While playing around with the purrr package of the Tidyverse in R, I saw that the map() function returns a list.
library(tidyverse) set.seed(123) map(1:5, ~rnorm(3)) #> [[1]] #> [1] -0.5604756 -0.2301775 1.5587083 #> #> [[2]] #> [1] 0.07050839 0.12928774 1.71506499 #> #> [[3]] #> [1] 0.4609162 -1.2650612 -0.6868529 #> ...... I want to convert this list to a data frame with 3 columns. One option would be using do.call(rbind, .). However, I also noticed that the map_dfr() function existed.
Using this function in the same way as the map() provides an error.
map_dfr(1:5, ~rnorm(3)) #> Error: Argument 1 must have names. Question
What are the differences between the map() and the map_dfr() functions that lead to this error? And how should you use the map_dfr() function to bind the rows directly in the mapping function?
1 Answer
map_dfr directly binds the rows when map output is a dataframe or a named vector:
library(dplyr) library(purrr) map(1:5, ~as.data.frame(matrix(rnorm(3),nrow=1))) [[1]] V1 V2 V3 1 1.326029 0.4581257 0.4367454 [[2]] V1 V2 V3 1 -0.3769822 -0.2488601 -1.441538 [[3]] V1 V2 V3 1 -0.4931225 1.145818 -0.6269974 [[4]] V1 V2 V3 1 -1.679398 1.035032 1.784175 [[5]] V1 V2 V3 1 0.5158901 -2.322314 -1.145897 map_dfr(1:5, ~as.data.frame(matrix(rnorm(3),nrow=1))) V1 V2 V3 1 0.29250530 -0.8325543 0.21013608 2 1.03348415 0.3333718 -0.08498664 3 1.01011329 0.6583516 -0.49360421 4 -0.06229409 -0.1200969 0.06078136 5 -1.92491929 0.3891900 -0.57046411 It's equivalent to :
map(1:5, ~as.data.frame(matrix(rnorm(3),nrow=1))) %>% bind_rows You get an error because you provide to map_dfr a vector without column names.
This works:
map_dfr(1:5, ~setNames(rnorm(3),LETTERS[1:3])) # A tibble: 5 x 3 A B C <dbl> <dbl> <dbl> 1 -0.360 -1.36 1.40 2 0.715 1.55 0.381 3 1.20 -0.179 0.315 4 0.126 -0.467 1.04 5 1.31 0.375 -2.21 
