Here is how to apply the ifelse function across a range of multiple R data frame columns. Sometimes it is necessary to do calculations by a condition and it could be time-consuming to do that for each of multiple columns. Or even worse. Maybe the necessary columns are changing position over time and you have to select necessary ones automatically.
Here is my data frame.
df <- structure(
list(
id = structure(1:4, .Label = c("a", "b", "c", "d"), class = "factor"),
actual_activity_dt = structure(
c(1613632351, 1613725221, 1613740219, 1613749521),
class = c("POSIXct", "POSIXt"), tzone = ""
),
last_call = structure(
c(NA, 1613405520, NA, NA),
class = c("POSIXct", "POSIXt"), tzone = ""
),
last_mail = structure(
c(1613244600, NA, 1613730081, 1613370915),
class = c("POSIXct", "POSIXt"), tzone = ""
),
last_chat = structure(
c(NA, 1613408525, NA, 1613564545),
class = c("POSIXct", "POSIXt"), tzone = ""
),
other_column = c(NA, NA, NA, NA)
),
row.names = c(NA, -4L),
class = c("tbl_df", "tbl", "data.frame")
)
df
## A tibble: 4 x 6
# id actual_activity_dt last_call last_mail last_chat other_column
#
#1 a 2021-02-18 09:12:31 NA 2021-02-13 21:30:00 NA NA
#2 b 2021-02-19 11:00:21 2021-02-15 18:12:00 NA 2021-02-15 19:02:05 NA
#3 c 2021-02-19 15:10:19 NA 2021-02-19 12:21:21 NA NA
#4 d 2021-02-19 17:45:21 NA 2021-02-15 08:35:15 2021-02-17 14:22:25 NAI would like to calculate the time difference in days by subtracting each of the column values that start with the prefix “last_” from the datetime in the second column and overwrite them with that results.
Select R data frame columns by part of the name
This is needed to automatically select columns if something is changing over ar time. It might be reasonable if you want to automate the R script. If there is a part that common that it is simple to do that with the grepl.
grepl('last_', names(df))
#[1] FALSE FALSE TRUE TRUE TRUE FALSEIn this case, there is possible to use the startsWith function that gives the same result.
startsWith(names(x), 'last_') #[1] FALSE FALSE TRUE TRUE TRUE FALSE
ifelse across a range of R data frame columns
If you have a location of necessary columns that should be transformed by using function ifelse, then it could be done with the function lapply.
loc <- grepl('last_', names(df))
df[loc] <-
lapply(df[loc], function(x) {
ifelse(is.na(x),
NA,
difftime(df$actual_activity_dt, x, units = 'days'))
})
df
## A tibble: 4 x 6
# id actual_activity_dt last_call last_mail last_chat other_column
#
#1 a 2021-02-18 09:12:31 NA 4.49 NA NA
#2 b 2021-02-19 11:00:21 3.70 NA 3.67 NA
#3 c 2021-02-19 15:10:19 NA 0.117 NA NA
#4 d 2021-02-19 17:45:21 NA 4.38 2.14 NAifelse across a range of column with dplyr
If you like to work with dplyr then there is a function across that makes it easy to apply transformations to multiple columns.
For example, if you want to do the same transformation for the range of columns with as.Posixct it could be done like this.
df <- df %>% mutate(across(2:5, as.POSIXct))
With the original data frame the same example with ifelse looks like this.
x %>% mutate(across(starts_with("last_"), ~ifelse(is.na(.x), NA, difftime(actual_activity_dt, .x, units = 'days'))))
## A tibble: 4 x 6
# id actual_activity_dt last_call last_mail last_chat other_column
#
#1 a 2021-02-18 09:12:31 NA 4.49 NA NA
#2 b 2021-02-19 11:00:21 3.70 NA 3.67 NA
#3 c 2021-02-19 15:10:19 NA 0.117 NA NA
#4 d 2021-02-19 17:45:21 NA 4.38 2.14 NAHere are a few other examples with function across that might be helpful.
If you like to use functions from R apply family then here is a bunch of examples from DataCornering.
Variance or other calculation across columns in R
Combine apply and match in R, return the name of the column
How to combine files with R and add filename column
How to combine numbers and find sums with R
Check if a column has a missing values (NA) in R
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