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Programming code for different programming languages.

Basic R commands – 1.2 – permutations and matrix functions

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

1 Reply

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# 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 ) )

100

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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 #

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

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

Using trigonometric functions in R

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R uses radiant as input for trigonometric functions.

# calculating rad
pi/2
90*pi/180 # transforming degree into radiant
degr<-c(0:360)
degr
rad<-grad*(pi/180)
rad

# calculating rad

pi/2

90*pi/180 # transforming degree into radiant

degr<-c(0:360)

degr

rad<-grad*(pi/180)

rad

Now we can plot the function.

sin(rad)
plot(sin(rad))

1 2	sin(rad) plot(sin(rad))

And by playing with the functions we get a funny graphic output.

plot(asin(sin(rad)))
plot(asin(sin(rad)),type="l")
points(sin(rad))
points(sin(rad)*-1,col="green")
lines(asin(sin(rad))*-1)
lines(1/sin(rad),col="red")
lines(1/sin(rad)*-1,col="blue")
points(x=91,y=0)
points(x=271,y=0)

plot(asin(sin(rad)))

plot(asin(sin(rad)),type="l")

points(sin(rad))

points(sin(rad)*-1,col="green")

lines(asin(sin(rad))*-1)

lines(1/sin(rad),col="red")

lines(1/sin(rad)*-1,col="blue")

points(x=91,y=0)

points(x=271,y=0)

And if we include the tangent, the graphic looks like this:

tan(rad)
tangens <- tan(rad)
tangens[91]<-0
tangens[271]<-0
plot(tangens,type="l")
lines(sin(rad),type="l",col="red")
lines(cos(rad),type="l",col="green")

tan(rad)

tangens <- tan(rad)

tangens[91]<-0

tangens[271]<-0

plot(tangens,type="l")

lines(sin(rad),type="l",col="red")

lines(cos(rad),type="l",col="green")

lines(tangens,type="l",col="red")
lines(1/tangens,type="l",col="green")
# dividing by 10 for visual reasons
lines(tangens/10,type="l",col="red")
lines(1/tangens/10,type="l",col="green")

lines(tangens,type="l",col="red")

lines(1/tangens,type="l",col="green")

# dividing by 10 for visual reasons

lines(tangens/10,type="l",col="red")

lines(1/tangens/10,type="l",col="green")

plot(tangens,type="l")
lines(1/tangens,type="l")
plot(tangens,type="l")
lines(1/tangens,type="l",col="green")

plot(tangens,type="l")

lines(1/tangens,type="l")

plot(tangens,type="l")

lines(1/tangens,type="l",col="green")

Audio file conversion with afconvert (mac)

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I was looking for a simple and elegant way to convert a high amount of audio files from one format (.caf) to another (.aif). The solution i found is a very elegant one and also comes included with your operating system – if using a MAC.

afconvert -f AIFF -d BEI24@48000 "Wow Bass 03.caf" "Wow Bass 03.aif"

1	afconvert -f AIFF -d BEI24@48000 "Wow Bass 03.caf" "Wow Bass 03.aif"

And now here is the most amazing part. It is super easy to execute the conversion of multiple files by just one command line.

for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done

1	for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done

or to run through subdirectories:

for d in */ ; do cd $d; pwd; cd ..; done
for d in */ ; do cd $d; for i in *.caf; do echo *caf; pwd; done; cd ..; done;
for d in */ ; do cd $d; for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done; cd ..; done

for d in */ ; do cd $d; pwd; cd ..; done

for d in */ ; do cd $d; for i in *.caf; do echo *caf; pwd; done; cd ..; done;

for d in */ ; do cd $d; for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done; cd ..; done

or with recursion by using find:

find -name "*.caf"
find . -type f -iname '*.caf' -print | while read -r name; do cp "$name" "/.../..."; done
find . -type f -iname '*.caf' -print | while read -r name; do afconvert -f AIFF -d BEI24@48000 "$name" "/Volumes/Daten/Logic/test/${name%.*}.aif"; done

find -name "*.caf"

find . -type f -iname '*.caf' -print | while read -r name; do cp "$name" "/.../..."; done

find . -type f -iname '*.caf' -print | while read -r name; do afconvert -f AIFF -d BEI24@48000 "$name" "/Volumes/Daten/Logic/test/${name%.*}.aif"; done

