跨越 NA 值

Question

我尝试使用 across 计算某些列的平均值，但出现问题为我使用的平均值的每一列制作新的不同列。是否正常工作？

    library(tidyverse)
    cars %>% 
as_tibble() %>% 
add_case(speed = 11, dist = NA, .before = 1) %>% 
add_column(names = str_c("a",1:51)) %>% 
rename_with(.cols =  -names, ~str_c("one_",.x)) %>% 
group_by(names) %>% 
mutate(two = across(starts_with("one"), .fns = mean))

在小插图中显示了这个例子：

df %>% mutate_at(vars(c(x, starts_with("y"))), mean)
# ->
df %>% mutate(across(c(x, starts_with("y")), mean, na.rm = TRUE))

我希望在每种情况下使用 NA 它都会产生 NA 而不是另一列。

Answer 1

如果要按行 mean 两列，不一定会在此处看到 across 的用法。

library(dplyr)
cars %>% 
  as_tibble() %>% 
  add_case(speed = 11, dist = NA, .before = 1) %>% 
  add_column(names = str_c("a",1:51)) %>% 
  rename_with(.cols =  -names, ~str_c("one_",.x)) %>% 
  mutate(two = rowMeans(select(., starts_with('one')), na.rm = TRUE))

您可以将 rowwise 与 c_across 一起使用，但那样效率会降低 rowMeans。

cars %>% 
  as_tibble() %>% 
  add_case(speed = 11, dist = NA, .before = 1) %>% 
  add_column(names = str_c("a",1:51)) %>% 
  rename_with(.cols =  -names, ~str_c("one_",.x)) %>% 
  rowwise() %>%
  mutate(two = mean(c_across(starts_with('one')), na.rm = TRUE))

跨越 NA 值

Across with NA values

r

tidyverse

across