# =========================================================
# A. MEMBACA DATA
# =========================================================
simpan <- read.table(file.choose(),header = TRUE,sep = ",")

names(simpan) # Melihat nama variabel
## [1] "Umur..bulan."      "Jenis.Kelamin"     "Tinggi.Badan..cm."
## [4] "Status.Gizi"
str(simpan) # Melihat struktur data
## 'data.frame':    120999 obs. of  4 variables:
##  $ Umur..bulan.     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Jenis.Kelamin    : chr  "laki-laki" "laki-laki" "laki-laki" "laki-laki" ...
##  $ Tinggi.Badan..cm.: num  44.6 56.7 46.9 47.5 42.7 ...
##  $ Status.Gizi      : chr  "stunted" "tinggi" "normal" "normal" ...
# =========================================================
# B. DATA TUNGGAL
# =========================================================

data_1 <- simpan[[1]] # data_1 menyimpan data umur
data_11 <- na.omit(data_1) # data_11 menyimpan data umur tanpa NA
data_2 <- simpan[[3]] # data_2 menyimpan data tinggi badan
data_21 <- na.omit(data_2) # data_21 menyimpan data tinggi badan tanpa NA

class(data_1)
## [1] "integer"
class(data_11)
## [1] "integer"
class(data_2)
## [1] "numeric"
class(data_21)
## [1] "numeric"
# =========================================================
# C. STATISTIK DESKRIPTIF DENGAN DESCRIBE()
# =========================================================
library(psych)
## Warning: package 'psych' was built under R version 4.5.3
describe(data_11) # Statistik deskriptif umur
##    vars      n  mean    sd median trimmed   mad min max range  skew kurtosis
## X1    1 120999 30.17 17.58     30   30.22 22.24   0  60    60 -0.02    -1.19
##      se
## X1 0.05
describe(data_21) # Statistik deskriptif tinggi badan
##    vars      n  mean   sd median trimmed   mad   min max range  skew kurtosis
## X1    1 120999 88.66 17.3   89.8    89.2 17.94 40.01 128 87.99 -0.27     -0.4
##      se
## X1 0.05
# =========================================================
# D. STATISTIK DESKRIPTIF DENGAN STAT.DESC()
# =========================================================
library(pastecs)
## Warning: package 'pastecs' was built under R version 4.5.2
stat.desc(data_11) # Statistik deskriptif umur
##      nbr.val     nbr.null       nbr.na          min          max        range 
## 1.209990e+05 1.999000e+03 0.000000e+00 0.000000e+00 6.000000e+01 6.000000e+01 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 3.651000e+06 3.000000e+01 3.017380e+01 5.052512e-02 9.902841e-02 3.088848e+02 
##      std.dev     coef.var 
## 1.757512e+01 5.824628e-01
stat.desc(data_21) # Statistik deskriptif tinggi badan
##      nbr.val     nbr.null       nbr.na          min          max        range 
## 1.209990e+05 0.000000e+00 0.000000e+00 4.001044e+01 1.280000e+02 8.798956e+01 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 1.072722e+07 8.980000e+01 8.865543e+01 4.973707e-02 9.748385e-02 2.993245e+02 
##      std.dev     coef.var 
## 1.730100e+01 1.951487e-01
# =========================================================
# E. DATA BERKELOMPOK
# =========================================================
library(psych)
describeBy(simpan[[1]],simpan[[2]])
## 
##  Descriptive statistics by group 
## group: laki-laki
##    vars     n  mean    sd median trimmed   mad min max range  skew kurtosis
## X1    1 59997 30.35 17.54     31   30.44 22.24   0  60    60 -0.03    -1.18
##      se
## X1 0.07
## ------------------------------------------------------------ 
## group: perempuan
##    vars     n mean    sd median trimmed   mad min max range skew kurtosis   se
## X1    1 61002   30 17.61     30      30 22.24   0  60    60    0     -1.2 0.07
describeBy(simpan[[3]],simpan[[2]])
## 
##  Descriptive statistics by group 
## group: laki-laki
##    vars     n  mean    sd median trimmed   mad   min max range  skew kurtosis
## X1    1 59997 89.18 17.13   90.4   89.82 17.35 41.02 127 85.98 -0.31    -0.33
##      se
## X1 0.07
## ------------------------------------------------------------ 
## group: perempuan
##    vars     n  mean    sd median trimmed   mad   min max range  skew kurtosis
## X1    1 61002 88.14 17.45   89.2   88.59 18.38 40.01 128 87.99 -0.22    -0.46
##      se
## X1 0.07
describeBy(simpan[[1]],simpan[[4]])
