                        
##  [922] "Prevalence of undernourishment (% of population)"                                                                                            
##  [923] "Prevalence of underweight, weight for age (% of children under 5)"                                                                           
##  [924] "Prevalence of underweight, weight for age, female (% of children under 5)"                                                                   
##  [925] "Prevalence of underweight, weight for age, male (% of children under 5)"                                                                     
##  [926] "Prevalence of wasting, weight for height (% of children under 5)"                                                                            
##  [927] "Prevalence of wasting, weight for height, female (% of children under 5)"                                                                    
##  [928] "Prevalence of wasting, weight for height, male (% of children under 5)"                                                                      
##  [929] "Price level ratio of PPP conversion factor (GDP) to market exchange rate"                                                                    
##  [930] "Primary completion rate, female (% of relevant age group)"                                                                                   
##  [931] "Primary completion rate, male (% of relevant age group)"                                                                                     
##  [932] "Primary completion rate, total (% of relevant age group)"                                                                                    
##  [933] "Primary education, duration (years)"                                                                                                         
##  [934] "Primary education, pupils"                                                                                                                   
##  [935] "Primary education, pupils (% female)"                                                                                                        
##  [936] "Primary education, teachers"                                                                                                                 
##  [937] "Primary education, teachers (% female)"                                                                                                      
##  [938] "Primary government expenditures as a proportion of original approved budget (%)"                                                             
##  [939] "Primary income payments (BoP, current US$)"                                                                                                  
##  [940] "Primary income receipts (BoP, current US$)"                                                                                                  
##  [941] "Primary school starting age (years)"                                                                                                         
##  [942] "Private credit bureau coverage (% of adults)"                                                                                                
##  [943] "Probability of dying at age 5-14 years (per 1,000 children age 5)"                                                                           
##  [944] "Procedures to build a warehouse (number)"                                                                                                    
##  [945] "Procedures to register property (number)"                                                                                                    
##  [946] "Profit tax (% of commercial profits)"                                                                                                        
##  [947] "Progression to secondary school (%)"                                                                                                         
##  [948] "Progression to secondary school, female (%)"                                                                                                 
##  [949] "Progression to secondary school, male (%)"                                                                                                   
##  [950] "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)"                      
##  [951] "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)"             
##  [952] "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)"             
##  [953] "Proportion of seats held by women in national parliaments (%)"                                                                               
##  [954] "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of women age 15-49)"                               
##  [955] "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)"                                            
##  [956] "Public and publicly guaranteed debt service (% of GNI)"                                                                                      
##  [957] "Public credit registry coverage (% of adults)"                                                                                               
##  [958] "Public private partnerships investment in energy (current US$)"                                                                              
##  [959] "Pump price for diesel fuel (US$ per liter)"                                                                                                  
##  [960] "Pump price for gasoline (US$ per liter)"                                                                                                     
##  [961] "Pupil-teacher ratio, lower secondary"                                                                                                        
##  [962] "Pupil-teacher ratio, preprimary"                                                                                                             
##  [963] "Pupil-teacher ratio, primary"                                                                                                                
##  [964] "Pupil-teacher ratio, secondary"                                                                                                              
##  [965] "Pupil-teacher ratio, tertiary"                                                                                                               
##  [966] "Pupil-teacher ratio, upper secondary"                                                                                                        
##  [967] "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"                                                           
##  [968] "Ratio of female to male labor force participation rate (%) (national estimate)"                                                              
##  [969] "Real interest rate (%)"                                                                                                                      
##  [970] "Refugee population by country or territory of asylum"                                                                                        
##  [971] "Refugee population by country or territory of origin"                                                                                        
##  [972] "Renewable electricity output (% of total electricity output)"                                                                                
##  [973] "Renewable energy consumption (% of total final energy consumption)"                                                                          
##  [974] "Renewable internal freshwater resources per capita (cubic meters)"                                                                           
##  [975] "Renewable internal freshwater resources, total (billion cubic meters)"                                                                       
##  [976] "Repeaters, primary, female (% of female enrollment)"                                                                                         
##  [977] "Repeaters, primary, male (% of male enrollment)"                                                                                             
##  [978] "Repeaters, primary, total (% of total enrollment)"                                                                                           
##  [979] "Reserves and related items (BoP, current US$)"                                                                                               
##  [980] "Revenue, excluding grants (% of GDP)"                                                                                                        
##  [981] "Revenue, excluding grants (current LCU)"                                                                                                     
##  [982] "Rural population"                                                                                                                            
##  [983] "Rural population (% of total population)"                                                                                                    
##  [984] "Rural population growth (annual %)"                                                                                                          
##  [985] "School enrollment, preprimary (% gross)"                                                                                                     
##  [986] "School enrollment, preprimary, female (% gross)"                                                                                             
##  [987] "School enrollment, preprimary, male (% gross)"                                                                                               
##  [988] "School enrollment, primary (% gross)"                                                                                                        
##  [989] "School enrollment, primary (% net)"                                                                                                          
##  [990] "School enrollment, primary (gross), gender parity index (GPI)"                                                                               
##  [991] "School enrollment, primary and secondary (gross), gender parity index (GPI)"                                                                 
##  [992] "School enrollment, primary, female (% gross)"                                                                                                
##  [993] "School enrollment, primary, female (% net)"                                                                                                  
##  [994] "School enrollment, primary, male (% gross)"                                                                                                  
##  [995] "School enrollment, primary, male (% net)"                                                                                                    
##  [996] "School enrollment, primary, private (% of total primary)"                                                                                    
##  [997] "School enrollment, secondary (% gross)"                                                                                                      
##  [998] "School enrollment, secondary (% net)"                                                                                                        
##  [999] "School enrollment, secondary (gross), gender parity index (GPI)"                                                                             
## [1000] "School enrollment, secondary, female (% gross)"                                                                                              
## [1001] "School enrollment, secondary, female (% net)"                                                                                                
## [1002] "School enrollment, secondary, male (% gross)"                                                                                                
## [1003] "School enrollment, secondary, male (% net)"                                                                                                  
## [1004] "School enrollment, secondary, private (% of total secondary)"                                                                                
## [1005] "School enrollment, tertiary (% gross)"                                                                                                       
## [1006] "School enrollment, tertiary (gross), gender parity index (GPI)"                                                                              
## [1007] "School enrollment, tertiary, female (% gross)"                                                                                               
## [1008] "School enrollment, tertiary, male (% gross)"                                                                                                 
## [1009] "Scientific and technical journal articles"                                                                                                   
## [1010] "Secondary education, duration (years)"                                                                                                       
## [1011] "Secondary education, general pupils"                                                                                                         
## [1012] "Secondary education, general pupils (% female)"                                                                                              
## [1013] "Secondary education, pupils"                                                                                                                 
## [1014] "Secondary education, pupils (% female)"                                                                                                      
## [1015] "Secondary education, teachers"                                                                                                               
## [1016] "Secondary education, teachers (% female)"                                                                                                    
## [1017] "Secondary education, teachers, female"                                                                                                       
## [1018] "Secondary education, vocational pupils"                                                                                                      
## [1019] "Secondary education, vocational pupils (% female)"                                                                                           
## [1020] "Secondary income receipts (BoP, current US$)"                                                                                                
## [1021] "Secondary income, other sectors, payments (BoP, current US$)"                                                                                
## [1022] "Secure Internet servers"                                                                                                                     
## [1023] "Secure Internet servers (per 1 million people)"                                                                                              
## [1024] "Self-employed, female (% of female employment) (modeled ILO estimate)"                                                                       
## [1025] "Self-employed, male (% of male employment) (modeled ILO estimate)"                                                                           
## [1026] "Self-employed, total (% of total employment) (modeled ILO estimate)"                                                                         
## [1027] "Service exports (BoP, current US$)"                                                                                                          
## [1028] "Service imports (BoP, current US$)"                                                                                                          
## [1029] "Services, value added (% of GDP)"                                                                                                            
## [1030] "Services, value added (annual % growth)"                                                                                                     
## [1031] "Services, value added (constant 2010 US$)"                                                                                                   
## [1032] "Services, value added (constant LCU)"                                                                                                        
## [1033] "Services, value added (current LCU)"                                                                                                         
## [1034] "Services, value added (current US$)"                                                                                                         
## [1035] "Services, value added per worker (constant 2010 US$)"                                                                                        
## [1036] "Sex ratio at birth (male births per female births)"                                                                                          
## [1037] "Share of tariff lines with international peaks, all products (%)"                                                                            
## [1038] "Share of tariff lines with international peaks, manufactured products (%)"                                                                   
## [1039] "Share of tariff lines with international peaks, primary products (%)"                                                                        
## [1040] "Share of tariff lines with specific rates, all products (%)"                                                                                 
## [1041] "Share of tariff lines with specific rates, manufactured products (%)"                                                                        
## [1042] "Share of tariff lines with specific rates, primary products (%)"                                                                             
## [1043] "Share of youth not in education, employment or training, female (% of female youth population)"                                              
## [1044] "Share of youth not in education, employment or training, male (% of male youth population)"                                                  
## [1045] "Share of youth not in education, employment or training, total (% of youth population)"                                                      
## [1046] "Short-term debt (% of exports of goods, services and primary income)"                                                                        
## [1047] "Short-term debt (% of total external debt)"                                                                                                  
## [1048] "Short-term debt (% of total reserves)"                                                                                                       
## [1049] "Social contributions (% of revenue)"                                                                                                         
## [1050] "Social contributions (current LCU)"                                                                                                          
## [1051] "Source data assessment of statistical capacity (scale 0 - 100)"                                                                              
## [1052] "Specialist surgical workforce (per 100,000 population)"                                                                                      
## [1053] "Start-up procedures to register a business (number)"                                                                                         
## [1054] "Start-up procedures to register a business, female (number)"                                                                                 
## [1055] "Start-up procedures to register a business, male (number)"                                                                                   
## [1056] "Statistical Capacity score (Overall average)"                                                                                                
## [1057] "Strength of legal rights index (0=weak to 12=strong)"                                                                                        
## [1058] "Subsidies and other transfers (% of expense)"                                                                                                
## [1059] "Subsidies and other transfers (current LCU)"                                                                                                 
## [1060] "Suicide mortality rate (per 100,000 population)"                                                                                             
## [1061] "Suicide mortality rate, female (per 100,000 female population)"                                                                              
## [1062] "Suicide mortality rate, male (per 100,000 male population)"                                                                                  
## [1063] "Surface area (sq. km)"                                                                                                                       
## [1064] "Survival to age 65, female (% of cohort)"                                                                                                    
## [1065] "Survival to age 65, male (% of cohort)"                                                                                                      
## [1066] "Tariff rate, applied, simple mean, all products (%)"                                                                                         
## [1067] "Tariff rate, applied, simple mean, manufactured products (%)"                                                                                
## [1068] "Tariff rate, applied, simple mean, primary products (%)"                                                                                     
## [1069] "Tariff rate, applied, weighted mean, all products (%)"                                                                                       
## [1070] "Tariff rate, applied, weighted mean, manufactured products (%)"                                                                              
## [1071] "Tariff rate, applied, weighted mean, primary products (%)"                                                                                   
## [1072] "Tariff rate, most favored nation, simple mean, all products (%)"                                                                             
## [1073] "Tariff rate, most favored nation, simple mean, manufactured products (%)"                                                                    
## [1074] "Tariff rate, most favored nation, simple mean, primary products (%)"                                                                         
## [1075] "Tariff rate, most favored nation, weighted mean, all products (%)"                                                                           
## [1076] "Tariff rate, most favored nation, weighted mean, manufactured products (%)"                                                                  
## [1077] "Tariff rate, most favored nation, weighted mean, primary products (%)"                                                                       
## [1078] "Tax payments (number)"                                                                                                                       
## [1079] "Tax revenue (% of GDP)"                                                                                                                      
## [1080] "Tax revenue (current LCU)"                                                                                                                   
## [1081] "Taxes less subsidies on products (constant LCU)"                                                                                             
## [1082] "Taxes less subsidies on products (current LCU)"                                                                                              
## [1083] "Taxes less subsidies on products (current US$)"                                                                                              
## [1084] "Taxes on exports (% of tax revenue)"                                                                                                         
## [1085] "Taxes on exports (current LCU)"                                                                                                              
## [1086] "Taxes on goods and services (% of revenue)"                                                                                                  
## [1087] "Taxes on goods and services (% value added of industry and services)"                                                                        
## [1088] "Taxes on goods and services (current LCU)"                                                                                                   
## [1089] "Taxes on income, profits and capital gains (% of revenue)"                                                                                   
## [1090] "Taxes on income, profits and capital gains (% of total taxes)"                                                                               
## [1091] "Taxes on income, profits and capital gains (current LCU)"                                                                                    
## [1092] "Taxes on international trade (% of revenue)"                                                                                                 
## [1093] "Taxes on international trade (current LCU)"                                                                                                  
## [1094] "Technical cooperation grants (BoP, current US$)"                                                                                             
## [1095] "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)"                                                     
## [1096] "Terrestrial and marine protected areas (% of total territorial area)"                                                                        
## [1097] "Terrestrial protected areas (% of total land area)"                                                                                          
## [1098] "Tertiary education, academic staff (% female)"                                                                                               
## [1099] "Time required to build a warehouse (days)"                                                                                                   
## [1100] "Time required to enforce a contract (days)"                                                                                                  
## [1101] "Time required to get electricity (days)"                                                                                                     
## [1102] "Time required to obtain an operating license (days)"                                                                                         
## [1103] "Time required to register property (days)"                                                                                                   
## [1104] "Time required to start a business (days)"                                                                                                    
## [1105] "Time required to start a business, female (days)"                                                                                            
## [1106] "Time required to start a business, male (days)"                                                                                              
## [1107] "Time spent dealing with the requirements of government regulations (% of senior management time)"                                            
## [1108] "Time to export, border compliance (hours)"                                                                                                   
## [1109] "Time to export, documentary compliance (hours)"                                                                                              
## [1110] "Time to import, border compliance (hours)"                                                                                                   
## [1111] "Time to import, documentary compliance (hours)"                                                                                              
## [1112] "Time to obtain an electrical connection (days)"                                                                                              
## [1113] "Time to prepare and pay taxes (hours)"                                                                                                       
## [1114] "Time to resolve insolvency (years)"                                                                                                          
## [1115] "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"                                        
## [1116] "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)"                         
## [1117] "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)"                             
## [1118] "Total debt service (% of exports of goods, services and primary income)"                                                                     
## [1119] "Total debt service (% of GNI)"                                                                                                               
## [1120] "Total fisheries production (metric tons)"                                                                                                    
## [1121] "Total greenhouse gas emissions (% change from 1990)"                                                                                         
## [1122] "Total greenhouse gas emissions (kt of CO2 equivalent)"                                                                                       
## [1123] "Total natural resources rents (% of GDP)"                                                                                                    
## [1124] "Total reserves (% of total external debt)"                                                                                                   
## [1125] "Total reserves (includes gold, current US$)"                                                                                                 
## [1126] "Total reserves in months of imports"                                                                                                         
## [1127] "Total reserves minus gold (current US$)"                                                                                                     
## [1128] "Total tax and contribution rate (% of profit)"                                                                                               
## [1129] "Trade (% of GDP)"                                                                                                                            
## [1130] "Trade in services (% of GDP)"                                                                                                                
## [1131] "Transport services (% of commercial service exports)"                                                                                        
## [1132] "Transport services (% of commercial service imports)"                                                                                        
## [1133] "Transport services (% of service exports, BoP)"                                                                                              
## [1134] "Transport services (% of service imports, BoP)"                                                                                              
## [1135] "Travel services (% of commercial service exports)"                                                                                           
## [1136] "Travel services (% of commercial service imports)"                                                                                           
## [1137] "Travel services (% of service exports, BoP)"                                                                                                 
## [1138] "Travel services (% of service imports, BoP)"                                                                                                 
## [1139] "Tuberculosis case detection rate (%, all forms)"                                                                                             
## [1140] "Tuberculosis treatment success rate (% of new cases)"                                                                                        
## [1141] "UHC service coverage index"                                                                                                                  
## [1142] "Unemployment with advanced education (% of total labor force with advanced education)"                                                       
## [1143] "Unemployment with advanced education, female (% of female labor force with advanced education)"                                              
## [1144] "Unemployment with advanced education, male (% of male labor force with advanced education)"                                                  
## [1145] "Unemployment with basic education (% of total labor force with basic education)"                                                             
## [1146] "Unemployment with basic education, female (% of female labor force with basic education)"                                                    
## [1147] "Unemployment with basic education, male (% of male labor force with basic education)"                                                        
## [1148] "Unemployment with intermediate education (% of total labor force with intermediate education)"                                               
## [1149] "Unemployment with intermediate education, female (% of female labor force with intermediate education)"                                      
## [1150] "Unemployment with intermediate education, male (% of male labor force with intermediate education)"                                          
## [1151] "Unemployment, female (% of female labor force) (modeled ILO estimate)"                                                                       
## [1152] "Unemployment, female (% of female labor force) (national estimate)"                                                                          
## [1153] "Unemployment, male (% of male labor force) (modeled ILO estimate)"                                                                           
## [1154] "Unemployment, male (% of male labor force) (national estimate)"                                                                              
## [1155] "Unemployment, total (% of total labor force) (modeled ILO estimate)"                                                                         
## [1156] "Unemployment, total (% of total labor force) (national estimate)"                                                                            
## [1157] "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)"                                                      
## [1158] "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)"                                                         
## [1159] "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)"                                                          
## [1160] "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)"                                                             
## [1161] "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)"                                                        
## [1162] "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)"                                                           
## [1163] "Unmet need for contraception (% of married women ages 15-49)"                                                                                
## [1164] "Urban population"                                                                                                                            
## [1165] "Urban population (% of total population)"                                                                                                    
## [1166] "Urban population growth (annual %)"                                                                                                          
## [1167] "Use of IMF credit (DOD, current US$)"                                                                                                        
## [1168] "Use of insecticide-treated bed nets (% of under-5 population)"                                                                               
## [1169] "Value lost due to electrical outages (% of sales for affected firms)"                                                                        
## [1170] "Vitamin A supplementation coverage rate (% of children ages 6-59 months)"                                                                    
## [1171] "Vulnerable employment, female (% of female employment) (modeled ILO estimate)"                                                               
## [1172] "Vulnerable employment, male (% of male employment) (modeled ILO estimate)"                                                                   
## [1173] "Vulnerable employment, total (% of total employment) (modeled ILO estimate)"                                                                 
## [1174] "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)"                                                           
## [1175] "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)"                                                               
## [1176] "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)"                                                             
## [1177] "Wanted fertility rate (births per woman)"                                                                                                    
## [1178] "Women Business and the Law Index Score (scale 1-100)"                                                                                        
## [1179] "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)"         
## [1180] "Women who believe a husband is justified in beating his wife (any of five reasons) (%)"                                                      
## [1181] "Women who believe a husband is justified in beating his wife when she argues with him (%)"                                                   
## [1182] "Women who believe a husband is justified in beating his wife when she burns the food (%)"                                                    
## [1183] "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)"                                      
## [1184] "Women who believe a husband is justified in beating his wife when she neglects the children (%)"                                             
## [1185] "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)"                                              
## [1186] "Women who were first married by age 15 (% of women ages 20-24)"                                                                              
## [1187] "Women who were first married by age 18 (% of women ages 20-24)"                                                                              
## [1188] "Women's share of population ages 15+ living with HIV (%)"

