使用map与自定义函数（返回数据框的函数）和多个输入。

huangapple
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英文:

Using map with a custom function (that returns a data frame) and multiple inputs

问题

I have a very simple function, that returns a data frame. The function takes three parameters, a dataset, and two variables that are present in the dataframe.

The function is pretty basic:

  2. # Function takes dataset and 2 categorical variables
  3. # Calculates number of records for each combination of values in the two variables
  4. # Calculates % of var 1 responses for each level of var2
  5. ugh<-function(data, var1, var2){
  6. # add checks to make sure vars on dataset
  8. tab_n<-data%>%
  9. group_by_at(c(var1, var2))%>%
  10. summarise(Numerator=n(), .groups="drop")%>%
  11. group_by_at(c(var2))%>%
  12. mutate(Denominator=sum(Numerator)
  13. , Pct=Numerator/Denominator*100
  14. # storing names of var1 and var2 for future subsetting
  15. , Var1=var1 
  16. , Var2=var2)%>% 
  17. rename(Var1_levels=var1
  18. , Var2_levels=var2
  19. )
  20. }
  22. # Sample output
  23. combo1<-mtcars%>ugh(var1="cyl", var2="gear")
  24. # can also run this as: 
  25. # combo1<-ugh(data=mtcars, var1="cyl", var2="gear")
  26. combo2<-mtcars%>ugh(var1="cyl", var2="carb")
  27. sampleOutput<-rbind(combo1, combo2)
  29. # Trying to use map to generate sampleOutput
  30. var1_vector=rep("cyl", 2) 
  31. var2_vector=c("gear", "carb")
  33. plswork<-mtcars%
  34. map2_dfr(var1=var1_vector, var2=var2_vector, ugh)

The error message I get is:

Error in as_mapper(.f, ...) : argument ".f" is missing, with no default

英文:

I have a very simple function, that returns a data frame. The function takes three parameters, a dataset, and two variables that are present in the dataframe.

I was hoping to use the map/pmap family of functions to feed a vector/list of inputs and produce a single (long) output dataset. I can't seem to get map/pmap tools to work for me. What can I try next?

The function is pretty basic:

  1. library(dplyr)
  3. # Function takes dataset and 2 categorical variables 
  4. # Calculates number of records for each combination of values in the two variables
  5. # Calculates % of var 1 responses for each level of var2
  6. ugh&lt;-function(data, var1, var2){
  7. # add checks to make sure vars on dataset
  9.   tab_n&lt;-data%&gt;%
  10.     group_by_at(c(var1, var2))%&gt;%
  11.     summarise(Numerator=n(), .groups=&quot;drop&quot;)%&gt;%
  12.     group_by_at(c(var2))%&gt;%
  13.     mutate(Denominator=sum(Numerator)
  14.            ,Pct=Numerator/Denominator*100
  15. # storing names of var1 and var2 for future subsetting
  16.            , Var1=var1 
  17.            , Var2=var2)%&gt;% 
  18.     rename(Var1_levels=var1
  19.            , Var2_levels=var2
  20.            )
  21. }
  24. # Sample output 
  25. combo1&lt;-mtcars%&gt;%ugh(var1=&quot;cyl&quot;, var2=&quot;gear&quot;)
  26. # can also run this as: 
  27. # combo1&lt;-ugh(data=mtcars, var1=&quot;cyl&quot;, var2=&quot;gear&quot;)
  28. combo2&lt;-mtcars%&gt;%ugh(var1=&quot;cyl&quot;, var2=&quot;carb&quot;)
  29. sampleOutput&lt;-rbind(combo1, combo2)
  31. # Trying to use map to generate sampleOutput
  32. var1_vector=rep(&quot;cyl&quot;, 2) 
  33. var2_vector=c(&quot;gear&quot;, &quot;carb&quot;)
  35. plswork&lt;-mtcars%&gt;%
  36.   map2_dfr(var1=var1_vector, var2=var2_vector, ugh)

The error message I get is:

> Error in as_mapper(.f, ...) : argument ".f" is missing, with no default

I've tried using ~ to specify the function and I've tried using map2 and binding rows separately, also tried pmap with a list of inputs... but am not having much luck.

(I am interested also in more efficient ways to summarise a subset of columns from a data frame by a different subset of columns.)

