Mandarel UAS
Muhammad Firlan Maulana
2023-12-01
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#mau nanya tidyverse tuh apa
library(tidyverse)## Warning: package 'tidyverse' 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 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ 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?tidyverse## starting httpd help server ... donelibrary(datasets)
data(iris)
iris = tibble::as.tibble(iris) ## 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.class(iris) #melihat tipe data## [1] "tbl_df" "tbl" "data.frame"view(iris) #memunculkan data
head(iris) #hanya meliat data yg ada di bagian atas## # A tibble: 6 × 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?glimpse
glimpse(iris) #melihat rangkuman (ada brp row, dsb)## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…mean(iris$Sepal.Length) #menghitung mean dari Sepal Length dari data iris## [1] 5.843333mean(iris$Sepal.Length) == iris$Sepal.Length %>% mean() #$ untuk memanggil data## [1] TRUEx = c(0.109, 0.359, 0.63, 0.996, 0.515, 0.142, 0.017, 0.829, 0.907)
x## [1] 0.109 0.359 0.630 0.996 0.515 0.142 0.017 0.829 0.907round(exp(diff(log(x))), 1) #tidak menerapkan tidyverse## [1] 3.3 1.8 1.6 0.5 0.3 0.1 48.8 1.1x %>% log() %>% #panggil data dulu baru dioperrasikan (sama seperti line 17)
diff() %>%
exp() %>%
round (1) #membulatkan jadi satu angka di belakang koma ## [1] 3.3 1.8 1.6 0.5 0.3 0.1 48.8 1.1#Summarize
## Menghitung rata-rata Sepal length setiap species
###memanggil data iris, digroup by species, dihitung mean dari sepal length
iris %>% group_by(Species) %>% summarize(mean(Sepal.Length))## # A tibble: 3 × 2
## Species `mean(Sepal.Length)`
## <fct> <dbl>
## 1 setosa 5.01
## 2 versicolor 5.94
## 3 virginica 6.59###memanggil data iris, digroup by species, dihitung mean dari sepal length trs di rename kolomnya jadi mean
iris %>% group_by(Species) %>% summarize(mean=mean(Sepal.Length))## # A tibble: 3 × 2
## Species mean
## <fct> <dbl>
## 1 setosa 5.01
## 2 versicolor 5.94
## 3 virginica 6.59## Mengurutkan berdasarkan peubah Sepal.Length dari nilai terkecil
iris %>% arrange(Sepal.Length) %>% print(n=150) #print 150 data## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 4.3 3 1.1 0.1 setosa