Key	linear PCM format
LE	Little Endian
BE	Big Endian
F	Floating point
I	Integer
UI	Unsigned integer
8/16/24/32/64	Number of bits

Number of bits	Information Size
8	256
16	65536
24	16777216
32	4294967296
64	18446744073709551616

afconvert -hf

1	afconvert -hf

Audio file and data formats:	data_formats:
‘3gpp’ = 3GP Audio (.3gp)	‘Qclp’ ‘aac ‘ ‘aace’ ‘aach’ ‘aacl’ ‘aacp’ ‘samr’
‘3gp2’ = 3GPP-2 Audio (.3g2)	Qclp’ ‘aac ‘ ‘aace’ ‘aach’ ‘aacl’ ‘aacp’ ‘samr’
‘adts’ = AAC ADTS (.aac, .adts)	‘aac ‘ ‘aach’ ‘aacp’
‘ac-3’ = AC3 (.ac3)	‘ac-3’
‘AIFC’ = AIFC (.aifc, .aiff, .aif)	I8 BEI16 BEI24 BEI32 BEF32 BEF64 UI8 ‘ulaw’ ‘alaw’ ‘MAC3’ ‘MAC6’ ‘ima4’ ‘QDMC’ ‘QDM2’ ‘Qclp’ ‘agsm’
‘AIFF’ = AIFF (.aiff, .aif)	I8 BEI16 BEI24 BEI32
‘amrf’ = AMR (.amr)	‘samr’
‘m4af’ = Apple MPEG-4 Audio (.m4a, .m4r)	‘aac ‘ ‘aace’ ‘aach’ ‘aacl’ ‘aacp’ ‘alac’
‘caff’ = CAF (.caf)	‘.mp1’ ‘.mp2’ ‘.mp3’ ‘QDM2’ ‘QDMC’ ‘Qclp’ ‘Qclq’ ‘aac ‘ ‘aace’ ‘aach’ ‘aacl’ ‘aacp’ ‘alac’ ‘alaw’ ‘dvi8’ ‘ilbc’ ‘ima4’ I8 BEI16 BEI24 BEI32 BEF32 BEF64 LEI16 LEI24 LEI32 LEF32 LEF64 ‘ms\x00\x02’ ‘ms\x00\x11’ ‘ms\x001’ ‘paac’ ‘samr’ ‘ulaw’
‘MPG1’ = MPEG Layer 1 (.mp1, .mpeg, .mpa)	‘.mp1’
‘MPG2’ = MPEG Layer 2 (.mp2, .mpeg, .mpa)	‘.mp2’
‘MPG3’ = MPEG Layer 3 (.mp3, .mpeg, .mpa)	‘.mp3’
‘mp4f’ = MPEG-4 Audio (.mp4)	data_formats: ‘aac ‘ ‘aace’ ‘aach’ ‘aacl’ ‘aacp’
‘NeXT’ = NeXT/Sun (.snd, .au)	I8 BEI16 BEI24 BEI32 BEF32 BEF64 ‘ulaw’
‘Sd2f’ = Sound Designer II (.sd2)	I8 BEI16 BEI24 BEI32
‘WAVE’ = WAVE (.wav)	UI8 LEI16 LEI24 LEI32 LEF32 LEF64 ‘ulaw’ ‘alaw’

Supported Audio File and Data Formats in OS X

ooRexx with BSF4ooRexx – “java,net.URL” Classes (2_getinfo.rxj)

