使用purr或map对因子变量的总百分比进行汇总。

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英文:

Summarising with purr or map the percentage of the total of a factor variable

问题

我正在尝试为列表的每个变量执行映射函数。为了执行此函数，我想要按某些类别进行分组，并显示每个变量因子的总百分比。

例如，我有这个列表：

  1. mtcars_list <- c("am","gear","carb")

我有要分组的变量："cyl"，以及我想要总结的变量。在这种情况下，我将转换mtcars数据库的变量"vs"为因子：

  1. mtcars$vs <- factor(mtcars$vs, levels=c('0', '1'))

然后我执行这个map::purr函数，当我在总结时使用count、prop.table或类似的函数时，它会给我一个错误：

  1. purrr::map(mtcars_list, ~ mtcars %>%
  2.   group_by(cyl, .data[[.x]]) %>%
  3.   summarise(count(vs), .groups = "drop")*100)

当我运行时，它会显示：

no applicable method for 'count' applied to an object of class "c('double', 'numeric')"

结果会类似于这样：

第一类别

  1.    0        1
  2. A 17.7%     83.3%
  3. B  5.0%     95.5%

第二类别

  1.     0        1 
  2. A   2.0     98.0
  3. B   4.0     96.0

谢谢！！！

英文:

I'm trying to do a map function for each of the variables of a list. To do this function, I want to group_by by some categories and show the percentage of the total of each factor of a variable

For instance, I have this list:

  1. mtcars_list &lt;- c (&quot;am&quot;,&quot;gear&quot;,&quot;carb&quot;)

I have the variable I want to group_by for: "cyl" And the variable I want to summarise. In this case I will transform the variable "vs" of the mtcars database as a factor:

  1. mtcars$vs &lt;- factor(mtcars$vs , levels=c(&#39;0&#39;, &#39;1&#39;))

I then do this map:: purr function, which gives me error when I use count, prop.table or similar when summarising...

  1. purrr::map(mtcars_list, ~ mtcars %&gt;%
  2.   group_by(cyl, .data[[.x]]) %&gt;%
  3.   summarise(count(vs), .groups = &quot;drop&quot;)*100)

When I run this it says:

no applicable method for 'count' applied to an object of class "c('double', 'numeric')

The result would be something like this

First category

  1.    0        1
  2. A 17.7%     83.3%
  3. B  5.0%     95.5%
  1. Second category 
  2.     0        1 
  3. A   2.0     98.0
  4. B   4.0     96.0

Thank you!!!

答案1

得分: 1

请检查这是否是预期输出：

  1. df1 <- purrr::map(mtcars_list, ~ mtcars %>%
  2.                     select(cyl, vs, !!sym(.x)) %>%
  3.                     mutate(n = n(), .by = c(cyl, .data[[.x]], vs)) %>%
  4.                     mutate(n2 = n(), .by = c(cyl, .data[[.x]])) %>%
  5.                     group_by(cyl, .data[[.x]], vs) %>%
  6.                     slice_tail(n = 1) %>%
  7.                     mutate(perc = (n / n2) * 100) %>%
  8.                     pivot_wider(id_cols = c(cyl, .data[[.x]]), names_from = vs, values_from = perc)
  9. )
  11. df1
  13. [[1]]
  14. # A tibble: 6 × 4
  15. # Groups:   cyl, am [6]
  16.     cyl    am   `1`   `0`
  17.   <dbl> <dbl> <dbl> <dbl>
  18. 1     4     0  100   NA  
  19. 2     4     1   87.5  12.5
  20. 3     6     0  100   NA  
  21. 4     6     1   NA   100  
  22. 5     8     0   NA   100  
  23. 6     8     1   NA   100  
  25. [[2]]
  26. # A tibble: 8 × 4
  27. # Groups:   cyl, gear [8]
  28.     cyl  gear   `1`   `0`
  29.   <dbl> <dbl> <dbl> <dbl>
  30. 1     4     3   100   NA
  31. 2     4     4   100   NA
  32. 3     4     5    50    50
  33. 4     6     3   100   NA
  34. 5     6     4    50    50
  35. 6     6     5    NA   100
  36. 7     8     3    NA   100
  37. 8     8     5    NA   100
  39. [[3]]
  40. # A tibble: 9 × 4
  41. # Groups:   cyl, carb [9]
  42.     cyl  carb   `1`   `0`
  43.   <dbl> <dbl> <dbl> <dbl>
  44. 1     4     1  100   NA  
  45. 2     4     2   83.3  16.7
  46. 3     6     1  100   NA  
  47. 4     6     4   50    50  
  48. 5     6     6   NA   100  
  49. 6     8     2   NA   100  
  50. 7     8     3   NA   100  
  51. 8     8     4   NA   100  
  52. 9     8     8   NA   100
英文:

