2.1 Apply Fungsi apply() bekerja dengan jenis data matrik atau array (jenis data homogen).
## membuat matriks
x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
x # print## x1 x2
## [1,] 3 4
## [2,] 3 3
## [3,] 3 2
## [4,] 3 1
## [5,] 3 2
## [6,] 3 3
## [7,] 3 4
## [8,] 3 5class(x) # cek kelas objek## [1] "matrix" "array"## menghitung mean masing-masing kolom
apply(x, MARGIN=2 ,FUN=mean, trim=0.2, na.rm=TRUE)## x1 x2
## 3 3## menghitung range pada masing-masing baris
## menggunakan user define function
apply(x, MARGIN=1,
FUN=function(x){
max(x)-min(x)
})## [1] 1 0 1 2 1 0 1 22.2 Lapply Fungsi ini melakukan loop fungsi terhadap input data berupa list.
## Membuat list
x <- list(a = 1:10, beta = exp(-4:6), logic = c(TRUE,FALSE,FALSE,TRUE))
x # print## $a
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $beta
## [1] 0.01831564 0.04978707 0.13533528 0.36787944 1.00000000
## [6] 2.71828183 7.38905610 20.08553692 54.59815003 148.41315910
## [11] 403.42879349
##
## $logic
## [1] TRUE FALSE FALSE TRUEclass(x) # cek kelas objek## [1] "list"## Menghitung nilai mean pada masing-masing baris lits
lapply(x, FUN=mean)## $a
## [1] 5.5
##
## $beta
## [1] 58.01857
##
## $logic
## [1] 0.5## $a2.3 Sapply Secara default sapply() menerima input utama berupa list (dapat pula dataframe atau vektor), namun tidak seperti lapply() jenis data output yang dihasilkan adalah vektor. Untuk mengubah output menjadi list perlu argumen tambahan berupa simplify=FALSE.
## membuat urutan
x <- list(a = 2:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))
## menghitung nilai mean setiap elemen
sapply(x, FUN=mean)## a beta logic
## 6.000000 4.535125 0.500000## menghitung nilai mean dengan output list
sapply(x, FUN=mean, simplify=FALSE)## $a
## [1] 6
##
## $beta
## [1] 4.535125
##
## $logic
## [1] 0.5## summary objek dataframe
sapply(mtcars, FUN=summary)## mpg cyl disp hp drat wt qsec vs
## Min. 10.40000 4.0000 71.1000 52.0000 2.760000 1.51300 14.50000 0.0000
## 1st Qu. 15.42500 4.0000 120.8250 96.5000 3.080000 2.58125 16.89250 0.0000
## Median 19.20000 6.0000 196.3000 123.0000 3.695000 3.32500 17.71000 0.0000
## Mean 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375
## 3rd Qu. 22.80000 8.0000 326.0000 180.0000 3.920000 3.61000 18.90000 1.0000
## Max. 33.90000 8.0000 472.0000 335.0000 4.930000 5.42400 22.90000 1.0000
## am gear carb
## Min. 0.00000 3.0000 1.0000
## 1st Qu. 0.00000 3.0000 2.0000
## Median 0.00000 4.0000 2.0000
## Mean 0.40625 3.6875 2.8125
## 3rd Qu. 1.00000 4.0000 4.0000
## Max. 1.00000 5.0000 8.0000## summary objek list
a <- list(mobil=mtcars, anggrek=iris)
sapply(a, FUN=summary)## $mobil
## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000
##
## $anggrek
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
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