Replacement of plyr::cbind.fill in dplyr?

Question

I apologize if this question is elementary, but I've been scouring the internet and I can't seem to find a simple solution.

I currently have a list of R objects (named vectors or dataframes of 1 variable, I can work with either), and I want to join them into 1 large dataframe with 1 row for each unique name/rowname, and 1 column for each element in the original list.

My starting list looks something like:

l1 <- list(df1 = data.frame(c(1,2,3), row.names = c("A", "B", "C")), 
       df2 = data.frame(c(2,6), row.names = c("B", "D")),
       df3 = data.frame(c(3,6,9), row.names = c("C", "D", "A")),
       df4 = data.frame(c(4,12), row.names = c("A", "E")))

And I want the output to look like:

data.frame("df1" = c(1,2,3,NA,NA),
+            "df2" = c(NA,2,NA,6,NA),
+            "df3" = c(9,NA,3,6,NA),
+            "df4" = c(4,NA,NA,NA,12), row.names = c("A", "B", "C", "D", "E"))
  df1 df2 df3 df4
A   1  NA   9   4
B   2   2  NA  NA
C   3  NA   3  NA
D  NA   6   6  NA
E  NA  NA  NA  12

I don't mind if the fill values are NA or 0 (ultimately I want 0 but that's an easy fix).

I'm almost positive that plyr::cbind.fill does exactly this, but I have been using dplyr in the rest of my script and I don't think using both is a good idea. dplyr::bind_cols does not seem to work with vectors of different lengths. I'm aware a very similar question has been asked here: R: Is there a good replacement for plyr::rbind.fill in dplyr? but as I mentioned, this solution doesn't actually seem to work. Neither does dplyr::full_join, even wrapped in a do.call. Is there a straightforward solution to this, or is the only solution to write a custom function?

Answer 1

We can convert the rownames to a column with rownames_to_column, then rename the second column, bind the list elements with bind_rows, and reshape to 'wide' with pivot_wider

library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
map_dfr(l1, ~ rownames_to_column(.x, 'rn') %>% 
              rename_at(2, ~'v1'), .id = 'grp') %>%        
   pivot_wider(names_from = grp, values_from = v1) %>% 
   column_to_rownames('rn')