Please check if this is the expected output

  1. df1 &lt;- purrr::map(mtcars_list, ~ mtcars %&gt;% select(cyl,vs,!!sym(.x)) %&gt;% 
  2.                     mutate(n=n() , .by=c(cyl, .data[[.x]], vs)) %&gt;% 
  3.                     mutate(n2=n(), .by=c(cyl, .data[[.x]]) ) %&gt;% 
  4.                     group_by(cyl, .data[[.x]], vs) %&gt;% 
  5.                     slice_tail(n=1) %&gt;% 
  6.                     mutate(perc=(n/n2)*100) %&gt;% 
  7.                     pivot_wider(id_cols = c(cyl,.data[[.x]]), names_from = vs, values_from = perc)
  8. )
  10. df1
  14. [[1]]
  15. # A tibble: 6 &#215; 4
  16. # Groups:   cyl, am [6]
  17.     cyl    am   `1`   `0`
  18.   &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
  19. 1     4     0 100    NA  
  20. 2     4     1  87.5  12.5
  21. 3     6     0 100    NA  
  22. 4     6     1  NA   100  
  23. 5     8     0  NA   100  
  24. 6     8     1  NA   100  
  26. [[2]]
  27. # A tibble: 8 &#215; 4
  28. # Groups:   cyl, gear [8]
  29.     cyl  gear   `1`   `0`
  30.   &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
  31. 1     4     3   100    NA
  32. 2     4     4   100    NA
  33. 3     4     5    50    50
  34. 4     6     3   100    NA
  35. 5     6     4    50    50
  36. 6     6     5    NA   100
  37. 7     8     3    NA   100
  38. 8     8     5    NA   100
  40. [[3]]
  41. # A tibble: 9 &#215; 4
  42. # Groups:   cyl, carb [9]
  43.     cyl  carb   `1`   `0`
  44.   &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
  45. 1     4     1 100    NA  
  46. 2     4     2  83.3  16.7
  47. 3     6     1 100    NA  
  48. 4     6     4  50    50  
  49. 5     6     6  NA   100  
  50. 6     8     2  NA   100  
  51. 7     8     3  NA   100  
  52. 8     8     4  NA   100  
  53. 9     8     8  NA   100

答案2

得分: 1

你的 dplyr 代码不起作用。对于你想要的输出格式，基本的 R 函数 table 和 prop.table 可以更快地达到目标：

  1. purrr::map(
  2.   mtcars_list, \(x) 
  3.   (table(mtcars$cyl, mtcars[[x]]) |&gt; prop.table(margin = 1)) * 100
  4. )
  5. # [[1]]
  6. #            0        1
  7. #   4 27.27273 72.72727
  8. #   6 57.14286 42.85714
  9. #   8 85.71429 14.28571
  10. # 
  11. # [[2]]
  12. #             3         4         5
  13. #   4  9.090909 72.727273 18.181818
  14. #   6 28.571429 57.142857 14.285714
  15. #   8 85.714286  0.000000 14.285714
  16. # 
  17. # [[3]]
  18. #             1         2         3         4         6         8
  19. #   4 45.454545 54.545455  0.000000  0.000000  0.000000  0.000000
  20. #   6 28.571429  0.000000  0.000000 57.142857 14.285714  0.000000
  21. #   8  0.000000 28.571429 21.428571 42.857143  0.000000  7.142857

注意：我将代码部分保持不变，只进行了翻译。

英文:

Your dplyr code doesn't work. For the output format you want, the base R functions table and prop.table get you there faster:

  1. purrr::map(
  2. mtcars_list, \(x) 
  3. (table(mtcars$cyl, mtcars[[x]]) |&gt; prop.table(margin = 1)) * 100
  4. )
  5. # [[1]]
  6. #            0        1
  7. #   4 27.27273 72.72727
  8. #   6 57.14286 42.85714
  9. #   8 85.71429 14.28571
  10. # 
  11. # [[2]]
  12. #             3         4         5
  13. #   4  9.090909 72.727273 18.181818
  14. #   6 28.571429 57.142857 14.285714
  15. #   8 85.714286  0.000000 14.285714
  16. # 
  17. # [[3]]
  18. #             1         2         3         4         6         8
  19. #   4 45.454545 54.545455  0.000000  0.000000  0.000000  0.000000
  20. #   6 28.571429  0.000000  0.000000 57.142857 14.285714  0.000000
  21. #   8  0.000000 28.571429 21.428571 42.857143  0.000000  7.142857

huangapple
  • 本文由 发表于 2023年7月13日 23:26:12
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  • dplyr
  • purrr
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