library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.3.2
## Warning: package 'dplyr' was built under R version 4.3.2
## Warning: package 'lubridate' was built under R version 4.3.2
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(datasets)
#Memanggil data airquality
data("airquality")
airquality<-tibble::as.tibble(airquality)
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#Mendapatkan atau menetapkan kelas objek
class(airquality)
## [1] "tbl_df" "tbl" "data.frame"
#Memberikan ringkasan atau tampilan yang lebih lengkap tentang struktur suatu dataset
glimpse(airquality)
## Rows: 153
## Columns: 6
## $ Ozone <int> 41, 36, 12, 18, NA, 28, 23, 19, 8, NA, 7, 16, 11, 14, 18, 14, …
## $ Solar.R <int> 190, 118, 149, 313, NA, NA, 299, 99, 19, 194, NA, 256, 290, 27…
## $ Wind <dbl> 7.4, 8.0, 12.6, 11.5, 14.3, 14.9, 8.6, 13.8, 20.1, 8.6, 6.9, 9…
## $ Temp <int> 67, 72, 74, 62, 56, 66, 65, 59, 61, 69, 74, 69, 66, 68, 58, 64…
## $ Month <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
## $ Day <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,…
#Menampilkan beberapa baris pertama dari suatu objek
head(airquality)
## # A tibble: 6 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 41 190 7.4 67 5 1
## 2 36 118 8 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
#Menghitung Rata-rata Temperatur udara
mean(airquality$Temp)
## [1] 77.88235
# %>% (dplyr) : setelah memanggil rata-rata airquality yang sudah dihitung
# kemudian membandingkan/mengecek dengan cara lain menggunakan fungsi dplyr
mean(airquality$Temp)==airquality$Temp%>%mean()
## [1] TRUE
#Summarise
#Menghitung rata-rata variabel Temp berdasarkan bulannya (Month)
airquality%>%group_by(Month)%>%summarize(mean=mean(Temp))%>%print(n=31)
## # A tibble: 5 × 2
## Month mean
## <int> <dbl>
## 1 5 65.5
## 2 6 79.1
## 3 7 83.9
## 4 8 84.0
## 5 9 76.9
#Arrange
#mengurutkan berdasarkan peubah Wind terkecil
airquality%>%arrange(Wind)%>%print(n=153)
## # A tibble: 153 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 NA 59 1.7 76 6 22
## 2 118 225 2.3 94 8 29
## 3 73 183 2.8 93 9 3
## 4 168 238 3.4 81 8 25
## 5 122 255 4 89 8 7
## 6 135 269 4.1 84 7 1
## 7 NA 91 4.6 76 6 23
## 8 64 175 4.6 83 7 5
## 9 66 NA 4.6 87 8 6
## 10 91 189 4.6 93 9 4
## 11 77 276 5.1 88 7 7
## 12 79 187 5.1 87 7 19
## 13 78 197 5.1 92 9 2
## 14 115 223 5.7 79 5 30
## 15 97 272 5.7 92 7 9
## 16 NA 153 5.7 88 8 27
## 17 NA 150 6.3 77 6 21
## 18 NA 250 6.3 76 6 24
## 19 97 267 6.3 92 7 8
## 20 61 285 6.3 84 7 18
## 21 59 51 6.3 79 8 17
## 22 84 237 6.3 96 8 30
## 23 85 188 6.3 94 8 31
## 24 28 238 6.3 77 9 13
## 25 7 NA 6.9 74 5 11
## 26 NA 273 6.9 87 6 8
## 27 48 260 6.9 81 7 16
## 28 16 7 6.9 74 7 21
## 29 39 83 6.9 81 8 1
## 30 78 NA 6.9 86 8 4
## 31 96 167 6.9 91 9 1
## 32 46 237 6.9 78 9 16
## 33 30 193 6.9 70 9 26
## 34 41 190 7.4 67 5 1
## 35 37 279 7.4 76 5 31
## 36 85 175 7.4 89 7 10
## 37 82 213 7.4 88 7 28
## 38 50 275 7.4 86 7 29
## 39 64 253 7.4 83 7 30
## 40 16 77 7.4 82 8 3
## 41 35 NA 7.4 85 8 5
## 42 23 115 7.4 76 8 18
## 43 47 95 7.4 87 9 5
## 44 36 118 8 72 5 2
## 45 NA NA 8 57 