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/* ***************************************************************************** */
/* This is just a short example of implementing the  "java,net.URL" classes      */
/* using BSF4ooRexx                                                              */   
/* created by Martin Stoppacher    26.12.2009                                    */
/* ***************************************************************************** */

say hello this reads a syndfeed
say please type in the url
url= "http://rss.orf.at/fm4.xml"

f=.bsf~new("java.net.URL", url) /* creating a java url object with the above url */

say f~getAuthority()                      /* gets the authority part of this URL */
say f~getDefaultPort()           /* gets the default port number of the protocol */
                                                     /* associated with this URL */
say f~getPort()                              /* gets the port number of this URL */
say f~getFile()                                /* gets the file name of this URL */
say f~getHost()                 /* gets the host name of this URL, if applicable */
say f~getProtocol()                        /* gets the protocol name of this URL */
say f~getQuery()                              /* gets the query part of this URL */
say f~hashCode()          /* creates an integer suitable for hash table indexing */

::requires BSF.cls                            /* get the Java support for ooRexx */

/* ***************************************************************************** */

/* This is just a short example of implementing the "java,net.URL" classes */

/* using BSF4ooRexx */

/* created by Martin Stoppacher 26.12.2009 */

/* ***************************************************************************** */

say hello this reads a syndfeed

say please type in the url

url= "http://rss.orf.at/fm4.xml"

f=.bsf~new("java.net.URL", url) /* creating a java url object with the above url */

say f~getAuthority() /* gets the authority part of this URL */

say f~getDefaultPort() /* gets the default port number of the protocol */

/* associated with this URL */

say f~getPort() /* gets the port number of this URL */

say f~getFile() /* gets the file name of this URL */

say f~getHost() /* gets the host name of this URL, if applicable */

say f~getProtocol() /* gets the protocol name of this URL */

say f~getQuery() /* gets the query part of this URL */

say f~hashCode() /* creates an integer suitable for hash table indexing */

::requires BSF.cls /* get the Java support for ooRexx */

Syndication Feed Reader (1_Read.rxj) – ooRexx with BSF4ooRexx

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/* ***************************************************************************** */
/* Example of a syndication feed reader using the Project Rome API with          */
/* BSF4ooRexx                                                                    */
/* current version (2009) of Rome: rome1.0.jar   https://rome.dev.java.net/      */
/* You need to implement this API plus the JDOM API                              */
/* jdom.jar  ,   you can find this at     https://jdom.org/                      */
/*                                                                               */
/* This class retrieves a syndfeed from the web by using a precreated            */
/* Java class via BSF4ooRexx "com.sun.syndication.io.SyndFeedInput" methods      */
/* created by Martin Stoppacher       date:  26.12.2009                          */ 
/* ***************************************************************************** */

javaclass = "FeedReader1"                         /* determine Java class to use */
get=.bsf~new(javaClass)                     /* create an instance of "javaClass" */
say get~getfeed             /* calls the getfeed method in the FeedReader1 class */

::requires BSF.CLS                                       /* get the Java support */

/* ***************************************************************************** */

/* Example of a syndication feed reader using the Project Rome API with */

/* BSF4ooRexx */

/* current version (2009) of Rome: rome1.0.jar https://rome.dev.java.net/ */

/* You need to implement this API plus the JDOM API */

/* jdom.jar , you can find this at https://jdom.org/ */

/* */

/* This class retrieves a syndfeed from the web by using a precreated */

/* Java class via BSF4ooRexx "com.sun.syndication.io.SyndFeedInput" methods */

/* created by Martin Stoppacher date: 26.12.2009 */

/* ***************************************************************************** */

javaclass = "FeedReader1" /* determine Java class to use */

get=.bsf~new(javaClass) /* create an instance of "javaClass" */

say get~getfeed /* calls the getfeed method in the FeedReader1 class */

::requires BSF.CLS /* get the Java support */

An example of a syndication feed reader using the Project Rome API with BSF4ooRexx – “java.net.URL” feed reader (2_Rome.rxj)

