Conditional filling NA rows with comparing non-NA labeled rows - r

Question

I want to fill NA rows based on checking the differences between the closest non-NA labeled rows.
For instance
data <- data.frame(sd_value=c(34,33,34,37,36,45),
value=c(383,428,437,455,508,509),
label=c(c("bad",rep(NA,4),"unable")))
> data
sd_value value label
1 34 383 bad
2 33 428 <NA>
3 34 437 <NA>
4 37 455 <NA>
5 36 508 <NA>
6 45 509 unable
I want to evaluate how to change NA rows with checking the difference between sd_value and value those close to bad and unablerows.
if we want to get differences between the rows we can do;
library(dplyr)
data%>%
mutate(diff_val=c(0,diff(value)), diff_sd_val=c(0,diff(sd_value)))
sd_value value label diff_val diff_sd_val
1 34 383 bad 0 0
2 33 428 <NA> 45 -1
3 34 437 <NA> 9 1
4 37 455 <NA> 18 3
5 36 508 <NA> 53 -1
6 45 509 unable 1 9
The condition how I want to label the NA rows is
if the diff_val<50 and diff_sd_val<9 label them with the last non-NA label else use the first non-NA label after the last NA row.
So that the expected output would be
sd_value value label diff_val diff_sd_val
1 34 383 bad 0 0
2 33 428 bad 45 -1
3 34 437 bad 9 1
4 37 455 bad 18 3
5 36 508 unable 53 -1
6 45 509 unable 1 9
The possible solution I cooked up so far:
custom_labelling <- function(x,y,label){
diff_sd_val<-c(NA,diff(x))
diff_val<-c(NA,diff(y))
label <- NA
for (i in 1:length(label)){
if(is.na(label[i])&diff_sd_val<9&diff_val<50){
label[i] <- label
}
else {
label <- label[i]
}
}
return(label)
}
which gives
data%>%
mutate(diff_val=c(0,diff(value)), diff_sd_val=c(0,diff(sd_value)))%>%
mutate(custom_label=custom_labelling(sd_value,value,label))
Error in mutate_impl(.data, dots) :
Evaluation error: missing value where TRUE/FALSE needed.
In addition: Warning message:
In if (is.na(label[i]) & diff_sd_val < 9 & diff_val < 50) { :
the condition has length > 1 and only the first element will be used

Answer 1

One option is to find NA and non-NA index and based on the condition select the closest label to it.
library(dplyr)
#Create a new dataframe with diff_val and diff_sd_val
data1 <- data%>% mutate(diff_val=c(0,diff(value)), diff_sd_val=c(0,diff(sd_value)))
#Get the NA indices
NA_inds <- which(is.na(data1$label))
#Get the non-NA indices
non_NA_inds <- setdiff(1:nrow(data1), NA_inds)
#For every NA index
for (i in NA_inds) {
#Check the condition
if(data1$diff_sd_val[i] < 9 & data1$diff_val[i] < 50)
#Get the last non-NA label
data1$label[i] <- data1$label[non_NA_inds[which.max(i > non_NA_inds)]]
else
#Get the first non-NA label after last NA value
data1$label[i] <- data1$label[non_NA_inds[i < non_NA_inds]]
}
data1
# sd_value value label diff_val diff_sd_val
#1 34 383 bad 0 0
#2 33 428 bad 45 -1
#3 34 437 bad 9 1
#4 37 455 bad 18 3
#5 36 508 unable 53 -1
#6 45 509 unable 1 9
You can remove diff_val and diff_sd_val columns later if not needed.
We can also create a function
custom_label <- function(label, diff_val, diff_sd_val) {
NA_inds <- which(is.na(label))
non_NA_inds <- setdiff(1:length(label), NA_inds)
new_label = label
for (i in NA_inds) {
if(diff_sd_val[i] < 9 & diff_val[i] < 50)
new_label[i] <- label[non_NA_inds[which.max(i > non_NA_inds)]]
else
new_label[i] <- label[non_NA_inds[i < non_NA_inds]]
}
return(new_label)
}
and then apply it
data%>%
mutate(diff_val = c(0, diff(value)),
diff_sd_val = c(0, diff(sd_value)),
new_label = custom_label(label, diff_val, diff_sd_val))
# sd_value value label diff_val diff_sd_val new_label
#1 34 383 bad 0 0 bad
#2 33 428 <NA> 45 -1 bad
#3 34 437 <NA> 9 1 bad
#4 37 455 <NA> 18 3 bad
#5 36 508 <NA> 53 -1 unable
#6 45 509 unable 1 9 unable
If we want to apply it by group we can add a group_by statement and it should work.
data%>%
group_by(group) %>%
mutate(diff_val = c(0, diff(value)),
diff_sd_val = c(0, diff(sd_value)),
new_label = custom_label(label, diff_val, diff_sd_val))