## 2 4.4 2.9 1.4 0.2 setosa
## 3 4.4 3 1.3 0.2 setosa
## 4 4.4 3.2 1.3 0.2 setosa
## 5 4.5 2.3 1.3 0.3 setosa
## 6 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 4.6 3.6 1 0.2 setosa
## 9 4.6 3.2 1.4 0.2 setosa
## 10 4.7 3.2 1.3 0.2 setosa
## 11 4.7 3.2 1.6 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3 1.4 0.1 setosa
## 14 4.8 3.4 1.9 0.2 setosa
## 15 4.8 3.1 1.6 0.2 setosa
## 16 4.8 3 1.4 0.3 setosa
## 17 4.9 3 1.4 0.2 setosa
## 18 4.9 3.1 1.5 0.1 setosa
## 19 4.9 3.1 1.5 0.2 setosa
## 20 4.9 3.6 1.4 0.1 setosa
## 21 4.9 2.4 3.3 1 versicolor
## 22 4.9 2.5 4.5 1.7 virginica
## 23 5 3.6 1.4 0.2 setosa
## 24 5 3.4 1.5 0.2 setosa
## 25 5 3 1.6 0.2 setosa
## 26 5 3.4 1.6 0.4 setosa
## 27 5 3.2 1.2 0.2 setosa
## 28 5 3.5 1.3 0.3 setosa
## 29 5 3.5 1.6 0.6 setosa
## 30 5 3.3 1.4 0.2 setosa
## 31 5 2 3.5 1 versicolor
## 32 5 2.3 3.3 1 versicolor
## 33 5.1 3.5 1.4 0.2 setosa
## 34 5.1 3.5 1.4 0.3 setosa
## 35 5.1 3.8 1.5 0.3 setosa
## 36 5.1 3.7 1.5 0.4 setosa
## 37 5.1 3.3 1.7 0.5 setosa
## 38 5.1 3.4 1.5 0.2 setosa
## 39 5.1 3.8 1.9 0.4 setosa
## 40 5.1 3.8 1.6 0.2 setosa
## 41 5.1 2.5 3 1.1 versicolor
## 42 5.2 3.5 1.5 0.2 setosa
## 43 5.2 3.4 1.4 0.2 setosa
## 44 5.2 4.1 1.5 0.1 setosa
## 45 5.2 2.7 3.9 1.4 versicolor
## 46 5.3 3.7 1.5 0.2 setosa
## 47 5.4 3.9 1.7 0.4 setosa
## 48 5.4 3.7 1.5 0.2 setosa
## 49 5.4 3.9 1.3 0.4 setosa
## 50 5.4 3.4 1.7 0.2 setosa
## 51 5.4 3.4 1.5 0.4 setosa
## 52 5.4 3 4.5 1.5 versicolor
## 53 5.5 4.2 1.4 0.2 setosa
## 54 5.5 3.5 1.3 0.2 setosa
## 55 5.5 2.3 4 1.3 versicolor
## 56 5.5 2.4 3.8 1.1 versicolor
## 57 5.5 2.4 3.7 1 versicolor
## 58 5.5 2.5 4 1.3 versicolor
## 59 5.5 2.6 4.4 1.2 versicolor
## 60 5.6 2.9 3.6 1.3 versicolor
## 61 5.6 3 4.5 1.5 versicolor
## 62 5.6 2.5 3.9 1.1 versicolor
## 63 5.6 3 4.1 1.3 versicolor
## 64 5.6 2.7 4.2 1.3 versicolor
## 65 5.6 2.8 4.9 2 virginica
## 66 5.7 4.4 1.5 0.4 setosa
## 67 5.7 3.8 1.7 0.3 setosa
## 68 5.7 2.8 4.5 1.3 versicolor
## 69 5.7 2.6 3.5 1 versicolor
## 70 5.7 3 4.2 1.2 versicolor
## 71 5.7 2.9 4.2 1.3 versicolor
## 72 5.7 2.8 4.1 1.3 versicolor
## 73 5.7 2.5 5 2 virginica
## 74 5.8 4 1.2 0.2 setosa
## 75 5.8 2.7 4.1 1 versicolor
## 76 5.8 2.7 3.9 1.2 versicolor
## 77 5.8 2.6 4 1.2 versicolor
## 78 5.8 2.7 5.1 1.9 virginica
## 79 5.8 2.8 5.1 2.4 virginica
## 80 5.8 2.7 5.1 1.9 virginica
## 81 5.9 3 4.2 1.5 versicolor
## 82 5.9 3.2 4.8 1.8 versicolor
## 83 5.9 3 5.1 1.8 virginica
## 84 6 2.2 4 1 versicolor
## 85 6 2.9 4.5 1.5 versicolor
## 86 6 2.7 5.1 1.6 versicolor
## 87 6 3.4 4.5 1.6 versicolor
## 88 6 2.2 5 1.5 virginica
## 89 6 3 4.8 1.8 virginica
## 90 6.1 2.9 4.7 1.4 versicolor
## 91 6.1 2.8 4 1.3 versicolor