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## [1] "year"

## [2] "Access to clean fuels and technologies for cooking (% of population)"

## [3] "Access to electricity (% of population)"

## [4] "Access to electricity, rural (% of rural population)"

## [5] "Access to electricity, urban (% of urban population)"

## [6] "Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)"

## [7] "Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)"

## [8] "Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)"

## [9] "Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)"

## [10] "Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)"

## [11] "Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)"

## [12] "Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)"

## [13] "Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)"

## [14] "Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)"

## [15] "Adequacy of social insurance programs (% of total welfare of beneficiary households)"

## [16] "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)"

## [17] "Adequacy of social safety net programs (% of total welfare of beneficiary households)"

## [18] "Adjusted net enrollment rate, primary (% of primary school age children)"

## [19] "Adjusted net enrollment rate, primary, female (% of primary school age children)"

## [20] "Adjusted net enrollment rate, primary, male (% of primary school age children)"

## [21] "Adjusted net national income (constant 2010 US$)"

## [22] "Adjusted net national income (current US$)"

## [23] "Adjusted net national income per capita (constant 2010 US$)"

## [24] "Adjusted net national income per capita (current US$)"

## [25] "Adjusted net savings, excluding particulate emission damage (% of GNI)"

## [26] "Adjusted net savings, excluding particulate emission damage (current US$)"

## [27] "Adjusted net savings, including particulate emission damage (% of GNI)"

## [28] "Adjusted net savings, including particulate emission damage (current US$)"

## [29] "Adjusted savings: carbon dioxide damage (% of GNI)"

## [30] "Adjusted savings: carbon dioxide damage (current US$)"

## [31] "Adjusted savings: consumption of fixed capital (% of GNI)"

## [32] "Adjusted savings: consumption of fixed capital (current US$)"

## [33] "Adjusted savings: education expenditure (% of GNI)"

## [34] "Adjusted savings: education expenditure (current US$)"

## [35] "Adjusted savings: energy depletion (% of GNI)"

## [36] "Adjusted savings: energy depletion (current US$)"

## [37] "Adjusted savings: gross savings (% of GNI)"

## [38] "Adjusted savings: mineral depletion (% of GNI)"

## [39] "Adjusted savings: mineral depletion (current US$)"

## [40] "Adjusted savings: natural resources depletion (% of GNI)"

## [41] "Adjusted savings: net forest depletion (% of GNI)"

## [42] "Adjusted savings: net forest depletion (current US$)"

## [43] "Adjusted savings: net national savings (% of GNI)"

## [44] "Adjusted savings: net national savings (current US$)"

## [45] "Adjusted savings: particulate emission damage (% of GNI)"

## [46] "Adjusted savings: particulate emission damage (current US$)"

## [47] "Adolescent fertility rate (births per 1,000 women ages 15-19)"

## [48] "Adolescents out of school (% of lower secondary school age)"

## [49] "Adolescents out of school, female (% of female lower secondary school age)"

## [50] "Adolescents out of school, male (% of male lower secondary school age)"

## [51] "Adults (ages 15+) and children (ages 0-14) newly infected with HIV"

## [52] "Adults (ages 15+) newly infected with HIV"

## [53] "Age dependency ratio (% of working-age population)"

## [54] "Age dependency ratio, old (% of working-age population)"

## [55] "Age dependency ratio, young (% of working-age population)"

## [56] "Agricultural irrigated land (% of total agricultural land)"

## [57] "Agricultural land (% of land area)"

## [58] "Agricultural land (sq. km)"

## [59] "Agricultural machinery, tractors"

## [60] "Agricultural machinery, tractors per 100 sq. km of arable land"

## [61] "Agricultural methane emissions (% of total)"

## [62] "Agricultural methane emissions (thousand metric tons of CO2 equivalent)"

## [63] "Agricultural nitrous oxide emissions (% of total)"

## [64] "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)"

## [65] "Agricultural raw materials exports (% of merchandise exports)"

## [66] "Agricultural raw materials imports (% of merchandise imports)"

## [67] "Agriculture, forestry, and fishing, value added (% of GDP)"

## [68] "Agriculture, forestry, and fishing, value added (annual % growth)"

## [69] "Agriculture, forestry, and fishing, value added (constant 2010 US$)"

## [70] "Agriculture, forestry, and fishing, value added (constant LCU)"

## [71] "Agriculture, forestry, and fishing, value added (current LCU)"

## [72] "Agriculture, forestry, and fishing, value added (current US$)"

## [73] "Agriculture, forestry, and fishing, value added per worker (constant 2010 US$)"

## [74] "Air transport, freight (million ton-km)"

## [75] "Air transport, passengers carried"

## [76] "Air transport, registered carrier departures worldwide"

## [77] "Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal)"

## [78] "Annual freshwater withdrawals, domestic (% of total freshwater withdrawal)"

## [79] "Annual freshwater withdrawals, industry (% of total freshwater withdrawal)"

## [80] "Annual freshwater withdrawals, total (% of internal resources)"

## [81] "Annual freshwater withdrawals, total (billion cubic meters)"

## [82] "Antiretroviral therapy coverage (% of people living with HIV)"

## [83] "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)"

## [84] "Aquaculture production (metric tons)"

## [85] "Arable land (% of land area)"

## [86] "Arable land (hectares per person)"

## [87] "Arable land (hectares)"

## [88] "ARI treatment (% of children under 5 taken to a health provider)"

## [89] "Armed forces personnel (% of total labor force)"

## [90] "Armed forces personnel, total"

## [91] "Arms exports (SIPRI trend indicator values)"

## [92] "Arms imports (SIPRI trend indicator values)"

## [93] "Automated teller machines (ATMs) (per 100,000 adults)"

## [94] "Average number of visits or required meetings with tax officials (for affected firms)"

## [95] "Average precipitation in depth (mm per year)"

## [96] "Average time to clear exports through customs (days)"

## [97] "Average transaction cost of sending remittances to a specific country (%)"

## [98] "Average working hours of children, study and work, ages 7-14 (hours per week)"

## [99] "Average working hours of children, study and work, female, ages 7-14 (hours per week)"

## [100] "Average working hours of children, study and work, male, ages 7-14 (hours per week)"

## [101] "Average working hours of children, working only, ages 7-14 (hours per week)"

## [102] "Average working hours of children, working only, female, ages 7-14 (hours per week)"

## [103] "Average working hours of children, working only, male, ages 7-14 (hours per week)"

## [104] "Bank capital to assets ratio (%)"

## [105] "Bank liquid reserves to bank assets ratio (%)"

## [106] "Bank nonperforming loans to total gross loans (%)"

## [107] "Battle-related deaths (number of people)"

## [108] "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)"

## [109] "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)"

## [110] "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)"

## [111] "Binding coverage, all products (%)"

## [112] "Binding coverage, manufactured products (%)"

## [113] "Binding coverage, primary products (%)"

## [114] "Bird species, threatened"

## [115] "Birth rate, crude (per 1,000 people)"

## [116] "Births attended by skilled health staff (% of total)"

## [117] "Borrowers from commercial banks (per 1,000 adults)"

## [118] "Bound rate, simple mean, all products (%)"

## [119] "Bound rate, simple mean, manufactured products (%)"

## [120] "Bound rate, simple mean, primary products (%)"

## [121] "Bribery incidence (% of firms experiencing at least one bribe payment request)"

## [122] "Broad money (% of GDP)"

## [123] "Broad money (current LCU)"

## [124] "Broad money growth (annual %)"

## [125] "Broad money to total reserves ratio"

## [126] "Business extent of disclosure index (0=less disclosure to 10=more disclosure)"

## [127] "Capture fisheries production (metric tons)"

## [128] "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)"

## [129] "Cause of death, by injury (% of total)"

## [130] "Cause of death, by non-communicable diseases (% of total)"

## [131] "Cereal production (metric tons)"

## [132] "Cereal yield (kg per hectare)"

## [133] "Charges for the use of intellectual property, payments (BoP, current US$)"

## [134] "Charges for the use of intellectual property, receipts (BoP, current US$)"

## [135] "Children (0-14) living with HIV"

## [136] "Children (ages 0-14) newly infected with HIV"

## [137] "Children in employment, female (% of female children ages 7-14)"

## [138] "Children in employment, male (% of male children ages 7-14)"

## [139] "Children in employment, study and work (% of children in employment, ages 7-14)"

## [140] "Children in employment, study and work, female (% of female children in employment, ages 7-14)"

## [141] "Children in employment, study and work, male (% of male children in employment, ages 7-14)"

## [142] "Children in employment, total (% of children ages 7-14)"

## [143] "Children in employment, unpaid family workers (% of children in employment, ages 7-14)"

## [144] "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)"

## [145] "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)"

## [146] "Children in employment, wage workers (% of children in employment, ages 7-14)"

## [147] "Children in employment, wage workers, female (% of female children in employment, ages 7-14)"

## [148] "Children in employment, wage workers, male (% of male children in employment, ages 7-14)"

## [149] "Children in employment, work only (% of children in employment, ages 7-14)"

## [150] "Children in employment, work only, female (% of female children in employment, ages 7-14)"

## [151] "Children in employment, work only, male (% of male children in employment, ages 7-14)"

## [152] "Children out of school (% of primary school age)"

## [153] "Children out of school, female (% of female primary school age)"

## [154] "Children out of school, male (% of male primary school age)"

## [155] "Children out of school, primary"

## [156] "Children out of school, primary, female"

## [157] "Children out of school, primary, male"

## [158] "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)"

## [159] "Claims on central government (annual growth as % of broad money)"

## [160] "Claims on central government, etc. (% GDP)"

## [161] "Claims on other sectors of the domestic economy (annual growth as % of broad money)"

## [162] "Claims on private sector (annual growth as % of broad money)"

## [163] "CO2 emissions (kg per 2010 US$ of GDP)"

## [164] "CO2 emissions (kg per 2017 PPP $ of GDP)"

## [165] "CO2 emissions (kg per PPP $ of GDP)"

## [166] "CO2 emissions (kt)"

## [167] "CO2 emissions (metric tons per capita)"

## [168] "CO2 emissions from gaseous fuel consumption (% of total)"

## [169] "CO2 emissions from gaseous fuel consumption (kt)"

## [170] "CO2 emissions from liquid fuel consumption (% of total)"

## [171] "CO2 emissions from liquid fuel consumption (kt)"

## [172] "CO2 emissions from solid fuel consumption (% of total)"

## [173] "CO2 emissions from solid fuel consumption (kt)"

## [174] "Coal rents (% of GDP)"

## [175] "Commercial bank branches (per 100,000 adults)"

## [176] "Commercial banks and other lending (PPG + PNG) (NFL, current US$)"

## [177] "Commercial service exports (current US$)"

## [178] "Commercial service imports (current US$)"

## [179] "Communications, computer, etc. (% of service exports, BoP)"

## [180] "Communications, computer, etc. (% of service imports, BoP)"

## [181] "Compensation of employees (% of expense)"

## [182] "Compensation of employees (current LCU)"

## [183] "Completeness of birth registration (%)"

## [184] "Completeness of birth registration, female (%)"

## [185] "Completeness of birth registration, male (%)"

## [186] "Completeness of birth registration, rural (%)"

## [187] "Completeness of birth registration, urban (%)"

## [188] "Compulsory education, duration (years)"

## [189] "Computer, communications and other services (% of commercial service exports)"

## [190] "Computer, communications and other services (% of commercial service imports)"

## [191] "Consumer price index (2010 = 100)"

## [192] "Consumption of iodized salt (% of households)"

## [193] "Contraceptive prevalence, any methods (% of women ages 15-49)"

## [194] "Contraceptive prevalence, modern methods (% of women ages 15-49)"

## [195] "Contributing family workers, female (% of female employment) (modeled ILO estimate)"

## [196] "Contributing family workers, male (% of male employment) (modeled ILO estimate)"

## [197] "Contributing family workers, total (% of total employment) (modeled ILO estimate)"

## [198] "Cost of business start-up procedures (% of GNI per capita)"

## [199] "Cost of business start-up procedures, female (% of GNI per capita)"

## [200] "Cost of business start-up procedures, male (% of GNI per capita)"

## [201] "Cost to export, border compliance (US$)"

## [202] "Cost to export, documentary compliance (US$)"

## [203] "Cost to import, border compliance (US$)"

## [204] "Cost to import, documentary compliance (US$)"

## [205] "Coverage of social insurance programs (% of population)"

## [206] "Coverage of social insurance programs in 2nd quintile (% of population)"

## [207] "Coverage of social insurance programs in 3rd quintile (% of population)"

## [208] "Coverage of social insurance programs in 4th quintile (% of population)"

## [209] "Coverage of social insurance programs in poorest quintile (% of population)"

## [210] "Coverage of social insurance programs in richest quintile (% of population)"

## [211] "Coverage of social protection and labor programs (% of population)"

## [212] "Coverage of social safety net programs (% of population)"

## [213] "Coverage of social safety net programs in 2nd quintile (% of population)"

## [214] "Coverage of social safety net programs in 3rd quintile (% of population)"

## [215] "Coverage of social safety net programs in 4th quintile (% of population)"

## [216] "Coverage of social safety net programs in poorest quintile (% of population)"

## [217] "Coverage of social safety net programs in richest quintile (% of population)"

## [218] "CPIA building human resources rating (1=low to 6=high)"

## [219] "CPIA business regulatory environment rating (1=low to 6=high)"

## [220] "CPIA debt policy rating (1=low to 6=high)"

## [221] "CPIA economic management cluster average (1=low to 6=high)"

## [222] "CPIA efficiency of revenue mobilization rating (1=low to 6=high)"

## [223] "CPIA equity of public resource use rating (1=low to 6=high)"

## [224] "CPIA financial sector rating (1=low to 6=high)"

## [225] "CPIA fiscal policy rating (1=low to 6=high)"

## [226] "CPIA gender equality rating (1=low to 6=high)"

## [227] "CPIA macroeconomic management rating (1=low to 6=high)"

## [228] "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)"

## [229] "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)"

## [230] "CPIA property rights and rule-based governance rating (1=low to 6=high)"

## [231] "CPIA public sector management and institutions cluster average (1=low to 6=high)"

## [232] "CPIA quality of budgetary and financial management rating (1=low to 6=high)"

## [233] "CPIA quality of public administration rating (1=low to 6=high)"

## [234] "CPIA social protection rating (1=low to 6=high)"

## [235] "CPIA structural policies cluster average (1=low to 6=high)"

## [236] "CPIA trade rating (1=low to 6=high)"

## [237] "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)"

## [238] "Crop production index (2004-2006 = 100)"

## [239] "Current account balance (% of GDP)"

## [240] "Current account balance (BoP, current US$)"

## [241] "Current education expenditure, primary (% of total expenditure in primary public institutions)"

## [242] "Current education expenditure, secondary (% of total expenditure in secondary public institutions)"

## [243] "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)"

## [244] "Current education expenditure, total (% of total expenditure in public institutions)"

## [245] "Current health expenditure (% of GDP)"

## [246] "Current health expenditure per capita (current US$)"

## [247] "Current health expenditure per capita, PPP (current international $)"

## [248] "Customs and other import duties (% of tax revenue)"

## [249] "Customs and other import duties (current LCU)"

## [250] "Death rate, crude (per 1,000 people)"

## [251] "Debt service (PPG and IMF only, % of exports of goods, services and primary income)"

## [252] "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)"

## [253] "Debt service on external debt, total (TDS, current US$)"

## [254] "DEC alternative conversion factor (LCU per US$)"

## [255] "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)"

## [256] "Depositors with commercial banks (per 1,000 adults)"

## [257] "Depth of credit information index (0=low to 8=high)"

## [258] "Diabetes prevalence (% of population ages 20 to 79)"

## [259] "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)"

## [260] "Diarrhea treatment (% of children under 5 who received ORS packet)"

## [261] "Discrepancy in expenditure estimate of GDP (current LCU)"

## [262] "Domestic credit to private sector (% of GDP)"

## [263] "Domestic credit to private sector by banks (% of GDP)"

## [264] "Domestic general government health expenditure (% of current health expenditure)"

## [265] "Domestic general government health expenditure (% of GDP)"

## [266] "Domestic general government health expenditure (% of general government expenditure)"

## [267] "Domestic general government health expenditure per capita (current US$)"

## [268] "Domestic general government health expenditure per capita, PPP (current international $)"

## [269] "Domestic private health expenditure (% of current health expenditure)"

## [270] "Domestic private health expenditure per capita (current US$)"

## [271] "Domestic private health expenditure per capita, PPP (current international $)"

## [272] "Droughts, floods, extreme temperatures (% of population, average 1990-2009)"

## [273] "Ease of doing business index (1=most business-friendly regulations)"

## [274] "Ease of doing business score (0 = lowest performance to 100 = best performance)"

## [275] "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)"

## [276] "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)"

## [277] "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)"

## [278] "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)"

## [279] "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)"

## [280] "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)"

## [281] "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)"

## [282] "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)"

## [283] "Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)"

## [284] "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)"

## [285] "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)"

## [286] "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)"

## [287] "Employers, female (% of female employment) (modeled ILO estimate)"

## [288] "Employers, male (% of male employment) (modeled ILO estimate)"

## [289] "Employers, total (% of total employment) (modeled ILO estimate)"

## [290] "Employment in agriculture (% of total employment) (modeled ILO estimate)"

## [291] "Employment in agriculture, female (% of female employment) (modeled ILO estimate)"

## [292] "Employment in agriculture, male (% of male employment) (modeled ILO estimate)"

## [293] "Employment in industry (% of total employment) (modeled ILO estimate)"

## [294] "Employment in industry, female (% of female employment) (modeled ILO estimate)"

## [295] "Employment in industry, male (% of male employment) (modeled ILO estimate)"

## [296] "Employment in services (% of total employment) (modeled ILO estimate)"

## [297] "Employment in services, female (% of female employment) (modeled ILO estimate)"

## [298] "Employment in services, male (% of male employment) (modeled ILO estimate)"

## [299] "Employment to population ratio, 15+, female (%) (modeled ILO estimate)"

## [300] "Employment to population ratio, 15+, female (%) (national estimate)"

## [301] "Employment to population ratio, 15+, male (%) (modeled ILO estimate)"

## [302] "Employment to population ratio, 15+, male (%) (national estimate)"

## [303] "Employment to population ratio, 15+, total (%) (modeled ILO estimate)"

