2.7 行操作

行操作指不同字段间的计算，如Excle的列与列之间计算,Excle中的函数对行列不敏感，没有明显区别，但是R中tidyverse里列计算简单，行间计算依赖rowwise()函数实现。

Excel-sum

df <- tibble(x = 1:2, y = 3:4, z = 5:6)
df %>% rowwise()
#> # A tibble: 2 x 3
#> # Rowwise: 
#>       x     y     z
#>   <int> <int> <int>
#> 1     1     3     5
#> 2     2     4     6
df %>% rowwise() %>% mutate(total = sum(c(x, y, z))) #返回结果与Excel一致
#> # A tibble: 2 x 4
#> # Rowwise: 
#>       x     y     z total
#>   <int> <int> <int> <int>
#> 1     1     3     5     9
#> 2     2     4     6    12

df %>% mutate(total = sum(c(x, y, z))) # 返回结果不符合预期
#> # A tibble: 2 x 4
#>       x     y     z total
#>   <int> <int> <int> <int>
#> 1     1     3     5    21
#> 2     2     4     6    21

通过自己定义函数实现：

fun1 <- function(x,y,z){
  x+y+z
}

df %>% mutate(total = fun1(x,y,z))
#> # A tibble: 2 x 4
#>       x     y     z total
#>   <int> <int> <int> <int>
#> 1     1     3     5     9
#> 2     2     4     6    12

2.7.1 比较差异

像group_by(),rowwise()并没有做任何事情，它的作用是改变其他动词的工作方式。

注意以下代码返回结果不同:

df %>% mutate(m = mean(c(x, y, z)))
#> # A tibble: 2 x 4
#>       x     y     z     m
#>   <int> <int> <int> <dbl>
#> 1     1     3     5   3.5
#> 2     2     4     6   3.5
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
#> # A tibble: 2 x 4
#> # Rowwise: 
#>       x     y     z     m
#>   <int> <int> <int> <dbl>
#> 1     1     3     5     3
#> 2     2     4     6     4

df %>% mutate(m = mean(c(x, y, z)))返回的结果是x,y,z散列全部数据的均值；df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))通过rowwise改变了mean的作为范围，返回的某行x,y,z列3个数字的均值。两种动词的作用的范围因为rowwise完全改变。

可以选择在调用中提供“标识符”变量rowwise()。这些变量在您调用时被保留summarise()，因此它们的行为与传递给的分组变量有些相似group_by()：

df <- tibble(name = c("Mara", "Hadley"), x = 1:2, y = 3:4, z = 5:6)

df %>% 
  rowwise() %>% 
  summarise(m = mean(c(x, y, z)))
#> `summarise()` has ungrouped output. You can override using the `.groups` argument.
#> # A tibble: 2 x 1
#>       m
#>   <dbl>
#> 1     3
#> 2     4

df %>% 
  rowwise(name) %>% 
  summarise(m = mean(c(x, y, z)))
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.
#> # A tibble: 2 x 2
#> # Groups:   name [2]
#>   name       m
#>   <chr>  <dbl>
#> 1 Mara       3
#> 2 Hadley     4

2.7.2 常用案例

df <- tibble(x = runif(6), y = runif(6), z = runif(6))
# Compute the mean of x, y, z in each row
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
#> # A tibble: 6 x 4
#> # Rowwise: 
#>       x     y      z     m
#>   <dbl> <dbl>  <dbl> <dbl>
#> 1 0.735 0.500 0.234  0.489
#> 2 0.104 0.423 0.706  0.411
#> 3 0.347 0.355 0.559  0.420
#> 4 0.600 0.461 0.0523 0.371
#> 5 0.826 0.114 0.481  0.474
#> 6 0.982 0.823 0.831  0.879


# Compute the minimum of x and y in each row
df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
#> # A tibble: 6 x 4
#> # Rowwise: 
#>       x     y      z      m
#>   <dbl> <dbl>  <dbl>  <dbl>
#> 1 0.735 0.500 0.234  0.234 
#> 2 0.104 0.423 0.706  0.104 
#> 3 0.347 0.355 0.559  0.347 
#> 4 0.600 0.461 0.0523 0.0523
#> 5 0.826 0.114 0.481  0.114 
#> 6 0.982 0.823 0.831  0.823
# In this case you can use an existing vectorised function:
df %>% mutate(m = pmin(x, y, z))
#> # A tibble: 6 x 4
#>       x     y      z      m
#>   <dbl> <dbl>  <dbl>  <dbl>
#> 1 0.735 0.500 0.234  0.234 
#> 2 0.104 0.423 0.706  0.104 
#> 3 0.347 0.355 0.559  0.347 
#> 4 0.600 0.461 0.0523 0.0523
#> 5 0.826 0.114 0.481  0.114 
#> 6 0.982 0.823 0.831  0.823

键入每个变量名称很繁琐，通过c_across()使更简单。

df <- tibble(id = 1:6, w = 10:15, x = 20:25, y = 30:35, z = 40:45)
rf <- df %>% rowwise(id)

rf %>% mutate(total = sum(c_across(w:z)))
rf %>% mutate(total = sum(c_across(where(is.numeric))))

rf %>% 
  mutate(total = sum(c_across(w:z))) %>% 
  ungroup() %>% 
  mutate(across(w:z, ~ . / total))

有关更多信息请查看 vignette(“rowwise”)。