dplyr中的across函数取代了之前的xx_if/xx_at/xx_all，用法更加灵活，初学时觉得不如xx_if/xx_at/xx_all简单易懂，用习惯后真是利器！
主要是介绍across函数的用法，这是dplyr1.0才出来的一个函数，大大简化了代码
可用于对多列做同一个操作。
- 一般用法
- 陷阱
- across其他连用
- 和filter()连用
一般用法
代码语言：javascript复制library(dplyr, warn.conflicts = FALSE)
across()有两个基本参数：
- .cols：选择你想操作的列
- .fn：你想进行的操作，可以使一个函数或者多个函数组成的列表
可以替代_if()，at_()，all_()
代码语言：javascript复制starwars %>%
summarise(across(where(is.character), n_distinct))
## # A tibble: 1 × 8
## 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
可以直接写列名：
代码语言：javascript复制starwars %>%
group_by(species) %>%
filter(n() > 1) %>%
summarise(across(c(sex, gender, homeworld), n_distinct))
## # A tibble: 9 × 4
## species sex gender homeworld
## <chr> <int> <int> <int>
## 1 Droid 1 2 3
## 2 Gungan 1 1 1
## 3 Human 2 2 16
## 4 Kaminoan 2 2 1
## 5 Mirialan 1 1 1
## 6 Twi'lek 2 2 1
## 7 Wookiee 1 1 1
## 8 Zabrak 1 1 2
## 9 <NA> 1 1 3
也可以和where函数连用，省时省力：
starwars %>%
group_by(homeworld) %>%
filter(n() > 1) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
## # A tibble: 10 × 4
## homeworld height mass birth_year
## <chr> <dbl> <dbl> <dbl>
## 1 Alderaan 176. 64 43
## 2 Corellia 175 78.5 25
## 3 Coruscant 174. 50 91
## 4 Kamino 208. 83.1 31.5
## 5 Kashyyyk 231 124 200
## 6 Mirial 168 53.1 49
## 7 Naboo 175. 64.2 55
## 8 Ryloth 179 55 48
## 9 Tatooine 170. 85.4 54.6
## 10 <NA> 139. 82 334.
如果没有缺失值，可以直接写mean，
代码语言：javascript复制library(tidyr)
starwars %>% drop_na() %>%
group_by(homeworld) %>%
filter(n() > 1) %>%
summarise(across(where(is.numeric), mean))
## # A tibble: 4 × 4
## homeworld height mass birth_year
## <chr> <dbl> <dbl> <dbl>
## 1 Corellia 175 78.5 25
## 2 Mirial 168 53.1 49
## 3 Naboo 177 62 60
## 4 Tatooine 181. 96 37.6
acorss支持多个函数同时使用，只要放入列表中即可：
min_max <- list(
min = ~min(.x, na.rm = TRUE),
max = ~max(.x, na.rm = TRUE)
)
starwars %>% summarise(across(where(is.numeric), min_max))
## # A tibble: 1 × 6
## height_min height_max mass_min mass_max birth_year_min birth_year_max
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 66 264 15 1358 8 896
starwars %>% summarise(across(c(height, mass, birth_year), min_max))
## # A tibble: 1 × 6
## height_min height_max mass_min mass_max birth_year_min birth_year_max
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 66 264 15 1358 8 896
当然也是支持glue的：
starwars %>% summarise(across(where(is.numeric), min_max, .names = "{.fn}.{.col}"))
## # A tibble: 1 × 6
## min.height max.height min.mass max.mass min.birth_year max.birth_year
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 66 264 15 1358 8 896
starwars %>% summarise(across(c(height, mass, birth_year), min_max, .names = "{.fn}.{.col}"))
## # A tibble: 1 × 6
## min.height max.height min.mass max.mass min.birth_year max.birth_year
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 66 264 15 1358 8 896
分开写也是可以的：
代码语言：javascript复制starwars %>% summarise(
across(c(height, mass, birth_year), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
across(c(height, mass, birth_year), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
)
## # A tibble: 1 × 6
## min_height min_mass min_birth_year max_height max_mass max_birth_year
## <int> <dbl> <dbl> <int> <dbl> <dbl>
## 1 66 15 8 264 1358 896
这种情况不能使用where(is.numeric)，因为第2个across会使用新创建的列（“min_height”, “min_mass” and “min_birth_year”）。
可以放在tibble里解决：
代码语言：javascript复制starwars %>% summarise(
tibble(
across(where(is.numeric), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
across(where(is.numeric), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
)
)
## # A tibble: 1 × 6
## min_height min_mass min_birth_year max_height max_mass max_birth_year
## <int> <dbl> <dbl> <int> <dbl> <dbl>
## 1 66 15 8 264 1358 896
陷阱
在使用where(is.numeric)，要注意下面这种情况：
df <- data.frame(x = c(1, 2, 3), y = c(1, 4, 9))
df %>%
summarise(n = n(), across(where(is.numeric), sd))
## n x y
## 1 NA 1 4.041452
n这里是3，是一个常数，所以它的sd变成了NA，可以通过换一下顺序解决：
代码语言：javascript复制df %>% summarise(across(where(is.numeric), sd),
n = n()
)
## x y n
## 1 1 4.041452 3
或者通过下面两种方法解决：
代码语言：javascript复制df %>%
summarise(n = n(), across(where(is.numeric) & !n, sd))
## n x y
## 1 3 1 4.041452
df %>%
summarise(
tibble(n = n(), across(where(is.numeric), sd))
)
## n x y
## 1 3 1 4.041452
across其他连用
还可以和group_by()/count()/distinct()连用。
和filter()连用
across()不能直接和filter()连用，和filter()连用的是if_any()和if_all()。
- if_any()：任何一列满足条件即可
- if_all()：所有列都要满足条件
starwars %>%
filter(if_any(everything(), ~ !is.na(.x)))
## # A tibble: 87 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
## 4 Darth V… 202 136 none white yellow 41.9 male mascu…
## 5 Leia Or… 150 49 brown light brown 19 fema… femin…
## 6 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 7 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 8 R5-D4 97 32 <NA> white, red red NA none mascu…
## 9 Biggs D… 183 84 black light brown 24 male mascu…
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>
starwars %>%
filter(if_all(everything(), ~ !is.na(.x)))
## # A tibble: 29 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 Darth V… 202 136 none white yellow 41.9 male mascu…
## 3 Leia Or… 150 49 brown light brown 19 fema… femin…
## 4 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 5 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 6 Biggs D… 183 84 black light brown 24 male mascu…
## 7 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## 8 Anakin … 188 84 blond fair blue 41.9 male mascu…
## 9 Chewbac… 228 112 brown unknown blue 200 male mascu…
## 10 Han Solo 180 80 brown fair brown 29 male mascu…
## # … with 19 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>
