Dynamically determine if a dataframe column exists and mutate if it does - r

Question

I have code that pulls and processes data from a database based upon a client name. Some clients may have data that does not include a specific column name, e.g., last_name or first_name. For clients that do not use last_name or first_name, I don't care. For clients that do use either of those fields, I need to mutate() those columns with toupper() so that I can join on those standardized fields later in the ETL process.
Right now, I'm using a series of if() statements and some helper functions to look into the names of a dataframe then mutate if they exist. I'm using if() statements because ifelse() is mostly vectorized and doesn't handle dataframes well.
library(dplyr)
set.seed(256)
b <- data.frame(id = sample(1:100, 5, FALSE),
col_name = sample(1000:9999, 5, FALSE),
another_col = sample(1000:9999, 5, FALSE))
d <- data.frame(id = sample(1:100, 5, FALSE),
col_name = sample(1000:9999, 5, FALSE),
last_name = sample(letters, 5, FALSE))
mutate_first_last <- function(df){
mutate_first_name <- function(df){
df %>%
mutate(first_name = first_name %>% toupper())
}
mutate_last_name <- function(df){
df %>%
mutate(last_name = last_name %>% toupper())
}
n <- c("first_name", "last_name") %in% names(df)
if (n[1] & n[2]) return(df %>% mutate_first_name() %>% mutate_last_name())
if (n[1] & !n[2]) return(df %>% mutate_first_name())
if (!n[1] & n[2]) return(df %>% mutate_last_name())
if (!n[1] & !n[2]) return(df)
}
I get what I expect to get this way
> b %>% mutate_first_last()
id col_name another_col
1 48 8318 6207
2 39 7155 7170
3 16 4486 4321
4 55 2521 8024
5 15 1412 4875
> d %>% mutate_first_last()
id col_name last_name
1 64 7438 A
2 43 4551 Q
3 48 7401 K
4 78 3682 Z
5 87 2554 J
but is this the best way to handle this kind of task? To dynamically look to see if a column name exists in a dataframe then mutate it if it does? It seems strange to have to have multiple if() statements in this function. Is there a more streamlined way to process these data?

Answer 1

You can use mutate_at with one_of, both from dplyr. This will mutate column only if it matches with one of c("first_name", "last_name"). If no match, it will generate a simple warning but you can ignore or suppress it.
library(dplyr)
d %>%
mutate_at(vars(one_of(c("first_name", "last_name")), toupper)
id col_name last_name
1 19 7461 V
2 52 9651 H
3 56 1901 P
4 13 7866 Z
5 25 9527 U
# example with no match
b %>%
mutate_at(vars(one_of(c("first_name", "last_name"))), toupper)
id col_name another_col
1 34 9315 8686
2 26 5598 4124
3 17 3318 2182
4 32 1418 4369
5 49 4759 6680
Warning message:
Unknown variables: `first_name`, `last_name`
Here are a bunch of other ?select_helpers in dplyr -
These functions allow you to select variables based on their names.
starts_with(): starts with a prefix
ends_with(): ends with a prefix
contains(): contains a literal string
matches(): matches a regular expression
num_range(): a numerical range like x01, x02, x03.
one_of(): variables in character vector.
everything(): all variables.

Answer 2

Update dplyr 1.0.0
In dplyr 1.0, the scoped variants of mutate such as _at or _all were replaced by across().
In addition, the best tidy_select helper for this case is any_of as it will perform on the variables which exist, but ignores those that don't exist (without warning message).
As result, you can write the following:
# purrr syntax
d %>% mutate(across(any_of(c("first_name", "last_name")), ~toupper(.x)))
# function name syntax
d %>% mutate(across(any_of(c("first_name", "last_name")), toupper))
which both return the mutated column
id col_name last_name
1 19 4398 Q
2 72 1135 S
3 54 9767 V
4 60 4364 K
5 35 1564 X
while
b %>% mutate(across(any_of(c("first_name", "last_name")), toupper))
ignores the columns and thus returns (without warning message):
id col_name another_col
1 42 7601 4482
2 22 1773 7072
3 47 2719 5884
4 1 9595 5945
5 81 8044 3927