Replace NA conditionally - r

Question

this an augmented version of my own question as i could not clearly explain it through the comments
There are only 2 farms, so each fruit is duplicated in the below df. i'd like to replace NA with 0 only if there is a value for either of the fruits, such as for a pear at y2019 with values c(NA, 7), i'd like to output c(0,7) instead.
sample data:
df <- data.frame(fruit = c("apple", "apple", "peach", "peach", "pear", "pear", "lime", "lime"),
farm = as.factor(c(1,2,1,2,1,2,1,2)), 'y2019' = c(NA,NA,3,12,NA,7,4,6),
'y2018' = c(5,3,NA,NA,8,2,NA,NA),'y2017' = c(4,5,7,15,NA,NA,1,NA))
> df
fruit farm y2019 y2018 y2017
1 apple 1 NA 5 4
2 apple 2 NA 3 5
3 peach 1 3 NA 7
4 peach 2 12 NA 15
5 pear 1 NA 8 NA
6 pear 2 7 2 NA
7 lime 1 4 NA 1
8 lime 2 6 NA NA
this is close
df %>%
group_by(fruit) %>%
mutate(across(where(is.numeric), ~ if (any(is.na(.))) 0 else .)) %>%
ungroup()
but :
7 gets wiped out in pear producing c(0,0).
i'd like to leave NA in when both farms are NA
#A tibble: 8 x 5
fruit farm y2019 y2018 y2017
<chr> <fct> <dbl> <dbl> <dbl>
1 apple 1 0 5 4
2 apple 2 0 3 5
3 peach 1 3 0 7
4 peach 2 12 0 15
5 pear 1 0 8 0
6 pear 2 0 2 0
7 lime 1 4 0 0
8 lime 2 6 0 0
desired outcome:
> df
fruit farm y2019 y2018 y2017
1 apple 1 NA 5 4
2 apple 2 NA 3 5
3 peach 1 3 NA 7
4 peach 2 12 NA 15
5 pear 1 0 8 NA
6 pear 2 7 2 NA
7 lime 1 4 NA 1
8 lime 2 6 NA 0

Answer 1

You can try :
library(dplyr)
df %>%
group_by(fruit) %>%
mutate(across(where(is.numeric), ~ if(any(!is.na(.)))
replace(., is.na(.), 0) else .)) %>%
ungroup()
# A tibble: 8 x 5
# fruit farm y2019 y2018 y2017
# <chr> <fct> <dbl> <dbl> <dbl>
#1 apple 1 NA 5 4
#2 apple 2 NA 3 5
#3 peach 1 3 NA 7
#4 peach 2 12 NA 15
#5 pear 1 0 8 NA
#6 pear 2 7 2 NA
#7 lime 1 4 NA 1
#8 lime 2 6 NA 0
So we replace NA to 0 only if there is any value in the group which is not NA.

Answer 2

We can use replace_na from tidyr if there are any non-NA elements to replace with 0 or else return the value
library(dplyr)
library(tidyr)
df %>%
group_by(fruit) %>%
mutate(across(where(is.numeric), ~ if(any(!is.na(.))) replace_na(., 0) else .)) %>%
ungroup()
# A tibble: 8 x 5
# fruit farm y2019 y2018 y2017
# <chr> <fct> <dbl> <dbl> <dbl>
#1 apple 1 NA 5 4
#2 apple 2 NA 3 5
#3 peach 1 3 NA 7
#4 peach 2 12 NA 15
#5 pear 1 0 8 NA
#6 pear 2 7 2 NA
#7 lime 1 4 NA 1
#8 lime 2 6 NA 0
or another option without if/else by having two logical expressions in replace after doing the group by 'fruit'
df %>%
group_by(fruit) %>%
mutate(across(where(is.numeric),
~ replace(., sum(!is.na(.)) > 0 & is.na(.), 0)))
# A tibble: 8 x 5
# Groups: fruit [4]
# fruit farm y2019 y2018 y2017
# <chr> <fct> <dbl> <dbl> <dbl>
#1 apple 1 NA 5 4
#2 apple 2 NA 3 5
#3 peach 1 3 NA 7
#4 peach 2 12 NA 15
#5 pear 1 0 8 NA
#6 pear 2 7 2 NA
#7 lime 1 4 NA 1
#8 lime 2 6 NA 0