问题

以下是您要翻译的内容：

"I have some data that I want to pivot_wider, using one or more names_from columns. At least one of the potential names_from columns is Boolean.

This results in uninformative default column names like TRUE, FALSE or TRUE_somevalue, FALSE_somevalue (where somevalue is an otherwise meaningful level from another column).

I would like to instead use some more informative column name when some names_from column or columns are Boolean, for instance, varnameTRUE, varnameFALSE or varnameTRUE_somevalue, varnameFALSE_somevalue (where varname is the name of the Boolean column in the first place).

Obviously, I could convert the Boolean column to character or factor with meaningful names in the first place, but I'm wondering if there's some generalizable way to do this using the arguments available to pivot_wider. I can't figure out a way to do it with e.g. names_glue or names_sep. Ideally I'd like to be able to do this without even manually specifying the relevant columns, but have it happen automatically if a names_from column is a Boolean one.

Is there some way to do this?

Some example data:

test_dat <- tibble(
  idx = c(rep("a", 6), rep("b", 6)), 
  boo = c(rep(c(TRUE, FALSE), 6)), 
  word = c(rep(c("foo", "bar", "baz"), 4)), 
  val = runif(12)
)

> test_dat
# A tibble: 12 × 4
   idx   boo   word     val
   <chr> <lgl> <chr>  <dbl>
 1 a     TRUE  foo   0.325 
 2 a     FALSE bar   0.770 
 3 a     TRUE  baz   0.824 
 4 a     FALSE foo   0.351 
 5 a     TRUE  bar   0.555 
 6 a     FALSE baz   0.698 
 7 b     TRUE  foo   0.0200
 8 b     FALSE bar   0.427 
 9 b     TRUE  baz   0.325 
10 b     FALSE foo   0.463 
11 b     TRUE  bar   0.987 
12 b     FALSE baz   0.345 

Default output:

> test_dat %>% pivot_wider(names_from=c("boo", "word"), values_from=val)
# A tibble: 2 × 7
  idx   TRUE_foo FALSE_bar TRUE_baz FALSE_foo TRUE_bar FALSE_baz
  <chr>    <dbl>     <dbl>    <dbl>     <dbl>    <dbl>     <dbl>
1 a       0.325      0.770    0.824     0.351    0.555     0.698
2 b       0.0200     0.427    0.325     0.463    0.987     0.345

Desired output would be identical except with column names more like booTRUE_foo, booFALSE_foo (or some variant thereof, e.g. boo_foo, notboo_foo)"

希望这能帮助您。

test_dat <- tibble(
  idx = c(rep("a", 6), rep("b", 6)), 
  boo = c(rep(c(TRUE, FALSE), 6)), 
  word = c(rep(c("foo", "bar", "baz"), 4)), 
  val = runif(12)
)

> test_dat
# A tibble: 12 × 4
   idx   boo   word     val
   <chr> <lgl> <chr>  <dbl>
 1 a     TRUE  foo   0.325 
 2 a     FALSE bar   0.770 
 3 a     TRUE  baz   0.824 
 4 a     FALSE foo   0.351 
 5 a     TRUE  bar   0.555 
 6 a     FALSE baz   0.698 
 7 b     TRUE  foo   0.0200
 8 b     FALSE bar   0.427 
 9 b     TRUE  baz   0.325 
10 b     FALSE foo   0.463 
11 b     TRUE  bar   0.987 
12 b     FALSE baz   0.345 

> test_dat %>% pivot_wider(names_from=c("boo", "word"), values_from=val)
# A tibble: 2 × 7
  idx   TRUE_foo FALSE_bar TRUE_baz FALSE_foo TRUE_bar FALSE_baz
  <chr>    <dbl>     <dbl>    <dbl>     <dbl>    <dbl>     <dbl>
1 a       0.325      0.770    0.824     0.351    0.555     0.698
2 b       0.0200     0.427    0.325     0.463    0.987     0.345

答案1

得分: 2

你可以在最开始使用 `dplyr` 函数将列名附加到那些布尔（逻辑）列上：

```R
mutate(across(where(is.logical), ...))

然后再进行宽表格的变换。通过这种方式，你不需要手动指定布尔列的名称。

library(tidyverse)

test_dat %>%
  mutate(across(where(is.logical), ~ paste0(cur_column(), .x))) %>%
  pivot_wider(names_from = c(boo, word), values_from = val)

# # A tibble: 2 × 7
#   idx   booTRUE_foo booFALSE_bar booTRUE_baz booFALSE_foo booTRUE_bar booFALSE_baz
#   <chr>       <dbl>        <dbl>       <dbl>        <dbl>       <dbl>        <dbl>
# 1 a          0.0556        0.679      0.409         0.666       0.675        0.312
# 2 b          0.281         0.477      0.0817        0.977       0.745        0.340

<details>
<summary>英文:</summary>

You can append column names to those boolean(logical) columns at first with `dplyr` functions:

mutate(across(where(is.logical), ...))

before pivoting to wide. By this way you don't need to require manual specification of the boolean column names.

```r
library(tidyverse)

test_dat %>%
  mutate(across(where(is.logical), ~ paste0(cur_column(), .x))) %>%
  pivot_wider(names_from = c(boo, word), values_from = val)

# # A tibble: 2 × 7
#   idx   booTRUE_foo booFALSE_bar booTRUE_baz booFALSE_foo booTRUE_bar booFALSE_baz
#   <chr>       <dbl>        <dbl>       <dbl>        <dbl>       <dbl>        <dbl>
# 1 a          0.0556        0.679      0.409         0.666       0.675        0.312
# 2 b          0.281         0.477      0.0817        0.977       0.745        0.340