如何合并两个嵌套列？

Question

我有以下数据集：

> ex
# A tibble: 98 × 5
   Country    pred_data         data             model     prdct     
   <chr>      <list>            <list>           <list>    <list>    
 1 Albania    <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 2 Angola     <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 3 Azerbaijan <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 4 Algeria    <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 5 Armenia    <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 6 Laos       <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 7 Argentina  <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 8 Austria    <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
 9 Barbados   <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
10 Belgium    <tibble [10 × 2]> <tibble [9 × 3]> <betareg> <dbl [10]>
# … with 88 more rows
# ℹ Use `print(n = ...)` to see more rows

我正在尝试创建一个新列，即merge或cbind of pred_data和prdct，它应该将数据集看作<tibble [10 × 3]>，但当我编写以下代码时：

res <- ex %>%
  group_by(Country) %>%
  nest() %>%
  mutate(pr2 = map2(pred_data, prdct, merge)) %>%
  ungroup()

它给出了以下错误：

Error in `mutate()`:
! Problem while computing `pr2 = map2(pred_data, prdct, merge)`.
ℹ The error occurred in group 1: Country = "Albania".
Caused by error in `map2()`:
! object 'pred_data' not found
Backtrace:
 1. ... %>% ungroup()
 8. purrr::map2(pred_data, prdct, merge)

有人知道我做错了什么吗？有人能帮我得到通缉的结果吗？

Answer 1

问题是，在使用nest之前，您需要对数据进行map2，这是不必要的。另外，对于您想要的结果，请使用cbind而不是merge。

使用一些基于gapminder数据集的假示例数据：

library(gapminder)
library(dplyr, warn=FALSE)
library(tidyr)
library(purrr)

# example data
gap_nested <- gapminder %>%
  group_by(country) %>%
  nest() %>%
  mutate(prdct = map(country, ~ runif(12))) 

gap_nested <- gap_nested %>%
  mutate(data = map2(data, prdct, ~ cbind(.x, prdct = .y)))

head(gap_nested)
#> # A tibble: 6 × 3
#> # Groups:   country [6]
#>   country     data          prdct     
#>   <fct>       <list>        <list>    
#> 1 Afghanistan <df [12 × 6]> <dbl [12]>
#> 2 Albania     <df [12 × 6]> <dbl [12]>
#> 3 Algeria     <df [12 × 6]> <dbl [12]>
#> 4 Angola      <df [12 × 6]> <dbl [12]>
#> 5 Argentina   <df [12 × 6]> <dbl [12]>
#> 6 Australia   <df [12 × 6]> <dbl [12]>

gap_nested$data[[1]]
#>    continent year lifeExp      pop gdpPercap      prdct
#> 1       Asia 1952  28.801  8425333  779.4453 0.08012722
#> 2       Asia 1957  30.332  9240934  820.8530 0.82086328
#> 3       Asia 1962  31.997 10267083  853.1007 0.80174251
#> 4       Asia 1967  34.020 11537966  836.1971 0.31049317
#> 5       Asia 1972  36.088 13079460  739.9811 0.32704996
#> 6       Asia 1977  38.438 14880372  786.1134 0.68114361
#> 7       Asia 1982  39.854 12881816  978.0114 0.75686794
#> 8       Asia 1987  40.822 13867957  852.3959 0.40456933
#> 9       Asia 1992  41.674 16317921  649.3414 0.83969222
#> 10      Asia 1997  41.763 22227415  635.3414 0.64467924
#> 11      Asia 2002  42.129 25268405  726.7341 0.34743910
#> 12      Asia 2007  43.828 31889923  974.5803 0.54325215

Answer 2

我们也可以不使用map，只要我们将它保留为列表列：

library(dplyr)

ex |>
  group_by(country)
  mutate(pr2 = list(bind_cols(data, tibble(prdct = prdct)))) |>
  ungroup()

输出：

# A tibble: 142 × 4
# Groups:   country [142]
   country     data              prdct      pr2              
   <fct>       <list>            <list>     <list>           
 1 Afghanistan <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 2 Albania     <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 3 Algeria     <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 4 Angola      <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 5 Argentina   <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 6 Australia   <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 7 Austria     <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 8 Bahrain     <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
 9 Bangladesh  <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
10 Belgium     <tibble [12 × 5]> <dbl [12]> <tibble [12 × 6]>
# … with 132 more rows

数据(受@stefan启发)：

library(gapminder)
library(dplyr)

ex <-
  gapminder |>
  group_by(country) |>
  nest() |>
  mutate(prdct = list(runif(12))) |>
  ungroup()