Convert all coumns ending in 'ID' to character in tidyverse - r

Question

I have numerous dataframes with many columns where the name of the column ends in "ID". What's the simplest way to change the type of every column ending in "ID". Ideally I'd pass the imported dataframe to a function which would return the same dataframe but with the column types changed. I definitely can't hardcode the column names as I will not know in advance what the columns are.
Here's some sample data:
test_data <- structure(list(ContactID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
), SystemID = c(3, 1, 5, 4, 3, 5, 35, 1, 55, 52, 9), Value1 = c("A",
"B", "C", "D", "E", "F", "E", "G", "D", "S", "C"), Value2 = c("1/01/2020",
"2/01/2020", "3/01/2020", "4/01/2020", "5/01/2020", "6/01/2020",
"7/01/2020", "8/01/2020", "9/01/2020", "10/01/2020", "11/01/2020"
), OtherID = c(10004, 10009, 10002, 10007, 10099, 10010, 10002,
10004, 10002, 10007, 10099)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -11L), spec = structure(list(
cols = list(ContactID = structure(list(), class = c("collector_double",
"collector")), SystemID = structure(list(), class = c("collector_double",
"collector")), Value1 = structure(list(), class = c("collector_character",
"collector")), Value2 = structure(list(), class = c("collector_character",
"collector")), OtherID = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
Columns ContactID, SystemID and OtherID have been imported from a CSV file (using read_csv from readr) and so have been designated numeric. I want a function where I can pass this (or any other dataframe) to change any columns ending in ID to character.
I've tried this but it seems very clumsy. Looking for a neater solution.
change_ID_cols <- function(x) {
id_cols <- grep("ID$", colnames(x))
for (i in id_cols) {
for (j in 1:nrow(x)) {
x[j,i] <- as.character(x[j,i])
}
}
x
}

Answer 1

Does this work:
library(dplyr)
test_data %>% mutate(across(ends_with('ID'), as.character))
# A tibble: 11 x 5
ContactID SystemID Value1 Value2 OtherID
<chr> <chr> <chr> <chr> <chr>
1 1 3 A 1/01/2020 10004
2 2 1 B 2/01/2020 10009
3 3 5 C 3/01/2020 10002
4 4 4 D 4/01/2020 10007
5 5 3 E 5/01/2020 10099
6 6 5 F 6/01/2020 10010
7 7 35 E 7/01/2020 10002
8 8 1 G 8/01/2020 10004
9 9 55 D 9/01/2020 10002
10 10 52 S 10/01/2020 10007
11 11 9 C 11/01/2020 10099
>

Answer 2

You don't have to change each value individually to character. You can turn the complete column into character at once. To do this for multiple columns use lapply.
change_ID_cols <- function(x) {
id_cols <- grep("ID$", colnames(x))
x[id_cols] <- lapply(x[id_cols], as.character)
x
}

Answer 3

An option with data.table would be
library(data.table)
nm <- grep('ID$', names(test_data), value = TRUE)
setDT(test_data)[, (nm) := lapply(.SD, as.character), .SDcols = nm]