5 27
## 46 23 148 8 82 6 13
## 47 NA 135 8 75 6 25
## 48 NA 127 8 78 6 26
## 49 NA 138 8 83 6 30
## 50 108 223 8 85 7 25
## 51 110 207 8 90 8 9
## 52 73 215 8 86 8 26
## 53 16 201 8 82 9 20
## 54 18 131 8 76 9 29
## 55 23 299 8.6 65 5 7
## 56 NA 194 8.6 69 5 10
## 57 NA 286 8.6 78 6 1
## 58 NA 220 8.6 85 6 5
## 59 NA 139 8.6 82 7 11
## 60 80 294 8.6 86 7 24
## 61 20 81 8.6 82 7 26
## 62 NA 222 8.6 92 8 10
## 63 11 290 9.2 66 5 13
## 64 NA 186 9.2 84 6 4
## 65 NA 250 9.2 92 6 12
## 66 20 37 9.2 65 6 18
## 67 49 248 9.2 85 7 2
## 68 32 236 9.2 81 7 3
## 69 59 254 9.2 81 7 31
## 70 23 14 9.2 71 9 22
## 71 16 256 9.7 69 5 12
## 72 11 44 9.7 62 5 20
## 73 1 8 9.7 59 5 21
## 74 4 25 9.7 61 5 23
## 75 NA 287 9.7 74 6 2
## 76 29 127 9.7 82 6 7
## 77 NA 258 9.7 81 7 22
## 78 65 157 9.7 80 8 14
## 79 45 212 9.7 79 8 24
## 80 76 203 9.7 97 8 28
## 81 24 259 9.7 73 9 10
## 82 13 137 10.3 76 6 20
## 83 NA 47 10.3 73 6 27
## 84 35 274 10.3 82 7 17
## 85 89 229 10.3 90 8 8
## 86 22 71 10.3 77 8 16
## 87 44 190 10.3 78 8 20
## 88 23 220 10.3 78 9 8
## 89 13 27 10.3 76 9 18
## 90 24 238 10.3 68 9 19
## 91 36 139 10.3 81 9 23
## 92 7 49 10.3 69 9 24
## 93 14 274 10.9 68 5 14
## 94 NA 259 10.9 93 6 11
## 95 NA 101 10.9 84 7 4
## 96 40 314 10.9 83 7 6
## 97 31 244 10.9 78 8 19
## 98 20 252 10.9 80 9 7
## 99 21 230 10.9 75 9 9
## 100 9 24 10.9 71 9 14
## 101 18 313 11.5 62 5 4
## 102 14 334 11.5 64 5 16
## 103 30 322 11.5 68 5 19
## 104 39 323 11.5 87 6 10
## 105 NA 322 11.5 79 6 15
## 106 12 120 11.5 73 6 19
## 107 NA 98 11.5 80 6 28
## 108 63 220 11.5 85 7 20
## 109 NA 295 11.5 82 7 23
## 110 NA 137 11.5 86 8 11
## 111 44 192 11.5 86 8 12
## 112 28 273 11.5 82 8 13
## 113 NA 64 11.5 79 8 15
## 114 13 112 11.5 71 9 15
## 115 20 223 11.5 68 9 30
## 116 34 307 12 66 5 17
## 117 32 92 12 61 5 24
## 118 23 13 12 67 5 28
## 119 52 82 12 86 7 27
## 120 12 149 12.6 74 5 3
## 121 NA 255 12.6 75 8 23
## 122 13 238 12.6 64 9 21
## 123 18 65 13.2 58 5 15
## 124 NA 145 13.2 77 9 27
## 125 19 99 13.8 59 5 8
## 126 71 291 13.8 90 6 9
## 127 NA 332 13.8 80 6 14
## 128 9 24 13.8 81 8 2
## 129 18 224 13.8 67 9 17
## 130 NA NA 14.3 56 5 5
## 131 NA 264 14.3 79 6 6
## 132 10 264 14.3 73 7 12
## 133 7 48 14.3 80 7 15
## 134 9 36 14.3 72 8 22
## 135 14 191 14.3 75 9 28
## 136 28 NA 14.9 66 5 6
## 137 NA 266 14.9 58 5 26
## 138 45 252 14.9 81 5 29
## 139 21 191 14.9 77 6 16
## 140 NA 31 14.9 77 6 29
## 141 27 175 14.9 81 7 13
## 142 NA 291 14.9 91 7 14
## 143 44 236 14.9 81 9 11
## 144 21 259 15.5 77 8 21
## 145 32 92 15.5 84 9 6
## 146 21 259 15.5 76 9 12
## 147 NA 242 16.1 67 6 3
## 148 11 320 16.6 73 5 22
## 149 NA 66 16.6 57 5 25
## 150 14 20 16.6 63 9 25
## 151 6 78 18.4 57 5 18
## 152 8 19 20.1 61 5 9
## 153 37 284 20.7 72 6 17
#mengurutkan berdasarkan peubah Wind terbesar
airquality%>%arrange(desc(Wind))%>%print(n=153)