## [304] "Employment to population ratio, 15+, total (%) (national estimate)"

## [305] "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)"

## [306] "Employment to population ratio, ages 15-24, female (%) (national estimate)"

## [307] "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)"

## [308] "Employment to population ratio, ages 15-24, male (%) (national estimate)"

## [309] "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)"

## [310] "Employment to population ratio, ages 15-24, total (%) (national estimate)"

## [311] "Energy intensity level of primary energy (MJ/$2011 PPP GDP)"

## [312] "Energy related methane emissions (% of total)"

## [313] "Exclusive breastfeeding (% of children under 6 months)"

## [314] "Expenditure on primary education (% of government expenditure on education)"

## [315] "Expenditure on secondary education (% of government expenditure on education)"

## [316] "Expenditure on tertiary education (% of government expenditure on education)"

## [317] "Expense (% of GDP)"

## [318] "Expense (current LCU)"

## [319] "Export unit value index (2000 = 100)"

## [320] "Export value index (2000 = 100)"

## [321] "Export volume index (2000 = 100)"

## [322] "Exports of goods and services (% of GDP)"

## [323] "Exports of goods and services (BoP, current US$)"

## [324] "Exports of goods and services (constant 2010 US$)"

## [325] "Exports of goods and services (current LCU)"

## [326] "Exports of goods and services (current US$)"

## [327] "Exports of goods, services and primary income (BoP, current US$)"

## [328] "External balance on goods and services (% of GDP)"

## [329] "External balance on goods and services (current LCU)"

## [330] "External balance on goods and services (current US$)"

## [331] "External debt stocks (% of GNI)"

## [332] "External debt stocks, long-term (DOD, current US$)"

## [333] "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)"

## [334] "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)"

## [335] "External debt stocks, short-term (DOD, current US$)"

## [336] "External debt stocks, total (DOD, current US$)"

## [337] "External health expenditure (% of current health expenditure)"

## [338] "External health expenditure per capita (current US$)"

## [339] "External health expenditure per capita, PPP (current international $)"

## [340] "Female headed households (% of households with a female head)"

## [341] "Female share of employment in senior and middle management (%)"

## [342] "Fertility rate, total (births per woman)"

## [343] "Fertilizer consumption (% of fertilizer production)"

## [344] "Fertilizer consumption (kilograms per hectare of arable land)"

## [345] "Final consumption expenditure (% of GDP)"

## [346] "Final consumption expenditure (constant 2010 US$)"

## [347] "Final consumption expenditure (current LCU)"

## [348] "Final consumption expenditure (current US$)"

## [349] "Firms competing against unregistered firms (% of firms)"

## [350] "Firms expected to give gifts in meetings with tax officials (% of firms)"

## [351] "Firms experiencing electrical outages (% of firms)"

## [352] "Firms experiencing losses due to theft and vandalism (% of firms)"

## [353] "Firms formally registered when operations started (% of firms)"

## [354] "Firms offering formal training (% of firms)"

## [355] "Firms that spend on R&D (% of firms)"

## [356] "Firms using banks to finance investment (% of firms)"

## [357] "Firms using banks to finance working capital (% of firms)"

## [358] "Firms visited or required meetings with tax officials (% of firms)"

## [359] "Firms with female participation in ownership (% of firms)"

## [360] "Firms with female top manager (% of firms)"

## [361] "Fish species, threatened"

## [362] "Fixed broadband subscriptions"

## [363] "Fixed broadband subscriptions (per 100 people)"

## [364] "Fixed telephone subscriptions"

## [365] "Fixed telephone subscriptions (per 100 people)"

## [366] "Food exports (% of merchandise exports)"

## [367] "Food imports (% of merchandise imports)"

## [368] "Food production index (2004-2006 = 100)"

## [369] "Foreign direct investment, net (BoP, current US$)"

## [370] "Foreign direct investment, net inflows (% of GDP)"

## [371] "Foreign direct investment, net inflows (BoP, current US$)"

## [372] "Foreign direct investment, net outflows (% of GDP)"

## [373] "Foreign direct investment, net outflows (BoP, current US$)"

## [374] "Forest area (% of land area)"

## [375] "Forest area (sq. km)"

## [376] "Forest rents (% of GDP)"

## [377] "Fuel exports (% of merchandise exports)"

## [378] "Fuel imports (% of merchandise imports)"

## [379] "GDP (constant 2010 US$)"

## [380] "GDP (constant LCU)"

## [381] "GDP (current LCU)"

## [382] "GDP (current US$)"

## [383] "GDP deflator (base year varies by country)"

## [384] "GDP deflator: linked series (base year varies by country)"

## [385] "GDP growth (annual %)"

## [386] "GDP per capita (constant 2010 US$)"

## [387] "GDP per capita (constant LCU)"

## [388] "GDP per capita (current LCU)"

## [389] "GDP per capita (current US$)"

## [390] "GDP per capita growth (annual %)"

## [391] "GDP per capita, PPP (constant 2017 international $)"

## [392] "GDP per capita, PPP (current international $)"

## [393] "GDP per person employed (constant 2017 PPP $)"

## [394] "GDP, PPP (constant 2017 international $)"

## [395] "GDP, PPP (current international $)"

## [396] "GDP: linked series (current LCU)"

## [397] "General government final consumption expenditure (% of GDP)"

## [398] "General government final consumption expenditure (constant 2010 US$)"

## [399] "General government final consumption expenditure (current LCU)"

## [400] "General government final consumption expenditure (current US$)"

## [401] "GNI (constant 2010 US$)"

## [402] "GNI (current LCU)"

## [403] "GNI (current US$)"

## [404] "GNI per capita (constant 2010 US$)"

## [405] "GNI per capita (current LCU)"

## [406] "GNI per capita, Atlas method (current US$)"

## [407] "GNI per capita, PPP (constant 2017 international $)"

## [408] "GNI per capita, PPP (current international $)"

## [409] "GNI, Atlas method (current US$)"

## [410] "GNI, PPP (constant 2017 international $)"

## [411] "GNI, PPP (current international $)"

## [412] "GNI: linked series (current LCU)"

## [413] "Goods and services expense (% of expense)"

## [414] "Goods and services expense (current LCU)"

## [415] "Goods exports (BoP, current US$)"

## [416] "Goods imports (BoP, current US$)"

## [417] "Government expenditure on education, total (% of GDP)"

## [418] "Government expenditure on education, total (% of government expenditure)"

## [419] "Government expenditure per student, primary (% of GDP per capita)"

## [420] "Government expenditure per student, secondary (% of GDP per capita)"

## [421] "Government expenditure per student, tertiary (% of GDP per capita)"

## [422] "Grants and other revenue (% of revenue)"

## [423] "Grants and other revenue (current LCU)"

## [424] "Grants, excluding technical cooperation (BoP, current US$)"

## [425] "Gross capital formation (% of GDP)"

## [426] "Gross capital formation (constant 2010 US$)"

## [427] "Gross capital formation (current LCU)"

## [428] "Gross capital formation (current US$)"

## [429] "Gross domestic savings (% of GDP)"

## [430] "Gross domestic savings (current LCU)"

## [431] "Gross domestic savings (current US$)"

## [432] "Gross fixed capital formation (% of GDP)"

## [433] "Gross fixed capital formation (constant 2010 US$)"

## [434] "Gross fixed capital formation (current LCU)"

## [435] "Gross fixed capital formation (current US$)"

## [436] "Gross intake ratio in first grade of primary education, female (% of relevant age group)"

## [437] "Gross intake ratio in first grade of primary education, male (% of relevant age group)"

## [438] "Gross intake ratio in first grade of primary education, total (% of relevant age group)"

## [439] "Gross national expenditure (% of GDP)"

## [440] "Gross national expenditure (constant 2010 US$)"

## [441] "Gross national expenditure (current LCU)"

## [442] "Gross national expenditure (current US$)"

## [443] "Gross savings (% of GDP)"

## [444] "Gross savings (% of GNI)"

## [445] "Gross savings (current LCU)"

## [446] "Gross savings (current US$)"

## [447] "Gross value added at basic prices (GVA) (constant 2010 US$)"

## [448] "Gross value added at basic prices (GVA) (constant LCU)"

## [449] "Gross value added at basic prices (GVA) (current LCU)"

## [450] "Gross value added at basic prices (GVA) (current US$)"

## [451] "Hospital beds (per 1,000 people)"

## [452] "Households and NPISHs final consumption expenditure (% of GDP)"

## [453] "Households and NPISHs Final consumption expenditure (constant 2010 US$)"

## [454] "Households and NPISHs Final consumption expenditure (current LCU)"

## [455] "Households and NPISHs Final consumption expenditure (current US$)"

## [456] "Households and NPISHs Final consumption expenditure per capita (constant 2010 US$)"

## [457] "Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $)"

## [458] "Households and NPISHs Final consumption expenditure, PPP (current international $)"

## [459] "Households and NPISHs final consumption expenditure: linked series (current LCU)"

## [460] "Human capital index (HCI) (scale 0-1)"

## [461] "Human capital index (HCI), female (scale 0-1)"

## [462] "Human capital index (HCI), female, lower bound (scale 0-1)"

## [463] "Human capital index (HCI), female, upper bound (scale 0-1)"

## [464] "Human capital index (HCI), lower bound (scale 0-1)"

## [465] "Human capital index (HCI), male (scale 0-1)"

## [466] "Human capital index (HCI), male, lower bound (scale 0-1)"

## [467] "Human capital index (HCI), male, upper bound (scale 0-1)"

## [468] "Human capital index (HCI), upper bound (scale 0-1)"

## [469] "IBRD loans and IDA credits (DOD, current US$)"

## [470] "ICT goods imports (% total goods imports)"

## [471] "ICT service exports (% of service exports, BoP)"

## [472] "ICT service exports (BoP, current US$)"

## [473] "IDA resource allocation index (1=low to 6=high)"

## [474] "IFC, private nonguaranteed (NFL, current US$)"

## [475] "IMF repurchases and charges (TDS, current US$)"

## [476] "Immunization, DPT (% of children ages 12-23 months)"

## [477] "Immunization, HepB3 (% of one-year-old children)"

## [478] "Immunization, measles (% of children ages 12-23 months)"

## [479] "Import unit value index (2000 = 100)"

## [480] "Import value index (2000 = 100)"

## [481] "Import volume index (2000 = 100)"

## [482] "Imports of goods and services (% of GDP)"

## [483] "Imports of goods and services (BoP, current US$)"

## [484] "Imports of goods and services (constant 2010 US$)"

## [485] "Imports of goods and services (current LCU)"

## [486] "Imports of goods and services (current US$)"

## [487] "Imports of goods, services and primary income (BoP, current US$)"

## [488] "Incidence of HIV (per 1,000 uninfected population ages 15-49)"

## [489] "Incidence of malaria (per 1,000 population at risk)"

## [490] "Incidence of tuberculosis (per 100,000 people)"

## [491] "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)"

## [492] "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)"

## [493] "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)"

## [494] "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)"

## [495] "Individuals using the Internet (% of population)"

## [496] "Industry (including construction), value added (% of GDP)"

## [497] "Industry (including construction), value added (annual % growth)"

## [498] "Industry (including construction), value added (constant 2010 US$)"

## [499] "Industry (including construction), value added (constant LCU)"

## [500] "Industry (including construction), value added (current LCU)"

## [501] "Industry (including construction), value added (current US$)"

## [502] "Industry (including construction), value added per worker (constant 2010 US$)"

## [503] "Inflation, consumer prices (annual %)"

## [504] "Inflation, GDP deflator (annual %)"

## [505] "Inflation, GDP deflator: linked series (annual %)"

## [506] "Informal payments to public officials (% of firms)"

## [507] "Insurance and financial services (% of commercial service exports)"

## [508] "Insurance and financial services (% of commercial service imports)"

## [509] "Insurance and financial services (% of service exports, BoP)"

## [510] "Insurance and financial services (% of service imports, BoP)"

## [511] "Intentional homicides (per 100,000 people)"

## [512] "Interest payments (% of expense)"

## [513] "Interest payments (% of revenue)"

## [514] "Interest payments (current LCU)"

## [515] "Internally displaced persons, new displacement associated with conflict and violence (number of cases)"

## [516] "Internally displaced persons, new displacement associated with disasters (number of cases)"

## [517] "Internally displaced persons, total displaced by conflict and violence (number of people)"

## [518] "International migrant stock (% of population)"

## [519] "International migrant stock, total"

## [520] "International tourism, expenditures (% of total imports)"

## [521] "International tourism, expenditures (current US$)"

## [522] "International tourism, expenditures for passenger transport items (current US$)"

## [523] "International tourism, expenditures for travel items (current US$)"

## [524] "International tourism, receipts (% of total exports)"

## [525] "International tourism, receipts (current US$)"

## [526] "International tourism, receipts for passenger transport items (current US$)"

## [527] "International tourism, receipts for travel items (current US$)"

## [528] "Investment in energy with private participation (current US$)"

## [529] "Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)"

## [530] "Labor force participation rate for ages 15-24, female (%) (national estimate)"

## [531] "Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)"

## [532] "Labor force participation rate for ages 15-24, male (%) (national estimate)"

## [533] "Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)"

## [534] "Labor force participation rate for ages 15-24, total (%) (national estimate)"

## [535] "Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)"

## [536] "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)"

## [537] "Labor force participation rate, female (% of female population ages 15+) (national estimate)"

## [538] "Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)"

## [539] "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)"

## [540] "Labor force participation rate, male (% of male population ages 15+) (national estimate)"

## [541] "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)"

## [542] "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)"

## [543] "Labor force participation rate, total (% of total population ages 15+) (national estimate)"

## [544] "Labor force with advanced education (% of total working-age population with advanced education)"

## [545] "Labor force with advanced education, female (% of female working-age population with advanced education)"

## [546] "Labor force with advanced education, male (% of male working-age population with advanced education)"

## [547] "Labor force with basic education (% of total working-age population with basic education)"

## [548] "Labor force with basic education, female (% of female working-age population with basic education)"

## [549] "Labor force with basic education, male (% of male working-age population with basic education)"

## [550] "Labor force with intermediate education (% of total working-age population with intermediate education)"

## [551] "Labor force with intermediate education, female (% of female working-age population with intermediate education)"

## [552] "Labor force with intermediate education, male (% of male working-age population with intermediate education)"

## [553] "Labor force, female (% of total labor force)"

## [554] "Labor force, total"

## [555] "Labor tax and contributions (% of commercial profits)"

## [556] "Land area (sq. km)"

## [557] "Land under cereal production (hectares)"

## [558] "Lead time to export, median case (days)"

## [559] "Lead time to import, median case (days)"

## [560] "Lending interest rate (%)"

## [561] "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources"

## [562] "Life expectancy at birth, female (years)"

## [563] "Life expectancy at birth, male (years)"

## [564] "Life expectancy at birth, total (years)"

## [565] "Lifetime risk of maternal death (%)"

## [566] "Lifetime risk of maternal death (1 in: rate varies by country)"

## [567] "Literacy rate, adult female (% of females ages 15 and above)"

## [568] "Literacy rate, adult male (% of males ages 15 and above)"

## [569] "Literacy rate, adult total (% of people ages 15 and above)"

## [570] "Literacy rate, youth (ages 15-24), gender parity index (GPI)"

## [571] "Literacy rate, youth female (% of females ages 15-24)"

## [572] "Literacy rate, youth male (% of males ages 15-24)"

## [573] "Literacy rate, youth total (% of people ages 15-24)"

## [574] "Livestock production index (2004-2006 = 100)"

## [575] "Logistics performance index: Ability to track and trace consignments (1=low to 5=high)"

## [576] "Logistics performance index: Competence and quality of logistics services (1=low to 5=high)"

## [577] "Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high)"

## [578] "Logistics performance index: Efficiency of customs clearance process (1=low to 5=high)"

## [579] "Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high)"

## [580] "Logistics performance index: Overall (1=low to 5=high)"

## [581] "Logistics performance index: Quality of trade and transport-related infrastructure (1=low to 5=high)"

## [582] "Losses due to theft and vandalism (% of annual sales of affected firms)"

## [583] "Lower secondary completion rate, female (% of relevant age group)"

## [584] "Lower secondary completion rate, male (% of relevant age group)"

## [585] "Lower secondary completion rate, total (% of relevant age group)"

## [586] "Lower secondary school starting age (years)"

## [587] "Mammal species, threatened"

## [588] "Manufactures exports (% of merchandise exports)"

## [589] "Manufactures imports (% of merchandise imports)"

## [590] "Manufacturing, value added (% of GDP)"

## [591] "Manufacturing, value added (annual % growth)"

## [592] "Manufacturing, value added (constant 2010 US$)"

## [593] "Manufacturing, value added (constant LCU)"

## [594] "Manufacturing, value added (current LCU)"

## [595] "Manufacturing, value added (current US$)"

## [596] "Maternal mortality ratio (modeled estimate, per 100,000 live births)"

## [597] "Maternal mortality ratio (national estimate, per 100,000 live births)"

## [598] "Medium and high-tech exports (% manufactured exports)"

## [599] "Medium and high-tech Industry (including construction) (% manufacturing value added)"

## [600] "Merchandise exports (current US$)"

## [601] "Merchandise exports by the reporting economy (current US$)"

## [602] "Merchandise exports by the reporting economy, residual (% of total merchandise exports)"

## [603] "Merchandise exports to economies in the Arab World (% of total merchandise exports)"

## [604] "Merchandise exports to high-income economies (% of total merchandise exports)"

## [605] "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)"

## [606] "Merchandise exports to low- and middle-income economies in Europe & Central Asia (% of total merchandise exports)"

## [607] "Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (% of total merchandise exports)"

## [608] "Merchandise exports to low- and middle-income economies in Middle East & North Africa (% of total merchandise exports)"

## [609] "Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports)"

## [610] "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)"

## [611] "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)"

## [612] "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)"

## [613] "Merchandise imports (current US$)"

## [614] "Merchandise imports by the reporting economy (current US$)"

## [615] "Merchandise imports by the reporting economy, residual (% of total merchandise imports)"

## [616] "Merchandise imports from economies in the Arab World (% of total merchandise imports)"

## [617] "Merchandise imports from high-income economies (% of total merchandise imports)"

## [618] "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)"

## [619] "Merchandise imports from low- and middle-income economies in Europe & Central Asia (% of total merchandise imports)"

## [620] "Merchandise imports from low- and middle-income economies in Latin America & the Caribbean (% of total merchandise imports)"

## [621] "Merchandise imports from low- and middle-income economies in Middle East & North Africa (% of total merchandise imports)"

