[英]Can we actually pass two sets of multiple variables into mutate across in dplyr
This question though having three answers raised me doubts as I am mulling my head over the problem.这个问题虽然有三个答案,但让我怀疑,因为我正在考虑这个问题。 Though I am aware that problem can be solved by other methods (and using purrr or apply group of functions especially), Yet I am not sure that can It be actually done through
mutate(across(...
? I am reproducing the problem for sake of clarity here. Note: I am not looking for its answer but only an answer to my doubt whether two sets of variables can actually be passed through mutate/across虽然我知道问题可以通过其他方法解决(特别是使用 purrr 或应用函数组),但我不确定它实际上可以通过
mutate(across(...
吗?我正在重现这个问题这里为了清楚起见。注意:我不是在寻找它的答案,而只是对我怀疑两组变量是否实际上可以通过 mutate/across 传递的答案
There are two sets of variables (one without suffix and one set with suffix avail).有两组变量(一组不带后缀,一组带后缀avail)。
df <- tibble(a = c(0, 1, 0, 0, 0),
a_avail = c(1, 1, 1, 0, 0),
b = c(1, 1, 1, 0, 0),
b_avail = c(1, 0, 0, 1, 0))
# A tibble: 5 x 4
a a_avail b b_avail
<dbl> <dbl> <dbl> <dbl>
1 0 1 1 1
2 1 1 1 0
3 0 1 1 0
4 0 0 0 1
5 0 0 0 0
Now If we want to mutate one set of variables say (a and b) but by comparing these by another set in tandem.现在,如果我们想改变一组变量,比如说(a 和 b),但是通过将它们与另一组串联比较。 That is to say when column a is mutating it may use its corresponding variable a_avail and while b is mutating it is
b_avail
and so on upto n variables.也就是说,当 a 列发生变异时,它可以使用其对应的变量 a_avail,而当 b 发生变异时,它是
b_avail
等等,最多 n 个变量。
I have tried these codes apart from OP has除了OP之外,我已经尝试过这些代码
df %>% mutate(d = row_number()) %>%
mutate(across(.cols = c(a_avail, b_avail),
.fns = ~case_when(
.x == 1 ~ {str_replace(cur_column(), "_avail", "")[d]},
.x == 0 ~ NA_character_
),
.names = "{.col}_new"))
OR或者
df %>%
mutate(across(.cols = c(a, b),
.fns = ~case_when(
glue::glue("{cur_column()}_avail") == 1 ~ .x,
glue::glue("{cur_column()}_avail") == 0 ~ as.numeric(NA)
),
.names = "{.col}_new"))
but to no avail.但无济于事。 can someone clarify that whether it can be done through mutate(across.. syntax?
有人可以澄清一下是否可以通过 mutate(cross.. 语法来完成?
You can do this with get
with cur_column()
.您可以使用
get
和cur_column()
来做到这一点。
library(dplyr)
df %>%
mutate(across(.cols = c(a, b),
.fns = ~case_when(
get(glue::glue("{cur_column()}_avail")) == 1 ~ .x,
get(glue::glue("{cur_column()}_avail")) == 0 ~ as.numeric(NA)
),
.names = "{.col}_new"))
# a a_avail b b_avail a_new b_new
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 0 1 1 1 0 1
#2 1 1 1 0 1 NA
#3 0 1 1 0 0 NA
#4 0 0 0 1 NA 0
#5 0 0 0 0 NA NA
PS - I am not sure if this should be an answer to the post that you linked. PS - 我不确定这是否应该是您链接的帖子的答案。
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