## # A tibble: 153 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 37 284 20.7 72 6 17
## 2 8 19 20.1 61 5 9
## 3 6 78 18.4 57 5 18
## 4 11 320 16.6 73 5 22
## 5 NA 66 16.6 57 5 25
## 6 14 20 16.6 63 9 25
## 7 NA 242 16.1 67 6 3
## 8 21 259 15.5 77 8 21
## 9 32 92 15.5 84 9 6
## 10 21 259 15.5 76 9 12
## 11 28 NA 14.9 66 5 6
## 12 NA 266 14.9 58 5 26
## 13 45 252 14.9 81 5 29
## 14 21 191 14.9 77 6 16
## 15 NA 31 14.9 77 6 29
## 16 27 175 14.9 81 7 13
## 17 NA 291 14.9 91 7 14
## 18 44 236 14.9 81 9 11
## 19 NA NA 14.3 56 5 5
## 20 NA 264 14.3 79 6 6
## 21 10 264 14.3 73 7 12
## 22 7 48 14.3 80 7 15
## 23 9 36 14.3 72 8 22
## 24 14 191 14.3 75 9 28
## 25 19 99 13.8 59 5 8
## 26 71 291 13.8 90 6 9
## 27 NA 332 13.8 80 6 14
## 28 9 24 13.8 81 8 2
## 29 18 224 13.8 67 9 17
## 30 18 65 13.2 58 5 15
## 31 NA 145 13.2 77 9 27
## 32 12 149 12.6 74 5 3
## 33 NA 255 12.6 75 8 23
## 34 13 238 12.6 64 9 21
## 35 34 307 12 66 5 17
## 36 32 92 12 61 5 24
## 37 23 13 12 67 5 28
## 38 52 82 12 86 7 27
## 39 18 313 11.5 62 5 4
## 40 14 334 11.5 64 5 16
## 41 30 322 11.5 68 5 19
## 42 39 323 11.5 87 6 10
## 43 NA 322 11.5 79 6 15
## 44 12 120 11.5 73 6 19
## 45 NA 98 11.5 80 6 28
## 46 63 220 11.5 85 7 20
## 47 NA 295 11.5 82 7 23
## 48 NA 137 11.5 86 8 11
## 49 44 192 11.5 86 8 12
## 50 28 273 11.5 82 8 13
## 51 NA 64 11.5 79 8 15
## 52 13 112 11.5 71 9 15
## 53 20 223 11.5 68 9 30
## 54 14 274 10.9 68 5 14
## 55 NA 259 10.9 93 6 11
## 56 NA 101 10.9 84 7 4
## 57 40 314 10.9 83 7 6
## 58 31 244 10.9 78 8 19
## 59 20 252 10.9 80 9 7
## 60 21 230 10.9 75 9 9
## 61 9 24 10.9 71 9 14
## 62 13 137 10.3 76 6 20
## 63 NA 47 10.3 73 6 27
## 64 35 274 10.3 82 7 17
## 65 89 229 10.3 90 8 8
## 66 22 71 10.3 77 8 16
## 67 44 190 10.3 78 8 20
## 68 23 220 10.3 78 9 8
## 69 13 27 10.3 76 9 18
## 70 24 238 10.3 68 9 19
## 71 36 139 10.3 81 9 23
## 72 7 49 10.3 69 9 24
## 73 16 256 9.7 69 5 12
## 74 11 44 9.7 62 5 20
## 75 1 8 9.7 59 5 21
## 76 4 25 9.7 61 5 23
## 77 NA 287 9.7 74 6 2
## 78 29 127 9.7 82 6 7
## 79 NA 258 9.7 81 7 22
## 80 65 157 9.7 80 8 14
## 81 45 212 9.7 79 8 24
## 82 76 203 9.7 97 8 28
## 83 24 259 9.7 73 9 10
## 84 11 290 9.2 66 5 13
## 85 NA 186 9.2 84 6 4
## 86 NA 250 9.2 92 6 12
## 87 20 37 9.2 65 6 18
## 88 49 248 9.2 85 7 2
## 89 32 236 9.2 81 7 3
## 90 59 254 9.2 81 7 31
## 91 23 14 9.2 71 9 22
## 92 23 299 8.6 65 5 7
## 93 NA 194 8.6 69 5 10
## 94 NA 286 8.6 78 6 1
## 95 NA 220 8.6 85 6 5
## 96 NA 139 8.6 82 7 11
## 97 80 294 8.6 86 7 24
## 98 20 81 8.6 82 7 26
## 99 NA 222 8.6 92 8 10
## 100 36 118 8 72 5 2
## 101 NA NA 8 57 5 27
## 102 23 148 8 82 6 13
## 103 NA 135 8 75 6 25
## 104 NA 127 8 78 6 26
## 105 NA 138 8 83 6 30
## 106 108 223 8 85 7 25
## 107 110 207 8 90 8 9
## 108 73 215 8 86 8 26
## 109 16 201 8 82 9 20
## 110 18 131 8 76 9 29
## 111 41 190 7.4 67 5 1
## 112 37 279 7.4 76 5 31
## 113 85 175 7.4 89 7 10
## 114 82 213 7.4 88 7 28
## 115 50 275 7.4 86 7 29
## 116 64 253 7.4 83 7 30
## 117 16 77 7.4 82 8 3
## 118 35 NA 7.4 85 8 5
## 119 23 115 7.4 76 8 18
## 120 47 95 7.4 87 9 5
## 121 7 NA 6.9 74 5 11
## 122 NA 273 6.9 87 6 8
## 123 48 260 6.9 81 7 16
## 124 16 7 6.9 74 7 21
## 125 39 83 6.9 81 8 1
## 126 78 NA 6.9 86 8 4
## 127 96 167 6.9 91 9 1
## 128 46 237 6.9 78 9 16
## 129 30 193 6.9 70 9 26
## 130 NA 150 6.3 77 6 21
## 131 NA 250 6.3 76 6 24
## 132 97 267 6.3 92 7 8