答案1

得分: 1

以下是代码的翻译部分：

  1. library(dplyr)
  2. library(purrr)
  4. # 函数接受数据集和两个分类变量
  5. # 计算两个变量每个组合的记录数
  6. # 计算每个var1响应的var2水平的百分比
  7. ugh <- function(data, var1, var2) {
  8.   # 添加检查以确保数据集中存在这些变量
  9.   
  10.   tab_n <- data %>%
  11.     group_by(across(all_of(c(var1, var2)))) %>%
  12.     summarise(Numerator = n(), .groups = "drop") %>%
  13.     group_by(across(all_of(var2))) %>%
  14.     mutate(Denominator = sum(Numerator),
  15.            Pct = Numerator / Denominator * 100,
  16.            # 存储var1和var2的名称以供将来子集
  17.            Var1 = .env$var1,
  18.            Var2 = .env$var2) %>%
  19.     rename(Var1_levels = all_of(var1),
  20.            Var2_levels = all_of(var2)
  21.     )
  22. }
  24. # 示例输出
  25. combo1 <- mtcars %>% ugh(var1 = "cyl", var2 = "gear")
  26. # 也可以这样运行：
  27. # combo1 <- ugh(data = mtcars, var1 = "cyl", var2 = "gear")
  28. combo2 <- mtcars %>% ugh(var1 = "cyl", var2 = "carb")
  30. sampleOutput <- rbind(combo1, combo2)
  32. sampleOutput

如果您需要关于代码中其他部分的翻译，请随时提出。

英文:

There are a few solutions to this problem. I recommend the first because I think its the most clear. I also tweaked your function to fix any use of deprecated/superseded functions/behaviour.