## [622] "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)"

## [623] "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)"

## [624] "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)"

## [625] "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)"

## [626] "Merchandise trade (% of GDP)"

## [627] "Methane emissions (% change from 1990)"

## [628] "Methane emissions (kt of CO2 equivalent)"

## [629] "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)"

## [630] "Methodology assessment of statistical capacity (scale 0 - 100)"

## [631] "Military expenditure (% of GDP)"

## [632] "Military expenditure (% of general government expenditure)"

## [633] "Military expenditure (current LCU)"

## [634] "Military expenditure (current USD)"

## [635] "Mineral rents (% of GDP)"

## [636] "Mobile cellular subscriptions"

## [637] "Mobile cellular subscriptions (per 100 people)"

## [638] "Monetary Sector credit to private sector (% GDP)"

## [639] "Mortality caused by road traffic injury (per 100,000 people)"

## [640] "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)"

## [641] "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)"

## [642] "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)"

## [643] "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)"

## [644] "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)"

## [645] "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)"

## [646] "Mortality rate attributed to unintentional poisoning (per 100,000 population)"

## [647] "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)"

## [648] "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)"

## [649] "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)"

## [650] "Mortality rate, adult, female (per 1,000 female adults)"

## [651] "Mortality rate, adult, male (per 1,000 male adults)"

## [652] "Mortality rate, infant (per 1,000 live births)"

## [653] "Mortality rate, infant, female (per 1,000 live births)"

## [654] "Mortality rate, infant, male (per 1,000 live births)"

## [655] "Mortality rate, neonatal (per 1,000 live births)"

## [656] "Mortality rate, under-5 (per 1,000 live births)"

## [657] "Mortality rate, under-5, female (per 1,000 live births)"

## [658] "Mortality rate, under-5, male (per 1,000 live births)"

## [659] "Multidimensional poverty headcount ratio (% of total population)"

## [660] "Multidimensional poverty headcount ratio, children (% of population ages 0-17)"

## [661] "Multidimensional poverty index (scale 0-1)"

## [662] "Multidimensional poverty index, children (population ages 0-17) (scale 0-1)"

## [663] "Multidimensional poverty intensity (average share of deprivations experienced by the poor)"

## [664] "Multilateral debt service (% of public and publicly guaranteed debt service)"

## [665] "Multilateral debt service (TDS, current US$)"

## [666] "Natural gas rents (% of GDP)"

## [667] "Net acquisition of financial assets (% of GDP)"

## [668] "Net acquisition of financial assets (current LCU)"

## [669] "Net barter terms of trade index (2000 = 100)"

## [670] "Net bilateral aid flows from DAC donors, Australia (current US$)"

## [671] "Net bilateral aid flows from DAC donors, Austria (current US$)"

## [672] "Net bilateral aid flows from DAC donors, Belgium (current US$)"

## [673] "Net bilateral aid flows from DAC donors, Canada (current US$)"

## [674] "Net bilateral aid flows from DAC donors, Czech Republic (current US$)"

## [675] "Net bilateral aid flows from DAC donors, Denmark (current US$)"

## [676] "Net bilateral aid flows from DAC donors, European Union institutions (current US$)"

## [677] "Net bilateral aid flows from DAC donors, Finland (current US$)"

## [678] "Net bilateral aid flows from DAC donors, France (current US$)"

## [679] "Net bilateral aid flows from DAC donors, Germany (current US$)"

## [680] "Net bilateral aid flows from DAC donors, Greece (current US$)"

## [681] "Net bilateral aid flows from DAC donors, Hungary (current US$)"

## [682] "Net bilateral aid flows from DAC donors, Iceland (current US$)"

## [683] "Net bilateral aid flows from DAC donors, Ireland (current US$)"

## [684] "Net bilateral aid flows from DAC donors, Italy (current US$)"

## [685] "Net bilateral aid flows from DAC donors, Japan (current US$)"

## [686] "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)"

## [687] "Net bilateral aid flows from DAC donors, Luxembourg (current US$)"

## [688] "Net bilateral aid flows from DAC donors, Netherlands (current US$)"

## [689] "Net bilateral aid flows from DAC donors, New Zealand (current US$)"

## [690] "Net bilateral aid flows from DAC donors, Norway (current US$)"

## [691] "Net bilateral aid flows from DAC donors, Poland (current US$)"

## [692] "Net bilateral aid flows from DAC donors, Portugal (current US$)"

## [693] "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)"

## [694] "Net bilateral aid flows from DAC donors, Slovenia (current US$)"

## [695] "Net bilateral aid flows from DAC donors, Spain (current US$)"

## [696] "Net bilateral aid flows from DAC donors, Sweden (current US$)"

## [697] "Net bilateral aid flows from DAC donors, Switzerland (current US$)"

## [698] "Net bilateral aid flows from DAC donors, Total (current US$)"

## [699] "Net bilateral aid flows from DAC donors, United Kingdom (current US$)"

## [700] "Net bilateral aid flows from DAC donors, United States (current US$)"

## [701] "Net capital account (BoP, current US$)"

## [702] "Net domestic credit (current LCU)"

## [703] "Net errors and omissions (BoP, current US$)"

## [704] "Net financial account (BoP, current US$)"

## [705] "Net financial flows, bilateral (NFL, current US$)"

## [706] "Net financial flows, IBRD (NFL, current US$)"

## [707] "Net financial flows, IDA (NFL, current US$)"

## [708] "Net financial flows, IMF concessional (NFL, current US$)"

## [709] "Net financial flows, IMF nonconcessional (NFL, current US$)"

## [710] "Net financial flows, multilateral (NFL, current US$)"

## [711] "Net financial flows, others (NFL, current US$)"

## [712] "Net financial flows, RDB concessional (NFL, current US$)"

## [713] "Net financial flows, RDB nonconcessional (NFL, current US$)"

## [714] "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)"

## [715] "Net foreign assets (current LCU)"

## [716] "Net incurrence of liabilities, total (% of GDP)"

## [717] "Net incurrence of liabilities, total (current LCU)"

## [718] "Net intake rate in grade 1 (% of official school-age population)"

## [719] "Net intake rate in grade 1, female (% of official school-age population)"

## [720] "Net intake rate in grade 1, male (% of official school-age population)"

## [721] "Net investment in nonfinancial assets (% of GDP)"

## [722] "Net investment in nonfinancial assets (current LCU)"

## [723] "Net lending (+) / net borrowing (-) (% of GDP)"

## [724] "Net lending (+) / net borrowing (-) (current LCU)"

## [725] "Net migration"

## [726] "Net ODA received (% of central government expense)"

## [727] "Net ODA received (% of GNI)"

## [728] "Net ODA received (% of gross capital formation)"

## [729] "Net ODA received (% of imports of goods, services and primary income)"

## [730] "Net ODA received per capita (current US$)"

## [731] "Net official development assistance and official aid received (constant 2015 US$)"

## [732] "Net official development assistance and official aid received (current US$)"

## [733] "Net official development assistance received (constant 2015 US$)"

## [734] "Net official development assistance received (current US$)"

## [735] "Net official flows from UN agencies, FAO (current US$)"

## [736] "Net official flows from UN agencies, IAEA (current US$)"

## [737] "Net official flows from UN agencies, IFAD (current US$)"

## [738] "Net official flows from UN agencies, ILO (current US$)"

## [739] "Net official flows from UN agencies, UNDP (current US$)"

## [740] "Net official flows from UN agencies, UNFPA (current US$)"

## [741] "Net official flows from UN agencies, UNHCR (current US$)"

## [742] "Net official flows from UN agencies, UNICEF (current US$)"

## [743] "Net official flows from UN agencies, UNTA (current US$)"

## [744] "Net official flows from UN agencies, WFP (current US$)"

## [745] "Net official flows from UN agencies, WHO (current US$)"

## [746] "Net primary income (BoP, current US$)"

## [747] "Net primary income (Net income from abroad) (current LCU)"

## [748] "Net primary income (Net income from abroad) (current US$)"

## [749] "Net secondary income (BoP, current US$)"

## [750] "Net secondary income (Net current transfers from abroad) (current LCU)"

## [751] "Net secondary income (Net current transfers from abroad) (current US$)"

## [752] "Net trade in goods (BoP, current US$)"

## [753] "Net trade in goods and services (BoP, current US$)"

## [754] "New business density (new registrations per 1,000 people ages 15-64)"

## [755] "New businesses registered (number)"

## [756] "Newborns protected against tetanus (%)"

## [757] "Nitrous oxide emissions (% change from 1990)"

## [758] "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)"

## [759] "Nitrous oxide emissions in energy sector (% of total)"

## [760] "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)"

## [761] "Number of deaths ages 5-14 years"

## [762] "Number of infant deaths"

## [763] "Number of maternal deaths"

## [764] "Number of neonatal deaths"

## [765] "Number of under-five deaths"

## [766] "Nurses and midwives (per 1,000 people)"

## [767] "Official exchange rate (LCU per US$, period average)"

## [768] "Oil rents (% of GDP)"

## [769] "Ores and metals exports (% of merchandise exports)"

## [770] "Ores and metals imports (% of merchandise imports)"

## [771] "Other expense (% of expense)"

## [772] "Other expense (current LCU)"

## [773] "Other greenhouse gas emissions (% change from 1990)"

## [774] "Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)"

## [775] "Other taxes (% of revenue)"

## [776] "Other taxes (current LCU)"

## [777] "Other taxes payable by businesses (% of commercial profits)"

## [778] "Out-of-pocket expenditure (% of current health expenditure)"

## [779] "Out-of-pocket expenditure per capita (current US$)"

## [780] "Out-of-pocket expenditure per capita, PPP (current international $)"

## [781] "Over-age students, primary (% of enrollment)"

## [782] "Over-age students, primary, female (% of female enrollment)"

## [783] "Over-age students, primary, male (% of male enrollment)"

## [784] "Part time employment, female (% of total female employment)"

## [785] "Part time employment, male (% of total male employment)"

## [786] "Part time employment, total (% of total employment)"

## [787] "People practicing open defecation (% of population)"

## [788] "People practicing open defecation, rural (% of rural population)"

## [789] "People practicing open defecation, urban (% of urban population)"

## [790] "People using at least basic drinking water services (% of population)"

## [791] "People using at least basic drinking water services, rural (% of rural population)"

## [792] "People using at least basic drinking water services, urban (% of urban population)"

## [793] "People using at least basic sanitation services (% of population)"

## [794] "People using at least basic sanitation services, rural (% of rural population)"

## [795] "People using at least basic sanitation services, urban (% of urban population)"

## [796] "People with basic handwashing facilities including soap and water (% of population)"

## [797] "People with basic handwashing facilities including soap and water, rural (% of rural population)"

## [798] "People with basic handwashing facilities including soap and water, urban (% of urban population)"

## [799] "Periodicity and timeliness assessment of statistical capacity (scale 0 - 100)"

## [800] "Permanent cropland (% of land area)"

## [801] "Persistence to grade 5, female (% of cohort)"

## [802] "Persistence to grade 5, male (% of cohort)"

## [803] "Persistence to grade 5, total (% of cohort)"

## [804] "Persistence to last grade of primary, female (% of cohort)"

## [805] "Persistence to last grade of primary, male (% of cohort)"

## [806] "Persistence to last grade of primary, total (% of cohort)"

## [807] "Personal remittances, paid (current US$)"

## [808] "Personal remittances, received (% of GDP)"

## [809] "Personal remittances, received (current US$)"

## [810] "Personal transfers, receipts (BoP, current US$)"

## [811] "Physicians (per 1,000 people)"

## [812] "Plant species (higher), threatened"

## [813] "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)"

## [814] "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)"

## [815] "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)"

## [816] "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)"

## [817] "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)"

## [818] "PNG, bonds (NFL, current US$)"

## [819] "PNG, commercial banks and other creditors (NFL, current US$)"

## [820] "Population ages 0-14 (% of total population)"

## [821] "Population ages 0-14, female"

## [822] "Population ages 0-14, female (% of female population)"

## [823] "Population ages 0-14, male"

## [824] "Population ages 0-14, male (% of male population)"

## [825] "Population ages 0-14, total"

## [826] "Population ages 00-04, female (% of female population)"

## [827] "Population ages 00-04, male (% of male population)"

## [828] "Population ages 05-09, female (% of female population)"

## [829] "Population ages 05-09, male (% of male population)"

## [830] "Population ages 10-14, female (% of female population)"

## [831] "Population ages 10-14, male (% of male population)"

## [832] "Population ages 15-19, female (% of female population)"

## [833] "Population ages 15-19, male (% of male population)"

## [834] "Population ages 15-64 (% of total population)"

## [835] "Population ages 15-64, female"

## [836] "Population ages 15-64, female (% of female population)"

## [837] "Population ages 15-64, male"

## [838] "Population ages 15-64, male (% of male population)"

## [839] "Population ages 15-64, total"

## [840] "Population ages 20-24, female (% of female population)"

## [841] "Population ages 20-24, male (% of male population)"

## [842] "Population ages 25-29, female (% of female population)"

## [843] "Population ages 25-29, male (% of male population)"

## [844] "Population ages 30-34, female (% of female population)"

## [845] "Population ages 30-34, male (% of male population)"

## [846] "Population ages 35-39, female (% of female population)"

## [847] "Population ages 35-39, male (% of male population)"

## [848] "Population ages 40-44, female (% of female population)"

## [849] "Population ages 40-44, male (% of male population)"

## [850] "Population ages 45-49, female (% of female population)"

## [851] "Population ages 45-49, male (% of male population)"

## [852] "Population ages 50-54, female (% of female population)"

## [853] "Population ages 50-54, male (% of male population)"

## [854] "Population ages 55-59, female (% of female population)"

## [855] "Population ages 55-59, male (% of male population)"

## [856] "Population ages 60-64, female (% of female population)"

## [857] "Population ages 60-64, male (% of male population)"

## [858] "Population ages 65 and above (% of total population)"

## [859] "Population ages 65 and above, female"

## [860] "Population ages 65 and above, female (% of female population)"

## [861] "Population ages 65 and above, male"

## [862] "Population ages 65 and above, male (% of male population)"

## [863] "Population ages 65 and above, total"

## [864] "Population ages 65-69, female (% of female population)"

## [865] "Population ages 65-69, male (% of male population)"

## [866] "Population ages 70-74, female (% of female population)"

## [867] "Population ages 70-74, male (% of male population)"

## [868] "Population ages 75-79, female (% of female population)"

## [869] "Population ages 75-79, male (% of male population)"

## [870] "Population ages 80 and above, female (% of female population)"

## [871] "Population ages 80 and above, male (% of male population)"

## [872] "Population density (people per sq. km of land area)"

## [873] "Population growth (annual %)"

## [874] "Population in largest city"

## [875] "Population in the largest city (% of urban population)"

## [876] "Population in urban agglomerations of more than 1 million"

## [877] "Population in urban agglomerations of more than 1 million (% of total population)"

## [878] "Population living in slums (% of urban population)"

## [879] "Population, female"

## [880] "Population, female (% of total population)"

## [881] "Population, male"

## [882] "Population, male (% of total population)"

## [883] "Population, total"

## [884] "Portfolio equity, net inflows (BoP, current US$)"

## [885] "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)"

## [886] "Portfolio investment, net (BoP, current US$)"

## [887] "Poverty headcount ratio at national poverty lines (% of population)"

## [888] "Power outages in firms in a typical month (number)"

## [889] "PPG, bonds (NFL, current US$)"

## [890] "PPG, commercial banks (NFL, current US$)"

## [891] "PPG, IBRD (DOD, current US$)"

## [892] "PPG, IDA (DOD, current US$)"

## [893] "PPG, official creditors (NFL, current US$)"

## [894] "PPG, other private creditors (NFL, current US$)"

## [895] "PPG, private creditors (NFL, current US$)"

## [896] "PPP conversion factor, GDP (LCU per international $)"

## [897] "PPP conversion factor, private consumption (LCU per international $)"

## [898] "Pregnant women receiving prenatal care (%)"

## [899] "Preprimary education, duration (years)"

## [900] "Presence of peace keepers (number of troops, police, and military observers in mandate)"

## [901] "Present value of external debt (% of exports of goods, services and primary income)"

## [902] "Present value of external debt (% of GNI)"

## [903] "Present value of external debt (current US$)"

## [904] "Prevalence of anemia among children (% of children under 5)"

## [905] "Prevalence of anemia among non-pregnant women (% of women ages 15-49)"

## [906] "Prevalence of anemia among pregnant women (%)"

## [907] "Prevalence of anemia among women of reproductive age (% of women ages 15-49)"

## [908] "Prevalence of HIV, female (% ages 15-24)"

## [909] "Prevalence of HIV, male (% ages 15-24)"

## [910] "Prevalence of HIV, total (% of population ages 15-49)"

## [911] "Prevalence of moderate or severe food insecurity in the population (%)"

## [912] "Prevalence of overweight, weight for height (% of children under 5)"

## [913] "Prevalence of overweight, weight for height, female (% of children under 5)"

## [914] "Prevalence of overweight, weight for height, male (% of children under 5)"

## [915] "Prevalence of severe food insecurity in the population (%)"

## [916] "Prevalence of severe wasting, weight for height (% of children under 5)"

## [917] "Prevalence of severe wasting, weight for height, female (% of children under 5)"

## [918] "Prevalence of severe wasting, weight for height, male (% of children under 5)"

## [919] "Prevalence of stunting, height for age (% of children under 5)"

## [920] "Prevalence of stunting, height