R Markdown

2 + 3

2+4
## [1] 6
6-4
## [1] 2
5*9
## [1] 45
90/2
## [1] 45
2^9
## [1] 512
a <- 10  
b <- 5 
c <- a + b
c
## [1] 15
vektor <-c(1,2,3,4,5)
vektor
## [1] 1 2 3 4 5
warna <- c("Merah", "Biru", "Hijau", "Merah", "Biru", "Hijau", "Merah")
warna  # Menampilkan isi vektor karakter
## [1] "Merah" "Biru"  "Hijau" "Merah" "Biru"  "Hijau" "Merah"
warna_factor <- factor(warna) 
str(warna_factor)  
##  Factor w/ 3 levels "Biru","Hijau",..: 3 1 2 3 1 2 3
levels(warna_factor)
## [1] "Biru"  "Hijau" "Merah"
table(warna_factor) 
## warna_factor
##  Biru Hijau Merah 
##     2     2     3
data_list <- list(
  angka = c(10, 20, 30, 40),
  teks = c("A", "B", "C"),
  kategori = factor(c("Baik", "Sedang", "Buruk"))
)
print(data_list)
## $angka
## [1] 10 20 30 40
## 
## $teks
## [1] "A" "B" "C"
## 
## $kategori
## [1] Baik   Sedang Buruk 
## Levels: Baik Buruk Sedang
data_list$angka
## [1] 10 20 30 40
data_list[[2]]
## [1] "A" "B" "C"
data_karyawan <- data.frame(
  Nama = c("Ancha", "Hardani", "Nurleli", "Atiqoh"),  # Kolom Nama
  Usia = c(25, 30, 27, 35),                   # Kolom Usia
  Pekerjaan = factor(c("Pegawai", "Wirausaha", "Mahasiswa", "Pegawai"))  # Kolom Pekerjaan
)
print(data_karyawan)
##      Nama Usia Pekerjaan
## 1   Ancha   25   Pegawai
## 2 Hardani   30 Wirausaha
## 3 Nurleli   27 Mahasiswa
## 4  Atiqoh   35   Pegawai
summary(data_karyawan)
##      Nama                Usia           Pekerjaan
##  Length:4           Min.   :25.00   Mahasiswa:1  
##  Class :character   1st Qu.:26.50   Pegawai  :2  
##  Mode  :character   Median :28.50   Wirausaha:1  
##                     Mean   :29.25                
##                     3rd Qu.:31.25                
##                     Max.   :35.00
array_data <- array(1:24, dim = c(3, 4, 2))
print(array_data)
## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]   13   16   19   22
## [2,]   14   17   20   23
## [3,]   15   18   21   24
dim(array_data)
## [1] 3 4 2
nilai <- c(90, 85, NA, 75, 80, NA, 95)  # Membuat vektor dengan beberapa nilai NA

