Extract a dataframe from a column of dataframes (tidyverse approach) - r

Question

I have been able to do some nice things with purrr to be able to work with dataframe columns within a dataframe. By which I am referring to a column of a dataframe where every cell contains a dataframe itself.
I am trying to find out the idiomatic approach for extracting one of these dataframes back out.
Example
# Create a couple of dataframes:
df1 <- tibble::tribble(~a, ~b,
1, 2,
3, 4)
df2 <- tibble::tribble(~a, ~b,
11, 12,
13, 14)
# Make a dataframe with a dataframe column containing
# our first two dfs as cells:
meta_df <- tibble::tribble(~df_name, ~dfs,
"One", df1,
"Two", df2)
My question is, what is the tidyverse-preferred way of getting one of these dataframes back out of meta_df? Say I get the cell I want using select() and filter():
library("magrittr")
# This returns a 1x1 tibble with the only cell containing the 2x2 tibble that
# I'm actually after:
meta_df %>%
dplyr::filter(df_name == "Two") %>%
dplyr::select(dfs)
This works, but seems non-tidyverse-ish:
# To get the actual tibble that I'm after I can wrap the whole lot in brackets
# and then use position [[1, 1]] index to get it:
(meta_df %>%
dplyr::filter(df_name == "Two") %>%
dplyr::select(dfs))[[1, 1]]
# Or a pipeable version:
meta_df %>%
dplyr::filter(df_name == "Two") %>%
dplyr::select(dfs) %>%
`[[`(1, 1)
I have a feeling that this might be a situation where the answer is in purrr rather than dplyr, and that it might be a simple trick once you know it, but I'm coming up blank so far.

Answer 1

better solution:
Use tidyr::unnest():
meta_df %>%
dplyr::filter(df_name == "Two") %>%
dplyr::select(dfs) %>%
tidyr::unnest()
other solution:
You can use pull (the tidyverse way to select the column, equivalent to $), but it returns a one-element list of tibbles, so you need to add %>% .[[1]] to the end.
meta_df %>%
dplyr::filter(df_name == "Two") %>%
dplyr::pull(dfs) %>% .[[1]]