bind_rows of different data types

We can use rbindlist from data.table

library(data.table)
rbindlist(list(ds_a, ds_b))
#    x
#1:  1
#2:  2
#3:  3
#4:  4
#5:  5
#6:  6
#7: z1
#8: z2

Recently I switched to an approach that keeps all columns as strings initially(when converting from plain-text to a data.frame), then stacks, and finally converts the columns to an appropriate data type after it has all the rows to make a decision (using readr::type_convert()).

It mimics this example. I haven't done any performance comparisons but there wasn't a noticeable difference (the internet is the real bottleneck). Also, I kinda like the idea of reducing the number of data type conversions.

library(magrittr)
col_types <- readr::cols(.default = readr::col_character())
raw_a <- "x,y\n1,21\n2,22\n3,23\n4,24\n5,25\n6,26"
raw_b <- "x,y\nz1,31\nz2,32"
ds_a <- readr::read_csv(raw_a, col_types=col_types)
ds_b <- readr::read_csv(raw_b, col_types=col_types)

list(ds_a, ds_b) %>% 
  dplyr::bind_rows() %>% 
  readr::type_convert()
#> Parsed with column specification:
#> cols(
#>   x = col_character(),
#>   y = col_double()
#> )
#> # A tibble: 8 x 2
#>   x         y
#>   <chr> <dbl>
#> 1 1        21
#> 2 2        22
#> 3 3        23
#> 4 4        24
#> 5 5        25
#> 6 6        26
#> 7 z1       31
#> 8 z2       32

^{Created on 2019-12-03 by the reprex package (v0.3.0)}