如何将相同的 function 应用于 R 中的几个变量？

Question

I know that similar questions have already been asked (eg Passing list element names as a variable to functions within lapply or R - iteratively apply a function of a list of variables ), but I couldn't manage to find a solution for my problem based on these posts.

I have an event dataset (~100 variables, >2000 observations) that contains variables with information on the involved actors. One variable can only contain one actor, so if several actors have been involved in the event, they are spread over several variables (eg actor1 , actor2 , ...). These actors can be classified into two groups ("s" and "nons"). For later use, I need two lists of actors : one that contains all actors of the category "s" and one that contains all actors of "nons". "s" only consists of three actors while "nons" consists of dozens of actors.

# create example data
df <- data.frame(id = c(1:8),
                 actor1 = c("A", "B", "D", "E", "F", "G", "H", NA),
                 actor2 = c("A", NA, "B", "C", "E", "I", "D", "G"))

df <-  
  df %>%
  mutate(actor1 = as.character(actor1),
         actor2 = as.character(actor2))

Since the script I am about to prepare is supposed to be used on updated versions of the dataset in the future, I would like to automate as much as possible and keep the parts of the script that would need to be adapted as limited as possible. My idea was to create one function per category that extracts the actors of the respective category (eg "nons") from one variable (eg actor1 ) in a list and then "loop" this function over the other variables (ideally with the apply family).

I know which category each actor belongs to ("A", "B", and "C" are category "s"), which allows me to define a separation rule as used in the function below (the filter command).

# create function
nons_function <- function(col) {
  col_ <- enquo(col)
  nons_list <-
    df %>%
    filter(!is.na(!!col_), !!col_ != "A", !!col_ != "B", !!col_ != "C") %>%
    distinct(!!col_) %>%
    pull()
  nons_list
}

# create list of variables to "loop" over
actorlist <- c("actor1", "actor2")

This results in the following. Instead of two lists of actors I get a list that contains the variable names as character strings.

> lapply(actorlist, nons_function)
[[1]]
[1] "actor1"

[[2]]
[1] "actor2"

What I would like to get is something like the following:

> lapply(actorlist, nons_function)
[[1]]
[1] "D" "E" "F" "G" "H"

[[2]]
[1] "E" "I" "D" "G"

The problem is probably the way I am passing the variable names to my function within lapply . Apparently, my function is not able use a character input as variable names. However, I have not found a way to either adapt my function in a way that allows for character input or to provide my function with a list of variables to loop over in a way it can digest.

Any help appreciated!

EDIT: Initially I had named the actors in a misleading way (actor names indicated which category an actor belongs to), which lead to answers that do not really help in my case. I have changed the actor names from "s1", "s2", "nons1", "nons2" etc to "A", "B", "C" etc now.

Answer 1

here is an option using base r.

for nons-actors:

lapply( df[, 2:3], function(x) grep( "^nons", x, value = TRUE ) )

#$actor1
#[1] "nons1" "nons2" "nons3" "nons4" "nons5"
#
#$actor2
#[1] "nons2" "nons6" "nons1" "nons4"

and for s-actors:

lapply( df[, 2:3], function(x) grep( "^s", x, value = TRUE ) )

# $actor1
# [1] "s1" "s2"
# 
# $actor2
# [1] "s1" "s2" "s3"

Answer 2

Here is an option

library(dplyr)
library(stringr)
library(purrr)
map(actorlist, ~ df %>% 
                  select(.x) %>%
                  filter(!str_detect(!! rlang::sym(.x), "^s\\d+$")) %>% 
                   pull(1))
#[[1]]
#[1] "nons1" "nons2" "nons3" "nons4" "nons5"

#[[2]]
#[1] "nons2" "nons6" "nons1" "nons4"

It can be wrapped as a function as well. Note that the input is string, so instead of enquo , use sym to convert to symbol and then evaluate ( !! )

f1 <- function(dat, colNm) {
                dat %>%
                  select(colNm) %>%
                   filter(!str_detect(!! rlang::sym(colNm), "^s\\d+$")) %>%
                    pull(1) %>%
                    unique
         }

map(actorlist, f1, dat = df)

NOTE: This can be done more easily, but here we are using similar code from the OP's post

---

Another option is to use split with grepl in base R and that returns a list of both 'nons' and 's' after removing the NA s

lapply(df[2:3], function(x)  {
           x1 <- x[!is.na(x)]
            split(x1, grepl("nons", x1))})

Answer 3

Check my solution and see if it works for you.

require("dplyr")


# create example data
df <- data.frame(id = c(1:8),
                 actor1 = c("s1", "s2", "nons1", "nons2", "nons3", "nons4", "nons5", NA),
                 actor2 = c("s1", NA, "s2", "s3", "nons2", "nons6", "nons1", "nons4"))

df <-  
  df %>%
  mutate(actor1 = as.character(actor1),
         actor2 = as.character(actor2))


# Function for getting the category
category_function <- function(col,categ){

  if(categ == "non"){
    outp = grep("^non",col,value = T)
  }else{
    outp = grep("^s",col,value = T)
  }

  return(outp)  

}

# Apply the function to all variables whose name starts with "actor"
sapply(df[grep("actor",names(df),value=T)],category_function,categ="non")
sapply(df[grep("actor",names(df),value=T)],category_function,categ="s")

My output was the following:

> sapply(df[grep("actor",names(df),value=T)],category_function,categ="non")
$actor1
[1] "nons1" "nons2" "nons3" "nons4" "nons5"

$actor2
[1] "nons2" "nons6" "nons1" "nons4"

> sapply(df[grep("actor",names(df),value=T)],category_function,categ="s")
$actor1
[1] "s1" "s2"

$actor2
[1] "s1" "s2" "s3"