Running the same code for many tibbles in the global environment in R-CodePudding

I am working with R.

I have many tibbles that look similar to this (I say similar because they contain 5 columns at the beginning and 11 columns at the and that need to be eliminated)

chair   table  hill  block  chain  ball  money house

  2       4     5      6      7     -2     4     5 
  1       3     6      1      8      3     9     1
 -2       1     1     -2      1      8    -2     3
  6       4    -2      4     -2      5     8     4
  5       5     5      5      3      2     6     7

First I need to eliminate the columns that I don't need at the beginning and at the end of the tibble. So, I used this code.

dummy1 <- at01 %>% 
  select(-1, -2, -3, -4, -5, -name, -time, -id, -class,
         -slot, -bracket, -app, -aal, -PHT, -END,
         -START)

Then, I need to rename the columns by eliminating the first 4 characters of the column names with this code.

names(dummy1) <- substring(names(dummy1), 5)

And, finally, I need to replace all the -2 with NA's

dummy1[dummy1 == -2] <- NA

I have 120 documents in my global environment. How can I pass all the code in order to obtain clean results rapidly without the need of doing these three steps with every tibble.

Thanks

CodePudding user response：

When applying the same functions to multiple dataframes, it is easiest to work with them in a list, so that you can use functions from apply or purrr. Here, I am assuming that only the 120 dataframes are in your global environment. I first put all dataframes in your global environment into a list. Then, we can apply your various functions (I put it all into one function) on each dataframe to clean them up. Then, you can apply that function using purrr::map to each dataframe.

library(tidyverse)

# Put all dataframes in your global environment into a list.
dfs <- Filter(function(x) is(x, "data.frame"), mget(ls()))

cleanup <- function(x){
  x <- x %>% 
    select(-c(1:5, name, time, id, class,
           slot, bracket, app, aal, PHT, END,
           START)) %>% 
    mutate_all(~replace(., . == -2, NA))
  
  names(x) <- substring(names(x), 5)
  return(x)
}

dfs_cleanedup <- purrr::map(dfs, cleanup)

Then, if you want to overwrite the 120 dataframes in your global environment with the cleaned dataframes, then you can do something like this (Caution: it will overwrite the dataframes in the global environment).

list2env(dfs_cleanedup, envir = .GlobalEnv)