2 + 3
## [1] 5
a <- 10  
b <- 5  
c <- a + b
vektor <- c(1, 2, 3, 4, 5)
vektor
## [1] 1 2 3 4 5
warna <- c("Merah", "Biru", "Hijau", "Merah", "Biru", "Hijau", "Merah")
warna
## [1] "Merah" "Biru"  "Hijau" "Merah" "Biru"  "Hijau" "Merah"
data_karyawan <- data.frame(
  Nama = c("Andi", "Budi", "Citra", "Dewi"),  
  Usia = c(25, 30, 27, 35),                  
  Pekerjaan = factor(c("Pegawai", "Wirausaha", "Mahasiswa", "Pegawai"))  
)
print(data_karyawan)
##    Nama Usia Pekerjaan
## 1  Andi   25   Pegawai
## 2  Budi   30 Wirausaha
## 3 Citra   27 Mahasiswa
## 4  Dewi   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)
is.na(nilai)
## [1] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
sum(is.na(nilai))
## [1] 2
x1 <- seq(0, 10, length=5)
x1
## [1]  0.0  2.5  5.0  7.5 10.0
x2 <- seq(0,10,length=6)  
x2
## [1]  0  2  4  6  8 10
x3 <- seq(0,10,length=7)
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
nilai<- c(90, 85, NA, 95)
is.na(nilai)
## [1] FALSE FALSE  TRUE FALSE
sum(is.na(nilai))
## [1] 1
x1 <- seq(0, 10, length=5)  
x1 
## [1]  0.0  2.5  5.0  7.5 10.0
x2 <- seq(0,10,length=6)  
x2
## [1]  0  2  4  6  8 10
x3 <- seq(0,10,length=7)
x3
## [1]  0.000000  1.666667  3.333333  5.000000  6.666667  8.333333 10.000000
x4<- seq(1,10,length=10)
x4
##  [1]  1  2  3  4  5  6  7  8  9 10
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)
##  [1] "A" "B" "C" "A" "B" "C" "A" "B" "C" "A" "B" "C" "A" "B" "C"
rep(c("A", "B", "C"), each=5)
##  [1] "A" "A" "A" "A" "A" "B" "B" "B" "B" "B" "C" "C" "C" "C" "C"
rep(c("A", "B", "C"), each=2, 5)
##  [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)
##  [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)
##  [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)
##  [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
x <- c(3, 4, 5, 6)  
y <- c(2, 3, 4, 5, 6, 6)
min(x)
## [1] 3
max(y)
## [1] 6
mean(x)
## [1] 4.5
var(y)
## [1] 2.666667
cor(x, y[1:length(x)])
## [1] 1
range(x)
## [1] 3 6
range(y)
## [1] 2 6
set.seed(123)  
sample(0:1, 30, replace = TRUE)
##  [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
sample(c("A", "G"), 15, replace = TRUE)
##  [1] "A" "G" "A" "G" "G" "A" "A" "A" "A" "G" "A" "G" "G" "A" "A"
sample(1:6, 30, replace = TRUE)
##  [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)
## Warning: package 'dplyr' was built under R version 4.3.3
## 
## 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)
## Warning: package 'data.table' was built under R version 4.3.3
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
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"
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

##TUGAS LATIHAN WEEK 2

1.Ketikkan perintah di bawah ini dan berikan pernyataan apa saja yang dapat kalian peroleh dari perintah tersebut! —————————————————– nama_vector <- c(5,FALSE,“true”,“8.3”,“Statistika”) nama_vector —————————————————–

nama_vector <- c(5, FALSE, "true", "8.3", "Statistika")
nama_vector
## [1] "5"          "FALSE"      "true"       "8.3"        "Statistika"

2.Cobalah untuk membuat List dengan nama contoh_list yang memiliki elemen sama dengan Latihan no.1, dan panggil seluruh elemen. Berikan perbedaan list dan vector yang dapat kalian peroleh setelah melakukan perintah tersebut

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"

3.Dalam melakukan pemanggilan elemen, kita dapat menggunakan index elemen atau nama kolom. Buatlah data frame dengan nama kelompok_kkn berupa tabel tiga kolom terdiri atas: nama,nim, dan prodi. Isikan minimal 10 baris. Lakukan eksperimen pemanggilan isi tabel, meliputi pemanggilan baris, kolom, dan elemen tabel. Jelaskan mengenai perbedaan cara pemanggilan dalam data frame tersebut

kelompok_kkn <- data.frame(
  nama = c("Ale", "Aya", "Claisa", "Didy", "Danu", "Ibay", "Kale", "Kafin", "Luna", "Opi"),
  nim = c(12388, 13308, 12439, 12510, 12389, 13450, 12151, 12992, 13377, 12054),
  prodi = c("Hubungan Inernasional", "Matematika", "Hukum", "Ilmu Komunikasi", "Hubungan Internasional", "Statistika", "IT", "Fisika", "Teknik Sipil", "Psikologi")
)
kelompok_kkn
##      nama   nim                  prodi
## 1     Ale 12388  Hubungan Inernasional
## 2     Aya 13308             Matematika
## 3  Claisa 12439                  Hukum
## 4    Didy 12510        Ilmu Komunikasi
## 5    Danu 12389 Hubungan Internasional
## 6    Ibay 13450             Statistika
## 7    Kale 12151                     IT
## 8   Kafin 12992                 Fisika
## 9    Luna 13377           Teknik Sipil
## 10    Opi 12054              Psikologi
kelompok_kkn$nama
##  [1] "Ale"    "Aya"    "Claisa" "Didy"   "Danu"   "Ibay"   "Kale"   "Kafin" 
##  [9] "Luna"   "Opi"
kelompok_kkn[ , "nim"]
##  [1] 12388 13308 12439 12510 12389 13450 12151 12992 13377 12054
kelompok_kkn[5, ]   # Baris ke-5
##   nama   nim                  prodi
## 5 Danu 12389 Hubungan Internasional
kelompok_kkn[2, 3]
## [1] "Matematika"

4.Buatlah data frame yang beberapa datanya berupa missing value. Carilah letak atau posisi data yang berupa missing value tersebut dengan menggunakan perintah is.na.

data_na <- data.frame(
  nama = c("Ale", "Aya", "Claisa", "Didy", "Danu", NA, "Kale", "Kafin", "Luna", "Opi"),
  umur = c(20, 23, NA, 21, 20, 21, NA, 19, 21, 20),
  nilai = c(90, 85, 88, NA, 92, 87, 89, NA, 86, 91)
)
# Mencari posisi missing values
which(is.na(data_na))
## [1]  6 13 17 24 28
which(is.na(data_na), arr.ind = TRUE)
##      row col
## [1,]   6   1
## [2,]   3   2
## [3,]   7   2
## [4,]   4   3
## [5,]   8   3