[英]Using “across” to change multiple columns in dplyr
I have 100 columns that are all numeric.我有 100 列都是数字的。 The first column is an ID number, which needs to stay numeric, and I need to change the other columns to factors.第一列是一个 ID 号,需要保持数字,我需要将其他列更改为因子。 I have been trying to use the new(ish) across
function from dplyr
to do this, but I cannot successfully apply the function to all columns except the first one.我一直在尝试使用 function 中的新(ish) across
dplyr
执行此操作,但我无法成功地将 function 应用于除第一列之外的所有列。 From what I've read, I should be able to do the following:根据我的阅读,我应该能够做到以下几点:
df %>% mutate(across(everything(!c(ID)), as.factor))
But this gives the error: "Can't subset columns that don't exist. Locations 101, 102, etc. do not exist."但这会产生错误:“不能对不存在的列进行子集化。位置 101、102 等不存在。” What am I doing wrong?我究竟做错了什么?
Instead of the negate ( !
) in everything
, we just need -
or as @27 ϕ 9 mentioned !
而不是everything
的否定( !
),我们只需要-
或@27 φ 9 提到的!
also works without the everything
没有everything
也可以工作
library(dplyr)
df <- df %>%
mutate(across(-ID, factor))
If there are more than one column, wrap it inside c
如果多于一列,将其包裹在c
内
df <- df %>%
mutate(across(-c(ID, ID2), factor))
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