为什么 purrr::map 函数没有正确地将函数映射到拆分数据帧的每一块？

Question

我有以下数据框，我们可以调用 df_all

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

我有以下数据框，我们可以调用 df_alt

structure(list(ID = c("1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385", 
"1738c0c7214e7fced61c1caa479a5385", "1738c0c7214e7fced61c1caa479a5385"
), Book = c("Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", 
"Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", 
"Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", "Pinnacle", 
"Pinnacle"), Home = c("Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide"), Away = c("San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs"), Team = c("Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"Alabama Crimson Tide", "Alabama Crimson Tide", "Alabama Crimson Tide", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "San Diego St Aztecs", 
"San Diego St Aztecs", "San Diego St Aztecs", "Alabama Crimson Tide", 
"San Diego St Aztecs"), Price = c(-149, -138, -126, -115, 105, 
114, 122, 132, 128, 119, 110, 102, -119, -131, -142, -154, -104, 
-108), Points = c(-5.5, -6, -6.5, -7, -8, -8.5, -9, -9.5, 5.5, 
6, 6.5, 7, 8, 8.5, 9, 9.5, -7.5, 7.5)), row.names = c(NA, -18L
), class = c("tbl_df", "tbl", "data.frame"))

我有以下功能，用于查找 df_all 和 df_alt 之间的公共/相交点值。

int_value <- function(df){
    
    df %>% 
            dplyr::select(c(ID, Team, Points)) %>%  
            dplyr::intersect(df_alt %>% dplyr::select(c(ID, Team,Points))) %>% 
            mutate(Book = 'Pinnacle')
    
    df %>% full_join(df_int)%>% left_join(df_alt %>% rename(price=Price)) %>% 
            mutate(Price=ifelse(is.na(price),Price,price))%>% 
            select(-price)
}

我正在尝试使用以下映射语法应用 int_value。

df_all %>% 
    group_split(ID, Book) %>% 
    map(int_value)

这是返回的输出，不是所需的输出。

[[1]]
# A tibble: 8 × 7
ID                               Book      Home                 Away                Team                 
Price Points
<chr>                            <chr>     <chr>                <chr>               <chr>                
<dbl>  <dbl>
1 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -110   -7.5
2 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -110    7.5
3 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -111   -7  
4 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -101    7  
5 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -104   -7.5
6 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -108    7.5
7 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -115   -7  
8 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs    102    7  

[[2]]
# A tibble: 8 × 7
ID                               Book      Home                 Away                Team                 
Price Points
<chr>                            <chr>     <chr>                <chr>               <chr>                
<dbl>  <dbl>
1 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -110   -7.5
2 1738c0c7214e7fced61c1caa479a5385 Bovada    Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -110    7.5
3 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -111   -7  
4 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -101    7  
5 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -104   -7.5
6 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -108    7.5
7 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -115   -7  
8 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs    102    7  

这是期望的输出和我期望返回的内容。

[[1]]
# A tibble: 6 × 7
ID                               Book     Home                 Away                Team                 
Price Points
<chr>                            <chr>    <chr>                <chr>               <chr>                
<dbl>  <dbl>
1 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -110   -7.5
2 1738c0c7214e7fced61c1caa479a5385 Bovada   Alabama Crimson Tide San Diego St Aztecs San Diego 
St Aztecs   -110    7.5
3 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -104   -7.5
4 1738c0c7214e7fced61c1caa479a5385 Pinnacle Alabama Crimson Tide San Diego St Aztecs San Diego 
St Aztecs   -108    7.5


[[2]]
# A tibble: 6 × 7
ID                               Book      Home                 Away                Team                 
Price Points
<chr>                            <chr>     <chr>                <chr>               <chr>                
<dbl>  <dbl>
1 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -111   -7  
2 1738c0c7214e7fced61c1caa479a5385 LowVig.ag Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs   -101    7  
3 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs Alabama 
Crimson Tide  -115   -7  
4 1738c0c7214e7fced61c1caa479a5385 Pinnacle  Alabama Crimson Tide San Diego St Aztecs San 
Diego St Aztecs    102    7 

map 函数似乎不支持基于 Book 列的隐含 group_by。我错过了什么？