# Mengecek nilai yang hilang
is.na(nilai)  # Mengecek apakah ada nilai NA dalam vektor
## [1] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
# Menghitung jumlah nilai NA dalam vektor
sum(is.na(nilai))  # Menghitung total nilai NA dalam vektor
## [1] 2
x1 <- seq(0, 10, length=5)  # Membuat sequence dari 0 hingga 10 dengan 5 elemen
x1  # Menampilkan sequence
## [1]  0.0  2.5  5.0  7.5 10.0
x2 <- seq(0,10,length=6)  # Membuat deret angka dengan 6 elemen
x2
## [1]  0  2  4  6  8 10
x3 <- seq(0,10,length=7)  # Membuat deret angka dengan 7 elemen
x3
## [1]  0.000000  1.666667  3.333333  5.000000  6.666667  8.333333 10.000000
round(x3)
## [1]  0  2  3  5  7  8 10
floor(x3)
## [1]  0  1  3  5  6  8 10
ceiling(x3)
## [1]  0  2  4  5  7  9 10
rep(c("A", "B", "C"), 5)  # Mengulang vektor "A", "B", "C" sebanyak 5 kali
##  [1] "A" "B" "C" "A" "B" "C" "A" "B" "C" "A" "B" "C" "A" "B" "C"
rep(c("A", "B", "C"), each=5)  # Mengulang setiap elemen vektor "A", "B", "C" sebanyak 5 kali
##  [1] "A" "A" "A" "A" "A" "B" "B" "B" "B" "B" "C" "C" "C" "C" "C"
rep(c("A", "B", "C"), each=2, 5)  # Mengulang setiap elemen vektor "A", "B", "C" sebanyak 2 kali, dan seluruhnya diulang 5 kali
##  [1] "A" "A" "B" "B" "C" "C" "A" "A" "B" "B" "C" "C" "A" "A" "B" "B" "C" "C" "A"
## [20] "A" "B" "B" "C" "C" "A" "A" "B" "B" "C" "C"
rep(1:5, 5)  # Mengulang vektor 1 hingga 5 sebanyak 5 kali
##  [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
rep(1:5, each=5)  # Mengulang setiap elemen vektor 1 hingga 5 sebanyak 5 kali
##  [1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5
rep(1:5, each=2, 5)  # Mengulang setiap elemen vektor 1 hingga 5 sebanyak 2 kali, dan seluruhnya diulang 5 kali
##  [1] 1 1 2 2 3 3 4 4 5 5 1 1 2 2 3 3 4 4 5 5 1 1 2 2 3 3 4 4 5 5 1 1 2 2 3 3 4 4
## [39] 5 5 1 1 2 2 3 3 4 4 5 5
# Membuat data x dan y
x <- c(3, 4, 5, 6)  # Membuat vektor x
y <- c(2, 3, 4, 5, 6, 6)  # Membuat vektor y
# Menghitung nilai statistik dasar
min(x)  # Menghitung nilai minimum dari vektor x
## [1] 3
max(y)  # Menghitung nilai maksimum dari vektor y
## [1] 6
mean(x) # Menghitung rata-rata dari vektor x
## [1] 4.5
var(y)  # Menghitung variansi dari vektor y
## [1] 2.666667
cor(x, y[1:length(x)])  # Menghitung korelasi antara vektor x dan y (panjang harus sama)
## [1] 1
# Menentukan range (jangkauan nilai)
range(x)  # Menghitung range dari vektor x
## [1] 3 6
range(y)  # Menghitung range dari vektor y
## [1] 2 6
# Simulasi pelemparan koin (0 = ekor, 1 = kepala)
set.seed(123)  # Mengatur seed untuk hasil yang konsisten
sample(0:1, 30, replace = TRUE)  # Simulasi pelemparan koin sebanyak 30 kali
##  [1] 0 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 1
# Simulasi pengambilan sampel huruf "A" dan "G" sebanyak 15 kali
sample(c("A", "G"), 15, replace = TRUE)  # Simulasi pengambilan sampel huruf "A" dan "G"
##  [1] "A" "G" "A" "G" "G" "A" "A" "A" "A" "G" "A" "G" "G" "A" "A"
# Simulasi pelemparan dadu sebanyak 30 kali
sample(1:6, 30, replace = TRUE)  # Simulasi pelemparan dadu sebanyak 30 kali
##  [1] 1 1 2 3 4 5 5 3 6 1 2 5 5 4 5 2 1 1 3 1 6 5 1 2 4 4 6 6 3 6
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(data.table)
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
library(ggplot2)
data <- tbl_df(iris)
## Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
## ℹ Please use `tibble::as_tibble()` instead.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
class(data)
## [1] "tbl_df"     "tbl"        "data.frame"
data
## # A tibble: 150 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # ℹ 140 more rows
data_slice <- slice(data, 1:10)
data_slice
## # A tibble: 10 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa
arrange(data_slice, desc(data_slice$Sepal.Length))
## # A tibble: 10 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.4         3.9          1.7         0.4 setosa 
##  2          5.1         3.5          1.4         0.2 setosa 
##  3          5           3.6          1.4         0.2 setosa 
##  4          5           3.4          1.5         0.2 setosa 
##  5          4.9         3            1.4         0.2 setosa 
##  6          4.9         3.1          1.5         0.1 setosa 
##  7          4.7         3.2          1.3         0.2 setosa 
##  8          4.6         3.1          1.5         0.2 setosa 
##  9          4.6         3.4          1.4         0.3 setosa 
## 10          4.4         2.9          1.4         0.2 setosa
datatable <- data.table(iris)
datatable
##      Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
##             <num>       <num>        <num>       <num>    <fctr>
##   1:          5.1         3.5          1.4         0.2    setosa
##   2:          4.9         3.0          1.4         0.2    setosa
##   3:          4.7         3.2          1.3         0.2    setosa
##   4:          4.6         3.1          1.5         0.2    setosa
##   5:          5.0         3.6          1.4         0.2    setosa
##  ---                                                            
## 146:          6.7         3.0          5.2         2.3 virginica
## 147:          6.3         2.5          5.0         1.9 virginica
## 148:          6.5         3.0          5.2         2.0 virginica
## 149:          6.2         3.4          5.4         2.3 virginica
## 150:          5.9         3.0          5.1         1.8 virginica
datatable$new_col <- datatable$Species
datatable$new_col
##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica
setkey(datatable, Species)
key(datatable)
## [1] "Species"
## [1] "Species"
datatable[,.(mean=mean(Sepal.Length), IQR=IQR(Sepal.Length), median=median(Sepal.Length)), by=Species]
## Key: <Species>
##       Species  mean   IQR median
##        <fctr> <num> <num>  <num>
## 1:     setosa 5.006 0.400    5.0
## 2: versicolor 5.936 0.700    5.9
## 3:  virginica 6.588 0.675    6.5
plot_data <- ggplot(data,aes(x=Sepal.Length, y=Sepal.Width)) + geom_point(aes(colour=Species)) 
plot_data