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/* ***************************************************************************** */
/* Example of a syndication feed reader using the Project Rome API with          */
/* BSF4ooRexx                                                                    */
/* current version (2009) of Rome: rome1.0.jar        https://rome.dev.java.net/ */
/* You need to implement this API plus the JDOM API                              */
/* jdom.jar,     you can find this at https://jdom.org/                          */
/* This class retrieves a syndfeed from the web and prints some                  */
/* information about the feed using                                              */
/* "com.sun.syndication.io.SyndFeedInput" methods                                */
/* created by Martin Stoppacher     date:   26.12.2009                           */
/* ***************************************************************************** */

say hello this reads a syndfeed 
--say please type in the url        /* possible input statement for the feed URL */ 
--pull url 

--parse arg url                                  /* optional input via arguments */ 

         /* The url String is predefined in this example, to change the feed url */ 
               /* replace to String in this file or use one of the above methods */ 

url= "http://rss.orf.at/fm4.xml" 
feedUrl=.bsf~new("java.net.URL", url)      /* create an instance of java.net.URL */ 
say connecting__ || feedUrl~getAuthority() 

input=.bsf~new("com.sun.syndication.io.SyndFeedInput")/* creates a SyndFeedInput */

xmlr=.bsf~new("com.sun.syndication.io.XmlReader", feedUrl) 
       /* Figures out the charset encoding of the XML document within the stream */ 
feed= input~build(xmlr) 
         /* Builds a SyndFeedImpl from an Reader , also possible with SAX or DOM */ 

say bb~getDefaultEncoding() 
say feed                                              /* returns the feed object */ 

::requires BSF.cls                                       /* get the Java support */

/* ***************************************************************************** */

/* Example of a syndication feed reader using the Project Rome API with */

/* BSF4ooRexx */

/* current version (2009) of Rome: rome1.0.jar https://rome.dev.java.net/ */

/* You need to implement this API plus the JDOM API */

/* jdom.jar, you can find this at https://jdom.org/ */

/* This class retrieves a syndfeed from the web and prints some */

/* information about the feed using */

/* "com.sun.syndication.io.SyndFeedInput" methods */

/* created by Martin Stoppacher date: 26.12.2009 */

/* ***************************************************************************** */

say hello this reads a syndfeed

--say please type in the url /* possible input statement for the feed URL */

--pull url

--parse arg url /* optional input via arguments */

/* The url String is predefined in this example, to change the feed url */

/* replace to String in this file or use one of the above methods */

url= "http://rss.orf.at/fm4.xml"

feedUrl=.bsf~new("java.net.URL", url) /* create an instance of java.net.URL */

say connecting__ || feedUrl~getAuthority()

input=.bsf~new("com.sun.syndication.io.SyndFeedInput")/* creates a SyndFeedInput */

xmlr=.bsf~new("com.sun.syndication.io.XmlReader", feedUrl)

/* Figures out the charset encoding of the XML document within the stream */

feed= input~build(xmlr)

/* Builds a SyndFeedImpl from an Reader , also possible with SAX or DOM */

say bb~getDefaultEncoding()

say feed /* returns the feed object */

::requires BSF.cls /* get the Java support */

Syndication feed reader using the Project Rome API – (1_FeedReader.java)

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/* ***************************************************************************** */
/* Example of a syndication feed reader using the Project Rome API               */
/* current version of Rome: rome1.0.jar (2009)      https://rome.dev.java.net/   */
/* You need to implement this API plus the JDOM API                              */
/* jdom.jar  ,   you can find this at               https://jdom.org/            */
/* Parts of this example are taken from the PRome Web Page tutorials             */
/* https://rome.dev.java.net/  author: Alejandro Abdelnur                        */
/*                                                                               */
/* This class retrieves a syndfeed from the web and prints its                   */
/* pure content to the system                                                    */
/* created by Martin Stoppacher       date:  26.12.2009                          */
/* ***************************************************************************** */

import java.net.URL;
   /* Class URL represents a Uniform Resource Locator, a pointer to a "resource" */
   /* on the World Wide Web                                                      */
import java.io.InputStreamReader;
   /* An InputStreamReader is a bridge from byte streams to character streams    */
import com.sun.syndication.feed.synd.SyndFeed;
   /* This is the Bean interface for all types of feeds.                         */
import com.sun.syndication.io.SyndFeedInput;
   /* Parses an XML document (File, InputStream, Reader, W3C SAX InputSource, W3C*/
   /* DOM Document or JDom DOcument) into an WireFeed (RSS/Atom).                */
import com.sun.syndication.io.XmlReader;
   /* Character stream that handles (or at least attemtps to) all the necessary  */
   /* Voodo to figure out the charset encoding of the XML document within        */
   /* the stream.                                                                */

public class 1_FeedReader {

    public static void main(String[] args) {
        boolean ok = false;
        if (args.length==1) {
            try {
                URL feedUrl = new URL(args[0]);   /*creates a string with the URL*/

