Mutate new columns and intercalate them with old ones.

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英文:

Mutate new columns and intercalate them with old ones

问题

以下是您要翻译的内容：

"我想使用 across 创建新列，并使这些新列与旧列交错。在示例中，我手动重新排列列以显示所需的输出。但我希望能够自动执行此操作，就像我在 mutate 中尝试的方式一样，显然这种方式不起作用。

  1. library(dplyr)
  3. df <- tibble(a = 1:2, x_b = 1:2, x_c = 1:2)
  5. df |&gt; 
  6.   mutate(across(starts_with(&quot;x_&quot;), 
  7.                 ~ .x * 2, 
  8.                 .names = &quot;{sub(&#39;x_&#39;, &#39;y_&#39;, .col)}&quot;),
  9.          .after = c(x_b, x_c)) |&gt; 
  10.   relocate(y_b, .after = x_b) |&gt; 
  11.   relocate(y_c, .after = x_c)
  13. #&gt; # A tibble: 2 &#215; 5
  14. #&gt;       a   x_b   y_b   x_c   y_c
  15. #&gt;   &lt;int&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt;
  16. #&gt; 1     1     1     2     1     2
  17. #&gt; 2     2     2     4     2     4

<sup>创建于2023年5月18日，使用 reprex v2.0.2</sup>"

英文:

I want to create new columns using across and that new columns being intercalated with the old ones. In the example I manually relocate the columns to show the desired output. But I would like to do this automatically, like my try inside mutate, which obviously does not work.

  1. library(dplyr)
  3. df &lt;- tibble(a = 1:2, x_b = 1:2, x_c = 1:2)
  5. df |&gt; 
  6.   mutate(across(starts_with(&quot;x_&quot;), 
  7.                 ~ .x * 2, 
  8.                 .names = &quot;{sub(&#39;x_&#39;, &#39;y_&#39;, .col)}&quot;),
  9.          .after = c(x_b, x_c)) |&gt; 
  10.   relocate(y_b, .after = x_b) |&gt; 
  11.   relocate(y_c, .after = x_c)
  13. #&gt; # A tibble: 2 &#215; 5
  14. #&gt;       a   x_b   y_b   x_c   y_c
  15. #&gt;   &lt;int&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt;
  16. #&gt; 1     1     1     2     1     2
  17. #&gt; 2     2     2     4     2     4

<sup>Created on 2023-05-18 with reprex v2.0.2</sup>

答案1

得分: 5

We could create a tibble/data.frame, use .unpack option and rename the columns

  1. library(dplyr)
  2. library(stringr)
  3. df %>%
  4.   mutate(across(starts_with("x_"), 
  5.                 ~ data.frame(x = .x, y = .x * 2), .unpack = TRUE),
  6.    .keep = 'unused') %>% 
  7.  rename_with(~ str_replace(.x, "x_(.)_(.)", "\_\"))

-output

  1. # A tibble: 2 × 5
  2.       a   x_b   y_b   x_c   y_c
  3.   <int> <int> <dbl> <int> <dbl>
  4. 1     1     1     2     1     2
  5. 2     2     2     4     2     4
英文:

We could create a tibble/data.frame, use .unpack option and rename the columns

  1. library(dplyr)
  2. library(stringr)
  3. df %&gt;%
  4.   mutate(across(starts_with(&quot;x_&quot;), 
  5.                 ~ data.frame(x = .x, y = .x * 2), .unpack = TRUE),
  6.    .keep = &#39;unused&#39;) %&gt;% 
  7.  rename_with(~ str_replace(.x, &quot;x_(.)_(.)&quot;, &quot;\_\&quot;))

-output

  1. # A tibble: 2 &#215; 5
  2.       a   x_b   y_b   x_c   y_c
  3.   &lt;int&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt;
  4. 1     1     1     2     1     2
  5. 2     2     2     4     2     4
  8. </details>
  12. # 答案2
  13. **得分**: 3
  15. A dirty workaround that I‘m using regularly:
  17.     library(tidyverse)
  18.     
  19.     prefix &lt;- c('x_', 'y_')
  20.     suffix &lt;- c('b', 'c')
  21.     
  22.     col_order &lt;- paste0(prefix, rep(suffix, each = length(prefix)))
  23.     
  24.     df %&gt;%
  25.       select(a, all_of(col_order))
  27.     # A tibble: 2 x 5
  28.           a   x_b   y_b   x_c   y_c
  29.       &lt;int&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt;
  30.     1     1     1     2     1     2
  31.     2     2     2     4     2     4
  33. <details>
  34. <summary>英文:</summary>
  36. A dirty workaround that I‘m using regularly:
  38.     library(tidyverse)
  39.     
  40.     prefix &lt;- c(&#39;x_&#39;, &#39;y_&#39;)
  41.     suffix &lt;- c(&#39;b&#39;, &#39;c&#39;)
  42.     
  43.     col_order &lt;- paste0(prefix, rep(suffix, each = length(prefix)))
  44.     
  45.     df %&gt;%
  46.       select(a, all_of(col_order))
  48.     # A tibble: 2 x 5
  49.           a   x_b   y_b   x_c   y_c
  50.       &lt;int&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt;
  51.     1     1     1     2     1     2
  52.     2     2     2     4     2     4
  54. </details>

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  • 本文由 发表于 2023年5月18日 13:19:00
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  • dplyr
  • r
  • tidyverse
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