## 92 6.1 2.8 4.7 1.2 versicolor
## 93 6.1 3 4.6 1.4 versicolor
## 94 6.1 3 4.9 1.8 virginica
## 95 6.1 2.6 5.6 1.4 virginica
## 96 6.2 2.2 4.5 1.5 versicolor
## 97 6.2 2.9 4.3 1.3 versicolor
## 98 6.2 2.8 4.8 1.8 virginica
## 99 6.2 3.4 5.4 2.3 virginica
## 100 6.3 3.3 4.7 1.6 versicolor
## 101 6.3 2.5 4.9 1.5 versicolor
## 102 6.3 2.3 4.4 1.3 versicolor
## 103 6.3 3.3 6 2.5 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.3 2.7 4.9 1.8 virginica
## 106 6.3 2.8 5.1 1.5 virginica
## 107 6.3 3.4 5.6 2.4 virginica
## 108 6.3 2.5 5 1.9 virginica
## 109 6.4 3.2 4.5 1.5 versicolor
## 110 6.4 2.9 4.3 1.3 versicolor
## 111 6.4 2.7 5.3 1.9 virginica
## 112 6.4 3.2 5.3 2.3 virginica
## 113 6.4 2.8 5.6 2.1 virginica
## 114 6.4 2.8 5.6 2.2 virginica
## 115 6.4 3.1 5.5 1.8 virginica
## 116 6.5 2.8 4.6 1.5 versicolor
## 117 6.5 3 5.8 2.2 virginica
## 118 6.5 3.2 5.1 2 virginica
## 119 6.5 3 5.5 1.8 virginica
## 120 6.5 3 5.2 2 virginica
## 121 6.6 2.9 4.6 1.3 versicolor
## 122 6.6 3 4.4 1.4 versicolor
## 123 6.7 3.1 4.4 1.4 versicolor
## 124 6.7 3 5 1.7 versicolor
## 125 6.7 3.1 4.7 1.5 versicolor
## 126 6.7 2.5 5.8 1.8 virginica
## 127 6.7 3.3 5.7 2.1 virginica
## 128 6.7 3.1 5.6 2.4 virginica
## 129 6.7 3.3 5.7 2.5 virginica
## 130 6.7 3 5.2 2.3 virginica
## 131 6.8 2.8 4.8 1.4 versicolor
## 132 6.8 3 5.5 2.1 virginica
## 133 6.8 3.2 5.9 2.3 virginica
## 134 6.9 3.1 4.9 1.5 versicolor
## 135 6.9 3.2 5.7 2.3 virginica
## 136 6.9 3.1 5.4 2.1 virginica
## 137 6.9 3.1 5.1 2.3 virginica
## 138 7 3.2 4.7 1.4 versicolor
## 139 7.1 3 5.9 2.1 virginica
## 140 7.2 3.6 6.1 2.5 virginica
## 141 7.2 3.2 6 1.8 virginica
## 142 7.2 3 5.8 1.6 virginica
## 143 7.3 2.9 6.3 1.8 virginica
## 144 7.4 2.8 6.1 1.9 virginica
## 145 7.6 3 6.6 2.1 virginica
## 146 7.7 3.8 6.7 2.2 virginica
## 147 7.7 2.6 6.9 2.3 virginica
## 148 7.7 2.8 6.7 2 virginica
## 149 7.7 3 6.1 2.3 virginica
## 150 7.9 3.8 6.4 2 virginica## Mengurutkan berdasarkan peubah Sepal.Length dari nilai terbesar
iris %>% arrange(desc(Sepal.Length)) #hanya print 10 teratas## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 7.9 3.8 6.4 2 virginica
## 2 7.7 3.8 6.7 2.2 virginica
## 3 7.7 2.6 6.9 2.3 virginica
## 4 7.7 2.8 6.7 2 virginica
## 5 7.7 3 6.1 2.3 virginica
## 6 7.6 3 6.6 2.1 virginica
## 7 7.4 2.8 6.1 1.9 virginica
## 8 7.3 2.9 6.3 1.8 virginica
## 9 7.2 3.6 6.1 2.5 virginica
## 10 7.2 3.2 6 1.8 virginica
## # ℹ 140 more rows# FILTER
iris %>% filter(Species == "setosa")## # A tibble: 50 × 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 40 more rowsiris %>% select(Petal.Width, Species, Petal.Length) #milih Petal.Width, species, petal.length dari Setosa## # A tibble: 150 × 3
