使用 dplyr 重新编码多列 [英] Recode multiple columns using dplyr

问题描述

我有一个数据框，我在其中重新编码了几列，以便将 999 设置为 NA

I had a dataframe where I recoded several columns so that 999 was set to NA

dfB <-dfA %>%
  mutate(adhere = if_else(adhere==999, as.numeric(NA), adhere)) %>%
  mutate(engage = if_else(engage==999, as.numeric(NA), engage)) %>%
  mutate(quality = if_else(quality==999, as.numeric(NA), quality)) %>%
  mutate(undrstnd = if_else(undrstnd==999, as.numeric(NA), undrstnd)) %>%
  mutate(sesspart = if_else(sesspart==999, as.numeric(NA), sesspart)) %>%
  mutate(attended = if_else(attended>=9, as.integer(NA), attended))

我想使用 mutate_at() 和一系列列和 recode() 而不是 if_else()，但我被困在如何给它条件上.我认为类似 999 = NA 之类的东西基于一些 mutate_all 示例——但我还需要 NA 来匹配 .x 的类型，我不确定如何让它成为类型敏感的

I want to use mutate_at() and a range of columns and recode() instead of if_else(), but I am stuck on how to give it the condition. I think something like 999 = NA based on some mutate_all examples -- but I also need the NA to match the type of .x and I am unsure how to get it to be type sensitive

我试过了:

y <- data.frame(y1=c(1,2,999,3,4), y2=c(1L, 2L, 999L, 3L, 4L), y3=c(T,T,F,F,T))
z <- y %>%
    mutate_at( vars(y1:y2), funs(recode(.,`999` = as.numeric(NA))))

但我收到警告未替换的值被视为 NA 作为 .x 不兼容.请彻底指定替换或提供 .default"，我可以看到它是针对数字列的，但不是针对整数列 y2 的

But I get a warning "Unreplaced values treated as NA as .x is not compatible. Please specify replacements exhaustively or supply .default " and I can see that it worded for the numeric column, but not for the integer column y2"

> z
  y1 y2    y3
1  1 NA  TRUE
2  2 NA  TRUE
3 NA NA FALSE
4  3 NA FALSE
5  4 NA  TRUE

推荐答案

目前，基于 dplyr文档:

across() 取代了范围变体"系列；如 summarise_at()、summarise_if() 和 summarise_all().

across() supersedes the family of "scoped variants" like summarise_at(), summarise_if(), and summarise_all().

因此，现在建议改用 mutate 和 across.

So, using mutate and across instead is now recommended.

喜欢 Chris LeBoa 说，如果你只想把一个烦人的值转换成NA，函数na_if()可能是最好的选择:

Like Chris LeBoa said, if you only want to convert an annoying value to NA, the function na_if() is probably the best choice:

y <- data.frame(y1=c(1,2,999,3,4), y2=c(1L, 2L, 999L, 3L, 4L), y3=c(T,T,F,F,T))

y
   y1  y2    y3
1   1   1  TRUE
2   2   2  TRUE
3 999 999 FALSE
4   3   3 FALSE
5   4   4  TRUE
 
z <- y %>%
    mutate(across(
        y1:y2,
        ~na_if(., 999)
    ))

z
  y1 y2    y3
1  1  1  TRUE
2  2  2  TRUE
3 NA NA FALSE
4  3  3 FALSE
5  4  4  TRUE

同样，如果你真的想recode多列中的值，你可以按照bcarothers 中的cross-not-working-in-a-function">示例:

Similarly, if you really want to recode values in multiple columns, you can follow the example from bcarothers:

df1 <- tibble(Q7_1=1:5,
              Q7_1_TEXT=c("let's","see","grogu","this","week"),
              Q8_1=6:10,
              Q8_1_TEXT=rep("grogu",5),
              Q8_2=11:15,
              Q8_2_TEXT=c("grogu","is","the","absolute","best"))

df2 <- df1 %>%
    mutate(across(
        starts_with("Q8") & ends_with("TEXT"),
        ~recode(., "grogu"="mando")
    ))

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