I’m trying to run a function on columns that have NA observations. When all observations are NA I would like it to return NA, but when only a fraction of rows has it, just apply na.rm=T. I’ve seen a few posts showing how to do this (link_1, link_2, link_3), but none of them seem to work for my function and I’m not sure where I’m going wrong.
# data frame
species_1<- c(NA, 10, 40)
species_2<- c(NA, NA, 30)
species_3<- c(NA, NA, NA)
group<- c(1, 1, 1)
df<- data.frame(species_1, species_2, species_3, group)
# function argument
y_true_test<- c(30, 20, 20)
# function
estimate = function(df, y_true, na.rm=T) {
if (all(is.na(df))) df[NA_integer_] else
sqrt(colSums((t(t(df) - y_true_test))^2, na.rm=T) / 3) / y_true_test * 100
}
# run
final<- df %>%
group_by(group) %>%
group_modify( ~ as.data.frame.list(estimate(., y_true_test))) #species 3 returns '0' when it should be NA
Any help would be greatly appreciated.
>Solution :
The function was checking the NA on the whole dataset columns instead it should be by each column. Here, is an option with across
library(dplyr)
names(y_true_test) <- grep("species", names(df), value = TRUE)
df %>%
group_by(group) %>%
summarise(across(everything(), ~ if(all(is.na(.x))) NA_real_ else
sqrt(sum((.x - y_true_test)^2, na.rm = TRUE)/n())/
(y_true_test[cur_column()]) * 100), .groups = 'drop')
-output
# A tibble: 1 × 4
group species_1 species_2 species_3
<dbl> <dbl> <dbl> <dbl>
1 1 43.0 28.9 NA
If we want to modify the OP’s function
estimate <- function(df, y_true, narm=TRUE) {
i1 <- colSums(is.na(df)) == nrow(df)
out <- sqrt(colSums((t(t(df) - y_true_test))^2,
na.rm= narm) / 3) / y_true_test * 100
out[i1] <- NA
out
}
-testing
> df %>%
+ group_by(group) %>%
+ group_modify( ~ as.data.frame.list(estimate(.,
y_true_test)))
# A tibble: 1 × 4
# Groups: group [1]
group species_1 species_2 species_3
<dbl> <dbl> <dbl> <dbl>
1 1 43.0 28.9 NA