## Petal.Width Species Petal.Length
## <dbl> <fct> <dbl>
## 1 0.2 setosa 1.4
## 2 0.2 setosa 1.4
## 3 0.2 setosa 1.3
## 4 0.2 setosa 1.5
## 5 0.2 setosa 1.4
## 6 0.4 setosa 1.7
## 7 0.3 setosa 1.4
## 8 0.2 setosa 1.5
## 9 0.2 setosa 1.4
## 10 0.1 setosa 1.5
## # ℹ 140 more rowsiris %>% select(-Petal.Width, -Species) #milih semua kecuali petal.width dan species## # A tibble: 150 × 3
## Sepal.Length Sepal.Width Petal.Length
## <dbl> <dbl> <dbl>
## 1 5.1 3.5 1.4
## 2 4.9 3 1.4
## 3 4.7 3.2 1.3
## 4 4.6 3.1 1.5
## 5 5 3.6 1.4
## 6 5.4 3.9 1.7
## 7 4.6 3.4 1.4
## 8 5 3.4 1.5
## 9 4.4 2.9 1.4
## 10 4.9 3.1 1.5
## # ℹ 140 more rows# MUTATE (menambah peubah baru)
##bikin peubah baru namanya sepal, isinya sepal.length + sepal.width
iris %>% mutate(sepal = Sepal.Length + Sepal.Width)## # A tibble: 150 × 6
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species sepal
## <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
## 1 5.1 3.5 1.4 0.2 setosa 8.6
## 2 4.9 3 1.4 0.2 setosa 7.9
## 3 4.7 3.2 1.3 0.2 setosa 7.9
## 4 4.6 3.1 1.5 0.2 setosa 7.7
## 5 5 3.6 1.4 0.2 setosa 8.6
## 6 5.4 3.9 1.7 0.4 setosa 9.3
## 7 4.6 3.4 1.4 0.3 setosa 8
## 8 5 3.4 1.5 0.2 setosa 8.4
## 9 4.4 2.9 1.4 0.2 setosa 7.3
## 10 4.9 3.1 1.5 0.1 setosa 8
## # ℹ 140 more rows##bikin data baru namanya irisNew
irisNew = iris %>% select(-Species, -Petal.Width) %>% mutate(sepal=Sepal.Length + Sepal.Width)
irisNew## # A tibble: 150 × 4
## Sepal.Length Sepal.Width Petal.Length sepal
## <dbl> <dbl> <dbl> <dbl>
## 1 5.1 3.5 1.4 8.6
## 2 4.9 3 1.4 7.9
## 3 4.7 3.2 1.3 7.9
## 4 4.6 3.1 1.5 7.7
## 5 5 3.6 1.4 8.6
## 6 5.4 3.9 1.7 9.3
## 7 4.6 3.4 1.4 8
## 8 5 3.4 1.5 8.4
## 9 4.4 2.9 1.4 7.3
## 10 4.9 3.1 1.5 8
## # ℹ 140 more rows#Summarize
## Menghitung rata-rata Sepal length setiap species
###memanggil data iris, digroup by species, dihitung mean dari sepal length
iris %>% group_by(Species) %>% summarize(mean(Sepal.Length))## # A tibble: 3 × 2
## Species `mean(Sepal.Length)`
## <fct> <dbl>
## 1 setosa 5.01
## 2 versicolor 5.94
## 3 virginica 6.59###memanggil data iris, digroup by species, dihitung mean dari sepal length trs di rename kolomnya jadi mean
iris %>% group_by(Species) %>% summarize(mean=mean(Sepal.Length))## # A tibble: 3 × 2