## 133 61 285 6.3 84 7 18
## 134 59 51 6.3 79 8 17
## 135 84 237 6.3 96 8 30
## 136 85 188 6.3 94 8 31
## 137 28 238 6.3 77 9 13
## 138 115 223 5.7 79 5 30
## 139 97 272 5.7 92 7 9
## 140 NA 153 5.7 88 8 27
## 141 77 276 5.1 88 7 7
## 142 79 187 5.1 87 7 19
## 143 78 197 5.1 92 9 2
## 144 NA 91 4.6 76 6 23
## 145 64 175 4.6 83 7 5
## 146 66 NA 4.6 87 8 6
## 147 91 189 4.6 93 9 4
## 148 135 269 4.1 84 7 1
## 149 122 255 4 89 8 7
## 150 168 238 3.4 81 8 25
## 151 73 183 2.8 93 9 3
## 152 118 225 2.3 94 8 29
## 153 NA 59 1.7 76 6 22
#Filter
#Mengambil records yang memiliki kriteria tertentu (misal Month==5)
airquality%>%filter(Month=="5")%>%print(n=31)
## # A tibble: 31 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 41 190 7.4 67 5 1
## 2 36 118 8 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8 57 5 27
## 28 23 13 12 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
#Select
#Mengambil variabel Ozone, Wind, Temp
airquality%>%select(Ozone, Wind, Temp)%>%print(n=153)
## # A tibble: 153 × 3
## Ozone Wind Temp
## <int> <dbl> <int>
## 1 41 7.4 67
## 2 36 8 72
## 3 12 12.6 74
## 4 18 11.5 62
## 5 NA 14.3 56
## 6 28 14.9 66
## 7 23 8.6 65
## 8 19 13.8 59
## 9 8 20.1 61
## 10 NA 8.6 69
## 11 7 6.9 74
## 12 16 9.7 69
## 13 11 9.2 66
## 14 14 10.9 68
## 15 18 13.2 58
## 16 14 11.5 64
## 17 34 12 66
## 18 6 18.4 57
## 19 30 11.5 68
## 20 11 9.7 62
## 21 1 9.7 59
## 22 11 16.6 73
## 23 4 9.7 61
## 24 32 12 61
## 25 NA 16.6 57
## 26 NA 14.9 58
## 27 NA 8 57
## 28 23 12 67
## 29 45 14.9 81
## 30 115 5.7 79
## 31 37 7.4 76
## 32 NA 8.6 78
## 33 NA 9.7 74
## 34 NA 16.1 67
## 35 NA 9.2 84
## 36 NA 8.6 85
## 37 NA 14.3 79
## 38 29 9.7 82
## 39 NA 6.9 87
## 40 71 13.8 90
## 41 39 11.5 87
## 42 NA 10.9 93
## 43 NA 9.2 92
## 44 23 8 82
## 45 NA 13.8 80
## 46 NA 11.5 79
## 47 21 14.9 77
## 48 37 20.7 72
## 49 20 9.2 65
## 50 12 11.5 73
## 51 13 10.3 76
## 52 NA 6.3 77
## 53 NA 1.7 76
## 54 NA 4.6 76
## 55 NA 6.3 76
## 56 NA 8 75
## 57 NA 8 78
## 58 NA 10.3 73
## 59 NA 11.5 80
## 60 NA 14.9 77
## 61 NA 8 83
## 62 135 4.1 84
## 63 49 9.2 85
## 64 32 9.2 81
## 65 NA 10.9 84
## 66 64 4.6 83
## 67 40 10.9 83
## 68 77 5.1 88
## 69 97 6.3 92
## 70 97 5.7 92
## 71 85 7.4 89
## 72 NA 8.6 82
## 73 10 14.3 73
## 74 27 14.9 81
## 75 NA 14.9 91
## 76 7 14.3 80
## 77 48 6.9 81
## 78 35 10.3 82
## 79 61 6.3 84
## 80 79 5.1 87
## 81 63 11.5 85
## 82 16 6.9 74
## 83 NA 9.7 81
## 84 NA 11.5 82
## 85 80 8.6 86
## 86 108 8 85
## 87 20 8.6 82
## 88 52 12 86
## 89 82 7.4 88
## 90 50 7.4 86
## 91 64 7.4 83
## 92 59 9.2 81
## 93 39 6.9 81
## 94 9 13.8 81
## 95 16 7.4 82
## 96 78 6.9 86
## 97 35 7.4 85
## 98 66 4.6 87
## 99 122 4 89
## 100 89 10.3 90
## 101 110 8 90
## 102 NA 8.6 92
## 103 NA 11.5 86
## 104 44 11.5 86
## 105 28 11.5 82
## 106 65 9.7 80
## 107 NA 11.5 79
## 108 22 10.3 77
## 109 59 6.3 79
## 110 23 7.4 76
## 111 31 10.9 78
## 112 44 10.3 78
## 113 21 15.5 77
## 114 9 14.3 72
## 115 NA 12.6 75
## 116 45 9.7 79
## 117 168 3.4 81
## 118 73 8 86
## 119 NA 5.7 88
## 120 76 9.7 97
## 121 118 2.3 94
## 122 84 6.3 96
## 123 85 6.3 94
## 124 96 6.9 91
## 125 78 