for age, female (% of children under 5)"

## [921] "Prevalence of stunting, height for age, male (% of children under 5)"

## [922] "Prevalence of undernourishment (% of population)"

## [923] "Prevalence of underweight, weight for age (% of children under 5)"

## [924] "Prevalence of underweight, weight for age, female (% of children under 5)"

## [925] "Prevalence of underweight, weight for age, male (% of children under 5)"

## [926] "Prevalence of wasting, weight for height (% of children under 5)"

## [927] "Prevalence of wasting, weight for height, female (% of children under 5)"

## [928] "Prevalence of wasting, weight for height, male (% of children under 5)"

## [929] "Price level ratio of PPP conversion factor (GDP) to market exchange rate"

## [930] "Primary completion rate, female (% of relevant age group)"

## [931] "Primary completion rate, male (% of relevant age group)"

## [932] "Primary completion rate, total (% of relevant age group)"

## [933] "Primary education, duration (years)"

## [934] "Primary education, pupils"

## [935] "Primary education, pupils (% female)"

## [936] "Primary education, teachers"

## [937] "Primary education, teachers (% female)"

## [938] "Primary government expenditures as a proportion of original approved budget (%)"

## [939] "Primary income payments (BoP, current US$)"

## [940] "Primary income receipts (BoP, current US$)"

## [941] "Primary school starting age (years)"

## [942] "Private credit bureau coverage (% of adults)"

## [943] "Probability of dying at age 5-14 years (per 1,000 children age 5)"

## [944] "Procedures to build a warehouse (number)"

## [945] "Procedures to register property (number)"

## [946] "Profit tax (% of commercial profits)"

## [947] "Progression to secondary school (%)"

## [948] "Progression to secondary school, female (%)"

## [949] "Progression to secondary school, male (%)"

## [950] "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)"

## [951] "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)"

## [952] "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)"

## [953] "Proportion of seats held by women in national parliaments (%)"

## [954] "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of women age 15-49)"

## [955] "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)"

## [956] "Public and publicly guaranteed debt service (% of GNI)"

## [957] "Public credit registry coverage (% of adults)"

## [958] "Public private partnerships investment in energy (current US$)"

## [959] "Pump price for diesel fuel (US$ per liter)"

## [960] "Pump price for gasoline (US$ per liter)"

## [961] "Pupil-teacher ratio, lower secondary"

## [962] "Pupil-teacher ratio, preprimary"

## [963] "Pupil-teacher ratio, primary"

## [964] "Pupil-teacher ratio, secondary"

## [965] "Pupil-teacher ratio, tertiary"

## [966] "Pupil-teacher ratio, upper secondary"

## [967] "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"

## [968] "Ratio of female to male labor force participation rate (%) (national estimate)"

## [969] "Real interest rate (%)"

## [970] "Refugee population by country or territory of asylum"

## [971] "Refugee population by country or territory of origin"

## [972] "Renewable electricity output (% of total electricity output)"

## [973] "Renewable energy consumption (% of total final energy consumption)"

## [974] "Renewable internal freshwater resources per capita (cubic meters)"

## [975] "Renewable internal freshwater resources, total (billion cubic meters)"

## [976] "Repeaters, primary, female (% of female enrollment)"

## [977] "Repeaters, primary, male (% of male enrollment)"

## [978] "Repeaters, primary, total (% of total enrollment)"

## [979] "Reserves and related items (BoP, current US$)"

## [980] "Revenue, excluding grants (% of GDP)"

## [981] "Revenue, excluding grants (current LCU)"

## [982] "Rural population"

## [983] "Rural population (% of total population)"

## [984] "Rural population growth (annual %)"

## [985] "School enrollment, preprimary (% gross)"

## [986] "School enrollment, preprimary, female (% gross)"

## [987] "School enrollment, preprimary, male (% gross)"

## [988] "School enrollment, primary (% gross)"

## [989] "School enrollment, primary (% net)"

## [990] "School enrollment, primary (gross), gender parity index (GPI)"

## [991] "School enrollment, primary and secondary (gross), gender parity index (GPI)"

## [992] "School enrollment, primary, female (% gross)"

## [993] "School enrollment, primary, female (% net)"

## [994] "School enrollment, primary, male (% gross)"

## [995] "School enrollment, primary, male (% net)"

## [996] "School enrollment, primary, private (% of total primary)"

## [997] "School enrollment, secondary (% gross)"

## [998] "School enrollment, secondary (% net)"

## [999] "School enrollment, secondary (gross), gender parity index (GPI)"

## [1000] "School enrollment, secondary, female (% gross)"

## [1001] "School enrollment, secondary, female (% net)"

## [1002] "School enrollment, secondary, male (% gross)"

## [1003] "School enrollment, secondary, male (% net)"

## [1004] "School enrollment, secondary, private (% of total secondary)"

## [1005] "School enrollment, tertiary (% gross)"

## [1006] "School enrollment, tertiary (gross), gender parity index (GPI)"

## [1007] "School enrollment, tertiary, female (% gross)"

## [1008] "School enrollment, tertiary, male (% gross)"

## [1009] "Scientific and technical journal articles"

## [1010] "Secondary education, duration (years)"

## [1011] "Secondary education, general pupils"

## [1012] "Secondary education, general pupils (% female)"

## [1013] "Secondary education, pupils"

## [1014] "Secondary education, pupils (% female)"

## [1015] "Secondary education, teachers"

## [1016] "Secondary education, teachers (% female)"

## [1017] "Secondary education, teachers, female"

## [1018] "Secondary education, vocational pupils"

## [1019] "Secondary education, vocational pupils (% female)"

## [1020] "Secondary income receipts (BoP, current US$)"

## [1021] "Secondary income, other sectors, payments (BoP, current US$)"

## [1022] "Secure Internet servers"

## [1023] "Secure Internet servers (per 1 million people)"

## [1024] "Self-employed, female (% of female employment) (modeled ILO estimate)"

## [1025] "Self-employed, male (% of male employment) (modeled ILO estimate)"

## [1026] "Self-employed, total (% of total employment) (modeled ILO estimate)"

## [1027] "Service exports (BoP, current US$)"

## [1028] "Service imports (BoP, current US$)"

## [1029] "Services, value added (% of GDP)"

## [1030] "Services, value added (annual % growth)"

## [1031] "Services, value added (constant 2010 US$)"

## [1032] "Services, value added (constant LCU)"

## [1033] "Services, value added (current LCU)"

## [1034] "Services, value added (current US$)"

## [1035] "Services, value added per worker (constant 2010 US$)"

## [1036] "Sex ratio at birth (male births per female births)"

## [1037] "Share of tariff lines with international peaks, all products (%)"

## [1038] "Share of tariff lines with international peaks, manufactured products (%)"

## [1039] "Share of tariff lines with international peaks, primary products (%)"

## [1040] "Share of tariff lines with specific rates, all products (%)"

## [1041] "Share of tariff lines with specific rates, manufactured products (%)"

## [1042] "Share of tariff lines with specific rates, primary products (%)"

## [1043] "Share of youth not in education, employment or training, female (% of female youth population)"

## [1044] "Share of youth not in education, employment or training, male (% of male youth population)"

## [1045] "Share of youth not in education, employment or training, total (% of youth population)"

## [1046] "Short-term debt (% of exports of goods, services and primary income)"

## [1047] "Short-term debt (% of total external debt)"

## [1048] "Short-term debt (% of total reserves)"

## [1049] "Social contributions (% of revenue)"

## [1050] "Social contributions (current LCU)"

## [1051] "Source data assessment of statistical capacity (scale 0 - 100)"

## [1052] "Specialist surgical workforce (per 100,000 population)"

## [1053] "Start-up procedures to register a business (number)"

## [1054] "Start-up procedures to register a business, female (number)"

## [1055] "Start-up procedures to register a business, male (number)"

## [1056] "Statistical Capacity score (Overall average)"

## [1057] "Strength of legal rights index (0=weak to 12=strong)"

## [1058] "Subsidies and other transfers (% of expense)"

## [1059] "Subsidies and other transfers (current LCU)"

## [1060] "Suicide mortality rate (per 100,000 population)"

## [1061] "Suicide mortality rate, female (per 100,000 female population)"

## [1062] "Suicide mortality rate, male (per 100,000 male population)"

## [1063] "Surface area (sq. km)"

## [1064] "Survival to age 65, female (% of cohort)"

## [1065] "Survival to age 65, male (% of cohort)"

## [1066] "Tariff rate, applied, simple mean, all products (%)"

## [1067] "Tariff rate, applied, simple mean, manufactured products (%)"

## [1068] "Tariff rate, applied, simple mean, primary products (%)"

## [1069] "Tariff rate, applied, weighted mean, all products (%)"

## [1070] "Tariff rate, applied, weighted mean, manufactured products (%)"

## [1071] "Tariff rate, applied, weighted mean, primary products (%)"

## [1072] "Tariff rate, most favored nation, simple mean, all products (%)"

## [1073] "Tariff rate, most favored nation, simple mean, manufactured products (%)"

## [1074] "Tariff rate, most favored nation, simple mean, primary products (%)"

## [1075] "Tariff rate, most favored nation, weighted mean, all products (%)"

## [1076] "Tariff rate, most favored nation, weighted mean, manufactured products (%)"

## [1077] "Tariff rate, most favored nation, weighted mean, primary products (%)"

## [1078] "Tax payments (number)"

## [1079] "Tax revenue (% of GDP)"

## [1080] "Tax revenue (current LCU)"

## [1081] "Taxes less subsidies on products (constant LCU)"

## [1082] "Taxes less subsidies on products (current LCU)"

## [1083] "Taxes less subsidies on products (current US$)"

## [1084] "Taxes on exports (% of tax revenue)"

## [1085] "Taxes on exports (current LCU)"

## [1086] "Taxes on goods and services (% of revenue)"

## [1087] "Taxes on goods and services (% value added of industry and services)"

## [1088] "Taxes on goods and services (current LCU)"

## [1089] "Taxes on income, profits and capital gains (% of revenue)"

## [1090] "Taxes on income, profits and capital gains (% of total taxes)"

## [1091] "Taxes on income, profits and capital gains (current LCU)"

## [1092] "Taxes on international trade (% of revenue)"

## [1093] "Taxes on international trade (current LCU)"

## [1094] "Technical cooperation grants (BoP, current US$)"

## [1095] "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)"

## [1096] "Terrestrial and marine protected areas (% of total territorial area)"

## [1097] "Terrestrial protected areas (% of total land area)"

## [1098] "Tertiary education, academic staff (% female)"

## [1099] "Time required to build a warehouse (days)"

## [1100] "Time required to enforce a contract (days)"

## [1101] "Time required to get electricity (days)"

## [1102] "Time required to obtain an operating license (days)"

## [1103] "Time required to register property (days)"

## [1104] "Time required to start a business (days)"

## [1105] "Time required to start a business, female (days)"

## [1106] "Time required to start a business, male (days)"

## [1107] "Time spent dealing with the requirements of government regulations (% of senior management time)"

## [1108] "Time to export, border compliance (hours)"

## [1109] "Time to export, documentary compliance (hours)"

## [1110] "Time to import, border compliance (hours)"

## [1111] "Time to import, documentary compliance (hours)"

## [1112] "Time to obtain an electrical connection (days)"

## [1113] "Time to prepare and pay taxes (hours)"

## [1114] "Time to resolve insolvency (years)"

## [1115] "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"

## [1116] "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)"

## [1117] "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)"

## [1118] "Total debt service (% of exports of goods, services and primary income)"

## [1119] "Total debt service (% of GNI)"

## [1120] "Total fisheries production (metric tons)"

## [1121] "Total greenhouse gas emissions (% change from 1990)"

## [1122] "Total greenhouse gas emissions (kt of CO2 equivalent)"

## [1123] "Total natural resources rents (% of GDP)"

## [1124] "Total reserves (% of total external debt)"

## [1125] "Total reserves (includes gold, current US$)"

## [1126] "Total reserves in months of imports"

## [1127] "Total reserves minus gold (current US$)"

## [1128] "Total tax and contribution rate (% of profit)"

## [1129] "Trade (% of GDP)"

## [1130] "Trade in services (% of GDP)"

## [1131] "Transport services (% of commercial service exports)"

## [1132] "Transport services (% of commercial service imports)"

## [1133] "Transport services (% of service exports, BoP)"

## [1134] "Transport services (% of service imports, BoP)"

## [1135] "Travel services (% of commercial service exports)"

## [1136] "Travel services (% of commercial service imports)"

## [1137] "Travel services (% of service exports, BoP)"

## [1138] "Travel services (% of service imports, BoP)"

## [1139] "Tuberculosis case detection rate (%, all forms)"

## [1140] "Tuberculosis treatment success rate (% of new cases)"

## [1141] "UHC service coverage index"

## [1142] "Unemployment with advanced education (% of total labor force with advanced education)"

## [1143] "Unemployment with advanced education, female (% of female labor force with advanced education)"

## [1144] "Unemployment with advanced education, male (% of male labor force with advanced education)"

## [1145] "Unemployment with basic education (% of total labor force with basic education)"

## [1146] "Unemployment with basic education, female (% of female labor force with basic education)"

## [1147] "Unemployment with basic education, male (% of male labor force with basic education)"

## [1148] "Unemployment with intermediate education (% of total labor force with intermediate education)"

## [1149] "Unemployment with intermediate education, female (% of female labor force with intermediate education)"

## [1150] "Unemployment with intermediate education, male (% of male labor force with intermediate education)"

## [1151] "Unemployment, female (% of female labor force) (modeled ILO estimate)"

## [1152] "Unemployment, female (% of female labor force) (national estimate)"

## [1153] "Unemployment, male (% of male labor force) (modeled ILO estimate)"

## [1154] "Unemployment, male (% of male labor force) (national estimate)"

## [1155] "Unemployment, total (% of total labor force) (modeled ILO estimate)"

## [1156] "Unemployment, total (% of total labor force) (national estimate)"

## [1157] "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)"

## [1158] "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)"

## [1159] "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)"

## [1160] "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)"

## [1161] "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)"

## [1162] "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)"

## [1163] "Unmet need for contraception (% of married women ages 15-49)"

## [1164] "Urban population"

## [1165] "Urban population (% of total population)"

## [1166] "Urban population growth (annual %)"

## [1167] "Use of IMF credit (DOD, current US$)"

## [1168] "Use of insecticide-treated bed nets (% of under-5 population)"

## [1169] "Value lost due to electrical outages (% of sales for affected firms)"

## [1170] "Vitamin A supplementation coverage rate (% of children ages 6-59 months)"

## [1171] "Vulnerable employment, female (% of female employment) (modeled ILO estimate)"

## [1172] "Vulnerable employment, male (% of male employment) (modeled ILO estimate)"

## [1173] "Vulnerable employment, total (% of total employment) (modeled ILO estimate)"

## [1174] "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)"

## [1175] "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)"

## [1176] "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)"

## [1177] "Wanted fertility rate (births per woman)"

## [1178] "Women Business and the Law Index Score (scale 1-100)"

## [1179] "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)"

## [1180] "Women who believe a husband is justified in beating his wife (any of five reasons) (%)"

## [1181] "Women who believe a husband is justified in beating his wife when she argues with him (%)"

## [1182] "Women who believe a husband is justified in beating his wife when she burns the food (%)"

## [1183] "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)"

## [1184] "Women who believe a husband is justified in beating his wife when she neglects the children (%)"

## [1185] "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)"

## [1186] "Women who were first married by age 15 (% of women ages 20-24)"

## [1187] "Women who were first married by age 18 (% of women ages 20-24)"

## [1188] "Women's share of population ages 15+ living with HIV (%)"

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

Basic R commands – 1.2 – permutations and matrix functions

05/05/2020Allgemein, Code, Programming, RMartin Stoppacher

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Basic Commands and Statistics with R - 1.2 - permutations
# path: ~/ownCloud/STA_Statistics/Basic_Statistics/
# file_name: statistic_basics3.R
# files_used:

#install.packages( 'gtools' )
library( gtools )
#install.packages( "combinat" )
library( combinat )

#install.packages( "magrittr" )
library( "magrittr" )

# - - - - - - - - - - - - - - - - 
#

x <- 6

factorial( x )
# [1] 720

for( i in 1:x ) print( factorial( i ) )
# [1] 1
# [1] 2
# [1] 6
# [1] 24
# [1] 120
# [1] 720

# - - - - - - - - - - - - - - - - 
#

x <- c( "a", "b", "c" )

combinat::permn( x )

# [[1]]
# [1] "a" "b" "c"
# [[2]]
# [1] "a" "c" "b"
# [[3]]
# [1] "c" "a" "b"
# [[4]]
# [1] "c" "b" "a"
# [[5]]
# [1] "b" "c" "a"
# [[6]]
# [1] "b" "a" "c"

# - - - - - - - - - - - - - - - - 
# string conversion using combinat::

x <- "ABCDEFG"
x <- strsplit( x, "" )[[1]]
y <- combinat::permn( x )
y

x <- LETTERS[1:5]
y <- combinat::permn( x )

paste( y[[1]], collapse = "" )
for( i in 1:length( y ) ) print( paste( y[[i]], collapse = "" ) )

# - - - - - - - - - - - - - - - - 
# using gtools:: for permutations

gtools::permutations( 5, 5, x )
# [,1] [,2] [,3] [,4] [,5]
# [1,] "A"  "C"  "I"  "M"  "P" 
# [2,] "A"  "C"  "I"  "P"  "M" 
# [3,] "A"  "C"  "M"  "I"  "P" 
# [4,] "A"  "C"  "M"  "P"  "I" 
# [5,] "A"  "C"  "P"  "I"  "M"
# ...

gtools::permutations( 5, 5, x, repeats.allowed = TRUE )
# [,1] [,2] [,3] [,4] [,5]
# [1,] "A"  "A"  "A"  "A"  "A" 
# [2,] "A"  "A"  "A"  "A"  "C" 
# [3,] "A"  "A"  "A"  "A"  "I" 
# [4,] "A"  "A"  "A"  "A"  "M" 
# [5,] "A"  "A"  "A"  "A"  "P" 
# [6,] "A"  "A"  "A"  "C"  "A" 
# [7,] "A"  "A"  "A"  "C"  "C" 
# [8,] "A"  "A"  "A"  "C"  "I" 
# [9,] "A"  "A"  "A"  "C"  "M" 
# [10,] "A"  "A"  "A"  "C"  "P" 
# ...

