How do I append columns from a list of data frames?

Question

Suppose I have the following list of dataframes:

lst <- list(
a=data.frame(key=c(1,2,3), val=c(10,20,30)), 
b=data.frame(key=c(1,2,3), val=c(100,200,300)), 
c=data.frame(key=c(1,2,3), val=c(1000,2000,3000)), 
d=data.frame(key=c(1,2), val=c(10000,20000)))

How do I create a data.frame as follows? (or similar):

  key val.a val.b val.c val.d
1   1    10   100  1000 10000
2   2    20   200  2000 20000
3   3    30   300  3000 NA

I tried to do it this way (but FAILED):

Browse[2]> Reduce(function(x,y) merge(x, y, by = 'key', all.x = T), lst)
  key val.x val.y val.x val.y
1   1    10   100  1000 10000
2   2    20   200  2000 20000
3   3    30   300  3000 NA
Warning message:
In merge.data.frame(x, y, by = "key", all.x = T) :
  column names ‘val.x’, ‘val.y’ are duplicated in the result

NOTE: I would prefer a base-R solution but am interested in other ways of doing this

Answer 1

We can use inner_join with reduce after renaming the second column with the corresponding list name

library(purrr)
library(dplyr)
library(stringr)
imap(lst, ~ { nm <- .y
              .x %>% 
                rename_at(vars(val), ~ str_c(., ".", nm))}) %>% 
    reduce(full_join, by = 'key')
#    key val.a val.b val.c val.d
#1   1    10   100  1000 10000
#2   2    20   200  2000 20000
#3   3    30   300  3000 30000

---

Or in base R, we use Map to do the renaming of the column, then as in the OP's post, do the merge within Reduce

Reduce(function(...) merge(..., by = 'key', all = TRUE), 
       Map(function(x, y) setNames(x, c('key',
           paste0(names(x)[-1], ".", y))), lst, names(lst)))
#  key val.a val.b val.c val.d
#1   1    10   100  1000 10000
#2   2    20   200  2000 20000
#3   3    30   300  3000 30000

Answer 2

If you are not too fussed about the column names etc looking exactly they way you have it, you can use the following:

df_merged = do.call(cbind,lst) %>% 
  select(key = a.key, 2,4,6,8) 

with output:

   key a.val b.val c.val d.val
1     1    10   100  1000 10000
2     2    20   200  2000 20000
3     3    30   300  3000 30000