My question is related to the conversion of a list of nested lists of dataframes into a single dataframe.
For a simple example let's use the iris dataset.
#import of library
library(purrr)
#import of the dataset
data(iris)
#creation of lists
lst=list(`0`=list(`1`=iris[1:5,]),`18`=c(`1`=list(iris[1:5,]),`2`=list(iris[1:5,])))
How can I merge all the dataframes of the nested lists into an one dataframe?
First, loop inside 0, 18 and bind_rows then use map_dfr to bind rows of the higher element i.e to bind 0 and 18 together.
library(purrr)
lst=list(`0`=list(`1`=iris[1:5,]),`18`=c(`1`=list(iris[1:5,]),`2`=list(iris[1:5,])))
map_dfr(ls,~bind_rows(.,.id='id'))
#OR
bind_rows(unlist(lst,recursive = FALSE),.id = 'id')
Another more save option is to use unlist and purrr::imap to produce column id represents the full path of each dataframe
imap_dfr(unlist(lst,recursive = FALSE), ~data.frame(id=.y, .x, stringsAsFactors = FALSE))
id Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 0.1 5.1 3.5 1.4 0.2 setosa
2 0.1 4.9 3.0 1.4 0.2 setosa
3 0.1 4.7 3.2 1.3 0.2 setosa
4 0.1 4.6 3.1 1.5 0.2 setosa
5 0.1 5.0 3.6 1.4 0.2 setosa
6 18.1 5.1 3.5 1.4 0.2 setosa
7 18.1 4.9 3.0 1.4 0.2 setosa
8 18.1 4.7 3.2 1.3 0.2 setosa
9 18.1 4.6 3.1 1.5 0.2 setosa
10 18.1 5.0 3.6 1.4 0.2 setosa
11 18.2 5.1 3.5 1.4 0.2 setosa
12 18.2 4.9 3.0 1.4 0.2 setosa
13 18.2 4.7 3.2 1.3 0.2 setosa
14 18.2 4.6 3.1 1.5 0.2 setosa
15 18.2 5.0 3.6 1.4 0.2 setosa