library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.5## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.3 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.0.5## Warning: package 'tibble' was built under R version 4.0.5## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'readr' was built under R version 4.0.5## Warning: package 'dplyr' was built under R version 4.0.5## Warning: package 'stringr' was built under R version 4.0.5## Warning: package 'forcats' was built under R version 4.0.5## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Melihat isi package
??tidyverse## starting httpd help server ... doneDataset yang akan digunakan adalah ‘HairEyeColor’ yaitu data warna rambut dan mata dari 592 siswa statistika.
Observasi ini mempunyai 3 variabel beserta levelnya: Hair (Black,Brown,Red,Blond) Eye(Brown,Blue,Hazel,Green) Sex(Male,Female)
library(datasets)
data("HairEyeColor")
HairEyeColor<-tibble::as_tibble(HairEyeColor)Memunculkan 6 baris teratas
head(HairEyeColor)## # A tibble: 6 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Black Brown Male 32
## 2 Brown Brown Male 53
## 3 Red Brown Male 10
## 4 Blond Brown Male 3
## 5 Black Blue Male 11
## 6 Brown Blue Male 50Menghitung rata-rata jumlah siswa menurut gender
HairEyeColor %>% group_by(Sex) %>% summarise(mean=mean(n), .groups='drop')## # A tibble: 2 x 2
## Sex mean
## <chr> <dbl>
## 1 Female 19.6
## 2 Male 17.4Menghitung rata-rata jumlah siswa menurut warna mata
HairEyeColor %>% group_by(Eye) %>% summarise(mean=mean(n), .groups='drop')## # A tibble: 4 x 2
## Eye mean
## <chr> <dbl>
## 1 Blue 26.9
## 2 Brown 27.5
## 3 Green 8
## 4 Hazel 11.6Menghitung rata-rata jumlah siswa menurut warna rambut
HairEyeColor %>% group_by(Hair) %>% summarise(mean=mean(n), .groups='drop')## # A tibble: 4 x 2
## Hair mean
## <chr> <dbl>
## 1 Black 13.5
## 2 Blond 15.9
## 3 Brown 35.8
## 4 Red 8.88Mengurutkan berdasarkan banyaknya siswa dari nilai terkecil
HairEyeColor %>% arrange(n)## # A tibble: 32 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Black Green Female 2
## 2 Blond Brown Male 3
## 3 Black Green Male 3
## 4 Blond Brown Female 4
## 5 Blond Hazel Male 5
## 6 Black Hazel Female 5
## 7 Blond Hazel Female 5
## 8 Red Hazel Male 7
## 9 Red Green Male 7
## 10 Red Blue Female 7
## # ... with 22 more rowsMengurutkan berdasarkan banyaknya siswa dari nilai terbesar
HairEyeColor %>% arrange(desc(n))## # A tibble: 32 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Brown Brown Female 66
## 2 Blond Blue Female 64
## 3 Brown Brown Male 53
## 4 Brown Blue Male 50
## 5 Black Brown Female 36
## 6 Brown Blue Female 34
## 7 Black Brown Male 32
## 8 Blond Blue Male 30
## 9 Brown Hazel Female 29
## 10 Brown Hazel Male 25
## # ... with 22 more rowsMelakukan filter pada data HairEyeColor sehingga diperoleh hanya data untuk Perempuan
HairEyeColor %>% filter(Sex=="Female")## # A tibble: 16 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Black Brown Female 36
## 2 Brown Brown Female 66
## 3 Red Brown Female 16
## 4 Blond Brown Female 4
## 5 Black Blue Female 9
## 6 Brown Blue Female 34
## 7 Red Blue Female 7
## 8 Blond Blue Female 64
## 9 Black Hazel Female 5
## 10 Brown Hazel Female 29
## 11 Red Hazel Female 7
## 12 Blond Hazel Female 5
## 13 Black Green Female 2
## 14 Brown Green Female 14
## 15 Red Green Female 7
## 16 Blond Green Female 8Melakukan filter pada data HairEyeColor sehingga diperoleh hanya data untuk Perempuan dengan mata biru
HairEyeColor %>% filter(Sex=="Female" & Eye=="Blue")## # A tibble: 4 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Black Blue Female 9
## 2 Brown Blue Female 34
## 3 Red Blue Female 7
## 4 Blond Blue Female 64Melakukan filter pada data HairEyeColor sehingga diperoleh hanya data untuk siswa rambut hitam dengan mata biru
HairEyeColor %>% filter(Hair=="Black" & Eye=="Blue")## # A tibble: 2 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Black Blue Male 11
## 2 Black Blue Female 9Memilih subset data
HairEyeColor %>% select(Sex,Eye,n)## # A tibble: 32 x 3
## Sex Eye n
## <chr> <chr> <dbl>
## 1 Male Brown 32
## 2 Male Brown 53
## 3 Male Brown 10
## 4 Male Brown 3
## 5 Male Blue 11
## 6 Male Blue 50
## 7 Male Blue 10
## 8 Male Blue 30
## 9 Male Hazel 10
## 10 Male Hazel 25
## # ... with 22 more rowsMencari proporsi tiap kriteria
a<-HairEyeColor %>% mutate(p=n/sum(n))
sum(a$p)## [1] 1Data Exploration
Mengetahui ukuran data
dim(HairEyeColor)## [1] 32 4Pratinjau data
glimpse(HairEyeColor)## Rows: 32
## Columns: 4
## $ Hair <chr> "Black", "Brown", "Red", "Blond", "Black", "Brown", "Red", "Blond~
## $ Eye <chr> "Brown", "Brown", "Brown", "Brown", "Blue", "Blue", "Blue", "Blue~
## $ Sex <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", "~
## $ n <dbl> 32, 53, 10, 3, 11, 50, 10, 30, 10, 25, 7, 5, 3, 15, 7, 8, 36, 66,~Filtering data dengan hanya memilih data siswa perempuan
color<-HairEyeColor %>% filter(Sex=="Female")
nrow(color)## [1] 16head(color)## # A tibble: 6 x 4
## Hair Eye Sex n
## <chr> <chr> <chr> <dbl>
## 1 Black Brown Female 36
## 2 Brown Brown Female 66
## 3 Red Brown Female 16
## 4 Blond Brown Female 4
## 5 Black Blue Female 9
## 6 Brown Blue Female 34Filtering data dengan hanya memilih data laki-laki yang jumlah orang tiap karakteristik antara 0 dan 10
color<-HairEyeColor %>%
filter(Sex=="Male" & n %in% 0:10)%>%
select(Hair, Eye, n)
color## # A tibble: 9 x 3
## Hair Eye n
## <chr> <chr> <dbl>
## 1 Red Brown 10
## 2 Blond Brown 3
## 3 Red Blue 10
## 4 Black Hazel 10
## 5 Red Hazel 7
## 6 Blond Hazel 5
## 7 Black Green 3
## 8 Red Green 7
## 9 Blond Green 8Mengganti nama tabel
color %>% rename( banyak = n) ## # A tibble: 9 x 3
## Hair Eye banyak
## <chr> <chr> <dbl>
## 1 Red Brown 10
## 2 Blond Brown 3
## 3 Red Blue 10
## 4 Black Hazel 10
## 5 Red Hazel 7
## 6 Blond Hazel 5
## 7 Black Green 3
## 8 Red Green 7
## 9 Blond Green 8