5.1 92
## 126 73 2.8 93
## 127 91 4.6 93
## 128 47 7.4 87
## 129 32 15.5 84
## 130 20 10.9 80
## 131 23 10.3 78
## 132 21 10.9 75
## 133 24 9.7 73
## 134 44 14.9 81
## 135 21 15.5 76
## 136 28 6.3 77
## 137 9 10.9 71
## 138 13 11.5 71
## 139 46 6.9 78
## 140 18 13.8 67
## 141 13 10.3 76
## 142 24 10.3 68
## 143 16 8 82
## 144 13 12.6 64
## 145 23 9.2 71
## 146 36 10.3 81
## 147 7 10.3 69
## 148 14 16.6 63
## 149 30 6.9 70
## 150 NA 13.2 77
## 151 14 14.3 75
## 152 18 8 76
## 153 20 11.5 68
#Menghilangkan variabel Solar.R dan Day
airquality%>%select(-Solar.R, -Day)%>%print(n=153)
## # A tibble: 153 × 4
## Ozone Wind Temp Month
## <int> <dbl> <int> <int>
## 1 41 7.4 67 5
## 2 36 8 72 5
## 3 12 12.6 74 5
## 4 18 11.5 62 5
## 5 NA 14.3 56 5
## 6 28 14.9 66 5
## 7 23 8.6 65 5
## 8 19 13.8 59 5
## 9 8 20.1 61 5
## 10 NA 8.6 69 5
## 11 7 6.9 74 5
## 12 16 9.7 69 5
## 13 11 9.2 66 5
## 14 14 10.9 68 5
## 15 18 13.2 58 5
## 16 14 11.5 64 5
## 17 34 12 66 5
## 18 6 18.4 57 5
## 19 30 11.5 68 5
## 20 11 9.7 62 5
## 21 1 9.7 59 5
## 22 11 16.6 73 5
## 23 4 9.7 61 5
## 24 32 12 61 5
## 25 NA 16.6 57 5
## 26 NA 14.9 58 5
## 27 NA 8 57 5
## 28 23 12 67 5
## 29 45 14.9 81 5
## 30 115 5.7 79 5
## 31 37 7.4 76 5
## 32 NA 8.6 78 6
## 33 NA 9.7 74 6
## 34 NA 16.1 67 6
## 35 NA 9.2 84 6
## 36 NA 8.6 85 6
## 37 NA 14.3 79 6
## 38 29 9.7 82 6
## 39 NA 6.9 87 6
## 40 71 13.8 90 6
## 41 39 11.5 87 6
## 42 NA 10.9 93 6
## 43 NA 9.2 92 6
## 44 23 8 82 6
## 45 NA 13.8 80 6
## 46 NA 11.5 79 6
## 47 21 14.9 77 6
## 48 37 20.7 72 6
## 49 20 9.2 65 6
## 50 12 11.5 73 6
## 51 13 10.3 76 6
## 52 NA 6.3 77 6
## 53 NA 1.7 76 6
## 54 NA 4.6 76 6
## 55 NA 6.3 76 6
## 56 NA 8 75 6
## 57 NA 8 78 6
## 58 NA 10.3 73 6
## 59 NA 11.5 80 6
## 60 NA 14.9 77 6
## 61 NA 8 83 6
## 62 135 4.1 84 7
## 63 49 9.2 85 7
## 64 32 9.2 81 7
## 65 NA 10.9 84 7
## 66 64 4.6 83 7
## 67 40 10.9 83 7
## 68 77 5.1 88 7
## 69 97 6.3 92 7
## 70 97 5.7 92 7
## 71 85 7.4 89 7
## 72 NA 8.6 82 7
## 73 10 14.3 73 7
## 74 27 14.9 81 7
## 75 NA 14.9 91 7
## 76 7 14.3 80 7
## 77 48 6.9 81 7
## 78 35 10.3 82 7
## 79 61 6.3 84 7
## 80 79 5.1 87 7
## 81 63 11.5 85 7
## 82 16 6.9 74 7
## 83 NA 9.7 81 7
## 84 NA 11.5 82 7
## 85 80 8.6 86 7
## 86 108 8 85 7
## 87 20 8.6 82 7
## 88 52 12 86 7
## 89 82 7.4 88 7
## 90 50 7.4 86 7
## 91 64 7.4 83 7
## 92 59 9.2 81 7
## 93 39 6.9 81 8
## 94 9 13.8 81 8
## 95 16 7.4 82 8
## 96 78 6.9 86 8
## 97 35 7.4 85 8
## 98 66 4.6 87 8
## 99 122 4 89 8
## 100 89 10.3 90 8
## 101 110 8 90 8
## 102 NA 8.6 92 8
## 103 NA 11.5 86 8
## 104 44 11.5 86 8
## 105 28 11.5 82 8
## 106 65 9.7 80 8
## 107 NA 11.5 79 8
## 108 22 10.3 77 8
## 109 59 6.3 79 8
## 110 23 7.4 76 8
## 111 31 10.9 78 8
## 112 44 10.3 78 8
## 113 21 15.5 77 8
## 114 9 14.3 72 8
## 115 NA 12.6 75 8
## 116 45 9.7 79 8
## 117 168 3.4 81 8
## 118 73 8 86 8
## 119 NA 5.7 88 8
## 120 76 9.7 97 8
## 121 118 2.3 94 8
## 122 84 6.3 96 8
## 123 85 6.3 94 8
## 124 96 6.9 91 9
## 125 78 5.1 92 9
## 126 73 2.8 93 9
## 127 91 4.6 93 9
## 128 47 7.4 87 9
## 129 32 15.5 84 9
## 130 20 10.9 80 9
## 131 23 10.3 78 9
## 132 21 10.9 75 9
## 133 24 9.7 73 9
## 134 44 14.9 81 9
## 135 21 15.5 76 9
## 136 28 6.3 77 9
## 137 9 10.9 71 9
## 138 13 11.5 71 9
## 139 46 6.9 78 9
## 140 18 13.8 67 9
## 141 13 10.3 76 9
## 142 24 10.3 68 9
## 143 16 8 82 9
## 144 13 12.6 64 9
## 145 23 9.2 71 9
## 146 36 10.3 81 9
## 147 7 10.3 69 9
## 148 14 16.6 63 9
## 149 30 6.9 70 9
## 150 NA 13.2 77 9
## 151 14 14.3 75 9
## 152 18 8 76 9
## 153 20 11.5 68 9
#Mutate
