How can I write the following condition (preferably using case_when)? I need to replace NAs with TRUE if two variables (i.e., name and salary) are NA.
df <- data.frame(
id = c (1:5),
name = c("Rick","Dan","Michelle",NA,"Gary"),
salary = c(623.3,515.2,611.0,NA,843.25),
start_date = as.Date(c(NA, "2013-09-23", "2014-11-15", "2014-05-11",
"2015-03-27")),
stringsAsFactors = FALSE
)
My desired output looks like this:
id name salary start_date
1 1 Rick 623.30 NA
2 2 Dan 515.20 2013-09-23
3 3 Michelle 611.00 2014-11-15
4 4 TRUE TRUE 2014-05-11
5 5 Gary 843.25 2015-03-27
The condition would be something like this but it does not replace the values in their columns:
case_when(is.na(df$name)& is.na(df$salary) ~TRUE)
Thanks in advance for your input.
In dplyr mutate/replace several columns on a subset of rows and also shown in the Note at the end mutate_cond provides a simple way to implement this. The arguments are the data frame, the condition and the assignments to make on the rows for which the condition holds.
library(dplyr)
df %>% mutate_cond(is.na(name) & is.na(salary), name = "TRUE", salary = "TRUE")
giving
id name salary start_date
1 1 Rick 623.3 <NA>
2 2 Dan 515.2 2013-09-23
3 3 Michelle 611 2014-11-15
4 4 TRUE TRUE 2014-05-11
5 5 Gary 843.25 2015-03-27
# see link above
mutate_cond <- function(.data, condition, ..., envir = parent.frame()) {
condition <- eval(substitute(condition), .data, envir)
.data[condition, ] <- .data[condition, ] %>% mutate(...)
.data
}