## Species mean
## <fct> <dbl>
## 1 setosa 5.01
## 2 versicolor 5.94
## 3 virginica 6.59## Mengurutkan berdasarkan peubah Sepal.Length dari nilai terkecil
iris %>% arrange(Sepal.Length) %>% print(n=150) #print 150 data## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 4.3 3 1.1 0.1 setosa
## 2 4.4 2.9 1.4 0.2 setosa
## 3 4.4 3 1.3 0.2 setosa
## 4 4.4 3.2 1.3 0.2 setosa
## 5 4.5 2.3 1.3 0.3 setosa
## 6 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 4.6 3.6 1 0.2 setosa
## 9 4.6 3.2 1.4 0.2 setosa
## 10 4.7 3.2 1.3 0.2 setosa
## 11 4.7 3.2 1.6 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3 1.4 0.1 setosa
## 14 4.8 3.4 1.9 0.2 setosa
## 15 4.8 3.1 1.6 0.2 setosa
## 16 4.8 3 1.4 0.3 setosa
## 17 4.9 3 1.4 0.2 setosa
## 18 4.9 3.1 1.5 0.1 setosa
## 19 4.9 3.1 1.5 0.2 setosa
## 20 4.9 3.6 1.4 0.1 setosa
## 21 4.9 2.4 3.3 1 versicolor
## 22 4.9 2.5 4.5 1.7 virginica
## 23 5 3.6 1.4 0.2 setosa
## 24 5 3.4 1.5 0.2 setosa
## 25 5 3 1.6 0.2 setosa
## 26 5 3.4 1.6 0.4 setosa
## 27 5 3.2 1.2 0.2 setosa
## 28 5 3.5 1.3 0.3 setosa
## 29 5 3.5 1.6 0.6 setosa
## 30 5 3.3 1.4 0.2 setosa
## 31 5 2 3.5 1 versicolor
## 32 5 2.3 3.3 1 versicolor
## 33 5.1 3.5 1.4 0.2 setosa
## 34 5.1 3.5 1.4 0.3 setosa
## 35 5.1 3.8 1.5 0.3 setosa
## 36 5.1 3.7 1.5 0.4 setosa
## 37 5.1 3.3 1.7 0.5 setosa
## 38 5.1 3.4 1.5 0.2 setosa
## 39 5.1 3.8 1.9 0.4 setosa
## 40 5.1 3.8 1.6 0.2 setosa
## 41 5.1 2.5 3 1.1 versicolor
## 42 5.2 3.5 1.5 0.2 setosa
## 43 5.2 3.4 1.4 0.2 setosa
## 44 5.2 4.1 1.5 0.1 setosa
## 45 5.2 2.7 3.9 1.4 versicolor
## 46 5.3 3.7 1.5 0.2 setosa
## 47 5.4 3.9 1.7 0.4 setosa
## 48 5.4 3.7 1.5 0.2 setosa
## 49 5.4 3.9 1.3 0.4 setosa
## 50 5.4 3.4 1.7 0.2 setosa
## 51 5.4 3.4 1.5 0.4 setosa
## 52 5.4 3 4.5 1.5 versicolor
## 53 5.5 4.2 1.4 0.2 setosa
## 54 5.5 3.5 1.3 0.2 setosa
## 55 5.5 2.3 4 1.3 versicolor
## 56 5.5 2.4 3.8 1.1 versicolor
## 57 5.5 2.4 3.7 1 versicolor
## 58 5.5 2.5 4 1.3 versicolor
## 59 5.5 2.6 4.4 1.2 versicolor
## 60 5.6 2.9 3.6 1.3 versicolor
## 61 5.6 3 4.5 1.5 versicolor
## 62 5.6 2.5 3.9 1.1 versicolor
## 63 5.6 3 4.1 1.3 versicolor
## 64 5.6 2.7 4.2 1.3 versicolor
## 65 5.6 2.8 4.9 2 virginica
## 66 5.7 4.4 1.5 0.4 setosa
## 67 5.7 3.8 1.7 0.3 setosa
## 68 5.7 2.8 4.5 1.3 versicolor
## 69 5.7 2.6 3.5 1 versicolor
## 70 5.7 3 4.2 1.2 versicolor
## 71 5.7 2.9 4.2 1.3 versicolor
## 72 5.7 2.8 4.1 1.3 versicolor
## 73 5.7 2.5 5 2 virginica
## 74 5.8 4 1.2 0.2 setosa
## 75 5.8 