# - - - - - - - - - - - - - - - - 
# using gtools:: for combinations

gtools::combinations( 4, 2, x ) 

gtools::combinations( 5, 2, x )

gtools::combinations( 5, 3, x )

gtools::combinations( 4, 2, x, repeats.allowed = TRUE )

gtools::combinations( 2, 5, x, repeats.allowed = TRUE )

# Martin Stoppacher                                                             #
# office@martinstoppacher.com                                                   #
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#################################################################################

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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Basic Commands and Statistics with R - 1.2 - permutations

# path: ~/ownCloud/STA_Statistics/Basic_Statistics/

# file_name: statistic_basics3.R

# files_used:

#install.packages( 'gtools' )

library( gtools )

#install.packages( "combinat" )

library( combinat )

#install.packages( "magrittr" )

library( "magrittr" )

# - - - - - - - - - - - - - - - -

x <- 6

factorial( x )

# [1] 720

for( i in 1:x ) print( factorial( i ) )

# [1] 1

# [1] 2

# [1] 6

# [1] 24

# [1] 120

# [1] 720

# - - - - - - - - - - - - - - - -

x <- c( "a", "b", "c" )

combinat::permn( x )

# [[1]]

# [1] "a" "b" "c"

# [[2]]

# [1] "a" "c" "b"

# [[3]]

# [1] "c" "a" "b"

# [[4]]

# [1] "c" "b" "a"

# [[5]]

# [1] "b" "c" "a"

# [[6]]

# [1] "b" "a" "c"

# - - - - - - - - - - - - - - - -

# string conversion using combinat::

x <- "ABCDEFG"

x <- strsplit( x, "" )[[1]]

y <- combinat::permn( x )

x <- LETTERS[1:5]

y <- combinat::permn( x )

paste( y[[1]], collapse = "" )

for( i in 1:length( y ) ) print( paste( y[[i]], collapse = "" ) )

# - - - - - - - - - - - - - - - -

# using gtools:: for permutations

gtools::permutations( 5, 5, x )

# [,1] [,2] [,3] [,4] [,5]

# [1,] "A" "C" "I" "M" "P"

# [2,] "A" "C" "I" "P" "M"

# [3,] "A" "C" "M" "I" "P"

# [4,] "A" "C" "M" "P" "I"

# [5,] "A" "C" "P" "I" "M"

# ...

gtools::permutations( 5, 5, x, repeats.allowed = TRUE )

# [,1] [,2] [,3] [,4] [,5]

# [1,] "A" "A" "A" "A" "A"

# [2,] "A" "A" "A" "A" "C"

# [3,] "A" "A" "A" "A" "I"

# [4,] "A" "A" "A" "A" "M"

# [5,] "A" "A" "A" "A" "P"

# [6,] "A" "A" "A" "C" "A"

# [7,] "A" "A" "A" "C" "C"

# [8,] "A" "A" "A" "C" "I"

# [9,] "A" "A" "A" "C" "M"

# [10,] "A" "A" "A" "C" "P"

# ...

# - - - - - - - - - - - - - - - -

# using gtools:: for combinations

gtools::combinations( 4, 2, x )

gtools::combinations( 5, 2, x )

gtools::combinations( 5, 3, x )

gtools::combinations( 4, 2, x, repeats.allowed = TRUE )

gtools::combinations( 2, 5, x, repeats.allowed = TRUE )

# Martin Stoppacher #

# office@martinstoppacher.com #

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

#################################################################################

# matrix algebra

m1 <- matrix( c( 1, 2, 3, 4, 5, 6, 7, 8, 9 ) , 3, 3 )
m1

m2 <- matrix( rep( 2, 9 ), 3, 3 )
m2

#       [,1] [,2] [,3]
# [1,]    1    4    7
# [2,]    2    5    8
# [3,]    3    6    9

m1 * m1  # element multipliction

#       [,1] [,2] [,3]
# [1,]    1   16   49
# [2,]    4   25   64
# [3,]    9   36   81

m1 * m2

#       [,1] [,2] [,3]
# [1,]    2    8   14
# [2,]    4   10   16
# [3,]    6   12   18


# Matrix Multiplication
m1 %*% m1

#       [,1] [,2] [,3]
# [1,]   30   66  102
# [2,]   36   81  126
# [3,]   42   96  150

# Transpose
t( m1 )

#       [,1] [,2] [,3]
# [1,]    1    2    3
# [2,]    4    5    6
# [3,]    7    8    9

# Outer Product

m1[ 1 , ]
m1[ , 1 ] 
m1[ 1 , ] %o%  m1[ , 1 ] 

m1 %o% m1

# , , 1, 1
# 
#       [,1] [,2] [,3]
# [1,]    1    4    7
# [2,]    2    5    8
# [3,]    3    6    9
# 
# , , 2, 1
# 
#       [,1] [,2] [,3]
# [1,]    2    8   14
# [2,]    4   10   16
# [3,]    6   12   18
# 
# , , 3, 1
# 
#       [,1] [,2] [,3]
# [1,]    3   12   21
# [2,]    6   15   24
# [3,]    9   18   27
# 
# , , 1, 2
# 
#       [,1] [,2] [,3]
# [1,]    4   16   28
# [2,]    8   20   32
# [3,]   12   24   36
# 
# , , 2, 2
# 
#       [,1] [,2] [,3]
# [1,]    5   20   35
# [2,]   10   25   40
# [3,]   15   30   45
# 
# , , 3, 2
# 
#       [,1] [,2] [,3]
# [1,]    6   24   42
# [2,]   12   30   48
# [3,]   18   36   54
# 
# , , 1, 3
# 
#       [,1] [,2] [,3]
# [1,]    7   28   49
# [2,]   14   35   56
# [3,]   21   42   63
# 
# , , 2, 3
# 
#       [,1] [,2] [,3]
# [1,]    8   32   56
# [2,]   16   40   64
# [3,]   24   48   72
# 
# , , 3, 3
# 
#       [,1] [,2] [,3]
# [1,]    9   36   63
# [2,]   18   45   72
# [3,]   27   54   81

# kroenecker product

m1 %x% m1

#       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
# [1,]    1    4    7    4   16   28    7   28   49
# [2,]    2    5    8    8   20   32   14   35   56
# [3,]    3    6    9   12   24   36   21   42   63
# [4,]    2    8   14    5   20   35    8   32   56
# [5,]    4   10   16   10   25   40   16   40   64
# [6,]    6   12   18   15   30   45   24   48   72
# [7,]    3   12   21    6   24   42    9   36   63
# [8,]    6   15   24   12   30   48   18   45   72
# [9,]    9   18   27   18   36   54   27   54   81

m1 %x% m2

#       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
# [1,]    2    2    2    8    8    8   14   14   14
# [2,]    2    2    2    8    8    8   14   14   14
# [3,]    2    2    2    8    8    8   14   14   14
# [4,]    4    4    4   10   10   10   16   16   16
# [5,]    4    4    4   10   10   10   16   16   16
# [6,]    4    4    4   10   10   10   16   16   16
# [7,]    6    6    6   12   12   12   18   18   18
# [8,]    6    6    6   12   12   12   18   18   18
# [9,]    6    6    6   12   12   12   18   18   18

# cross Product 

m1[ , 1 ]
m1[ , 2 ] 

crossprod( m1, m2[ , 1 ]  )

#       [,1]
# [1,]   32
# [2,]   77
# [3,]  122

crossprod( m1 )

#      [,1] [,2] [,3]
# [1,]   14   32   50
# [2,]   32   77  122
# [3,]   50  122  194

crossprod( m1, m2 )

#       [,1] [,2] [,3]
# [1,]   12   12   12
# [2,]   30   30   30
# [3,]   48   48   48

# Diagonal Matrix with elements of x in the diagonal

diag( nrow = 3 )

# [,1] [,2] [,3]
# [1,]    1    0    0
# [2,]    0    1    0
# [3,]    0    0    1

diag( 3,  nrow = 3 )

# [,1] [,2] [,3]
# [1,]    3    0    0
# [2,]    0    3    0
# [3,]    0    0    3

# returns a vector of the principal diagonal elements
m1
diag( m1 )

# [,1] [,2] [,3]
# [1,]    1    0    0
# [2,]    0    2    0
# [3,]    0    0    3

# k * k identity matrix for m1[ , 1 ]
diag( m1[ , 1 ] )

# [,1] [,2] [,3]
# [1,]    1    0    0
# [2,]    0    2    0
# [3,]    0    0   

# simple combinations

cbind( m1, m2 )
rbind( m1, m2 )

# means and sums

rowMeans( m1 )
rowSums( m1 )

colMeans( m1 )
colSums( m1 )

# eigenvalues, eigenvectors

eigen( m1 )
e <- eigen( m1 )
e$values
e$vectors

# eigen() decomposition
# $values
# [1]  1.611684e+01 -1.116844e+00 -5.700691e-16
#
# $vectors
# [,1]       [,2]       [,3]
# [1,] -0.4645473 -0.8829060  0.4082483
# [2,] -0.5707955 -0.2395204 -0.8164966
# [3,] -0.6770438  0.4038651  0.4082483

# single value decomposition

svd( m1 )

# $d    singular values of m1
# [1] 1.684810e+01 1.068370e+00 5.543107e-16
#
# $u    left singular vectors of m1
# [,1]        [,2]       [,3]
# [1,] -0.4796712  0.77669099  0.4082483
# [2,] -0.5723678  0.07568647 -0.8164966
# [3,] -0.6650644 -0.62531805  0.4082483
#
# $v    right singular vectors of m1
# [,1]       [,2]       [,3]
# [1,] -0.2148372 -0.8872307  0.4082483
# [2,] -0.5205874 -0.2496440 -0.8164966
# [3,] -0.8263375  0.3879428  0.4082483


# QR decomposition 

qr( m1 )

# $qr
# [,1]      [,2]          [,3]
# [1,] -3.7416574 -8.552360 -1.336306e+01
# [2,]  0.5345225  1.963961  3.927922e+00
# [3,]  0.8017837  0.988693  1.776357e-15
#
# $rank
# [1] 2
#
# $qraux  
# [1] 1.267261e+00 1.149954e+00 1.776357e-15
#
# $pivot
# [1] 1 2 3
#
# attr(,"class")
# [1] "qr"


# inverse 

solve( m1 )

# Error in solve.default(m1) : 
#   Lapack routine dgesv: system is exactly singular: U[3,3] = 0

m3 <- matrix( c( 2, 6, 1, 4 ) , 2, 2 )
m3

# [,1] [,2]
# [1,]    2    1
# [2,]    6    4

sm3 <- solve( m3 )

# [,1] [,2]
# [1,]    2 -0.5
# [2,]   -3  1.0

m3 %*% sm3

# [,1] [,2]
# [1,]    1    0
# [2,]    0    1

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# matrix algebra

m1 <- matrix( c( 1, 2, 3, 4, 5, 6, 7, 8, 9 ) , 3, 3 )

m2 <- matrix( rep( 2, 9 ), 3, 3 )

# [,1] [,2] [,3]

# [1,] 1 4 7

# [2,] 2 5 8

# [3,] 3 6 9

m1 * m1 # element multipliction

# [,1] [,2] [,3]

# [1,] 1 16 49

# [2,] 4 25 64

# [3,] 9 36 81

m1 * m2

# [,1] [,2] [,3]

# [1,] 2 8 14

# [2,] 4 10 16

# [3,] 6 12 18

# Matrix Multiplication

m1 %*% m1

# [,1] [,2] [,3]

# [1,] 30 66 102

# [2,] 36 81 126

# [3,] 42 96 150

# Transpose

t( m1 )

# [,1] [,2] [,3]

# [1,] 1 2 3

# [2,] 4 5 6

# [3,] 7 8 9

# Outer Product

m1[ 1 , ]

m1[ , 1 ]

m1[ 1 , ] %o% m1[ , 1 ]

m1 %o% m1

# , , 1, 1

# [,1] [,2] [,3]

# [1,] 1 4 7

# [2,] 2 5 8

# [3,] 3 6 9

# , , 2, 1

# [,1] [,2] [,3]

# [1,] 2 8 14

# [2,] 4 10 16

# [3,] 6 12 18

# , , 3, 1

# [,1] [,2] [,3]

# [1,] 3 12 21

# [2,] 6 15 24

# [3,] 9 18 27

# , , 1, 2

# [,1] [,2] [,3]

# [1,] 4 16 28

# [2,] 8 20 32

# [3,] 12 24 36

# , , 2, 2

# [,1] [,2] [,3]

# [1,] 5 20 35

# [2,] 10 25 40

# [3,] 15 30 45

# , , 3, 2

# [,1] [,2] [,3]

# [1,] 6 24 42

# [2,] 12 30 48

# [3,] 18 36 54

# , , 1, 3

# [,1] [,2] [,3]

# [1,] 7 28 49

# [2,] 14 35 56

# [3,] 21 42 63

# , , 2, 3

# [,1] [,2] [,3]

# [1,] 8 32 56

# [2,] 16 40 64

# [3,] 24 48 72

# , , 3, 3

# [,1] [,2] [,3]

# [1,] 9 36 63

# [2,] 18 45 72

# [3,] 27 54 81

# kroenecker product

m1 %x% m1

# [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]

# [1,] 1 4 7 4 16 28 7 28 49

# [2,] 2 5 8 8 20 32 14 35 56

# [3,] 3 6 9 12 24 36 21 42 63

# [4,] 2 8 14 5 20 35 8 32 56

# [5,] 4 10 16 10 25 40 16 40 64

# [6,] 6 12 18 15 30 45 24 48 72

# [7,] 3 12 21 6 24 42 9 36 63

# [8,] 6 15 24 12 30 48 18 45 72

# [9,] 9 18 27 18 36 54 27 54 81

m1 %x% m2

# [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]

# [1,] 2 2 2 8 8 8 14 14 14

# [2,] 2 2 2 8 8 8 14 14 14

# [3,] 2 2 2 8 8 8 14 14 14

# [4,] 4 4 4 10 10 10 16 16 16

# [5,] 4 4 4 10 10 10 16 16 16

# [6,] 4 4 4 10 10 10 16 16 16

# [7,] 6 6 6 12 12 12 18 18 18

# [8,] 6 6 6 12 12 12 18 18 18

# [9,] 6 6 6 12 12 12 18 18 18

# cross Product

m1[ , 1 ]

m1[ , 2 ]

crossprod( m1, m2[ , 1 ] )

# [,1]

# [1,] 32

# [2,] 77

# [3,] 122

crossprod( m1 )

# [,1] [,2] [,3]

# [1,] 14 32 50

# [2,] 32 77 122

# [3,] 50 122 194

crossprod( m1, m2 )

# [,1] [,2] [,3]

# [1,] 12 12 12

# [2,] 30 30 30

# [3,] 48 48 48

# Diagonal Matrix with elements of x in the diagonal

diag( nrow = 3 )

# [,1] [,2] [,3]

# [1,] 1 0 0

# [2,] 0 1 0

# [3,] 0 0 1

diag( 3, nrow = 3 )

# [,1] [,2] [,3]

# [1,] 3 0 0

# [2,] 0 3 0

# [3,] 0 0 3

# returns a vector of the principal diagonal elements

diag( m1 )

# [,1] [,2] [,3]

# [1,] 1 0 0

# [2,] 0 2 0

# [3,] 0 0 3

# k * k identity matrix for m1[ , 1 ]

diag( m1[ , 1 ] )

# [,1] [,2] [,3]

# [1,] 1 0 0

# [2,] 0 2 0

# [3,] 0 0

# simple combinations

cbind( m1, m2 )

rbind( m1, m2 )

# means and sums

rowMeans( m1 )

rowSums( m1 )

colMeans( m1 )

colSums( m1 )

# eigenvalues, eigenvectors

eigen( m1 )

e <- eigen( m1 )

e$values

e$vectors

# eigen() decomposition

# $values

# [1] 1.611684e+01 -1.116844e+00 -5.700691e-16

# $vectors

# [,1] [,2] [,3]

# [1,] -0.4645473 -0.8829060 0.4082483

# [2,] -0.5707955 -0.2395204 -0.8164966

# [3,] -0.6770438 0.4038651 0.4082483

# single value decomposition

svd( m1 )

# $d singular values of m1

# [1] 1.684810e+01 1.068370e+00 5.543107e-16

# $u left singular vectors of m1

# [,1] [,2] [,3]

# [1,] -0.4796712 0.77669099 0.4082483

# [2,] -0.5723678 0.07568647 -0.8164966

# [3,] -0.6650644 -0.62531805 0.4082483

# $v right singular vectors of m1

# [,1] [,2] [,3]

# [1,] -0.2148372 -0.8872307 0.4082483

# [2,] -0.5205874 -0.2496440 -0.8164966

# [3,] -0.8263375 0.3879428 0.4082483

# QR decomposition

qr( m1 )

# $qr

# [,1] [,2] [,3]

# [1,] -3.7416574 -8.552360 -1.336306e+01

# [2,] 0.5345225 1.963961 3.927922e+00

# [3,] 0.8017837 0.988693 1.776357e-15

# $rank

# [1] 2

# $qraux

# [1] 1.267261e+00 1.149954e+00 1.776357e-15

# $pivot

# [1] 1 2 3

# attr(,"class")

# [1] "qr"

# inverse

solve( m1 )

# Error in solve.default(m1) :

# Lapack routine dgesv: system is exactly singular: U[3,3] = 0

m3 <- matrix( c( 2, 6, 1, 4 ) , 2, 2 )

# [,1] [,2]

# [1,] 2 1

# [2,] 6 4

sm3 <- solve( m3 )

# [,1] [,2]

# [1,] 2 -0.5

# [2,] -3 1.0

m3 %*% sm3

# [,1] [,2]

# [1,] 1 0

# [2,] 0 1

Basic R commands – 1

04/21/2020Allgemein, Code, Data Analysis, Programming, R, StatisticsMartin Stoppacher

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Basic Commands and Statistics with R

rm(list = ls(all = TRUE))
getwd()
#system("ls")
setwd("~/ownCloud/STA_Statistics/basicR/")

search()

options(scipen=100)   # scientific off
options(scipen=0)     # scientific on
options(digits = 3)     

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/mtcars.html

attach(mtcars)
search()
detach(mtcars)
search()
mtcars         # The data was extracted from the 1974 Motor Trend US magazine 
# ?mtcars

class(mtcars)  # determine the class of an object

str(mtcars)    # Compactly display the internal structure of an R object

# 'data.frame':	32 obs. of  11 variables:

#                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
# Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
# Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
# Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
# Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
# Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
# Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
# Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
# Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
# Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
# Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
# Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
# Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
# Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
# Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
# Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
# Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
# Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
# Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
# Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
# Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
# Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
# Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
# AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
# Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
# Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
# Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
# Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
# Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
# Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
# Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
# Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
# Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

# A data frame with 32 observations on 11 (numeric) variables.

#[, 1]	mpg	Miles/(US) gallon
#[, 2]	cyl	Number of cylinders
#[, 3]	disp	Displacement (cu.in.)
#[, 4]	hp	Gross horsepower
#[, 5]	drat	Rear axle ratio
#[, 6]	wt	Weight (1000 lbs)
#[, 7]	qsec	1/4 mile time
#[, 8]	vs	Engine (0 = V-shaped, 1 = straight)
#[, 9]	am	Transmission (0 = automatic, 1 = manual)
#[,10]	gear	Number of forward gears
#[,11]	carb	Number of carburetors

colnames(mtcars) <- c("mpg","cyl","disp","hp","drat","wt",
                      "qsec","vs","am","gear","carb")

# colnames(mtcars) <- c("miles-per-gallon","cylinders","displacement",
#                       "horsepower","rear-axle-ratio","weight","qsec",
#                       "engine-vs","Transmissionam","gear","carburetors")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# data structures

letters
l <- letters
str(l)
# chr [1:26]  "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" 
#             "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 
# Vectors

t <- c(1:3,"Hello",NA,FALSE,TRUE)
t
# [1] "1"     "2"     "3"     "Hello" NA      "FALSE" "TRUE" 
str(t)
# chr [1:7] "1" "2" "3" "Hello" NA "FALSE" "TRUE"

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Factors

mtcars$am[mtcars$am==1]
which(mtcars$am==1)

am.names <- mtcars$am
am.names[which(am.names==1)] <- "Automatic"
am.names
am.names[which(am.names==0)] <- "Manual"
am.names

am.names.factor <- factor(am.names)
am.names.factor
# [1] Automatic Automatic Automatic Manual    Manual  
# Levels: Automatic Manual

# internally stored as a table of:
# 1 Automatic
# 2 Manual

# Automatic and Manual are the levels of the factor 
levels(am.names.factor)

relevel(am.names.factor, ref="Manual")
# [1] Automatic Automatic Automatic Manual    Manual  
# Levels: Manual Automatic

am.names.factor[1]
as.character(am.names.factor[1])
as.numeric(am.names.factor[1])  # numeric takes the faktor not the level
levels(am.names.factor)[1]      # can be used to get the factor

table(am.names.factor)

is.factor(am.names.factor)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 
# Data Frames

mtcars
str(mtcars)
# 'data.frame':	32 obs. of  11 variables:

df <- data.frame(1:20,60:41,letters[1:20])
df
colnames(df) <- c("n1","n2","letters")
df
t(df)

df$letters      # get the vectors from the dataframe
df$n1
df[,2]
df[1,3]
df[[3]]
levels(df$letters)
is.factor(df[,3])

df <- data.frame(1:20,60:41,letters[1:20], stringsAsFactors = FALSE)
df
is.factor(df[,3])

trees
mtcars
USMortality 
data()    # list of data associated with all current packages in the serch path

