Mutate multiple columns if grouping variable has all observations missing - r

Question

I'm trying to mutate the columns "a" and "b" only if the grouping variable "group" has all observations missing. The attempted solution changes the group "blue", in which not all observations are missing. Thanks in advance for your valuable time!
Code below:
library(tidyverse)
# sample data
a <- c(NA,NA,1,1,NA,1)
b <- c(1,1,NA,NA,1,NA)
c <- letters[1:6]
group <- c("yellow","yellow","black","black", "blue", "blue")
(data <- as_tibble(data.frame(a,b,c,group)))
# a b c group
# <dbl> <dbl> <fct> <fct>
# 1 NA 1 a yellow
# 2 NA 1 b yellow
# 3 1 NA c black
# 4 1 NA d black
# 5 NA 1 e blue
# 6 1 NA f blue
# failed attempt: observations from group "blue" change
(data %>%
dplyr::group_by(group) %>%
dplyr::mutate(across(1:2, ~ ifelse(all(is.na(.x)), 99999,.x))))
# a b c group
# <dbl> <dbl> <fct> <fct>
# 1 99999 1 a yellow
# 2 99999 1 b yellow
# 3 1 99999 c black
# 4 1 99999 d black
# 5 NA 1 e blue
# 6 NA 1 f blue
# desired output - observations from blue remain the same
a2 <- c(99999,99999,1,1,NA,1)
b2 <- c(1,1,99999,99999,1,NA)
c2 <- letters[1:6]
group2 <- c("yellow","yellow","black","black", "blue", "blue")
(data_desired <- as_tibble(data.frame(a2,b2,c2,group2)))
# a2 b2 c2 group2
# <dbl> <dbl> <fct> <fct>
# 1 99999 1 a yellow
# 2 99999 1 b yellow
# 3 1 99999 c black
# 4 1 99999 d black
# 5 NA 1 e blue
# 6 1 NA f blue

Answer 1

You could try this:
library(tidyverse)
# sample data
a <- c(NA,NA,1,1,NA,1)
b <- c(1,1,NA,NA,1,NA)
c <- letters[1:6]
group <- c("yellow","yellow","black","black", "blue", "blue")
(data <- as_tibble(data.frame(a,b,c,group)))
(data %>%
dplyr::group_by(group) %>%
dplyr::mutate(across(1:2, ~ ifelse(is.na(.x), 99999,.x))))
# A tibble: 6 x 4
# Groups: group [3]
a b c group
<dbl> <dbl> <fct> <fct>
1 99999 1 a yellow
2 99999 1 b yellow
3 1 99999 c black
4 1 99999 d black
5 99999 1 e blue
6 1 99999 f blue

Answer 2

Not the best solution but you can deal with it...
data <- data %>%
group_by(group) %>%
mutate(new = paste0(a, "_", b),
new1 = if_else(new == lag(new), str_replace(new, "NA", "99999"), new),
new2 = if_else(new == lead(new), str_replace(new, "NA", "99999"), new)
) %>%
separate(col = new1, into = c("a_new1", "b_new1"), sep = "_", extra = "drop") %>%
separate(col = new2, into = c("a_new2", "b_new2"), sep = "_", extra = "drop") %>%
mutate(a2 = if_else(is.na(a_new1), replace_na(a_new2), a_new1),
b2 = if_else(is.na(b_new1), replace_na(b_new2), b_new1)
) %>%
select(a, b, c, group, a2, b2) %>%
type_convert()
data
# A tibble: 6 x 6
# Groups: group [3]
a b c group a2 b2
<dbl> <dbl> <fct> <fct> <dbl> <dbl>
1 NA 1 a yellow 99999 1
2 NA 1 b yellow 99999 1
3 1 NA c black 1 99999
4 1 NA d black 1 99999
5 NA 1 e blue NA 1
6 1 NA f blue 1 NA

Answer 3

Thanks all for the input!
Finally, this is how I resolved this with lists and purrr.
library(tidyverse)
library(purrr)
# sample data
a <- c(NA,NA,1,1,NA,1)
b <- c(1,1,NA,NA,1,NA)
c <- letters[1:6]
group <- c("yellow","yellow","black","black", "blue", "blue")
(data <- as_tibble(data.frame(a,b,c,group)))
# list with groups in which all cases are NA
list1 <- data %>%
split(.,.$group) %>%
map(~select(.x,as.vector(which(colSums(is.na(.)) == nrow(.))))) %>%
map(~mutate_all(.x, replace_na, 99999))
# list with groups in which there is at least one valid observation
list2 <- data %>%
split(.,.$group) %>%
map(~select(.x, as.vector(which(colSums(is.na(.)) != nrow(.)))))
# putting the groups together into a dataframe
list3 <- mapply(cbind, list1, list2, SIMPLIFY=FALSE)
(desired_output <- do.call(rbind.data.frame, list3))