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 'stringr' 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 1.5.1
## ✔ 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 errorslibrary(datasets)
data(Orange)
Orange <- tibble::as_tibble(Orange)#melihat tipe data
class(Orange)## [1] "tbl_df" "tbl" "data.frame"#melihat tampilan data dari airquality
View(Orange)#melihat ringkasan dari data
head(Orange)## # A tibble: 6 × 3
## Tree age circumference
## <ord> <dbl> <dbl>
## 1 1 118 30
## 2 1 484 58
## 3 1 664 87
## 4 1 1004 115
## 5 1 1231 120
## 6 1 1372 142glimpse(Orange)## Rows: 35
## Columns: 3
## $ Tree <ord> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3,…
## $ age <dbl> 118, 484, 664, 1004, 1231, 1372, 1582, 118, 484, 664, 10…
## $ circumference <dbl> 30, 58, 87, 115, 120, 142, 145, 33, 69, 111, 156, 172, 2…#menghitung rata-rata umur pohon
mean(Orange$age)## [1] 922.1429#Setelah memanggil %>% (dilanjutkan) menghitung mean
mean(Orange$age) == Orange$age %>% mean()## [1] TRUE#memasukkan data
C <- c(30, 58, 87, 115, 120, 142, 145, 33, 69)
C## [1] 30 58 87 115 120 142 145 33 69#dilogaritmakan, didiferensialkan, dieksponensialkan, dibulatkan 1 angka dibelakang koma
round(exp(diff(log(C))),1)## [1] 1.9 1.5 1.3 1.0 1.2 1.0 0.2 2.1C %>% log () %>%
diff () %>%
exp () %>%
round (1)## [1] 1.9 1.5 1.3 1.0 1.2 1.0 0.2 2.1#summarize
##Menghitung rata-rata kecepatan angin setiap bulan
Orange %>% group_by(Tree) %>% summarize (mean=mean(circumference))## # A tibble: 5 × 2
## Tree mean
## <ord> <dbl>
## 1 3 94
## 2 1 99.6
## 3 5 111.
## 4 2 135.
## 5 4 139.##Mengurutkkan berdasarkan peubah circumference dengan ascending
Orange %>% arrange(circumference)## # A tibble: 35 × 3
## Tree age circumference
## <ord> <dbl> <dbl>
## 1 1 118 30
## 2 3 118 30
## 3 5 118 30
## 4 4 118 32
## 5 2 118 33
## 6 5 484 49
## 7 3 484 51
## 8 1 484 58
## 9 4 484 62
## 10 2 484 69
## # ℹ 25 more rows##Mengurutkkan berdasarkan peubah circumference dengan descending
Orange %>% arrange (desc(circumference))## # A tibble: 35 × 3
## Tree age circumference
## <ord> <dbl> <dbl>
## 1 4 1582 214
## 2 4 1372 209
## 3 2 1372 203
## 4 2 1582 203
## 5 4 1231 179
## 6 5 1582 177
## 7 5 1372 174
## 8 2 1231 172
## 9 4 1004 167
## 10 2 1004 156
## # ℹ 25 more rows#Filter
##Ngambil data yang pada ordo 4
Orange %>% filter(Tree=="4")## # A tibble: 7 × 3
## Tree age circumference
## <ord> <dbl> <dbl>
## 1 4 118 32
## 2 4 484 62
## 3 4 664 112
## 4 4 1004 167
## 5 4 1231 179
## 6 4 1372 209
## 7 4 1582 214##Ngambil data yang age, circumference
Orange %>% select(age,circumference)## # A tibble: 35 × 2
## age circumference
## <dbl> <dbl>
## 1 118 30
## 2 484 58
## 3 664 87
## 4 1004 115
## 5 1231 120
## 6 1372 142
## 7 1582 145
## 8 118 33
## 9 484 69
## 10 664 111
## # ℹ 25 more rows##Ngambil data yang bukan Tree dan bukan age
Orange %>% select(-Tree,-age)## # A tibble: 35 × 1
## circumference
## <dbl>
## 1 30
## 2 58
## 3 87
## 4 115
## 5 120
## 6 142
## 7 145
## 8 33
## 9 69
## 10 111
## # ℹ 25 more rows#Mutate = menambah peubah baru
##menghitung rasio circumference dan age
Orange %>% mutate(Ratio=Orange$age / Orange$circumference)## # A tibble: 35 × 4
## Tree age circumference Ratio
## <ord> <dbl> <dbl> <dbl>
## 1 1 118 30 3.93
## 2 1 484 58 8.34
## 3 1 664 87 7.63
## 4 1 1004 115 8.73
## 5 1 1231 120 10.3
## 6 1 1372 142 9.66
## 7 1 1582 145 10.9
## 8 2 118 33 3.58
## 9 2 484 69 7.01
## 10 2 664 111 5.98
## # ℹ 25 more rows##menghilangkan peubah Tree, lalu memasukkan peubah ratio
Orangebaru <- Orange %>% select(-Tree) %>% mutate(Ratio=Orange$age / Orange$circumference)
Orangebaru## # A tibble: 35 × 3
## age circumference Ratio
## <dbl> <dbl> <dbl>
## 1 118 30 3.93
## 2 484 58 8.34
## 3 664 87 7.63
## 4 1004 115 8.73
## 5 1231 120 10.3
## 6 1372 142 9.66
## 7 1582 145 10.9
## 8 118 33 3.58
## 9 484 69 7.01
## 10 664 111 5.98
## # ℹ 25 more rows