#Membuat Variabel baru yaitu Level_Ozone yang merupakan deskripsi dari tingkat ozone dengan membuatnya menjadi 3 kategori yaitu "low", "moderate", dan "high"
airquality %>% mutate(Level_Ozone = case_when(
Ozone < 50 ~ "Low",
Ozone >= 50 & Ozone < 100 ~ "Moderate",
Ozone >= 100 ~ "High"
))%>%print(n=153)
## # A tibble: 153 × 7
## Ozone Solar.R Wind Temp Month Day Level_Ozone
## <int> <int> <dbl> <int> <int> <int> <chr>
## 1 41 190 7.4 67 5 1 Low
## 2 36 118 8 72 5 2 Low
## 3 12 149 12.6 74 5 3 Low
## 4 18 313 11.5 62 5 4 Low
## 5 NA NA 14.3 56 5 5 <NA>
## 6 28 NA 14.9 66 5 6 Low
## 7 23 299 8.6 65 5 7 Low
## 8 19 99 13.8 59 5 8 Low
## 9 8 19 20.1 61 5 9 Low
## 10 NA 194 8.6 69 5 10 <NA>
## 11 7 NA 6.9 74 5 11 Low
## 12 16 256 9.7 69 5 12 Low
## 13 11 290 9.2 66 5 13 Low
## 14 14 274 10.9 68 5 14 Low
## 15 18 65 13.2 58 5 15 Low
## 16 14 334 11.5 64 5 16 Low
## 17 34 307 12 66 5 17 Low
## 18 6 78 18.4 57 5 18 Low
## 19 30 322 11.5 68 5 19 Low
## 20 11 44 9.7 62 5 20 Low
## 21 1 8 9.7 59 5 21 Low
## 22 11 320 16.6 73 5 22 Low
## 23 4 25 9.7 61 5 23 Low
## 24 32 92 12 61 5 24 Low
## 25 NA 66 16.6 57 5 25 <NA>
## 26 NA 266 14.9 58 5 26 <NA>
## 27 NA NA 8 57 5 27 <NA>
## 28 23 13 12 67 5 28 Low
## 29 45 252 14.9 81 5 29 Low
## 30 115 223 5.7 79 5 30 High
## 31 37 279 7.4 76 5 31 Low
## 32 NA 286 8.6 78 6 1 <NA>
## 33 NA 287 9.7 74 6 2 <NA>
## 34 NA 242 16.1 67 6 3 <NA>
## 35 NA 186 9.2 84 6 4 <NA>
## 36 NA 220 8.6 85 6 5 <NA>
## 37 NA 264 14.3 79 6 6 <NA>
## 38 29 127 9.7 82 6 7 Low
## 39 NA 273 6.9 87 6 8 <NA>
## 40 71 291 13.8 90 6 9 Moderate
## 41 39 323 11.5 87 6 10 Low
## 42 NA 259 10.9 93 6 11 <NA>
## 43 NA 250 9.2 92 6 12 <NA>
## 44 23 148 8 82 6 13 Low
## 45 NA 332 13.8 80 6 14 <NA>
## 46 NA 322 11.5 79 6 15 <NA>
## 47 21 191 14.9 77 6 16 Low
## 48 37 284 20.7 72 6 17 Low
## 49 20 37 9.2 65 6 18 Low
## 50 12 120 11.5 73 6 19 Low
## 51 13 137 10.3 76 6 20 Low
## 52 NA 150 6.3 77 6 21 <NA>
## 53 NA 59 1.7 76 6 22 <NA>
## 54 NA 91 4.6 76 6 23 <NA>
## 55 NA 250 6.3 76 6 24 <NA>
## 56 NA 135 8 75 6 25 <NA>
## 57 NA 127 8 78 6 26 <NA>
## 58 NA 47 10.3 73 6 27 <NA>
## 59 NA 98 11.5 80 6 28 <NA>
## 60 NA 31 14.9 77 6 29 <NA>
## 61 NA 138 8 83 6 30 <NA>
## 62 135 269 4.1 84 7 1 High
## 63 49 248 9.2 85 7 2 Low
## 64 32 236 9.2 81 7 3 Low
## 65 NA 101 10.9 84 7 4 <NA>
## 66 64 175 4.6 83 7 5 Moderate
## 67 40 314 10.9 83 7 6 Low
## 68 77 276 5.1 88 7 7 Moderate
## 69 97 267 6.3 92 7 8 Moderate
## 70 97 272 5.7 92 7 9 Moderate
## 71 85 175 7.4 89 7 10 Moderate
## 72 NA 139 8.6 82 7 11 <NA>
## 73 10 264 14.3 73 7 12 Low
## 74 27 175 14.9 81 7 13 Low
## 75 NA 291 14.9 91 7 14 <NA>
## 76 7 48 14.3 80 7 15 Low
## 77 48 260 6.9 81 7 16 Low
## 78 