accessing nested lists in R

Question

I have created a double nested structure for some data. How can I Access the data on the 2nd Level ( or for that matter the nth Level?)

library(gapminder)
library(purrr)
library(tidyr)
gapminder
nest_data <- gapminder %>% group_by(continent) %>% nest(.key = by_continent) 

nest_2<-nest_data %>% mutate(by_continent = map(by_continent, ~.x %>% group_by(country) %>% nest(.key = by_country)))

How can I now get the data for China into a dataframe or tibble from nest_2?

I can get the data for all of Asia, but I'm unable to isolate China.

a<-nest_2[nest_2$continent=="Asia",]$by_continent  ##Any better way of isolating Asia from nest_2?

I thought I could then do

b<-a[a$country=="China",]$by_country 

But I get the following error

Error in a[a$country == "China", ] : incorrect number of dimensions 



> glimpse(a)
List of 1
 $ :Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   33 obs. of  2 variables:
  ..$ country   : Factor w/ 142 levels "Afghanistan",..: 1 8 9 19 25 56 59 60 61 62 ...
  ..$ by_country:List of 33

So my big error was not recognizing that the product was a list, which could be remedied by adding [[1]] in the end. However, I very much liked the solution by @Floo0. I took the liberty of providing a function taking the names of the variables in case the sequence of columns are different from the one provided.

select_unnest <- function(df, listcol, var, var_val){  ###listcol, var and var_val must enclosed by ""
  df[[listcol]][df[[var]]==var_val][[1]]
}

nest_2 %>% select_unnest(listcol = "by_continent", var = "continent", var_val = "Asia") %>% 
  select_unnest(listcol = "by_country", var = "country", var_val = "China")

Answer 1

This is a pipe-able (%>%) base R approach

select_unnest <- function(x, select_val){
  x[[2]][x[[1]]==select_val][[1]]
}

nest_2 %>% select_unnest("Asia") %>% select_unnest("China")

Comparing the timings:

Unit: microseconds

                min        lq      mean   median        uq       max neval
aosmith1   3202.105 3354.0055 4045.9602 3612.126 4179.9610 17119.495   100
aosmith2   5797.744 6191.9380 7327.6619 6716.445 7662.6415 24245.779   100
Floo0       227.169  303.3280  414.3779  346.135  400.6735  4804.500   100
Ben Bolker  622.267  720.6015  852.9727  775.172  875.5985  1942.495   100

Code:

microbenchmark::microbenchmark(
  {a<-nest_2[nest_2$continent=="Asia",]$by_continent
  flatten_df(a) %>%
    filter(country == "China") %>%
    unnest},
  {nest_2 %>%
      filter(continent == "Asia") %>%
      select(by_continent) %>%
      unnest%>%
      filter(country == "China") %>%
      unnest},
  {nest_2 %>% select_unnest("Asia") %>% select_unnest("China")},
  {n1 <- nest_2$by_continent[nest_2$continent=="Asia"][[1]]
  n2 <- n1 %>% filter(country=="China")
  n2$by_country[[1]]}
)