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Matrix

as.matrix(mtcars)
mtcars.mat <- as.matrix(mtcars) # all elements of a matrix have the same mode 
                                #(numeric, character)
x <- as.vector(mtcars.mat)      # all in order in the vector X
x

mat <- matrix(1:100,nco=10)
mat
as.vector(mat)

mat44 <- matrix(1:(4*4),nco=4)
mat44
as.vector(mat44)

dimnames(mtcars.mat)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Arrays

x <- 1:10000             # this is a matrix
dim(x) <- c(100,100)
x

x <- 1:1000              # array - a matrix with more than 2 dim
dim(x) <- c(10,10,10)
x

x <- 1:(2*3*3)           # array - a matrix with more than 2 dim
dim(x) <- c(2,3,3)
x

str(x)
# int [1:2, 1:3, 1:3] 1 2 3 4 5 6 7 8 9 10 ...

x <- letters[1:(2*3*3)]           # array - a matrix with more than 2 dim
dim(x) <- c(2,3,3)
x
str(x)
# chr [1:2, 1:3, 1:3] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" 
#                     "m" "n" "o" "p" "q" "r"

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Lists - collect different types of data objects

data()

l <- list(mtcars,barley,environmental,ethanol,melanoma)
                                            # most data from lattice package
str(l)

# List of 5
# $ :'data.frame':	32 obs. of  11 variables:
# ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
# ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
# ..$ disp: num [1:32] 160 160 108 258 360 ...
# ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
# ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
# ..$ wt  : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
# ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
# ..$ vs  : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
# ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
# ..$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
# ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
# $ :'data.frame':	120 obs. of  4 variables:
# ..$ yield  : num [1:120] 27 48.9 27.4 39.9 33 ...
# ..$ variety: Factor w/10 levels "Svansota","No.462",..: 3 3 3 3 3 3 7 7 7 7...
# ..$ year   : Factor w/2 levels "1932","1931": 2 2 2 2 2 2 2 2 2 2...
# ..$ site   : Factor w/6 levels "Grand Rapids",..: 3 6 4 5 1 2 3 6 4 5...
# $ :'data.frame':	111 obs. of  4 variables:
# ..$ ozone      : num [1:111] 41 36 12 18 23 19 8 16 11 14 ...
# ..$ radiation  : num [1:111] 190 118 149 313 299 99 19 256 290 274 ...
# ..$ temperature: num [1:111] 67 72 74 62 65 59 61 69 66 68 ...
# ..$ wind       : num [1:111] 7.4 8 12.6 11.5 8.6 13.8 20.1 9.7 9.2 10.9 ...
# $ :'data.frame':	88 obs. of  3 variables:
# ..$ NOx: num [1:88] 3.74 2.29 1.5 2.88 0.76 ...
# ..$ C  : num [1:88] 12 12 12 12 12 9 9 9 12 12 ...
# ..$ E  : num [1:88] 0.907 0.761 1.108 1.016 1.189 ...
# $ :'data.frame':	37 obs. of  2 variables:
# ..$ year     : num [1:37] 1936 1937 1938 1939 1940 ...
# ..$ incidence: num [1:37] 0.9 0.8 0.8 1.3 1.4 1.2 1.7 1.8 1.6 1.5 ...

l[[1]]
l[[2]]
l[[3]]

l[[1]]

model <- lm(mpg~wt,data=l[[1]])   # building a model from a list object
names(model) 
str(model)                        # the model itselfe is returned as a list

model$coefficients
model[[1]]

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# some basic R functions

mtcars$mpg
# [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 ...
mtcars$mpg^2
# [1]  441.00  441.00 519.84 457.96 349.69 327.61 204.49  595.36  519.84  ...
sqrt(mtcars$mpg^2)
# [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 ...

pi
# [1] 3.141593
90*pi/180 # radiant conversion
sin(90*pi/180)
cos(90*pi/180)
cos(0*pi/180)

celsius <- 20
9/5*celsius+32

celsius <- -20:40
fahrenheit <- 9/5*celsius+32
plot(celsius,fahrenheit,type="s")

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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Basic Commands and Statistics with R

rm(list = ls(all = TRUE))

getwd()

#system("ls")

setwd("~/ownCloud/STA_Statistics/basicR/")

search()

options(scipen=100) # scientific off

options(scipen=0) # scientific on

options(digits = 3)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/mtcars.html

attach(mtcars)

search()

detach(mtcars)

search()

mtcars # The data was extracted from the 1974 Motor Trend US magazine

# ?mtcars

class(mtcars) # determine the class of an object

str(mtcars) # Compactly display the internal structure of an R object

# 'data.frame': 32 obs. of 11 variables:

# mpg cyl disp hp drat wt qsec vs am gear carb

# Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

# Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

# Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2

# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1

# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4

# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2

# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2

# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4

# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4

# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3

# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3

# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3

# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4

# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4

# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4

# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1

# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2

# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1

# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1

# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2

# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2

# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4

# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2

# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1

# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2

# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2

# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4

# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6

# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8

# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

# A data frame with 32 observations on 11 (numeric) variables.

#[, 1] mpg Miles/(US) gallon

#[, 2] cyl Number of cylinders

#[, 3] disp Displacement (cu.in.)

#[, 4] hp Gross horsepower

#[, 5] drat Rear axle ratio

#[, 6] wt Weight (1000 lbs)

#[, 7] qsec 1/4 mile time

#[, 8] vs Engine (0 = V-shaped, 1 = straight)

#[, 9] am Transmission (0 = automatic, 1 = manual)

#[,10] gear Number of forward gears

#[,11] carb Number of carburetors

colnames(mtcars) <- c("mpg","cyl","disp","hp","drat","wt",

"qsec","vs","am","gear","carb")

# colnames(mtcars) <- c("miles-per-gallon","cylinders","displacement",

# "horsepower","rear-axle-ratio","weight","qsec",

# "engine-vs","Transmissionam","gear","carburetors")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# data structures

letters

l <- letters

str(l)

# chr [1:26] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p"

# "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Vectors

t <- c(1:3,"Hello",NA,FALSE,TRUE)

# [1] "1" "2" "3" "Hello" NA "FALSE" "TRUE"

str(t)

# chr [1:7] "1" "2" "3" "Hello" NA "FALSE" "TRUE"

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Factors

mtcars$am[mtcars$am==1]

which(mtcars$am==1)

am.names <- mtcars$am

am.names[which(am.names==1)] <- "Automatic"

am.names

am.names[which(am.names==0)] <- "Manual"

am.names

am.names.factor <- factor(am.names)

am.names.factor

# [1] Automatic Automatic Automatic Manual Manual

# Levels: Automatic Manual

# internally stored as a table of:

# 1 Automatic

# 2 Manual

# Automatic and Manual are the levels of the factor

levels(am.names.factor)

relevel(am.names.factor, ref="Manual")

# [1] Automatic Automatic Automatic Manual Manual

# Levels: Manual Automatic

am.names.factor[1]

as.character(am.names.factor[1])

as.numeric(am.names.factor[1]) # numeric takes the faktor not the level

levels(am.names.factor)[1] # can be used to get the factor

table(am.names.factor)

is.factor(am.names.factor)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Data Frames

mtcars

str(mtcars)

# 'data.frame': 32 obs. of 11 variables:

df <- data.frame(1:20,60:41,letters[1:20])

colnames(df) <- c("n1","n2","letters")

t(df)

df$letters # get the vectors from the dataframe

df$n1

df[,2]

df[1,3]

df[[3]]

levels(df$letters)

is.factor(df[,3])

df <- data.frame(1:20,60:41,letters[1:20], stringsAsFactors = FALSE)

is.factor(df[,3])

trees

mtcars

USMortality

data() # list of data associated with all current packages in the serch path

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Matrix

as.matrix(mtcars)

mtcars.mat <- as.matrix(mtcars) # all elements of a matrix have the same mode

#(numeric, character)

x <- as.vector(mtcars.mat) # all in order in the vector X

mat <- matrix(1:100,nco=10)

mat

as.vector(mat)

mat44 <- matrix(1:(4*4),nco=4)

mat44

as.vector(mat44)

dimnames(mtcars.mat)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Arrays

x <- 1:10000 # this is a matrix

dim(x) <- c(100,100)

x <- 1:1000 # array - a matrix with more than 2 dim

dim(x) <- c(10,10,10)

x <- 1:(2*3*3) # array - a matrix with more than 2 dim

dim(x) <- c(2,3,3)

str(x)

# int [1:2, 1:3, 1:3] 1 2 3 4 5 6 7 8 9 10 ...

x <- letters[1:(2*3*3)] # array - a matrix with more than 2 dim

dim(x) <- c(2,3,3)

str(x)

# chr [1:2, 1:3, 1:3] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"

# "m" "n" "o" "p" "q" "r"

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Lists - collect different types of data objects

data()

l <- list(mtcars,barley,environmental,ethanol,melanoma)

# most data from lattice package

str(l)

# List of 5

# $ :'data.frame': 32 obs. of 11 variables:

# ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...

# ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...

# ..$ disp: num [1:32] 160 160 108 258 360 ...

# ..$ hp : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...

# ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...

# ..$ wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...

# ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...

# ..$ vs : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...

# ..$ am : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...

# ..$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...

# ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...

# $ :'data.frame': 120 obs. of 4 variables:

# ..$ yield : num [1:120] 27 48.9 27.4 39.9 33 ...

# ..$ variety: Factor w/10 levels "Svansota","No.462",..: 3 3 3 3 3 3 7 7 7 7...

# ..$ year : Factor w/2 levels "1932","1931": 2 2 2 2 2 2 2 2 2 2...

# ..$ site : Factor w/6 levels "Grand Rapids",..: 3 6 4 5 1 2 3 6 4 5...

# $ :'data.frame': 111 obs. of 4 variables:

# ..$ ozone : num [1:111] 41 36 12 18 23 19 8 16 11 14 ...

# ..$ radiation : num [1:111] 190 118 149 313 299 99 19 256 290 274 ...

# ..$ temperature: num [1:111] 67 72 74 62 65 59 61 69 66 68 ...

# ..$ wind : num [1:111] 7.4 8 12.6 11.5 8.6 13.8 20.1 9.7 9.2 10.9 ...

# $ :'data.frame': 88 obs. of 3 variables:

# ..$ NOx: num [1:88] 3.74 2.29 1.5 2.88 0.76 ...

# ..$ C : num [1:88] 12 12 12 12 12 9 9 9 12 12 ...

# ..$ E : num [1:88] 0.907 0.761 1.108 1.016 1.189 ...

# $ :'data.frame': 37 obs. of 2 variables:

# ..$ year : num [1:37] 1936 1937 1938 1939 1940 ...

# ..$ incidence: num [1:37] 0.9 0.8 0.8 1.3 1.4 1.2 1.7 1.8 1.6 1.5 ...

l[[1]]

l[[2]]

l[[3]]

l[[1]]

model <- lm(mpg~wt,data=l[[1]]) # building a model from a list object

names(model)

str(model) # the model itselfe is returned as a list

model$coefficients

model[[1]]

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# some basic R functions

mtcars$mpg

# [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 ...

mtcars$mpg^2

# [1] 441.00 441.00 519.84 457.96 349.69 327.61 204.49 595.36 519.84 ...

sqrt(mtcars$mpg^2)

# [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 ...

# [1] 3.141593

90*pi/180 # radiant conversion

sin(90*pi/180)

cos(90*pi/180)

cos(0*pi/180)

celsius <- 20

9/5*celsius+32

celsius <- -20:40

fahrenheit <- 9/5*celsius+32

plot(celsius,fahrenheit,type="s")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
sum(mtcars$mpg)
# [1] 642.9
cumsum(mtcars$mpg)
# [1]  21.0  42.0  64.8  86.2 104.9 123.0 137.3 161.7
cumprod(mtcars$mpg)
# [1] 2.100000e+01 4.410000e+02 1.005480e+04 2.151727e+05 
#     4.023730e+06 7.282951e+07

c(1,2,3,4,5)    # vectors # concatenating function
c(1:5)
c(TRUE,FALSE)
c("TRUE","FALSE") # character vector

x <- runif(5)
x
# [1] 0.832 0.101 0.926 0.253 0.619
sort(x)

order(x)
# [1] 0.101 0.253 0.619 0.832 0.926
x[order(x)]
# [1] 0.101 0.253 0.619 0.832 0.926

x <- c(1:5)
x
y <- c("TRUE","FALSE",NA) 
y
z <- c(x,y)
z
# [1] "1"     "2"     "3"     "4"     "5"     "TRUE"  "FALSE" NA    
rev(z)
# [1] NA      "FALSE" "TRUE"  "5"     "4"     "3"     "2"     "1"   

z[2]
z[2:5]
# [1] "2" "3" "4" "5"
z[z<4]
# [1] "1" "2" "3" NA 
z[z>4]
# [1] "5"     "TRUE"  "FALSE" NA 
z[z>=4]
# [1] "4"     "5"     "TRUE"  "FALSE" NA  
z[-c(3,4)]
# [1] "1"     "2"     "5"     "TRUE"  "FALSE" NA 
z[c(3,4)]
# [1] "3" "4"

z[is.na(z)]
z[is.na(z)]<-0
z
z[z==TRUE]<-1
z[z==FALSE]<-0
z
# [1] "1" "2" "3" "4" "5" "1" "0" "0"

plot(z)
plot(z,type="b")

x <- runif(length(z))*5

plot(x,z,type="b")
plot(x~z,type="b")

plot(z,x,type="b")
lines(z~x,type="b",col="red")

plot(sin((1:360)*pi/180),type="l")
plot(sin((1:360)*pi/180),cos((1:360)*pi/180),type="l")

plot(sin((1:360)*pi/18),cos((1:360)*pi/10),type="l")

plot(sin((1:360)*pi/18),cos((1:360)*pi/10),type="l")

plot(tan((1:360)*pi/180),cos((1:360)*pi/180),type="l")
plot(tan((1:360)*pi/180),sin((1:360)*pi/180),type="l")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

sum(mtcars$mpg)

# [1] 642.9

cumsum(mtcars$mpg)

# [1] 21.0 42.0 64.8 86.2 104.9 123.0 137.3 161.7

cumprod(mtcars$mpg)

# [1] 2.100000e+01 4.410000e+02 1.005480e+04 2.151727e+05

# 4.023730e+06 7.282951e+07

c(1,2,3,4,5) # vectors # concatenating function

c(1:5)

c(TRUE,FALSE)

c("TRUE","FALSE") # character vector

x <- runif(5)

# [1] 0.832 0.101 0.926 0.253 0.619

sort(x)

order(x)

# [1] 0.101 0.253 0.619 0.832 0.926

x[order(x)]

# [1] 0.101 0.253 0.619 0.832 0.926

x <- c(1:5)

y <- c("TRUE","FALSE",NA)

z <- c(x,y)

# [1] "1" "2" "3" "4" "5" "TRUE" "FALSE" NA

rev(z)

# [1] NA "FALSE" "TRUE" "5" "4" "3" "2" "1"

z[2]

z[2:5]

# [1] "2" "3" "4" "5"

z[z<4]

# [1] "1" "2" "3" NA

z[z>4]

# [1] "5" "TRUE" "FALSE" NA

z[z>=4]

# [1] "4" "5" "TRUE" "FALSE" NA

z[-c(3,4)]

# [1] "1" "2" "5" "TRUE" "FALSE" NA

z[c(3,4)]

# [1] "3" "4"

z[is.na(z)]

z[is.na(z)]<-0

z[z==TRUE]<-1

z[z==FALSE]<-0

# [1] "1" "2" "3" "4" "5" "1" "0" "0"

plot(z)

plot(z,type="b")

x <- runif(length(z))*5

plot(x,z,type="b")

plot(x~z,type="b")

plot(z,x,type="b")

lines(z~x,type="b",col="red")

plot(sin((1:360)*pi/180),type="l")

plot(sin((1:360)*pi/180),cos((1:360)*pi/180),type="l")

plot(sin((1:360)*pi/18),cos((1:360)*pi/10),type="l")

plot(tan((1:360)*pi/180),cos((1:360)*pi/180),type="l")

plot(tan((1:360)*pi/180),sin((1:360)*pi/180),type="l")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# some loops

for(i in 1:360){
    plot(cos((1:360)*pi/i),sin((1:360)*pi/i),type="l")
    print(i)
    Sys.sleep(0.1)
}

for(i in 360:1){
  plot(cos((1:360)*pi/i),sin((1:360)*pi/i),type="l")
  print(i)
  Sys.sleep(0.1)
}

for(i in 1:360){
  for(e in 1:360){
  plot(cos((1:360)*pi/i),sin((1:360)*pi/e),type="l")
  print(i)
  Sys.sleep(0.1)
  }
}

for(i in 1:360){
  for(e in 1:360){
    plot(sin((1:360)*pi/i),cos((1:360)*pi/e),type="l")
    print(i)
    Sys.sleep(0.1)
  }
}

for(i in 1:10){
  for(e in 1:10){
    plot(sin((1:360)*pi/i),cos((1:360)*pi/e),type="l")
    mtext(paste("i:",i,"e:",e), side=3, outer=TRUE, line=-3)
    print(paste("i:",i,"e:",e))
    Sys.sleep(0.1)
  }
}

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# function collection

letters
# "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" 
# "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
# "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" 
# "S" "T" "U" "V" "W" "X" "Y" "Z"
letters[1:15]
# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"

plot(mtcars$mpg)
print(mtcars$mpg)

table(mtcars$mpg)
table(mtcars$cyl)
#   4   6   8 
#   11  7   14 

table(mtcars[,9:10])
str(table(mtcars[,9:10])) # table with 2 dimmensions
table(mtcars[,9:11])
str(table(mtcars[,9:11]))
# 'table' int [1:2, 1:3, 1:6] 3 0 0 4 0 0 4 0 2 2 ...
# - attr(*, "dimnames")=List of 3
# ..$ am  : chr [1:2] "0" "1"
# ..$ gear: chr [1:3] "3" "4" "5"
# ..$ carb: chr [1:6] "1" "2" "3" "4" ...

length(mtcars$mpg)
cat(mtcars$mpg)
mean(mtcars$mpg)
median(mtcars$mpg)
range(mtcars$mpg)
unique(mtcars$mpg)

rep(mtcars$mpg,10)

names()
colnames()
rownames()

diff(mtcars$mpg)
plot(diff(mtcars$mpg),type="h",xlab="",ylab="difference")
points(diff(mtcars$mpg),col="red")

sort(mtcars$mpg)
order(mtcars$mpg)
rev(mtcars$mpg)
rev(sort(mtcars$mpg))

cumsum(mtcars$mpg)
cumprod(mtcars$mpg)

rank(mtcars$vs) # Returns the sample ranks of the values in a vector.
(r1 <- rank(x1 <- c(3, 1, 4, 15, 92)))
rank(mtcars$vs, ties.method= "first")  # first occurrence wins 
                                       ## ranks without averaging
rank(mtcars$vs, ties.method= "last")   #  last occurrence wins 
                                       ## ranks without averaging
rank(mtcars$vs, ties.method= "random") # ties broken at random 
                                       ## ranks without averaging
rank(mtcars$vs, ties.method= "random") # and again 
                                       ## ranks without averaging

1:10
7:20
intersect(1:10, 7:20)

match(1:10,7:20)    # match returns a vector of the positions of (first) 
                    # matches of its first argument in its second.