  1. library(dplyr)
  2. library(purrr)
  4. # Function takes dataset and 2 categorical variables 
  5. # Calculates number of records for each combination of values in the two variables
  6. # Calculates % of var 1 responses for each level of var2
  7. ugh&lt;-function(data, var1, var2){
  8.   # add checks to make sure vars on dataset
  9.   
  10.   tab_n&lt;-data%&gt;%
  11.     group_by(across(all_of(c(var1, var2))))%&gt;%
  12.     summarise(Numerator=n(), .groups=&quot;drop&quot;)%&gt;%
  13.     group_by(across(all_of(var2)))%&gt;%
  14.     mutate(Denominator=sum(Numerator)
  15.            ,Pct=Numerator/Denominator*100
  16.            # storing names of var1 and var2 for future subsetting
  17.            , Var1= .env$var1 
  18.            , Var2= .env$var2)%&gt;% 
  19.     rename(Var1_levels= all_of(var1)
  20.            , Var2_levels= all_of(var2)
  21.     )
  22. }
  24. # Sample output 
  25. combo1&lt;-mtcars%&gt;%ugh(var1=&quot;cyl&quot;, var2=&quot;gear&quot;)
  26. # can also run this as: 
  27. # combo1&lt;-ugh(data=mtcars, var1=&quot;cyl&quot;, var2=&quot;gear&quot;)
  28. combo2&lt;-mtcars%&gt;%ugh(var1=&quot;cyl&quot;, var2=&quot;carb&quot;)
  30. sampleOutput&lt;-rbind(combo1, combo2)
  32. sampleOutput
  33. #&gt; # A tibble: 17 &#215; 7
  34. #&gt; # Groups:   Var2_levels [7]
  35. #&gt;    Var1_levels Var2_levels Numerator Denominator    Pct Var1  Var2 
  36. #&gt;          &lt;dbl&gt;       &lt;dbl&gt;     &lt;int&gt;       &lt;int&gt;  &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt;
  37. #&gt;  1           4           3         1          15   6.67 cyl   gear 
  38. #&gt;  2           4           4         8          12  66.7  cyl   gear 
  39. #&gt;  3           4           5         2           5  40    cyl   gear 
  40. #&gt;  4           6           3         2          15  13.3  cyl   gear 
  41. #&gt;  5           6           4         4          12  33.3  cyl   gear 
  42. #&gt;  6           6           5         1           5  20    cyl   gear 
  43. #&gt;  7           8           3        12          15  80    cyl   gear 
  44. #&gt;  8           8           5         2           5  40    cyl   gear 
  45. #&gt;  9           4           1         5           7  71.4  cyl   carb 
  46. #&gt; 10           4           2         6          10  60    cyl   carb 
  47. #&gt; 11           6           1         2           7  28.6  cyl   carb 
  48. #&gt; 12           6           4         4          10  40    cyl   carb 
  49. #&gt; 13           6           6         1           1 100    cyl   carb 
  50. #&gt; 14           8           2         4          10  40    cyl   carb 
  51. #&gt; 15           8           3         3           3 100    cyl   carb 
  52. #&gt; 16           8           4         6          10  60    cyl   carb 
  53. #&gt; 17           8           8         1           1 100    cyl   carb
  55. # Trying to use map to generate sampleOutput
  56. var1_vector=rep(&quot;cyl&quot;, 2) 
  57. var2_vector=c(&quot;gear&quot;, &quot;carb&quot;)
  59. # Method 1 (recommended): use of anonymous functions
  60. map2(var1_vector, var2_vector, \(var1, var2) ugh(mtcars, var1, var2)) %&gt;%
  61.   list_rbind()
  62. #&gt; # A tibble: 17 &#215; 7
  63. #&gt; # Groups:   Var2_levels [7]
  64. #&gt;    Var1_levels Var2_levels Numerator Denominator    Pct Var1  Var2 
  65. #&gt;          &lt;dbl&gt;       &lt;dbl&gt;     &lt;int&gt;       &lt;int&gt;  &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt;
  66. #&gt;  1           4           3         1          15   6.67 cyl   gear 
  67. #&gt;  2           4           4         8          12  66.7  cyl   gear 
  68. #&gt;  3           4           5         2           5  40    cyl   gear 
  69. #&gt;  4           6           3         2          15  13.3  cyl   gear 
  70. #&gt;  5           6           4         4          12  33.3  cyl   gear 
  71. #&gt;  6           6           5         1           5  20    cyl   gear 
  72. #&gt;  7           8           3        12          15  80    cyl   gear 
  73. #&gt;  8           8           5         2           5  40    cyl   gear 
  74. #&gt;  9           4           1         5           7  71.4  cyl   carb 
  75. #&gt; 10           4           2         6          10  60    cyl   carb 
  76. #&gt; 11           6           1         2           7  28.6  cyl   carb 
  77. #&gt; 12           6           4         4          10  40    cyl   carb 
  78. #&gt; 13           6           6         1           1 100    cyl   carb 
  79. #&gt; 14           8           2         4          10  40    cyl   carb 
  80. #&gt; 15           8           3         3           3 100    cyl   carb 
  81. #&gt; 16           8           4         6          10  60    cyl   carb 
  82. #&gt; 17           8           8         1           1 100    cyl   carb
  84. # If you aren&#39;t using a version of R with anonymous functions:
  85. map2(var1_vector, var2_vector, ~ ugh(mtcars, .x, .y)) %&gt;%
  86.   list_rbind()
  87. #&gt; # A tibble: 17 &#215; 7
  88. #&gt; # Groups:   Var2_levels [7]
  89. #&gt;    Var1_levels Var2_levels Numerator Denominator    Pct Var1  Var2 
  90. #&gt;          &lt;dbl&gt;       &lt;dbl&gt;     &lt;int&gt;       &lt;int&gt;  &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt;
  91. #&gt;  1           4           3         1          15   6.67 cyl   gear 
  92. #&gt;  2           4           4         8          12  66.7  cyl   gear 
  93. #&gt;  3           4           5         2           5  40    cyl   gear 
  94. #&gt;  4           6           3         2          15  13.3  cyl   gear 
  95. #&gt;  5           6           4         4          12  33.3  cyl   gear 
  96. #&gt;  6           6           5         1           5  20    cyl   gear 
  97. #&gt;  7           8           3        12          15  80    cyl   gear 
  98. #&gt;  8           8           5         2           5  40    cyl   gear 
  99. #&gt;  9           4           1         5           7  71.4  cyl   carb 
  100. #&gt; 10           4           2         6          10  60    cyl   carb 
  101. #&gt; 11           6           1         2           7  28.6  cyl   carb 
  102. #&gt; 12           6           4         4          10  40    cyl   carb 
  103. #&gt; 13           6           6         1           1 100    cyl   carb 
  104. #&gt; 14           8           2         4          10  40    cyl   carb 
  105. #&gt; 15           8           3         3           3 100    cyl   carb 
  106. #&gt; 16           8           4         6          10  60    cyl   carb 
  107. #&gt; 17           8           8         1           1 100    cyl   carb
  109. # Alternatively, using pmap():
  110. args &lt;- list(
  111.   var1 = var1_vector,
  112.   var2 = var2_vector
  113. )
  115. pmap(args, ugh, mtcars) %&gt;%
  116.   list_rbind()
  117. #&gt; # A tibble: 17 &#215; 7
  118. #&gt; # Groups:   Var2_levels [7]
  119. #&gt;    Var1_levels Var2_levels Numerator Denominator    Pct Var1  Var2 
  120. #&gt;          &lt;dbl&gt;       &lt;dbl&gt;     &lt;int&gt;       &lt;int&gt;  &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt;
  121. #&gt;  1           4           3         1          15   6.67 cyl   gear 
  122. #&gt;  2           4           4         8          12  66.7  cyl   gear 
  123. #&gt;  3           4           5         2           5  40    cyl   gear 
  124. #&gt;  4           6           3         2          15  13.3  cyl   gear 
  125. #&gt;  5           6           4         4          12  33.3  cyl   gear 
  126. #&gt;  6           6           5         1           5  20    cyl   gear 
  127. #&gt;  7           8           3        12          15  80    cyl   gear 
  128. #&gt;  8           8           5         2           5  40    cyl   gear 
  129. #&gt;  9           4           1         5           7  71.4  cyl   carb 
  130. #&gt; 10           4           2         6          10  60    cyl   carb 
  131. #&gt; 11           6           1         2           7  28.6  cyl   carb 
  132. #&gt; 12           6           4         4          10  40    cyl   carb 
  133. #&gt; 13           6           6         1           1 100    cyl   carb 
  134. #&gt; 14           8           2         4          10  40    cyl   carb 
  135. #&gt; 15           8           3         3           3 100    cyl   carb 
  136. #&gt; 16           8           4         6          10  60    cyl   carb 
  137. #&gt; 17           8           8         1           1 100    cyl   carb

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  • 本文由 发表于 2023年4月11日 11:47:07
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