使用pivot_longer转换后列名反转，如何将x、y设为数据框列名？

问题场景 #

尝试将datasaurus_dozen_wide数据集转换为长格式，希望最终数据框以x、y作为列名，但转换后列名呈现反转状态，原代码及错误输出如下：

原代码 #

library(pacman)
p_load(tidyverse, purrr, datasauRus)
datasaurus_dozen_wide %>% 
  pivot_longer(everything(),
               names_to = c(".value", "set"),
               names_pattern = "(.*)_(.)")

错误输出 #

# A tibble: 284 × 14
    set    away bullseye circle  dino  dots h_lines high_…¹ slant…² slant…³  star
    <chr> <dbl>    <dbl>  <dbl> <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>
  1 x      32.3     51.2   56.0  55.4  51.1    53.4    57.6    52.9    47.7  58.2
  2 y      61.4     83.3   79.3  97.2  90.9    90.2    83.9    97.3    95.2  91.9
  3 x      53.4     59.0   50.0  51.5  50.5    52.8    51.3    59.0    44.6  58.2
  4 y      26.2     85.5   79.0  96.0  89.1    90.1    82.8    93.6    93.1  92.2
  5 x      63.9     51.9   51.3  46.2  50.2    47.1    50.8    56.4    43.9  58.7
  6 y      30.8     85.8   82.4  94.5  85.5    90.5    76.8    96.3    94.1  90.3
  7 x      70.3     48.2   51.2  42.8  50.1    42.4    37.0    37.8    41.6  57.3
  8 y      82.5     85.0   79.2  91.4  83.1    89.5    82.0    94.4    90.3  89.9
  9 x      34.1     41.7   44.4  40.8  50.6    42.7    42.9    39.9    49.2  58.1
 10 y      45.7     84.0   78.2  88.3  82.9    90.4    80.2    90.6    96.6  92.0
# … with 274 more rows, 3 more variables: v_lines <dbl>, wide_lines <dbl>,
#   x_shape <dbl>, and abbreviated variable names ¹​high_lines, ²​slant_down,
#   ³​slant_up

问题原因 #

datasaurus_dozen_wide的列名格式为[数据集名称]_[x/y]（例如away_x、away_y），原代码存在两处错误：

• names_to = c(".value", "set") 顺序颠倒，应该先提取数据集名称作为set，再提取x/y作为.value对应的列名
• names_pattern = "(.*)_(.)" 匹配逻辑不精准，(.*)会过度匹配，且未限定只匹配x或y

修复代码 #

调整names_to的顺序，并修正names_pattern以精准匹配列名结构：

library(pacman)
p_load(tidyverse, purrr, datasauRus)
datasaurus_dozen_wide %>% 
  pivot_longer(everything(),
               names_to = c("set", ".value"),
               names_pattern = "(.*)_(x|y)")

正确输出示例 #

# A tibble: 1,846 × 3
   set       x     y
   <chr> <dbl> <dbl>
 1 away   32.3  61.4
 2 away   53.4  26.2
 3 away   63.9  30.8
 4 away   70.3  82.5
 5 away   34.1  45.7
 6 away   67.5  39.4
 7 away   43.7  65.3
 8 away   54.2  67.2
 9 away   61.1  57.4
10 away   46.1  57.5
# … with 1,836 more rows

内容的提问来源于stack exchange，提问作者Ramakrishna S