Combine two or more columns in a dataframe into a new column with a new name

Question

For example if I have this:

n = c(2, 3, 5) 
s = c("aa", "bb", "cc") 
b = c(TRUE, FALSE, TRUE) 
df = data.frame(n, s, b)

  n  s     b
1 2 aa  TRUE
2 3 bb FALSE
3 5 cc  TRUE

Then how do I combine the two columns n and s into a new column named x such that it looks like this:

  n  s     b     x
1 2 aa  TRUE  2 aa
2 3 bb FALSE  3 bb
3 5 cc  TRUE  5 cc

Answer 1

Use paste.

 df$x <- paste(df$n,df$s)
 df
#   n  s     b    x
# 1 2 aa  TRUE 2 aa
# 2 3 bb FALSE 3 bb
# 3 5 cc  TRUE 5 cc

Answer 2

For inserting a separator:

df$x <- paste(df$n, "-", df$s)

Answer 3

As already mentioned in comments by Uwe and UseR, a general solution in the tidyverse format would be to use the command unite:

library(tidyverse)

n = c(2, 3, 5) 
s = c("aa", "bb", "cc") 
b = c(TRUE, FALSE, TRUE) 

df = data.frame(n, s, b) %>% 
  unite(x, c(n, s), sep = " ", remove = FALSE)

Answer 4

Using dplyr::mutate:

library(dplyr)
df <- mutate(df, x = paste(n, s)) 

df 
> df
  n  s     b    x
1 2 aa  TRUE 2 aa
2 3 bb FALSE 3 bb
3 5 cc  TRUE 5 cc

Answer 5

Some examples with NAs and their removal using apply

n = c(2, NA, NA) 
s = c("aa", "bb", NA) 
b = c(TRUE, FALSE, NA) 
c = c(2, 3, 5) 
d = c("aa", NA, "cc") 
e = c(TRUE, NA, TRUE) 
df = data.frame(n, s, b, c, d, e)

paste_noNA <- function(x,sep=", ") {
gsub(", " ,sep, toString(x[!is.na(x) & x!="" & x!="NA"] ) ) }

sep=" "
df$x <- apply( df[ , c(1:6) ] , 1 , paste_noNA , sep=sep)
df

Answer 6

We can use paste0:

df$combField <- paste0(df$x, df$y)

If you do not want any padding space introduced in the concatenated field. This is more useful if you are planning to use the combined field as a unique id that represents combinations of two fields.

Answer 7

Instead of

• paste (default spaces),
• paste0 (force the inclusion of missing NA as character) or
• unite (constrained to 2 columns and 1 separator),

I'd suggest an alternative as flexible as paste0 but more careful with NA: stringr::str_c

library(tidyverse)

# check the missing value!!
df <- tibble(
  n = c(2, 2, 8),
  s = c("aa", "aa", NA_character_),
  b = c(TRUE, FALSE, TRUE)
)

df %>% 
  mutate(
    paste = paste(n,"-",s,".",b),
    paste0 = paste0(n,"-",s,".",b),
    str_c = str_c(n,"-",s,".",b)
  ) %>% 

  # convert missing value to ""
  mutate(
    s_2=str_replace_na(s,replacement = "")
  ) %>% 
  mutate(
    str_c_2 = str_c(n,"-",s_2,".",b)
  )
#> # A tibble: 3 x 8
#>       n s     b     paste          paste0     str_c      s_2   str_c_2   
#>   <dbl> <chr> <lgl> <chr>          <chr>      <chr>      <chr> <chr>     
#> 1     2 aa    TRUE  2 - aa . TRUE  2-aa.TRUE  2-aa.TRUE  "aa"  2-aa.TRUE 
#> 2     2 aa    FALSE 2 - aa . FALSE 2-aa.FALSE 2-aa.FALSE "aa"  2-aa.FALSE
#> 3     8 <NA>  TRUE  8 - NA . TRUE  8-NA.TRUE  <NA>       ""    8-.TRUE

^{Created on 2020-04-10 by the reprex package (v0.3.0)}

extra note from str_c documentation

Like most other R functions, missing values are "infectious": whenever a missing value is combined with another string the result will always be missing. Use str_replace_na() to convert NA to "NA"

Answer 8

There are other great answers, but in the case where you don't know the column names or the number of columns you want to concatenate beforehand, the following is useful.

df = data.frame(x = letters[1:5], y = letters[6:10], z = letters[11:15])
colNames = colnames(df) # could be any number of column names here
df$newColumn = apply(df[, colNames, drop = F], MARGIN = 1, FUN = function(i) paste(i, collapse = ""))