Mandarel
Angelika Anggreni Batubara
30/11/2021
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library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.2## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.0 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.2## Warning: package 'tidyr' was built under R version 4.1.2## Warning: package 'readr' was built under R version 4.1.2## Warning: package 'purrr' was built under R version 4.1.2## Warning: package 'stringr' was built under R version 4.1.2## Warning: package 'forcats' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()?tidyverse## starting httpd help server ...## donelibrary(datasets)
data(iris)
iris<-tibble::as_tibble(iris)
class(iris)## [1] "tbl_df" "tbl" "data.frame"View(iris)
glimpse(iris)## 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$Sepal.Length) ## [1] 5.843333mean(iris$Sepal.Length) == iris$Sepal.Length %>% mean()## [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)## [1] 3.3 1.8 1.6 0.5 0.3 0.1 48.8 1.1x %>% log() %>%
diff() %>%
exp() %>%
round(1)## [1] 3.3 1.8 1.6 0.5 0.3 0.1 48.8 1.1#menghitung rata-rata Sepal length setiap species
iris %>% group_by(Species) %>% summarise(mean=mean(Sepal.Length), .groups='drop')## # A tibble: 3 x 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)## # A tibble: 150 x 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
## # ... with 140 more rows#mengurutkan berdasarkan peubah Sepal.Length dari nilai terbesar
iris %>% arrange(desc(Sepal.Length))## # A tibble: 150 x 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
## # ... with 140 more rowsiris %>% filter(Species=="setosa")## # A tibble: 50 x 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
## # ... with 40 more rowsiris %>% select(Species,Petal.Width,Petal.Length)## # A tibble: 150 x 3
## Species Petal.Width Petal.Length
## <fct> <dbl> <dbl>
## 1 setosa 0.2 1.4
## 2 setosa 0.2 1.4
## 3 setosa 0.2 1.3
## 4 setosa 0.2 1.5
## 5 setosa 0.2 1.4
## 6 setosa 0.4 1.7
## 7 setosa 0.3 1.4
## 8 setosa 0.2 1.5
## 9 setosa 0.2 1.4
## 10 setosa 0.1 1.5
## # ... with 140 more rowsiris %>% select(-Species,-Petal.Width)## # A tibble: 150 x 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
## # ... with 140 more rowsiris %>% mutate(sepal=Sepal.Length+Sepal.Width)## # A tibble: 150 x 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
## # ... with 140 more rowsiris %>% select(-Species,-Petal.Width) %>% mutate(sepal=Sepal.Length+Sepal.Width)## # A tibble: 150 x 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
## # ... with 140 more rowsirisbaru <- iris %>% select(-Species,-Petal.Width) %>% mutate(sepal=Sepal.Length+Sepal.Width)
irisbaru## # A tibble: 150 x 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
## # ... with 140 more rowsIncluding Plots
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