Is it possible to "fill up" NA values based on a condition in two other columns?
A similar answer is found here: Replace missing values (NA) with most recent non-NA by group
This question is different because I need the fill up to be based on the values of another column and the value above the value in said column. Explanation below.
I have a dataframe and I need something that looks at the value above and the value beside and the value above that to evaluate. The process should go like this: x = value that is not null.
if value above x is null and
value beside x is the same as the value above the value of x then
fill up.
Here is some example code from mtcars
mtcars1 <- as_tibble(mtcars)
mtcars1 <- mtcars1 %>%
select(cyl, mpg, disp, hp) %>%
group_by(cyl) %>%
arrange(cyl, disp)
ex <- rep("NA", length(mtcars1$cyl))
mtcars1$ex=ex
mtcars1$ex <- if_else(mtcars$cyl = 4 & mtcars$disp > 100, ex==31.4, ex)
mtcars1$ex <- sapply(mtcars1, function(x) ifelse((mtcars1$cyl <= 4 &&
mtcars$disp > 100), 23.5, "NA"))
See the picture below. I want 23.5 to fill up until the values in the "cyl" column change. So,
if value above is NULL()
and cyl value = cyl value above,
fill up.
Is this possible in R?
