合并列表列数据框

Question

I have a data frame / tibble with several list-columns that I want to merge / join with each other, but I'm struggling to find an elegant way to do so using the mutate(map()) workflow.

Suppose I have a data frame with the following structure:

library(gapminder)

# Setup
gap_1 <- gapminder %>% 
    select(-pop, -gdpPercap) %>% 
    group_by(country, continent) %>% 
    nest() %>% 
    rename(lifeExp = data) 

gap_2 <- gapminder %>% 
    select(-lifeExp, -gdpPercap) %>% 
    group_by(country, continent) %>% 
    nest() %>% 
    rename(pop = data)  

gap_3 <- gapminder %>% 
    select(-lifeExp, -pop) %>% 
    group_by(country, continent) %>% 
    nest() %>% 
    rename(gdpPercap = data)

# What my data looks like 
gap_main <- reduce(list(gap_1, gap_2, gap_3), left_join, by = c("country", "continent")) %>% ungroup()  

What I'm hoping to do is to merge the list-columns lifeExp , pop , and gdpPercap by year (There are more than three such columns in my data). But I'm not sure how one would go about this using mutate and pmap / map2 .

My attempts so far have been to use pmap with bind_cols like so: (this is assuming rows correspond)

gap_main %>% 
     mutate(all = pmap(list(lifeExp, pop, gdpPercap), ~ bind_cols(...))) 

or repeatedly use map2 with left_join . Is there a more elegant way of doing this perhaps using reduce?

Answer 1

I am not sure how your desired output should look like, would the approach below work for you? It uses {dplyr}'s rowwise notation and then basically purrr::reduce to join all three columns in one call.

library(tidyverse)
library(gapminder)

gap_main %>% 
  rowwise %>% 
  mutate(data = list(reduce(list(lifeExp, pop, gdpPercap), left_join, by = "year")))

#> # A tibble: 142 x 6
#> # Rowwise: 
#>    country    continent lifeExp        pop           gdpPercap      data        
#>    <fct>      <fct>     <list>         <list>        <list>         <list>      
#>  1 Afghanist… Asia      <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  2 Albania    Europe    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  3 Algeria    Africa    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  4 Angola     Africa    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  5 Argentina  Americas  <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  6 Australia  Oceania   <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  7 Austria    Europe    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  8 Bahrain    Asia      <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  9 Bangladesh Asia      <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#> 10 Belgium    Europe    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#> # … with 132 more rows

Alternatively you could use pmap instead of rowwise in the following way:

gap_main %>% 
  mutate(all = pmap(list(lifeExp, pop, gdpPercap),
                    ~ reduce(list(..1, ..2, ..3), left_join, by = "year"))) 

#> # A tibble: 142 x 6
#>    country    continent lifeExp        pop           gdpPercap      all         
#>    <fct>      <fct>     <list>         <list>        <list>         <list>      
#>  1 Afghanist… Asia      <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  2 Albania    Europe    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  3 Algeria    Africa    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  4 Angola     Africa    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  5 Argentina  Americas  <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  6 Australia  Oceania   <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  7 Austria    Europe    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  8 Bahrain    Asia      <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#>  9 Bangladesh Asia      <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#> 10 Belgium    Europe    <tibble [12 ×… <tibble [12 … <tibble [12 ×… <tibble [12…
#> # … with 132 more rows