本節(jié)繼續(xù)來介紹tidyverse中的重要函數(shù)across，需要更新至 dplyr-1-0-0

across()它可以輕松地對多列執(zhí)行相同的操作

across() 有兩個主要參數(shù)：

• 第一個參數(shù).cols選擇要操作的列
• 第二個參數(shù).fns是要應用于每一列的一個函數(shù)或函數(shù)列表

創(chuàng)建數(shù)據

gdf <- tibble(g = c(1,1,2,3),v1 = 10:13,v2 = 20:23,v3=1:4)
gdf

      g    v1    v2    v3
  <dbl> <int> <int> <int>
1     1    10    20     1
2     1    11    21     2
3     2    12    22     3
4     3    13    23     4

給每一列加1

gdf %>% mutate(across(v1:v3, ~ .x +1))

      g    v1    v2    v3
  <dbl> <dbl> <dbl> <dbl>
1     1    11    21     2
2     1    12    22     3
3     2    13    23     4
4     3    14    24     5

前兩列四舍五入

iris %>%
  as_tibble() %>%
  mutate(across(c(1,2),round))

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4          1.4         0.2 setosa 
 2            5           3          1.4         0.2 setosa 
 3            5           3          1.3         0.2 setosa 

還有如下2種寫法

iris %>%
  as_tibble() %>%
  mutate(across(1:Sepal.Width, round))

iris %>%
  as_tibble() %>%
  mutate(across(where(is.double) & !c(Petal.Length, Petal.Width), round))

按列求均值

iris %>% summarize(across(is.numeric,mean))

按行求和

iris %>% as_tibble() %>% 
  rowwise() %>%
  mutate(mean = sum(across(where(is.numeric))))

分組求均值

iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), ~ mean(.x, na.rm = TRUE)))

  Species    Sepal.Length Sepal.Width
  <fct>             <dbl>       <dbl>
1 setosa             5.01        3.43
2 versicolor         5.94        2.77
3 virginica          6.59        2.97

分組求和

iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), ~ sum(.x, na.rm=TRUE)))

  Species    Sepal.Length Sepal.Width
  <fct>             <dbl>       <dbl>
1 setosa             250.        171.
2 versicolor         297.        138.
3 virginica          329.        149.

iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))

  Species    Sepal.Length_mean Sepal.Length_sd Sepal.Width_mean Sepal.Width_sd
  <fct>                  <dbl>           <dbl>            <dbl>          <dbl>
1 setosa                  5.01           0.352             3.43          0.379
2 versicolor              5.94           0.516             2.77          0.314
3 virginica               6.59           0.636             2.97          0.322

使用.name參數(shù)控制輸出名

iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean, .names = "mean_{.col}"))

  Species    mean_Sepal.Length mean_Sepal.Width
  <fct>                  <dbl>            <dbl>
1 setosa                  5.01             3.43
2 versicolor              5.94             2.77
3 virginica               6.59             2.97

篩選沒有缺失值的行

starwars %>% filter(across(everything(), ~ !is.na(.x)))

使用時mutate()，所有轉換across()都將立即應用

df <- tibble(x = 2, y = 4, z = 8)
df %>% mutate(across(everything(), ~ .x / y))

      x     y     z
  <dbl> <dbl> <dbl>
1   0.5     1     2

統(tǒng)計字符長度

starwars %>% 
  summarise(across(where(is.character), ~ length(unique(.x))))

   name hair_color skin_color eye_color   sex gender homeworld species
  <int>      <int>      <int>     <int> <int>  <int>     <int>   <int>
1    87         13         31        15     5      3        49      38

統(tǒng)計列最小/最大值

min_max <- list(
  min = ~min(.x, na.rm = TRUE), 
  max = ~max(.x, na.rm = TRUE)
)
iris %>% summarise(across(where(is.numeric),min_max))

  Sepal.Length_min Sepal.Length_max Sepal.Width_min Sepal.Width_max Petal.Length_min Petal.Length_max Petal.Width_min Petal.Width_max
1              4.3              7.9               2             4.4                1              6.9             0.1             2.5

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