NULL
and NA
are distinct. NULL
means "not defined" and NA
means "missing". The screenshot and the output of colSums()
show that some NA
s are present.
sum(is.na(your_data))
will return the total number in the data frame.
colSums(is.na(your_data))
gives the count of NA
by column, and
rowSums(your_data)
shows the rows containing NA
.
If
isn't working, we'd need to look at a representative data set. See the FAQ: How to do a minimal reproducible example reprex
for beginners.