前面主要介紹了dplyr 中的三大函數(shù)select filter mutate,這一節(jié)來(lái)介紹dplyr tidyr中執(zhí)行一些特定功能的函數(shù)

• arrange 對(duì)列進(jìn)行排序
• desc 降序排列
• distinct 去重復(fù)行
• raname 更改列名
• relocate 更改列的順序
• drop_na 刪除含有缺失值的行
• pull 提取單列

arrange() 通過(guò)選定的列進(jìn)行排序，默認(rèn)為升序

arrange(mtcars,mpg) %>% as_tibble()

     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  10.4     8  472    205  2.93  5.25  18.0     0     0     3     4
 2  10.4     8  460    215  3     5.42  17.8     0     0     3     4
 3  13.3     8  350    245  3.73  3.84  15.4     0     0     3     4

arrange結(jié)合desc對(duì)數(shù)據(jù)進(jìn)行降序

arrange(mtcars,desc(mpg)) %>% as_tibble()

    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
 2  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
 3  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2

distinct 針對(duì)數(shù)據(jù)框進(jìn)行去重，unique針對(duì)向量進(jìn)行去重

df <- tibble(
  x = sample(10, 100, rep = TRUE),
  y = sample(10, 100, rep = TRUE))

df %>% distinct()

rename 更改列名

rename(iris,petal_length=Petal.Length) %>% as_tibble()

   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 

reclocate 更改列順序

iris %>% as_tibble() %>% relocate(Species)

#下述方法也可以實(shí)現(xiàn)，但是較為麻煩
iris %>% as_tibble() %>% select(Species,everything()) 

   Species Sepal.Length Sepal.Width Petal.Length Petal.Width
   <fct>          <dbl>       <dbl>        <dbl>       <dbl>
 1 setosa           5.1         3.5          1.4         0.2
 2 setosa           4.9         3            1.4         0.2
 3 setosa           4.7         3.2          1.3         0.2

df <- tibble(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")

      a     b     c d     e     f    
  <dbl> <dbl> <dbl> <chr> <chr> <chr>
1     1     1     1 a     a     a   

df %>% relocate(a, .after = c) # 指定列的順序
df %>% relocate(f, .before = b)
df %>% relocate(a, .after = last_col()) # 移至最后一列
df %>% relocate(ff = f) #更改列名
df %>% relocate(where(is.character)) # 選擇所有字符列
df %>% relocate(where(is.numeric), .after = last_col())

drop_na 刪除含有缺失值的行

df <- tibble(x = c(1, 2, NA), y = c("a", NA, "b"))
df

      x y    
  <dbl> <chr>
1     1 a    
2     2 NA   
3    NA b 

df %>% drop_na()

> df %>% drop_na()
# A tibble: 1 x 2
      x y    
  <dbl> <chr>
1     1 a    

df %>% drop_na(x)

      x y    
  <dbl> <chr>
1     1 a    
2     2 NA 

pull 提取單列

pull( )與$相似，在管道中使用pull更加優(yōu)雅

iris %>% as_tibble() %>% 
  mutate(mean = rowMeans(across(where(is.numeric)))) %>% 
  pull(mean)

不使用pull函數(shù)稱(chēng)為點(diǎn)過(guò)濾

iris %>% as_tibble() %>% 
  mutate(mean = rowMeans(across(where(is.numeric)))) %>%
  .$mean

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