1:10 %in% c(1,3,5,9)
sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%")
sstr %in% c(letters, LETTERS)
sstr[sstr %in% c(letters, LETTERS)]

apply()
sapply()
aggregate()
aggregate(state.x77, list(Region = state.region), mean)
tapply()

merge(mtcars$vs,c(1,2))

read.csv()
read.table()

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# histograms

hist(mtcars$mpg)

# looping with R
par(mfrow=c(3,4))
for(i in 1:length(mtcars[1,])){ 
  hist(mtcars[,i],main=paste("Data: MTcars",colnames(mtcars)[i]),
                              xlab=paste(colnames(mtcars)[i]))
}
par(mfrow=c(1,1))

hist(mtcars$mpg, main="Data: MTcars - mpg Miles per gallon", 
     xlab="mpg Miles per gallon")

hist(mtcars$mpg, breaks=10, main="Data: MTcars - mpg Miles per gallon", 
     xlab="mpg Miles per gallon")

hist(mtcars$mpg, breaks=length(mtcars$mpg), 
     main="Data: MTcars - mpg Miles per gallon", 
     xlab="mpg Miles per gallon")

# probability densities, component
hist(mtcars$mpg, breaks=length(mtcars$mpg), freq=FALSE, 
     main="Data: MTcars - mpg Miles per gallon", xlab="mpg Miles per gallon")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#  Boxplot with Scatterplot

par( fig = c( 0, 0.8, 0, 0.8 ), new = TRUE)
plot( mtcars$wt, mtcars$mpg, xlab = "Car Weight",
      ylab = "Miles Per Gallon" )
abline( lm( mpg~wt ), col = "red" )      # regression line (y~x)
lines( lowess( wt, mpg ), col = "blue" ) # lowess line (x,y) 
                                         # LOWESS smoother which uses 
                                         # locally-weighted 
                                         # polynomial regression
model <- lm( mpg~wt )
text( 4, 32, paste( "Intercept", model$coefficients[1] ) )
text( 4, 30, paste( "wt", model$coefficients[2] ) )

par( fig = c( 0, 0.8, 0.55, 1 ), new = TRUE )
boxplot(mtcars$wt, horizontal = TRUE, axes=FALSE, 
                                 col= "green", notch = TRUE )
m.wt <- mean( mtcars$wt )
mtext( paste( "mean wt", m.wt ), side = 3, outer = TRUE, line = -8 )

par( fig = c( 0.65, 1, 0, 0.8 ), new = TRUE)
boxplot( mtcars$mpg, axes = FALSE, col = "green", notch = TRUE)
m.mpg <- mean( mtcars$mpg )
mtext( paste( "mean mpg", m.mpg ), side = 2, outer = TRUE, line = -28)

mtext( "Miles per gallon vs Car Weight", side = 3, outer = TRUE, line = -3 )

par( mfrow = c( 1, 1 ) )

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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# some loops

for(i in 1:360){

plot(cos((1:360)*pi/i),sin((1:360)*pi/i),type="l")

print(i)

Sys.sleep(0.1)

}

for(i in 360:1){

plot(cos((1:360)*pi/i),sin((1:360)*pi/i),type="l")

print(i)

Sys.sleep(0.1)

}

for(i in 1:360){

for(e in 1:360){

plot(cos((1:360)*pi/i),sin((1:360)*pi/e),type="l")

print(i)

Sys.sleep(0.1)

}

for(i in 1:360){

for(e in 1:360){

plot(sin((1:360)*pi/i),cos((1:360)*pi/e),type="l")

print(i)

Sys.sleep(0.1)

}

for(i in 1:10){

for(e in 1:10){

plot(sin((1:360)*pi/i),cos((1:360)*pi/e),type="l")

mtext(paste("i:",i,"e:",e), side=3, outer=TRUE, line=-3)

print(paste("i:",i,"e:",e))

Sys.sleep(0.1)

}

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# function collection

letters

# "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r"

# "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

# "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R"

# "S" "T" "U" "V" "W" "X" "Y" "Z"

letters[1:15]

# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"

plot(mtcars$mpg)

print(mtcars$mpg)

table(mtcars$mpg)

table(mtcars$cyl)

# 4 6 8

# 11 7 14

table(mtcars[,9:10])

str(table(mtcars[,9:10])) # table with 2 dimmensions

table(mtcars[,9:11])

str(table(mtcars[,9:11]))

# 'table' int [1:2, 1:3, 1:6] 3 0 0 4 0 0 4 0 2 2 ...

# - attr(*, "dimnames")=List of 3

# ..$ am : chr [1:2] "0" "1"

# ..$ gear: chr [1:3] "3" "4" "5"

# ..$ carb: chr [1:6] "1" "2" "3" "4" ...

length(mtcars$mpg)

cat(mtcars$mpg)

mean(mtcars$mpg)

median(mtcars$mpg)

range(mtcars$mpg)

unique(mtcars$mpg)

rep(mtcars$mpg,10)

names()

colnames()

rownames()

diff(mtcars$mpg)

plot(diff(mtcars$mpg),type="h",xlab="",ylab="difference")

points(diff(mtcars$mpg),col="red")

sort(mtcars$mpg)

order(mtcars$mpg)

rev(mtcars$mpg)

rev(sort(mtcars$mpg))

cumsum(mtcars$mpg)

cumprod(mtcars$mpg)

rank(mtcars$vs) # Returns the sample ranks of the values in a vector.

(r1 <- rank(x1 <- c(3, 1, 4, 15, 92)))

rank(mtcars$vs, ties.method= "first") # first occurrence wins

## ranks without averaging

rank(mtcars$vs, ties.method= "last") # last occurrence wins

## ranks without averaging

rank(mtcars$vs, ties.method= "random") # ties broken at random

## ranks without averaging

rank(mtcars$vs, ties.method= "random") # and again

## ranks without averaging

1:10

7:20

intersect(1:10, 7:20)

match(1:10,7:20) # match returns a vector of the positions of (first)

# matches of its first argument in its second.

1:10 %in% c(1,3,5,9)

sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%")

sstr %in% c(letters, LETTERS)

sstr[sstr %in% c(letters, LETTERS)]

apply()

sapply()

aggregate()

aggregate(state.x77, list(Region = state.region), mean)

tapply()

merge(mtcars$vs,c(1,2))

read.csv()

read.table()

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# histograms

hist(mtcars$mpg)

# looping with R

par(mfrow=c(3,4))

for(i in 1:length(mtcars[1,])){

hist(mtcars[,i],main=paste("Data: MTcars",colnames(mtcars)[i]),

xlab=paste(colnames(mtcars)[i]))

}

par(mfrow=c(1,1))

hist(mtcars$mpg, main="Data: MTcars - mpg Miles per gallon",

xlab="mpg Miles per gallon")

hist(mtcars$mpg, breaks=10, main="Data: MTcars - mpg Miles per gallon",

xlab="mpg Miles per gallon")

hist(mtcars$mpg, breaks=length(mtcars$mpg),

main="Data: MTcars - mpg Miles per gallon",

xlab="mpg Miles per gallon")

# probability densities, component

hist(mtcars$mpg, breaks=length(mtcars$mpg), freq=FALSE,

main="Data: MTcars - mpg Miles per gallon", xlab="mpg Miles per gallon")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

# Boxplot with Scatterplot

par( fig = c( 0, 0.8, 0, 0.8 ), new = TRUE)

plot( mtcars$wt, mtcars$mpg, xlab = "Car Weight",

ylab = "Miles Per Gallon" )

abline( lm( mpg~wt ), col = "red" ) # regression line (y~x)

lines( lowess( wt, mpg ), col = "blue" ) # lowess line (x,y)

# LOWESS smoother which uses

# locally-weighted

# polynomial regression

model <- lm( mpg~wt )

text( 4, 32, paste( "Intercept", model$coefficients[1] ) )

text( 4, 30, paste( "wt", model$coefficients[2] ) )

par( fig = c( 0, 0.8, 0.55, 1 ), new = TRUE )

boxplot(mtcars$wt, horizontal = TRUE, axes=FALSE,

col= "green", notch = TRUE )

m.wt <- mean( mtcars$wt )

mtext( paste( "mean wt", m.wt ), side = 3, outer = TRUE, line = -8 )

par( fig = c( 0.65, 1, 0, 0.8 ), new = TRUE)

boxplot( mtcars$mpg, axes = FALSE, col = "green", notch = TRUE)

m.mpg <- mean( mtcars$mpg )

mtext( paste( "mean mpg", m.mpg ), side = 2, outer = TRUE, line = -28)

mtext( "Miles per gallon vs Car Weight", side = 3, outer = TRUE, line = -3 )

par( mfrow = c( 1, 1 ) )

# Martin Stoppacher                                                             #
# office@martinstoppacher.com                                                   #
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#################################################################################

# Martin Stoppacher #

# office@martinstoppacher.com #

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #

#################################################################################

Renewable electricity output – Regions

12/07/2019Allgemein, Data Analysis, Programming, RMartin Stoppacher

https://host6.ssl-net.net/martinstoppacher_com/blog/?p=1194

# -------------

# region

# World
# Euro area
# European Union
# Europe & Central Asia
# Arab World
# North America
# Latin America & Caribbean
# Middle East & North Africa
# Sub-Saharan Africa 
# East Asia & Pacific
# South Asia

target.area <- which(renewable.electricity.output$country=="World")
renewable.electricity.output[target.area,]
plot(renewable.electricity.output[target.area,4:3],type="l",
     main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),
     axes=TRUE)
text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")
text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")
text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)
text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")
text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")
# renewable.electricity.output[target.area,]
# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")
# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)
text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)
text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)
text(1985, 28, "South Asia", col="pink",cex=0.7)

# -------------

# region

# World

# Euro area

# European Union

# Europe & Central Asia

# Arab World

# North America

# Latin America & Caribbean

# Middle East & North Africa

# Sub-Saharan Africa

# East Asia & Pacific

# South Asia

target.area <- which(renewable.electricity.output$country=="World")

renewable.electricity.output[target.area,]

plot(renewable.electricity.output[target.area,4:3],type="l",

main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),

axes=TRUE)

text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")

text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")

text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)

text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")

text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")

# renewable.electricity.output[target.area,]

# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")

# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)

text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)

text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)

text(1985, 28, "South Asia", col="pink",cex=0.7)

Renewable electricity output – World Map

12/07/2019Allgemein, Data Analysis, Programming, RMartin Stoppacher

https://host6.ssl-net.net/martinstoppacher_com/blog/?p=1194

# ----------------

library(rworldmap)

options("scipen"=100, "digits"=4)

year <- 2015
target.year <- which(renewable.electricity.output$year==year)
head(renewable.electricity.output[target.year,])

renewable.electricity.output.year <- na.omit(renewable.electricity.output[target.year,])
renewable.electricity.output.year <- renewable.electricity.output.year[which(renewable.electricity.output.year$EG.ELC.RNEW.ZS>0),]

mapped_data <- joinCountryData2Map(renewable.electricity.output.year, joinCode = "ISO2", nameJoinColumn = "iso2c")
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapTitle=paste(titel,subtitel))

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),
               numCats=2,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","green"))

# ----------------

library(rworldmap)

options("scipen"=100, "digits"=4)

year <- 2015

target.year <- which(renewable.electricity.output$year==year)

head(renewable.electricity.output[target.year,])

renewable.electricity.output.year <- na.omit(renewable.electricity.output[target.year,])

renewable.electricity.output.year <- renewable.electricity.output.year[which(renewable.electricity.output.year$EG.ELC.RNEW.ZS>0),]

mapped_data <- joinCountryData2Map(renewable.electricity.output.year, joinCode = "ISO2", nameJoinColumn = "iso2c")

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapTitle=paste(titel,subtitel))

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),

numCats=2,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","green"))

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),
               numCats=3,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","yellow","green"))
legend(-179, -10, legend=c("low", "medium","high"), col=c("red", "yellow","green"), lty=1:3, cex=0.6)

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),

numCats=3,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","yellow","green"))

legend(-179, -10, legend=c("low", "medium","high"), col=c("red", "yellow","green"), lty=1:3, cex=0.6)

Renewable electricity output – Statistics

12/07/2019Data Analysis, Programming, RMartin Stoppacher

# Renewable electricity output (% of total electricity output) - EG.ELC.RNEW.ZS

1	# Renewable electricity output (% of total electricity output) - EG.ELC.RNEW.ZS

library("XML")
library("WDI")
library("gridExtra")
library("scatterplot3d")

options(scipen=100) # turning of scientific notation

titel<-"Renewable electricity output"
subtitel<-"(% of total electricity output)"

renewable.electricity.output <- WDI(country="all", indicator="EG.ELC.RNEW.ZS", start=1900, end=2019)
str(renewable.electricity.output)

# 'data.frame':	15840 obs. of  4 variables:
#   $ iso2c         : chr  "1A" "1A" "1A" "1A" ...
# $ country       : chr  "Arab World" "Arab World" "Arab World" "Arab World" ...
# $ EG.ELC.RNEW.ZS: num  NA NA NA NA 2.92 ...
# $ year          : num  2019 2018 2017 2016 2015 ...

head(renewable.electricity.output)

# iso2c    country EG.ELC.RNEW.ZS year
# 1    1A Arab World             NA 2019
# 2    1A Arab World             NA 2018
# 3    1A Arab World             NA 2017
# 4    1A Arab World             NA 2016
# 5    1A Arab World       2.920702 2015
# 6    1A Arab World       3.278073 2014

library("XML")

library("WDI")

library("gridExtra")

library("scatterplot3d")

options(scipen=100) # turning of scientific notation

titel<-"Renewable electricity output"

subtitel<-"(% of total electricity output)"

renewable.electricity.output <- WDI(country="all", indicator="EG.ELC.RNEW.ZS", start=1900, end=2019)

str(renewable.electricity.output)

# 'data.frame': 15840 obs. of 4 variables:

# $ iso2c : chr "1A" "1A" "1A" "1A" ...

# $ country : chr "Arab World" "Arab World" "Arab World" "Arab World" ...

# $ EG.ELC.RNEW.ZS: num NA NA NA NA 2.92 ...

# $ year : num 2019 2018 2017 2016 2015 ...

head(renewable.electricity.output)

# iso2c country EG.ELC.RNEW.ZS year

# 1 1A Arab World NA 2019

# 2 1A Arab World NA 2018

# 3 1A Arab World NA 2017

# 4 1A Arab World NA 2016

# 5 1A Arab World 2.920702 2015

# 6 1A Arab World 3.278073 2014

# selection of the year 2015

year <- 2015
target.year <- which(renewable.electricity.output$year==year)
head(renewable.electricity.output[target.year,])

# iso2c                                     country EG.ELC.RNEW.ZS year
# 5      1A                                  Arab World       2.920702 2015
# 65     S3                      Caribbean small states       8.914780 2015
# 125    B8              Central Europe and the Baltics      20.252851 2015
# 185    V2                  Early-demographic dividend      18.104862 2015
# 245    Z4                         East Asia & Pacific      20.412466 2015
# 305    4E East Asia & Pacific (excluding high income)      23.394930 2015

# selection of the year 2015

year <- 2015

target.year <- which(renewable.electricity.output$year==year)

head(renewable.electricity.output[target.year,])

# iso2c country EG.ELC.RNEW.ZS year

# 5 1A Arab World 2.920702 2015

# 65 S3 Caribbean small states 8.914780 2015

# 125 B8 Central Europe and the Baltics 20.252851 2015

# 185 V2 Early-demographic dividend 18.104862 2015

# 245 Z4 East Asia & Pacific 20.412466 2015

# 305 4E East Asia & Pacific (excluding high income) 23.394930 2015

# income categories

– Low income
– Lower middle income
– Upper middle income
– High income

target.income <- which(renewable.electricity.output$country=="Low income")
renewable.electricity.output[target.income,]
plot(renewable.electricity.output[target.income,4:3],type="l",
     main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(10,90),
     axes=TRUE)
text(1985, 65, "Low income",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Low income")

renewable.electricity.output[target.income,]

plot(renewable.electricity.output[target.income,4:3],type="l",

main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(10,90),

axes=TRUE)

text(1985, 65, "Low income",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Lower middle income")
renewable.electricity.output[target.income,]
lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="red")
text(1985, 20, "Lower middle income", col="red",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Upper middle income")
renewable.electricity.output[target.income,]
lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="green")
text(1985, 25, "Upper middle income", col="green",cex=0.7)

target.income <- which(renewable.electricity.output$country=="High income")
renewable.electricity.output[target.income,]
lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="blue")
text(1985, 15, "High income", col="blue",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Lower middle income")

renewable.electricity.output[target.income,]

lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="red")

text(1985, 20, "Lower middle income", col="red",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Upper middle income")

renewable.electricity.output[target.income,]

lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="green")

text(1985, 25, "Upper middle income", col="green",cex=0.7)

target.income <- which(renewable.electricity.output$country=="High income")

renewable.electricity.output[target.income,]

lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="blue")

text(1985, 15, "High income", col="blue",cex=0.7)

# -------------

# region

# World
# Euro area
# European Union
# Europe & Central Asia
# Arab World
# North America
# Latin America & Caribbean
# Middle East & North Africa
# Sub-Saharan Africa 
# East Asia & Pacific
# South Asia

target.area <- which(renewable.electricity.output$country=="World")
renewable.electricity.output[target.area,]
plot(renewable.electricity.output[target.area,4:3],type="l",
     main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),
     axes=TRUE)
text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")
text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")
text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)
text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")
text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")
# renewable.electricity.output[target.area,]
# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")
# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)
text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)
text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)
text(1985, 28, "South Asia", col="pink",cex=0.7)

# -------------

# region

# World

# Euro area

# European Union

# Europe & Central Asia

# Arab World

# North America

# Latin America & Caribbean

# Middle East & North Africa

# Sub-Saharan Africa

# East Asia & Pacific

# South Asia

target.area <- which(renewable.electricity.output$country=="World")

renewable.electricity.output[target.area,]

plot(renewable.electricity.output[target.area,4:3],type="l",

main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),

axes=TRUE)

text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")

text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")

text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)

text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")

text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")

# renewable.electricity.output[target.area,]

# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")

# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)

text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)

text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)

text(1985, 28, "South Asia", col="pink",cex=0.7)

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