## 
##  Descriptive statistics by group 
## group: normal
##    vars     n  mean    sd median trimmed   mad min max range  skew kurtosis
## X1    1 67755 32.61 17.01     34   33.06 20.76   0  60    60 -0.17    -1.09
##      se
## X1 0.07
## ------------------------------------------------------------ 
## group: severely stunted
##    vars     n  mean    sd median trimmed   mad min max range skew kurtosis   se
## X1    1 19869 26.29 17.58     24   25.54 20.76   0  60    60 0.29    -1.13 0.12
## ------------------------------------------------------------ 
## group: stunted
##    vars     n  mean    sd median trimmed   mad min max range  skew kurtosis
## X1    1 13815 32.76 17.06     34   33.24 20.76   0  60    60 -0.18    -1.11
##      se
## X1 0.15
## ------------------------------------------------------------ 
## group: tinggi
##    vars     n  mean    sd median trimmed   mad min max range skew kurtosis   se
## X1    1 19560 23.86 17.57     21   22.71 20.76   0  60    60 0.43       -1 0.13
describeBy(simpan[[3]],simpan[[4]])
## 
##  Descriptive statistics by group 
## group: normal
##    vars     n mean    sd median trimmed   mad   min   max range  skew kurtosis
## X1    1 67755 92.7 15.68   94.9   93.72 15.57 45.43 123.9 78.47 -0.57    -0.12
##      se
## X1 0.06
## ------------------------------------------------------------ 
## group: severely stunted
##    vars     n  mean    sd median trimmed   mad   min  max range  skew kurtosis
## X1    1 19869 73.18 13.65   75.1   73.99 15.42 40.01 95.9 55.89 -0.43    -0.77
##     se
## X1 0.1
## ------------------------------------------------------------ 
## group: stunted
##    vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
## X1    1 13815 82.21 12.44   84.9    83.6 12.01 43.62 100.7 57.08 -0.91      0.3
##      se
## X1 0.11
## ------------------------------------------------------------ 
## group: tinggi
##    vars     n  mean    sd median trimmed   mad   min max range  skew kurtosis
## X1    1 19560 94.91 18.26     96   95.25 22.24 54.72 128 73.28 -0.13    -0.99
##      se
## X1 0.13
# =========================================================
# F. SUMMARYBY()
# =========================================================
library(doBy)
## Warning: package 'doBy' was built under R version 4.5.3
summaryBy(
  Umur..bulan. + Tinggi.Badan..cm. ~
    Jenis.Kelamin + Status.Gizi,
  data = simpan,
  FUN = function(x)
  {
    c(
      ratarata = mean(x),
      standar_deviasi = sd(x),
      jumlah = sum(x)
    )
  }
)
##   Jenis.Kelamin      Status.Gizi Umur..bulan..ratarata
## 1     laki-laki           normal              32.84995
## 2     laki-laki severely stunted              26.17746
## 3     laki-laki          stunted              33.08092
## 4     laki-laki           tinggi              24.11008
## 5     perempuan           normal              32.37194
## 6     perempuan severely stunted              26.41496
## 7     perempuan          stunted              32.45561
## 8     perempuan           tinggi              23.62450
##   Umur..bulan..standar_deviasi Umur..bulan..jumlah Tinggi.Badan..cm..ratarata
## 1                     16.91927             1097287                   93.40348
## 2                     17.67419              271565                   73.74070
## 3                     17.01695              225248                   83.12258
## 4                     17.49105              226900                   95.61650
## 5                     17.08948             1112041                   92.02125
## 6                     17.48153              250810                   72.55949
## 7                     17.10172              227384                   81.32056
## 8                     17.64450              239765                   94.26046
##   Tinggi.Badan..cm..standar_deviasi Tinggi.Badan..cm..jumlah
## 1                          15.39194                3119956.5
## 2                          13.66266                 764986.1
## 3                          12.17793                 565981.7
## 4                          17.98551                 899846.9
## 5                          15.91854                3161114.1
## 6                          13.60068                 688952.3
## 7                          12.62028                 569731.8
## 8                          18.48611                 956649.4