r - 在所有变量中应用 `dplyr::rowwise`

Question

我有一个数据:

df_1 <- data.frame(
  x = replicate(4, runif(30, 20, 100)), 
  y = sample(1:3, 30, replace = TRUE)
)

以下函数工作:

library(tidyverse)

df_1 %>% 
  select(-y) %>% 
  rowwise() %>% 
  mutate(var = sum(c(x.1, x.3)))

但是，以下函数(对于所有变量)不起作用:

与。:

df_1 %>% 
  select(-y) %>% 
  rowwise() %>% 
  mutate(var = sum(.))

使用select_if:

df_1 %>% 
  select(-y) %>% 
  rowwise() %>% 
  mutate(var = sum(select_if(., is.numeric)))

两种方法都返回:

Source: local data frame [30 x 5]
Groups: <by row>

# A tibble: 30 x 5
     x.1   x.2   x.3   x.4   var
   <dbl> <dbl> <dbl> <dbl> <dbl>
 1  32.7  42.7  50.1  20.8 7091.
 2  75.9  71.3  83.6  77.6 7091.
 3  49.6  28.7  97.0  59.7 7091.
 4  47.4  96.1  31.9  79.7 7091.
 5  54.2  47.1  81.7  41.6 7091.
 6  27.9  58.1  97.4  25.9 7091.
 7  61.8  78.3  52.6  67.7 7091.
 8  85.4  51.3  38.8  82.0 7091.
 9  27.9  72.6  68.9  25.2 7091.
10  87.2  42.1  27.6  73.9 7091.
# ... with 20 more rows

7091 是一个不正确的总和。

如何调整这个功能？

Answer 1

这可以使用 purrr::pmap 来完成，它将参数列表传递给接受“点”的函数。由于 mean、sd 等大多数函数都使用矢量，因此您需要将调用与 domain lifter 配对。 :

df_1 %>% select(-y) %>% mutate( var = pmap(., lift_vd(mean)) )
#         x.1      x.2      x.3      x.4      var
# 1  70.12072 62.99024 54.00672 86.81358 68.48282
# 2  49.40462 47.00752 21.99248 78.87789 49.32063

df_1 %>% select(-y) %>% mutate( var = pmap(., lift_vd(sd)) )
#         x.1      x.2      x.3      x.4      var
# 1  70.12072 62.99024 54.00672 86.81358 13.88555
# 2  49.40462 47.00752 21.99248 78.87789 23.27958

sum 函数直接接受点，所以你不需要提升它的域:

df_1 %>% select(-y) %>% mutate( var = pmap(., sum) )
#         x.1      x.2      x.3      x.4      var
# 1  70.12072 62.99024 54.00672 86.81358 273.9313
# 2  49.40462 47.00752 21.99248 78.87789 197.2825

一切都符合标准的 dplyr 数据处理，因此所有这三个都可以组合为 mutate 的单独参数:

df_1 %>% select(-y) %>% 
  mutate( v1 = pmap(., lift_vd(mean)),
          v2 = pmap(., lift_vd(sd)),
          v3 = pmap(., sum) )
#         x.1      x.2      x.3      x.4       v1       v2       v3
# 1  70.12072 62.99024 54.00672 86.81358 68.48282 13.88555 273.9313
# 2  49.40462 47.00752 21.99248 78.87789 49.32063 23.27958 197.2825