35 274 10.3 82 7 17 Low
## 79 61 285 6.3 84 7 18 Moderate
## 80 79 187 5.1 87 7 19 Moderate
## 81 63 220 11.5 85 7 20 Moderate
## 82 16 7 6.9 74 7 21 Low
## 83 NA 258 9.7 81 7 22 <NA>
## 84 NA 295 11.5 82 7 23 <NA>
## 85 80 294 8.6 86 7 24 Moderate
## 86 108 223 8 85 7 25 High
## 87 20 81 8.6 82 7 26 Low
## 88 52 82 12 86 7 27 Moderate
## 89 82 213 7.4 88 7 28 Moderate
## 90 50 275 7.4 86 7 29 Moderate
## 91 64 253 7.4 83 7 30 Moderate
## 92 59 254 9.2 81 7 31 Moderate
## 93 39 83 6.9 81 8 1 Low
## 94 9 24 13.8 81 8 2 Low
## 95 16 77 7.4 82 8 3 Low
## 96 78 NA 6.9 86 8 4 Moderate
## 97 35 NA 7.4 85 8 5 Low
## 98 66 NA 4.6 87 8 6 Moderate
## 99 122 255 4 89 8 7 High
## 100 89 229 10.3 90 8 8 Moderate
## 101 110 207 8 90 8 9 High
## 102 NA 222 8.6 92 8 10 <NA>
## 103 NA 137 11.5 86 8 11 <NA>
## 104 44 192 11.5 86 8 12 Low
## 105 28 273 11.5 82 8 13 Low
## 106 65 157 9.7 80 8 14 Moderate
## 107 NA 64 11.5 79 8 15 <NA>
## 108 22 71 10.3 77 8 16 Low
## 109 59 51 6.3 79 8 17 Moderate
## 110 23 115 7.4 76 8 18 Low
## 111 31 244 10.9 78 8 19 Low
## 112 44 190 10.3 78 8 20 Low
## 113 21 259 15.5 77 8 21 Low
## 114 9 36 14.3 72 8 22 Low
## 115 NA 255 12.6 75 8 23 <NA>
## 116 45 212 9.7 79 8 24 Low
## 117 168 238 3.4 81 8 25 High
## 118 73 215 8 86 8 26 Moderate
## 119 NA 153 5.7 88 8 27 <NA>
## 120 76 203 9.7 97 8 28 Moderate
## 121 118 225 2.3 94 8 29 High
## 122 84 237 6.3 96 8 30 Moderate
## 123 85 188 6.3 94 8 31 Moderate
## 124 96 167 6.9 91 9 1 Moderate
## 125 78 197 5.1 92 9 2 Moderate
## 126 73 183 2.8 93 9 3 Moderate
## 127 91 189 4.6 93 9 4 Moderate
## 128 47 95 7.4 87 9 5 Low
## 129 32 92 15.5 84 9 6 Low
## 130 20 252 10.9 80 9 7 Low
## 131 23 220 10.3 78 9 8 Low
## 132 21 230 10.9 75 9 9 Low
## 133 24 259 9.7 73 9 10 Low
## 134 44 236 14.9 81 9 11 Low
## 135 21 259 15.5 76 9 12 Low
## 136 28 238 6.3 77 9 13 Low
## 137 9 24 10.9 71 9 14 Low
## 138 13 112 11.5 71 9 15 Low
## 139 46 237 6.9 78 9 16 Low
## 140 18 224 13.8 67 9 17 Low
## 141 13 27 10.3 76 9 18 Low
## 142 24 238 10.3 68 9 19 Low
## 143 16 201 8 82 9 20 Low
## 144 13 238 12.6 64 9 21 Low
## 145 23 14 9.2 71 9 22 Low
## 146 36 139 10.3 81 9 23 Low
## 147 7 49 10.3 69 9 24 Low
## 148 14 20 16.6 63 9 25 Low
## 149 30 193 6.9 70 9 26 Low
## 150 NA 145 13.2 77 9 27 <NA>
## 151 14 191 14.3 75 9 28 Low
## 152 18 131 8 76 9 29 Low
## 153 20 223 11.5 68 9 30 Low
#Menggunakan funsi select dan mutate secara bersamaan
#Menghapus variabel Solar.R dan Wind kemudian menambahkan variabel baru yaitu Level_Ozone
airqualitybaru<-airquality%>%select(-Solar.R, -Wind)%>%mutate(Level_Ozone = case_when(
Ozone < 50 ~ "Low",
Ozone >= 50 & Ozone < 100 ~ "Moderate",
Ozone >= 100 ~ "High"))
airqualitybaru%>%print(n=153)
