purrr::map函数为什么不能正确地将一个函数映射到拆分数据框的每个部分？

huangapple
go评论95阅读模式
英文:

Why is the purrr::map function not correctly mapping a function to each piece of a split dataframe?

问题

以下是您提供的代码的翻译部分：

  1. 我有以下的数据框，我们可以称之为df_all
  3. 我有以下的数据框，我们可以称之为df_alt
  5. 我有以下的函数，它查找df_all和df_alt之间的共同/交集的Points值。
  7. 我试图使用以下的map语法应用int_value函数。
  9. 这是返回的输出，这不是期望的输出。
  11. 这是期望的输出和我预期返回的内容。
  13. map函数似乎没有遵循基于Book列的隐含分组。我漏掉了什么？

请注意，我已经忽略了代码的部分，只提供了翻译的内容。如果您需要进一步的帮助或解释，请随时告诉我。

英文:

I have the following dataframe that we can call df_all

  1. structure(list(ID = c("1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  2. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385"
  3. ), Book = c("Bovada", "Bovada", "LowVig.ag", "LowVig.ag"), Home = c("Alabama Crimson Tide", 
  4. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide"
  5. ), Away = c("San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  6. "San Diego St Aztecs"), Team = c("Alabama Crimson Tide", "San Diego St Aztecs", 
  7. "Alabama Crimson Tide", "San Diego St Aztecs"), Price = c(-110, 
  8. -110, -111, -101), Points = c(-7.5, 7.5, -7, 7)), row.names = c(NA, 
  9. -4L), class = c("tbl_df", "tbl", "data.frame"))

and I have the following dataframe that we can call df_alt

  1. structure(list(ID = c("1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  2. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  3. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  4. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  5. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  6. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  7. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  8. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
  9. "1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385"
  10. ), Book = c("Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", 
  11. "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", 
  12. "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", 
  13. "Pinnacle"), Home = c("Alabama Crimson Tide", "Alabama Crimson Tide", 
  14. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  15. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  16. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  17. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  18. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  19. "Alabama Crimson Tide"), Away = c("San Diego St Aztecs", "San Diego St Aztecs", 
  20. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  21. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  22. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  23. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  24. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  25. "San Diego St Aztecs"), Team = c("Alabama Crimson Tide", "Alabama Crimson Tide", 
  26. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  27. "Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
  28. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  29. "San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
  30. "San Diego St Aztecs", "San Diego St Aztecs", "Alabama Crimson Tide", 
  31. "San Diego St Aztecs"), Price = c(-149, -138, -126, -115, 105, 
  32. 114, 122, 132, 128, 119, 110, 102, -119, -131, -142, -154, -104, 
  33. -108), Points = c(-5.5, -6, -6.5, -7, -8, -8.5, -9, -9.5, 5.5, 
  34. 6, 6.5, 7, 8, 8.5, 9, 9.5, -7.5, 7.5)), row.names = c(NA, -18L
  35. ), class = c("tbl_df", "tbl", "data.frame"))

I have the following function which looks for common/intersecting Points values between df_all and df_alt.

  1. int_value <- function(df){
  2.     
  3.     df %>% 
  4.             dplyr::select(c(ID, Team, Points)) %>%  
  5.             dplyr::intersect(df_alt %>% dplyr::select(c(ID, Team,Points))) %>% 
  6.             mutate(Book = 'Pinnacle')
  7.     
  8.     df %>% full_join(df_int)%>% left_join(df_alt %>% rename(price=Price)) %>% 
  9.             mutate(Price=ifelse(is.na(price),Price,price))%>% 
  10.             select(-price)
  11. }

I am trying to apply int_value using the following map syntax.

  1. df_all %>% 
  2.     group_split(ID, Book) %>% 
  3.     map(int_value)

This is the output that is returned which is not the desired output.

  1. [[1]]
  2. # A tibble: 8 × 7
  3. ID                               Book      Home                 Away                Team                 
  4. Price Points
  5. <chr>                            <chr>     <chr>                <chr>               <chr>                
  6. <dbl>  <dbl>
  7. 1 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs Alabama 
  8. Crimson Tide  -110   -7.5
  9. 2 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs San 
  10. Diego St Aztecs   -110    7.5
  11. 3 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs Alabama 
  12. Crimson Tide  -111   -7  
  13. 4 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs San 
  14. Diego St Aztecs   -101    7  
  15. 5 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
  16. Crimson Tide  -104   -7.5
  17. 6 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
  18. Diego St Aztecs   -108    7.5
  19. 7 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
  20. Crimson Tide  -115   -7  
  21. 8 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
  22. Diego St Aztecs    102    7  
  24. [[2]]
  25. # A tibble: 8 × 7
  26. ID                               Book      Home                 Away                Team                 
  27. Price Points
  28. <chr>                            <chr>     <chr>                <chr>               <chr>                
  29. <dbl>  <dbl>
  30. 1 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs Alabama 
  31. Crimson Tide  -110   -7.5
  32. 2 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs San 
  33. Diego St Aztecs   -110    7.5
  34. 3 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs Alabama 
  35. Crimson Tide  -111   -7  
  36. 4 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs San 
  37. Diego St Aztecs   -101    7  
  38. 5 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
  39. Crimson Tide  -104   -7.5
  40. 6 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
  41. Diego St Aztecs   -108    7.5
  42. 7 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
  43. Crimson Tide  -115   -7  
  44. 8 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
  45. Diego St Aztecs    102    7

This is the desired output and what I expected to be returned.

