CODEWITHSUNDEEP
rtidyversedplyrrecode
Quinterpret
Quinterpret

Reputation: 143

Recoding several variables in for loop

I am working with some survey data where some of the responses have been coded inconsistently. For instance' "I don't know" may be coded as 4, 5, 97, or 777. I want to standardize all of these responses as "77" as efficiently as possible. I would like to use a crosswalk and want to avoid creating a new recode command for every variable in which this occurs, if possible, since there are several.

library(tidyverse)

#df with the inconsistent fourth category
var1 <- c("1", "2", "3", "4")
var2 <- c("1", "2", "3", "5")
var3 <- c("1", "2", "3", "97")
var4 <- c("1", "2", "3", "777")

df <- data.frame(var1, var2, var3, var4)

var <- c("var1", "var2", "var3", "var4")
oldvalue <- c("4", "6", "97", "777")
newvalue <- c("77", "77", "77", "77")

#crosswalk of old values to new values
cw <- data.frame(var, oldvalue, newvalue)

recodevars = cw$var

A few things I have tried are as follows, although I haven't had any luck with any. Please let me know if you have any suggestions.

rec_all = df %>%
  transmute_at(vars(recodevars), funs(recode(., cw$oldvalue = cw$newvalue)))


for(i in recodevars){
  rec_all = df %>%
    transmute_at(vars(recodevars), funs(ifelse(i == cw$oldval, cw$newval, i)))
}

Upvotes: 1

Views: 798

Answers (7)

hello_friend
hello_friend

Reputation: 5798

# Split-Apply-Combine: 
do.call(
  cbind, 
  Map(
    function(x, y){
    replace(
      x,
      x == y$oldvalue,
      y$newvalue
    )
  },
    split.default(
      df,
      seq_len(
        ncol(
          df
        )
      )
    ),
    split(
      cw, 
      seq_len(
        nrow(
          cw
        )
      )
    )
  )
)

Upvotes: 0

Ronak Shah
Ronak Shah

Reputation: 389235

Using purrr::pmap_dfc -

purrr::pmap_dfc(cw, ~{
  tmp <- df[..1]
  tmp[tmp == ..2] <- ..3
  tmp
})

#  var1 var2 var3 var4
#1    1    1    1    1
#2    2    2    2    2
#3    3    3    3    3
#4   77    5   77   77

Upvotes: 0

ThomasIsCoding
ThomasIsCoding

Reputation: 102625

A base R option

setNames(list2DF(
    lapply(
        names(df),
        function(k) {
            with(
                cw,
                replace(df[[k]], df[[k]] == oldvalue[var == k], newvalue[var == k])
            )
        }
    )
), names(df))

gives

  var1 var2 var3 var4
1    1    1    1    1
2    2    2    2    2
3    3    3    3    3
4   77    5   77   77

Upvotes: 2

Tech Commodities
Tech Commodities

Reputation: 1959

If you're dealing with factors ("1") and not numeric responses (1), the forcats package may be your friend.

This will recode anything not in the notForRecode string as "77"

notForRecode <- c("1", "2", "3") #  Add others as wanted
df <- df %>% 
  mutate(across(everything(), ~ fct_other(.x, keep = notForRecode, other_level = "77")))

Upvotes: 3

Bernhard
Bernhard

Reputation: 4427

Though not part of the tidyverse apply is the old standard function to apply a function to any value in a data.frame, essentially making this a one liner:

var1 <- c("1", "2", "3", "4")
var2 <- c("1", "2", "3", "5")
var3 <- c("1", "2", "3", "97")
var4 <- c("1", "2", "3", "777")

df <- data.frame(var1, var2, var3, var4)

var <- c("var1", "var2", "var3", "var4")
oldvalue <- c("4", "6", "97", "777")

new.df <- apply(df[, var], 1:2, function(x) if(x %in% oldvalue) 77 else x)

print(new.df)

Upvotes: 4

DPH
DPH

Reputation: 4344

if the values are independed of the column like in your example, then you can simply use this code:

df %>%
    dplyr::mutate(across(everything(), ~ ifelse(.x %in% c(4,6,97,777), 77, .x)))

Upvotes: 4

akrun
akrun

Reputation: 887831

Loop across the 'recodevars' columns, subset the 'cw' dataset rows based on the column name (cur_column()) while removing the first column, use that to match and replace the values of the data, and coalesce with the original column so that the NA elements for non-matches are replaced with corresponding original column values

library(dplyr)
library(tibble)
df %>% 
    mutate(across(all_of(recodevars), 
       ~ coalesce(deframe(cw[cw$var ==cur_column(),][-1])[as.character(.)], .)))

-output

  var1 var2 var3 var4
1    1    1    1    1
2    2    2    2    2
3    3    3    3    3
4   77    5   77   77

Upvotes: 5

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