Mencari data
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 1.5.0
## ✔ 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 errorslibrary(datasets)
data()
data(Orange)
Orange## Tree age circumference
## 1 1 118 30
## 2 1 484 58
## 3 1 664 87
## 4 1 1004 115
## 5 1 1231 120
## 6 1 1372 142
## 7 1 1582 145
## 8 2 118 33
## 9 2 484 69
## 10 2 664 111
## 11 2 1004 156
## 12 2 1231 172
## 13 2 1372 203
## 14 2 1582 203
## 15 3 118 30
## 16 3 484 51
## 17 3 664 75
## 18 3 1004 108
## 19 3 1231 115
## 20 3 1372 139
## 21 3 1582 140
## 22 4 118 32
## 23 4 484 62
## 24 4 664 112
## 25 4 1004 167
## 26 4 1231 179
## 27 4 1372 209
## 28 4 1582 214
## 29 5 118 30
## 30 5 484 49
## 31 5 664 81
## 32 5 1004 125
## 33 5 1231 142
## 34 5 1372 174
## 35 5 1582 177Orange <- tibble::as.tibble(Orange)## 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.Orange## # A tibble: 35 × 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 142
## 7 1 1582 145
## 8 2 118 33
## 9 2 484 69
## 10 2 664 111
## # ℹ 25 more rowsclass(Orange)## [1] "tbl_df" "tbl" "data.frame"view(Orange)
glimpse(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…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 142mean(Orange$circumference)## [1] 115.8571mean(Orange$circumference) == Orange$circumference%>% mean()## [1] TRUE## Menghitung rata-rata Tree
Orange %>% group_by(Tree) %>% summarize(mean=mean(age))## # A tibble: 5 × 2
## Tree mean
## <ord> <dbl>
## 1 3 922.
## 2 1 922.
## 3 5 922.
## 4 2 922.
## 5 4 922.Orange## # A tibble: 35 × 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 142
## 7 1 1582 145
## 8 2 118 33
## 9 2 484 69
## 10 2 664 111
## # ℹ 25 more rows## Mengurutkan dari nilai terkecil
Orange %>% arrange(age) %>%
print(n=10)## # A tibble: 35 × 3
## Tree age circumference
## <ord> <dbl> <dbl>
## 1 1 118 30
## 2 2 118 33
## 3 3 118 30
## 4 4 118 32
## 5 5 118 30
## 6 1 484 58
## 7 2 484 69
## 8 3 484 51
## 9 4 484 62
## 10 5 484 49
## # ℹ 25 more rows## Mengurutkan dari nilai terbesar
Orange %>% arrange(desc(age))## # A tibble: 35 × 3
## Tree age circumference
## <ord> <dbl> <dbl>
## 1 1 1582 145
## 2 2 1582 203
## 3 3 1582 140
## 4 4 1582 214
## 5 5 1582 177
## 6 1 1372 142
## 7 2 1372 203
## 8 3 1372 139
## 9 4 1372 209
## 10 5 1372 174
## # ℹ 25 more rows#Filter
Orange %>% filter(Tree=="1")## # A tibble: 7 × 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 142
## 7 1 1582 145Orange %>% select(Tree,circumference)## # A tibble: 35 × 2
## Tree circumference
## <ord> <dbl>
## 1 1 30
## 2 1 58
## 3 1 87
## 4 1 115
## 5 1 120
## 6 1 142
## 7 1 145
## 8 2 33
## 9 2 69
## 10 2 111
## # ℹ 25 more rowsOrange %>% select(Tree,age,-circumference)## # A tibble: 35 × 2
## Tree age
## <ord> <dbl>
## 1 1 118
## 2 1 484
## 3 1 664
## 4 1 1004
## 5 1 1231
## 6 1 1372
## 7 1 1582
## 8 2 118
## 9 2 484
## 10 2 664
## # ℹ 25 more rows## Mutate bikin variabel baru
Orange %>% mutate(Total=age+circumference)## # A tibble: 35 × 4
## Tree age circumference Total
## <ord> <dbl> <dbl> <dbl>
## 1 1 118 30 148
## 2 1 484 58 542
## 3 1 664 87 751
## 4 1 1004 115 1119
## 5 1 1231 120 1351
## 6 1 1372 142 1514
## 7 1 1582 145 1727
## 8 2 118 33 151
## 9 2 484 69 553
## 10 2 664 111 775
## # ℹ 25 more rowsNewOrange <- Orange %>% select(Tree,age, circumference) %>% mutate(Total=age+circumference)
NewOrange## # A tibble: 35 × 4
## Tree age circumference Total
## <ord> <dbl> <dbl> <dbl>
## 1 1 118 30 148
## 2 1 484 58 542
## 3 1 664 87 751
## 4 1 1004 115 1119
## 5 1 1231 120 1351
## 6 1 1372 142 1514
## 7 1 1582 145 1727
## 8 2 118 33 151
## 9 2 484 69 553
## 10 2 664 111 775
## # ℹ 25 more rows