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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) 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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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 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") |
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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 ) ) |
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# Martin Stoppacher # # office@martinstoppacher.com # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # ################################################################################# |