Tugas Mandarel UAS

library(datasets)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data(HairEyeColor)

HairEyeColor <- tibble::as_tibble(HairEyeColor)
View(HairEyeColor)

class(HairEyeColor)

## [1] "tbl_df"     "tbl"        "data.frame"

head(HairEyeColor)

## # A tibble: 6 × 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     50

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,…

mean(HairEyeColor$n)

## [1] 18.5

mean(HairEyeColor$n) == HairEyeColor$n%>% mean()

## [1] TRUE

#Menghitung Rata-rata n
HairEyeColor %>% group_by(Sex) %>% summarize(mean=mean(n))

## # A tibble: 2 × 2
##   Sex     mean
##   <chr>  <dbl>
## 1 Female  19.6
## 2 Male    17.4

#Mengurutkan
HairEyeColor %>% arrange(n)

## # A tibble: 32 × 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
## # ℹ 22 more rows

#Filter
HairEyeColor%>% filter(Eye=="Green")

## # A tibble: 8 × 4
##   Hair  Eye   Sex        n
##   <chr> <chr> <chr>  <dbl>
## 1 Black Green Male       3
## 2 Brown Green Male      15
## 3 Red   Green Male       7
## 4 Blond Green Male       8
## 5 Black Green Female     2
## 6 Brown Green Female    14
## 7 Red   Green Female     7
## 8 Blond Green Female     8

HairEyeColor %>% select(Hair,Sex)

## # A tibble: 32 × 2
##    Hair  Sex  
##    <chr> <chr>
##  1 Black Male 
##  2 Brown Male 
##  3 Red   Male 
##  4 Blond Male 
##  5 Black Male 
##  6 Brown Male 
##  7 Red   Male 
##  8 Blond Male 
##  9 Black Male 
## 10 Brown Male 
## # ℹ 22 more rows

HairEyeColor %>% select(-n)

## # A tibble: 32 × 3
##    Hair  Eye   Sex  
##    <chr> <chr> <chr>
##  1 Black Brown Male 
##  2 Brown Brown Male 
##  3 Red   Brown Male 
##  4 Blond Brown Male 
##  5 Black Blue  Male 
##  6 Brown Blue  Male 
##  7 Red   Blue  Male 
##  8 Blond Blue  Male 
##  9 Black Hazel Male 
## 10 Brown Hazel Male 
## # ℹ 22 more rows

#Mutate
HairEyeColor %>% mutate(Statistikawan=n)

## # A tibble: 32 × 5
##    Hair  Eye   Sex       n Statistikawan
##    <chr> <chr> <chr> <dbl>         <dbl>
##  1 Black Brown Male     32            32
##  2 Brown Brown Male     53            53
##  3 Red   Brown Male     10            10
##  4 Blond Brown Male      3             3
##  5 Black Blue  Male     11            11
##  6 Brown Blue  Male     50            50
##  7 Red   Blue  Male     10            10
##  8 Blond Blue  Male     30            30
##  9 Black Hazel Male     10            10
## 10 Brown Hazel Male     25            25
## # ℹ 22 more rows

NewLook<- HairEyeColor %>% select(-Sex) %>% mutate(Statistikawan=n)
NewLook

## # A tibble: 32 × 4
##    Hair  Eye       n Statistikawan
##    <chr> <chr> <dbl>         <dbl>
##  1 Black Brown    32            32
##  2 Brown Brown    53            53
##  3 Red   Brown    10            10
##  4 Blond Brown     3             3
##  5 Black Blue     11            11
##  6 Brown Blue     50            50
##  7 Red   Blue     10            10
##  8 Blond Blue     30            30
##  9 Black Hazel    10            10
## 10 Brown Hazel    25            25
## # ℹ 22 more rows