  1. [[1]]
  2. # A tibble: 6 × 7
  3. ID                               Book     Home                 Away                Team                 
  4. Price Points
  5. <chr>                            <chr>    <chr>                <chr>               <chr>                
  6. <dbl>  <dbl>
  7. 1 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St Aztecs Alabama 
  8. Crimson Tide  -110   -7.5
  9. 2 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St Aztecs San Diego 
  10. St Aztecs   -110    7.5
  11. 3 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St Aztecs Alabama 
  12. Crimson Tide  -104   -7.5
  13. 4 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St Aztecs San Diego 
  14. St Aztecs   -108    7.5
  17. [[2]]
  18. # A tibble: 6 × 7
  19. ID                               Book      Home                 Away                Team                 
  20. Price Points
  21. <chr>                            <chr>     <chr>                <chr>               <chr>                
  22. <dbl>  <dbl>
  23. 1 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs Alabama 
  24. Crimson Tide  -111   -7  
  25. 2 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs San 
  26. Diego St Aztecs   -101    7  
  27. 3 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
  28. Crimson Tide  -115   -7  
  29. 4 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
  30. Diego St Aztecs    102    7

The map function doesn't appear to be honoring the implied group_by based on the Book column. What am I missing?

答案1

得分: 1

以下是翻译的部分：

解决方案如@stefan建议的那样。 在定义df_int并分配必要变量之后，输出是准确的。 这是更新后的函数

  1. int_value <- function(df){
  2.     
  3.     df_int <- df %>% 
  4.             dplyr::select(c(ID, Home, Away, Team, Points)) %>%  
  5.             dplyr::intersect(df_alt %>% dplyr::select(c(ID, Home, Away, Team, 
  6. Points))) %>% 
  7.             mutate(Book = 'Pinnacle')
  8.     
  9.     df_join <- df %>% full_join(df_int)
  10.     
  11.     df_final <- df_join %>% left_join(df_alt %>% rename(price=Price)) %>% 
  12.             mutate(Price=ifelse(is.na(price),Price,price))%>% 
  13.             select(-price)
  14.     
  15. }

这是更新后的输出

  1. [[1]]
  2. # A tibble: 4 × 7
  3. ID                               Book     Home                 Away                
  4. Team                 Price Points
  5. <chr>                            <chr>    <chr>                <chr>               
  6. <chr>                <dbl>  <dbl>
  7. 1 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St 
  8. Aztecs Alabama Crimson Tide  -110   -7.5
  9. 2 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St 
  10. Aztecs San Diego St Aztecs   -110    7.5
  11. 3 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St 
  12. Aztecs Alabama Crimson Tide  -104   -7.5
  13. 4 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St 
  14. Aztecs San Diego St Aztecs   -108    7.5
  16. [[2]]
  17. # A tibble: 4 × 7
  18. ID                               Book      Home                 Away                
  19. Team                 Price Points
  20. <chr>                            <chr>     <chr>                <chr>               
  21. <chr>                <dbl>  <dbl>
  22. 1 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St 
  23. Aztecs Alabama Crimson Tide  -111     -7
  24. 2 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St 
  25. Aztecs San Diego St Aztecs   -101      7
  26. 3 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St 
  27. Aztecs Alabama Crimson Tide  -115     -7
  28. 4 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St 
  29. Aztecs San Diego St Aztecs    102      7
英文:

The solution was as @stefan had recommended. After defining df_int and assigning the necessary variables the output is accurate. Here is the updated function

  1. int_value &lt;- function(df){
  2.     
  3.     df_int &lt;- df %&gt;% 
  4.             dplyr::select(c(ID, Home, Away, Team, Points)) %&gt;%  
  5.             dplyr::intersect(df_alt %&gt;% dplyr::select(c(ID, Home, Away, Team, 
  6. Points))) %&gt;% 
  7.             mutate(Book = &#39;Pinnacle&#39;)
  8.     
  9.     df_join &lt;- df %&gt;% full_join(df_int)
  10.     
  11.     df_final &lt;- df_join %&gt;% left_join(df_alt %&gt;% rename(price=Price)) %&gt;% 
  12.             mutate(Price=ifelse(is.na(price),Price,price))%&gt;% 
  13.             select(-price)
  14.     
  15. }

And here is the updated output

  1. [[1]]
  2. # A tibble: 4 &#215; 7
  3. ID                               Book     Home                 Away                
  4. Team                 Price Points
  5. &lt;chr&gt;                            &lt;chr&gt;    &lt;chr&gt;                &lt;chr&gt;               
  6. &lt;chr&gt;                &lt;dbl&gt;  &lt;dbl&gt;
  7. 1 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St 
  8. Aztecs Alabama Crimson Tide  -110   -7.5
  9. 2 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St 
  10. Aztecs San Diego St Aztecs   -110    7.5
  11. 3 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St 
  12. Aztecs Alabama Crimson Tide  -104   -7.5
  13. 4 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St 
  14. Aztecs San Diego St Aztecs   -108    7.5
  16. [[2]]
  17. # A tibble: 4 &#215; 7
  18. ID                               Book      Home                 Away                
  19. Team                 Price Points
  20. &lt;chr&gt;                            &lt;chr&gt;     &lt;chr&gt;                &lt;chr&gt;               
  21. &lt;chr&gt;                &lt;dbl&gt;  &lt;dbl&gt;
  22. 1 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St 
  23. Aztecs Alabama Crimson Tide  -111     -7
  24. 2 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St 
  25. Aztecs San Diego St Aztecs   -101      7
  26. 3 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St 
  27. Aztecs Alabama Crimson Tide  -115     -7
  28. 4 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St 
  29. Aztecs San Diego St Aztecs    102      7

huangapple
  • 本文由 发表于 2023年3月23日 11:32:27
  • 转载请务必保留本文链接：https://go.coder-hub.com/75819037.html
  • purrr
  • r
  • tidyverse
匿名

发表评论

匿名网友

:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen:

确定