Binding dataframes with different column names by row - r

Question

I imported this excel sheet as a list of dataframes. I want to merge the list into one dataframe. bind_rows() allow me to easily add together the dataframes, but the issue is that I have a variable/column that has different names in each dataframe. bind_row() will by default create two separate columns, with empty values for the data from the other data frames. How can I join these columns?
Sample code:
# Sample dataframes
df1 <- tibble(A = c(1,2,3),
B = c("X","Y","Z"),
C = c(T,F,F)
)
df2 <- tibble(A = c(3,4,5),
B = c("U","V","W"),
D = c(T,T,F)
)
# List of dataframes
my_ls <- list(df1, df2)
my_ls
[[1]]
# A tibble: 3 x 3
A B C
<dbl> <chr> <lgl>
1 1 X TRUE
2 2 Y FALSE
3 3 Z FALSE
[[2]]
# A tibble: 3 x 3
A B D
<dbl> <chr> <lgl>
1 3 U TRUE
2 4 V TRUE
3 5 W FALSE
# Creating joined dataframe:
my_df <- bind_rows(my_ls)
my_df
# Current outcome: A tibble: 6 x 4
A B C D
<dbl> <chr> <lgl> <lgl>
1 1 X TRUE NA
2 2 Y FALSE NA
3 3 Z FALSE NA
4 3 U NA TRUE
5 4 V NA TRUE
6 5 W NA FALSE
The desired outcome:
# Desired outcome: A tibble: 6 x 3
A B C
<dbl> <chr> <lgl>
1 1 X TRUE
2 2 Y FALSE
3 3 Z FALSE
4 3 U TRUE
5 4 V TRUE
6 5 W FALSE
Currently, I've been using mutate() with case_when(), where I check which column is not empty (!is.na()). This works, but I can't help but think there must be an easier way.
# Example using mutate
my_df <- my_df %>%
mutate(
C = case_when(is.na(C) & !is.na(D) ~ D,
!is.na(C) & is.na(D) ~ C,
# The lines below may be a bit redundant for my purpose, since the dataframes either have the C or D variable.
!is.na(C) & !is.na(D) ~ C, # Better would be to return that variable has overlapping information
is.na(C) & is.na(D) ~ NA
)
) %>%
select(-D)
my_df
# A tibble: 6 x 3
A B C
<dbl> <chr> <lgl>
1 1 X TRUE
2 2 Y FALSE
3 3 Z FALSE
4 3 U TRUE
5 4 V TRUE
6 5 W FALSE

Answer 1

You can bind_rows and then select non-NA value using coalesce :
library(dplyr)
bind_rows(my_ls) %>% mutate(C = coalesce(C, D)) %>% select(A:C)
# A B C
# <dbl> <chr> <lgl>
#1 1 X TRUE
#2 2 Y FALSE
#3 3 Z FALSE
#4 3 U TRUE
#5 4 V TRUE
#6 5 W FALSE

Answer 2

Following the comment by #KarthikS you can rename your columns before binding. My approach using rename_with does not require the columns to be in a specific order. To illusrate this I used somewhat different example dataframes:
library(purrr)
library(dplyr)
d1 <- data.frame(A = 1, B = 2, C = 3)
d2 <- data.frame(A = 4, B = 5, D = 6)
d3 <- data.frame(D = 7, A = 8, B = 9)
d <- list(d1, d2, d3)
map(d, ~ rename_with(.x, ~ "C", matches("^D$"))) %>%
bind_rows()
#> A B C
#> 1 1 2 3
#> 2 4 5 6
#> 3 8 9 7
And now four your dataset:
d <- list(df1, df2)
map(d, ~ rename_with(.x, ~ "C", matches("^D$"))) %>%
bind_rows()
#> # A tibble: 6 x 3
#> A B C
#> <dbl> <chr> <lgl>
#> 1 1 X TRUE
#> 2 2 Y FALSE
#> 3 3 Z FALSE
#> 4 3 U TRUE
#> 5 4 V TRUE
#> 6 5 W FALSE
And if we add an addtional one with a different order:
df3 <- tibble(D = c(T,T,F),
A = c(7,8,9),
B = c("A","B","C"))
d <- list(df1, df2, df3)
map(d, ~ rename_with(.x, ~ "C", matches("^D$"))) %>%
bind_rows()
#> # A tibble: 9 x 3
#> A B C
#> <dbl> <chr> <lgl>
#> 1 1 X TRUE
#> 2 2 Y FALSE
#> 3 3 Z FALSE
#> 4 3 U TRUE
#> 5 4 V TRUE
#> 6 5 W FALSE
#> 7 7 A TRUE
#> 8 8 B TRUE
#> 9 9 C FALSE
Created on 2020-10-16 by the reprex package (v0.3.0)

Answer 3

Apologize for breaking out of the tidyverse for a quick answer
expl <- read.table(text= " A B C D
1 1 X TRUE NA
2 2 Y FALSE NA
3 3 Z FALSE NA
4 3 U NA TRUE
5 4 V NA TRUE
6 5 W NA FALSE")
expl$E <- ifelse(is.na(expl$C), expl$D, expl$C)
print(expl)
or maybe
expl[,c("C", "D")] %>% rowMeans(na.rm = TRUE) %>% as.logical()
EDIT: Translated the latter to tidy:
expl %>% select("C", "D") %>% rowMeans(na.rm = TRUE) %>% as.logical()
EDIT after first comment:
If you want more control you should probably write the things you want to do in each case in a function similar to the following example:
library(magrittr)
expl <- read.table(text= " A B C D
1 1 X TRUE NA
2 2 Y FALSE NA
3 3 Z FALSE NA
4 3 U NA TRUE
5 4 V NA TRUE
6 5 W NA FALSE
7 7 I NA NA
8 9 J TRUE TRUE")
myfun <- function(a, b){
if(is.na(a) & is.na(b))
return(NA)
if(!is.na(a) & !is.na(b)) {
warning("too much information, a and b set!")
return(NaN)
}
return(max(a, b, na.rm=TRUE))
}
myfun = Vectorize(myfun)
myfun(expl$C, expl$D) %>% as.logical()