2.7 4.1 1 versicolor
## 76 5.8 2.7 3.9 1.2 versicolor
## 77 5.8 2.6 4 1.2 versicolor
## 78 5.8 2.7 5.1 1.9 virginica
## 79 5.8 2.8 5.1 2.4 virginica
## 80 5.8 2.7 5.1 1.9 virginica
## 81 5.9 3 4.2 1.5 versicolor
## 82 5.9 3.2 4.8 1.8 versicolor
## 83 5.9 3 5.1 1.8 virginica
## 84 6 2.2 4 1 versicolor
## 85 6 2.9 4.5 1.5 versicolor
## 86 6 2.7 5.1 1.6 versicolor
## 87 6 3.4 4.5 1.6 versicolor
## 88 6 2.2 5 1.5 virginica
## 89 6 3 4.8 1.8 virginica
## 90 6.1 2.9 4.7 1.4 versicolor
## 91 6.1 2.8 4 1.3 versicolor
## 92 6.1 2.8 4.7 1.2 versicolor
## 93 6.1 3 4.6 1.4 versicolor
## 94 6.1 3 4.9 1.8 virginica
## 95 6.1 2.6 5.6 1.4 virginica
## 96 6.2 2.2 4.5 1.5 versicolor
## 97 6.2 2.9 4.3 1.3 versicolor
## 98 6.2 2.8 4.8 1.8 virginica
## 99 6.2 3.4 5.4 2.3 virginica
## 100 6.3 3.3 4.7 1.6 versicolor
## 101 6.3 2.5 4.9 1.5 versicolor
## 102 6.3 2.3 4.4 1.3 versicolor
## 103 6.3 3.3 6 2.5 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.3 2.7 4.9 1.8 virginica
## 106 6.3 2.8 5.1 1.5 virginica
## 107 6.3 3.4 5.6 2.4 virginica
## 108 6.3 2.5 5 1.9 virginica
## 109 6.4 3.2 4.5 1.5 versicolor
## 110 6.4 2.9 4.3 1.3 versicolor
## 111 6.4 2.7 5.3 1.9 virginica
## 112 6.4 3.2 5.3 2.3 virginica
## 113 6.4 2.8 5.6 2.1 virginica
## 114 6.4 2.8 5.6 2.2 virginica
## 115 6.4 3.1 5.5 1.8 virginica
## 116 6.5 2.8 4.6 1.5 versicolor
## 117 6.5 3 5.8 2.2 virginica
## 118 6.5 3.2 5.1 2 virginica
## 119 6.5 3 5.5 1.8 virginica
## 120 6.5 3 5.2 2 virginica
## 121 6.6 2.9 4.6 1.3 versicolor
## 122 6.6 3 4.4 1.4 versicolor
## 123 6.7 3.1 4.4 1.4 versicolor
## 124 6.7 3 5 1.7 versicolor
## 125 6.7 3.1 4.7 1.5 versicolor
## 126 6.7 2.5 5.8 1.8 virginica
## 127 6.7 3.3 5.7 2.1 virginica
## 128 6.7 3.1 5.6 2.4 virginica
## 129 6.7 3.3 5.7 2.5 virginica
## 130 6.7 3 5.2 2.3 virginica
## 131 6.8 2.8 4.8 1.4 versicolor
## 132 6.8 3 5.5 2.1 virginica
## 133 6.8 3.2 5.9 2.3 virginica
## 134 6.9 3.1 4.9 1.5 versicolor
## 135 6.9 3.2 5.7 2.3 virginica
## 136 6.9 3.1 5.4 2.1 virginica
## 137 6.9 3.1 5.1 2.3 virginica
## 138 7 3.2 4.7 1.4 versicolor
## 139 7.1 3 5.9 2.1 virginica
## 140 7.2 3.6 6.1 2.5 virginica
## 141 7.2 3.2 6 1.8 virginica
## 142 7.2 3 5.8 1.6 virginica
## 143 7.3 2.9 6.3 1.8 virginica
## 144 7.4 2.8 6.1 1.9 virginica
## 145 7.6 3 6.6 2.1 virginica
## 146 7.7 3.8 6.7 2.2 virginica
## 147 7.7 2.6 6.9 2.3 virginica
## 148 7.7 2.8 6.7 2 virginica
## 149 7.7 3 6.1 2.3 virginica
## 150 7.9 3.8 6.4 2 virginica## Mengurutkan berdasarkan peubah Sepal.Length dari nilai terbesar
iris %>% arrange(desc(Sepal.Length)) #hanya print 10 teratas## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 7.9 3.8 6.4 2 virginica
