r - 如何根据左相邻列中的值替换多列中的值

Question

我有与此类似的数据(尽管数据集更大):

  correct.trial1 RT.trial1 correct.trial2 RT.trial2 correct.trial3 RT.trial3
1              1       473              0       337              1       426
2              1       496              1       407              1       421
3              1       368              0       405              1       470
4              0       333              1       475              0       473
5              0       435              0       312              1       402

我们可以用这个来制作这个示例:

set.seed(12)
df <- data.frame(correct.trial1 = sample(0:1, 5, replace=T),
                 RT.trial1 = sample(300:500, 5, replace=T),
                 correct.trial2 = sample(0:1, 5, replace=T),
                 RT.trial2 = sample(300:500, 5, replace=T),
                 correct.trial3 = sample(0:1, 5, replace=T),
                 RT.trial3 = sample(300:500, 5, replace=T))

当相邻(左侧)列 starts_with("Correct.Trial"时，我想将值 starts_with("RT.Trial") 替换为 NA ) 的值为 0。当然，我可以一次执行一个，例如:

library(dplyr)
df %>%
  mutate(RT.trial1 = ifelse(correct.trial1==1, RT.trial1, NA),
         RT.trial2 = ifelse(correct.trial2==1, RT.trial2, NA),
         RT.trial3 = ifelse(correct.trial3==1, RT.trial3, NA))

所以它看起来像这样:

  correct.trial1 RT.trial1 correct.trial2 RT.trial2 correct.trial3 RT.trial3
1              1       473              0        NA              1       426
2              1       496              1       407              1       421
3              1       368              0        NA              1       470
4              0        NA              1       475              0        NA
5              0        NA              0        NA              1       402

但是对于数千列来说这是不切实际的。

问题

如何同时对所有列执行此操作？ (注意:我更喜欢 dplyr 解决方案，并且使用 across 比使用 mutate_at 更好。)

尝试

不确定，但基于此 related post ，它(也许)看起来像这样:

df %>%
  mutate_at(vars(starts_with("RT.trial")),
  ~ifelse(vars(starts_with("correct.trial"))==0, NA, .x))

Answer 1

我们可以 reshape 为“长”格式，然后进行转换

library(dplyr)
library(tidyr)
df %>% 
    mutate(rn = row_number()) %>% 
    pivot_longer(cols = -rn, names_to = c(".value", "grp"), 
          names_sep="\\.") %>%
    mutate(RT = case_when(as.logical(correct) ~ RT)) %>% 
    pivot_wider(names_from = grp, values_from = c(correct, RT), 
          names_sep = ".") %>%
    select(names(df))

-输出

# A tibble: 5 x 6
#  correct.trial1 RT.trial1 correct.trial2 RT.trial2 correct.trial3 RT.trial3
#           <int>     <int>          <int>     <int>          <int>     <int>
#1              0        NA              0        NA              0        NA
#2              1       394              1       458              0        NA
#3              0        NA              1       337              0        NA
#4              1       479              0        NA              0        NA
#5              0        NA              0        NA              0        NA

---

在base R中，这可以通过更简单的方式完成

i1 <- grepl('correct', names(df))
df[!i1] <- (NA^!df[i1]) * df[!i1]

数据

df <- structure(list(correct.trial1 = c(0L, 1L, 0L, 1L, 0L), RT.trial1 = c(417L, 
394L, 345L, 479L, 368L), correct.trial2 = c(0L, 1L, 1L, 0L, 0L
), RT.trial2 = c(382L, 458L, 337L, 406L, 306L), correct.trial3 = c(0L, 
0L, 0L, 0L, 0L), RT.trial3 = c(469L, 364L, 361L, 359L, 309L)),
 class = "data.frame", row.names = c("1", 
"2", "3", "4", "5"))