nama_vector <- c(5, FALSE, "true", "8.3", "Statistika")
nama_vector
## [1] "5"          "FALSE"      "true"       "8.3"        "Statistika"
contoh_list <- list(5, FALSE, "true", "8.3", "Statistika")
contoh_list
## [[1]]
## [1] 5
## 
## [[2]]
## [1] FALSE
## 
## [[3]]
## [1] "true"
## 
## [[4]]
## [1] "8.3"
## 
## [[5]]
## [1] "Statistika"
kelompok_kkn <- data.frame(
  nama = c("Andi", "Budi", "Caca", "Dedi", "Eka", "Fani", "Gita", "Hadi", "Ira", "Joko"),
  nim = c("12345", "67890", "13579", "24680", "11223", "44556", "77889", "99001", "22446", "88990"),
  prodi = c("Statistika", "Matematika", "Informatika", "Biologi", "Fisika", "Kimia", "Ekonomi", "Hukum", "Kedokteran", "Teknik")
)

# Memanggil seluruh tabel
kelompok_kkn
##    nama   nim       prodi
## 1  Andi 12345  Statistika
## 2  Budi 67890  Matematika
## 3  Caca 13579 Informatika
## 4  Dedi 24680     Biologi
## 5   Eka 11223      Fisika
## 6  Fani 44556       Kimia
## 7  Gita 77889     Ekonomi
## 8  Hadi 99001       Hukum
## 9   Ira 22446  Kedokteran
## 10 Joko 88990      Teknik
# Memanggil baris ke-3
kelompok_kkn[3, ]
##   nama   nim       prodi
## 3 Caca 13579 Informatika
# Memanggil kolom "nama"
kelompok_kkn[, "nama"]
##  [1] "Andi" "Budi" "Caca" "Dedi" "Eka"  "Fani" "Gita" "Hadi" "Ira"  "Joko"
# Memanggil elemen baris ke-2, kolom "nim"
kelompok_kkn[2, "nim"]
## [1] "67890"
# Membuat data frame dengan missing value
df_missing <- data.frame(
  A = c(1, 2, NA, 4, 5),
  B = c(NA, 2, 3, 4, NA),
  C = c(1, 2, 3, NA, 5)
)

# Mencari posisi missing value
is.na(df_missing)
##          A     B     C
## [1,] FALSE  TRUE FALSE
## [2,] FALSE FALSE FALSE
## [3,]  TRUE FALSE FALSE
## [4,] FALSE FALSE  TRUE
## [5,] FALSE  TRUE FALSE