                SyndFeedInput input = new SyndFeedInput();    /* new input object*/
                
                SyndFeed feed = input.build(new XmlReader(feedUrl));
                                     /* reads feed from URL and puts it into feed*/

                System.out.println(feed);              /* outputs file to system */

                ok = true;
            }
            catch (Exception ex) {
                ex.printStackTrace();
                System.out.println("ERROR: "+ex.getMessage());
            }
        }

      if (!ok) {
      System.out.println();
      System.out.println("FeedReader reads and prints any RSS/Atom feed type.");
      System.out.println("The first parameter must be the" 
                                                   +"URL of the feed to read.");
      
      System.out.println();
      }
    }

}

/* ***************************************************************************** */

/* Example of a syndication feed reader using the Project Rome API */

/* current version of Rome: rome1.0.jar (2009) https://rome.dev.java.net/ */

/* You need to implement this API plus the JDOM API */

/* jdom.jar , you can find this at https://jdom.org/ */

/* Parts of this example are taken from the PRome Web Page tutorials */

/* https://rome.dev.java.net/ author: Alejandro Abdelnur */

/* */

/* This class retrieves a syndfeed from the web and prints its */

/* pure content to the system */

/* created by Martin Stoppacher date: 26.12.2009 */

/* ***************************************************************************** */

import java.net.URL;

/* Class URL represents a Uniform Resource Locator, a pointer to a "resource" */

/* on the World Wide Web */

import java.io.InputStreamReader;

/* An InputStreamReader is a bridge from byte streams to character streams */

import com.sun.syndication.feed.synd.SyndFeed;

/* This is the Bean interface for all types of feeds. */

import com.sun.syndication.io.SyndFeedInput;

/* Parses an XML document (File, InputStream, Reader, W3C SAX InputSource, W3C*/

/* DOM Document or JDom DOcument) into an WireFeed (RSS/Atom). */

import com.sun.syndication.io.XmlReader;

/* Character stream that handles (or at least attemtps to) all the necessary */

/* Voodo to figure out the charset encoding of the XML document within */

/* the stream. */

public class 1_FeedReader {

public static void main(String[] args) {

boolean ok = false;

if (args.length==1) {

try {

URL feedUrl = new URL(args[0]); /*creates a string with the URL*/

SyndFeedInput input = new SyndFeedInput(); /* new input object*/

SyndFeed feed = input.build(new XmlReader(feedUrl));

/* reads feed from URL and puts it into feed*/

System.out.println(feed); /* outputs file to system */

ok = true;

}

catch (Exception ex) {

ex.printStackTrace();

System.out.println("ERROR: "+ex.getMessage());

}

if (!ok) {

System.out.println();

System.out.println("FeedReader reads and prints any RSS/Atom feed type.");

System.out.println("The first parameter must be the"

+"URL of the feed to read.");

System.out.println();

}

MST Blog

DataAnalytics, Programming, Engineering, …

Category Archives: Code

Basic R commands – 1.2 – permutations and matrix functions

Basic R commands – 1

Using trigonometric functions in R

Audio file conversion with afconvert (mac)

ooRexx with BSF4ooRexx – “java,net.URL” Classes (2_getinfo.rxj)

Syndication Feed Reader (1_Read.rxj) – ooRexx with BSF4ooRexx

An example of a syndication feed reader using the Project Rome API with BSF4ooRexx – “java.net.URL” feed reader (2_Rome.rxj)

Syndication feed reader using the Project Rome API – (1_FeedReader.java)