## # A tibble: 153 × 5
## Ozone Temp Month Day Level_Ozone
## <int> <int> <int> <int> <chr>
## 1 41 67 5 1 Low
## 2 36 72 5 2 Low
## 3 12 74 5 3 Low
## 4 18 62 5 4 Low
## 5 NA 56 5 5 <NA>
## 6 28 66 5 6 Low
## 7 23 65 5 7 Low
## 8 19 59 5 8 Low
## 9 8 61 5 9 Low
## 10 NA 69 5 10 <NA>
## 11 7 74 5 11 Low
## 12 16 69 5 12 Low
## 13 11 66 5 13 Low
## 14 14 68 5 14 Low
## 15 18 58 5 15 Low
## 16 14 64 5 16 Low
## 17 34 66 5 17 Low
## 18 6 57 5 18 Low
## 19 30 68 5 19 Low
## 20 11 62 5 20 Low
## 21 1 59 5 21 Low
## 22 11 73 5 22 Low
## 23 4 61 5 23 Low
## 24 32 61 5 24 Low
## 25 NA 57 5 25 <NA>
## 26 NA 58 5 26 <NA>
## 27 NA 57 5 27 <NA>
## 28 23 67 5 28 Low
## 29 45 81 5 29 Low
## 30 115 79 5 30 High
## 31 37 76 5 31 Low
## 32 NA 78 6 1 <NA>
## 33 NA 74 6 2 <NA>
## 34 NA 67 6 3 <NA>
## 35 NA 84 6 4 <NA>
## 36 NA 85 6 5 <NA>
## 37 NA 79 6 6 <NA>
## 38 29 82 6 7 Low
## 39 NA 87 6 8 <NA>
## 40 71 90 6 9 Moderate
## 41 39 87 6 10 Low
## 42 NA 93 6 11 <NA>
## 43 NA 92 6 12 <NA>
## 44 23 82 6 13 Low
## 45 NA 80 6 14 <NA>
## 46 NA 79 6 15 <NA>
## 47 21 77 6 16 Low
## 48 37 72 6 17 Low
## 49 20 65 6 18 Low
## 50 12 73 6 19 Low
## 51 13 76 6 20 Low
## 52 NA 77 6 21 <NA>
## 53 NA 76 6 22 <NA>
## 54 NA 76 6 23 <NA>
## 55 NA 76 6 24 <NA>
## 56 NA 75 6 25 <NA>
## 57 NA 78 6 26 <NA>
## 58 NA 73 6 27 <NA>
## 59 NA 80 6 28 <NA>
## 60 NA 77 6 29 <NA>
## 61 NA 83 6 30 <NA>
## 62 135 84 7 1 High
## 63 49 85 7 2 Low
## 64 32 81 7 3 Low
## 65 NA 84 7 4 <NA>
## 66 64 83 7 5 Moderate
## 67 40 83 7 6 Low
## 68 77 88 7 7 Moderate
## 69 97 92 7 8 Moderate
## 70 97 92 7 9 Moderate
## 71 85 89 7 10 Moderate
## 72 NA 82 7 11 <NA>
## 73 10 73 7 12 Low
## 74 27 81 7 13 Low
## 75 NA 91 7 14 <NA>
## 76 7 80 7 15 Low
## 77 48 81 7 16 Low
## 78 35 82 7 17 Low
## 79 61 84 7 18 Moderate
## 80 79 87 7 19 Moderate
## 81 63 85 7 20 Moderate
## 82 16 74 7 21 Low
## 83 NA 81 7 22 <NA>
## 84 NA 82 7 23 <NA>
## 85 80 86 7 24 Moderate
## 86 108 85 7 25 High
## 87 20 82 7 26 Low
## 88 52 86 7 27 Moderate
## 89 82 88 7 28 Moderate
## 90 50 86 7 29 Moderate
## 91 64 83 7 30 Moderate
## 92 59 81 7 31 Moderate
## 93 39 81 8 1 Low
## 94 9 81 8 2 Low
## 95 16 82 8 3 Low
## 96 78 86 8 4 Moderate
## 97 35 85 8 5 Low
## 98 66 87 8 6 Moderate
## 99 122 89 8 7 High
## 100 89 90 8 8 Moderate
## 101 110 90 8 9 High
## 102 NA 92 8 10 <NA>
## 103 NA 86 8 11 <NA>
## 104 44 86 8 12 Low
## 105 28 82 8 13 Low
## 106 65 80 8 14 Moderate
## 107 NA 79 8 15 <NA>
## 108 22 77 8 16 Low
## 109 59 79 8 17 Moderate
## 110 23 76 8 18 Low
## 111 31 78 8 19 Low
## 112 44 78 8 20 Low
## 113 21 77 8 21 Low
## 114 9 72 8 22 Low
## 115 NA 75 8 23 <NA>
## 116 45 79 8 24 Low
## 117 168 81 8 25 High
## 118 73 86 8 26 Moderate
## 119 NA 88 8 27 <NA>
## 120 76 97 8 28 Moderate
## 121 118 94 8 29 High
## 122 84 96 8 30 Moderate
## 123 85 94 8 31 Moderate
## 124 96 91 9 1 Moderate
## 125 78 92 9 2 Moderate
## 126 73 93 9 3 Moderate
## 127 91 93 9 4 Moderate
## 128 47 87 9 5 Low
## 129 32 84 9 6 Low
## 130 20 80 9 7 Low
## 131 23 78 9 8 Low
## 132 21 75 9 9 Low
## 133 24 73 9 10 Low
## 134 44 81 9 11 Low
## 135 21 76 9 12 Low
## 136 28 77 9 13 Low
## 137 9 71 9 14 Low
## 138 13 71 9 15 Low
## 139 46 78 9 16 Low
## 140 18 67 9 17 Low
## 141 13 76 9 18 Low
## 142 24 68 9 19 Low
## 143 16 82 9 20 Low
## 144 13 64 9 21 Low
## 145 23 71 9 22 Low
## 146 36 81 9 23 Low
## 147 7 69 9 24 Low
## 148 14 63 9 25 Low
## 149 30 70 9 26 Low
## 150 NA 77 9 27 <NA>
## 151 14 75 9 28 Low
## 152 18 76 9 29 Low
## 153 20 68 9 30 Low