Add missing columns from different data.frame filled with 0 [duplicate]

Answer 1

df1 <- data.frame(a = 1, b = 2, c = 3, d = 4)

df2 <- data.frame(a = 5, c = 6)

library(tidyverse)

right_join(df1, df2)

 a  b c  d
1 5 NA 6 NA

You'll have to change NA's to 0.

Answer 2

We can use setdiff to find out columns which are not present in df2 and assign the value 0 to those columns.

df2[setdiff(names(df1), names(df2))] <- 0

#  a c b d
#1 5 6 0 0

If we want to maintain the same order of columns as in df1 we can later do

df2[names(df1)]
#  a b c d
#1 5 0 6 0

Answer 3

There's probably a more elegant solution, but I think this works for your situation. If you're not too fussed about mixing your workflow up with dplyr and data.table syntax, you can use setdiff() to identify non-matching column names, and use data.table syntax to create those zero-value columns efficiently without using loops or apply() functions. Once you've made sure this works for all the possible situations, you can wrap it in a function and scale this across more datasets.

df1 <- data.frame(a = 1, b = 2, c = 3, d = 4)
df2 <- data.frame(a = 5, c = 6)

# Variables in df1 but not in df2
diff_vars <- dplyr::setdiff(names(df1),names(df2))

df2 %>%
  data.table::data.table() %>%
  .[,c(diff_vars):=0] %>%
  tibble::as_tibble() # Can choose to keep this in data.table