Apply same recoding rules to multiple data frames-CodePudding

I have 5 data frames. I want to recode all variables ending with "_comfort", "_agree", and "effective" using the same rules for each data frame. As is, the values in each column are 1:5 and I want is to recode 5's to 1, 4's to 2, 2's to 4, and 5's to 1 (3 will stay the same).
I do not want the end result to one merged dataset, but instead to apply the same recoding rules across all 5 independent data frames. For simplicity sake, let's just assume I have 2 data frames:

df1 <- data.frame(a_comfort = c(1, 2, 3, 4, 5),
        b_comfort = c(1, 2, 3, 4, 5),
        c_effective = c(1, 2, 3, 4, 5))

df2 <- data.frame(a_comfort = c(1, 2, 3, 4, 5),
        b_comfort = c(1, 2, 3, 4, 5),
        c_effective = c(1, 2, 3, 4, 5))

What I want is:

df1 <- data.frame(a_comfort = c(5, 4, 3, 2, 1),
        b_comfort = c(5, 4, 3, 2, 1),
        c_effective = c(5, 4, 3, 2, 1))

df2 <- data.frame(a_comfort = c(5, 4, 3, 2, 1),
        b_comfort = c(5, 4, 3, 2, 1),
        c_effective = c(5, 4, 3, 2, 1))

Conventionally, I would use dplyr's mutate_atand ends_withto achieve my goal, but have not been successful with this method across multiple data frames. I am thinking a combination of the purr and dplyr packages will work, but haven't nailed down the exact technique.

Thanks in advance for any help!

CodePudding user response：

You can use get() and assign() in a loop:

library(dplyr)

for (df_name in c("df1", "df2")) {
  df <- mutate(
    get(df_name),
    across(
      ends_with(c("_comfort", "_agree", "_effective")),
      \(x) 6 - x
    )
  )
  assign(df_name, df)
}

Result:

#> df1
  a_comfort b_comfort c_effective
1         5         5           5
2         4         4           4
3         3         3           3
4         2         2           2
5         1         1           1

#> df2
  a_comfort b_comfort c_effective
1         5         5           5
2         4         4           4
3         3         3           3
4         2         2           2
5         1         1           1

Note, however, it’s often better practice to keep multiple related dataframes in a list than loose in the global environment (see). In this case, you can use purrr::map() (or base::lapply()):

library(dplyr)
library(purrr)

dfs <- list(df1, df2)

dfs <- map(
  dfs,
  \(df) mutate(
    df,
    across(
      ends_with(c("_comfort", "_agree", "_effective")),
      \(x) 6 - x
    )
  )
)

Result:

#> dfs
[[1]]
  a_comfort b_comfort c_effective
1         5         5           5
2         4         4           4
3         3         3           3
4         2         2           2
5         1         1           1

[[2]]
  a_comfort b_comfort c_effective
1         5         5           5
2         4         4           4
3         3         3           3
4         2         2           2
5         1         1           1

CodePudding user response：

You can use ls(pattern = 'df\\d ') to find all objects whose names match a certain pattern. Then store them into a list and pass to purrr::map or lapply for recoding.

library(dplyr)

df.lst <- purrr::map(
  mget(ls(pattern = 'df\\d ')),
  ~ .x %>% mutate(6 - across(ends_with(c("_comfort", "_agree", "effective"))))
)

# $df1
#   a_comfort b_comfort c_effective
# 1         5         5           5
# 2         4         4           4
# 3         3         3           3
# 4         2         2           2
# 5         1         1           1
# 
# $df2
#   a_comfort b_comfort c_effective
# 1         5         5           5
# 2         4         4           4
# 3         3         3           3
# 4         2         2           2
# 5         1         1           1

You can further overwrite those dataframes in your workspace from the list through list2env().

list2env(df.lst, .GlobalEnv)

CodePudding user response：

Please try the below code where i convert the columns to factor and then recode them

data

  a_comfort b_comfort c_effective
1         1         1           1
2         2         2           2
3         3         3           3
4         4         4           4
5         5         5           5

code

library(tidyverse)

df1 %>% mutate(across(c(ends_with('comfort'),ends_with('effective')), ~ factor(.x, levels=c('1','2','3','4','5'), labels=c('5','4','3','2','1'))))

output

  a_comfort b_comfort c_effective
1         5         5           5
2         4         4           4
3         3         3           3
4         2         2           2
5         1         1           1