## 2 7.7 3.8 6.7 2.2 virginica
## 3 7.7 2.6 6.9 2.3 virginica
## 4 7.7 2.8 6.7 2 virginica
## 5 7.7 3 6.1 2.3 virginica
## 6 7.6 3 6.6 2.1 virginica
## 7 7.4 2.8 6.1 1.9 virginica
## 8 7.3 2.9 6.3 1.8 virginica
## 9 7.2 3.6 6.1 2.5 virginica
## 10 7.2 3.2 6 1.8 virginica
## # ℹ 140 more rows# FILTER
iris %>% filter(Species == "setosa")## # A tibble: 50 × 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 40 more rowsiris %>% select(Petal.Width, Species, Petal.Length) #milih Petal.Width, species, petal.length dari Setosa## # A tibble: 150 × 3
## Petal.Width Species Petal.Length
## <dbl> <fct> <dbl>
## 1 0.2 setosa 1.4
## 2 0.2 setosa 1.4
## 3 0.2 setosa 1.3
## 4 0.2 setosa 1.5
## 5 0.2 setosa 1.4
## 6 0.4 setosa 1.7
## 7 0.3 setosa 1.4
## 8 0.2 setosa 1.5
## 9 0.2 setosa 1.4
## 10 0.1 setosa 1.5
## # ℹ 140 more rowsiris %>% select(-Petal.Width, -Species) #milih semua kecuali petal.width dan species## # A tibble: 150 × 3
## Sepal.Length Sepal.Width Petal.Length
## <dbl> <dbl> <dbl>
## 1 5.1 3.5 1.4
## 2 4.9 3 1.4
## 3 4.7 3.2 1.3
## 4 4.6 3.1 1.5
## 5 5 3.6 1.4
## 6 5.4 3.9 1.7
## 7 4.6 3.4 1.4
## 8 5 3.4 1.5
## 9 4.4 2.9 1.4
## 10 4.9 3.1 1.5
## # ℹ 140 more rows# MUTATE (menambah peubah baru)
##bikin peubah baru namanya sepal, isinya sepal.length + sepal.width
iris %>% mutate(sepal = Sepal.Length + Sepal.Width)## # A tibble: 150 × 6
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species sepal
## <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
## 1 5.1 3.5 1.4 0.2 setosa 8.6
## 2 4.9 3 1.4 0.2 setosa 7.9
## 3 4.7 3.2 1.3 0.2 setosa 7.9
## 4 4.6 3.1 1.5 0.2 setosa 7.7
## 5 5 3.6 1.4 0.2 setosa 8.6
## 6 5.4 3.9 1.7 0.4 setosa 9.3
## 7 4.6 3.4 1.4 0.3 setosa 8
## 8 5 3.4 1.5 0.2 setosa 8.4
## 9 4.4 2.9 1.4 0.2 setosa 7.3
## 10 4.9 3.1 1.5 0.1 setosa 8
## # ℹ 140 more rows##bikin data baru namanya irisNew
irisNew = iris %>% select(-Species, -Petal.Width) %>% mutate(sepal=Sepal.Length + Sepal.Width)
irisNew## # A tibble: 150 × 4
## Sepal.Length Sepal.Width Petal.Length sepal
## <dbl> <dbl> <dbl> <dbl>
## 1 5.1 3.5 1.4 8.6
## 2 4.9 3 1.4 7.9
## 3 4.7 3.2 1.3 7.9
## 4 4.6 3.1 1.5 7.7
## 5 5 3.6 1.4 8.6
## 6 5.4 3.9 1.7 9.3
## 7 4.6 3.4 1.4 8
## 8 5 3.4 1.5 8.4
## 9 4.4 2.9 1.4 7.3
## 10 4.9 3.1 1.5 8
## # ℹ 140 more rows