We can use na.aggregate. One option would be to separately apply the na.aggregate on each column. We can do this with lapply. If we are using data.table, convert the 'data.frame' to 'data.table' (setDT(df)), loop over the columns and apply na.aggregate. This will replace NA with the mean of the non-NA values.
library(zoo)
library(data.table)
setDT(df)[, names(df) := lapply(.SD, na.aggregate)][]
# x y z
# 1: 1 1.0 8
# 2: 2 2.0 8
# 3: 3 3.0 8
# 4: 4 4.0 8
# 5: 5 5.0 8
# 6: 6 6.0 1
# 7: 7 7.0 2
# 8: 8 8.0 3
# 9: 9 9.0 4
#10: 10 10.0 5
#11: 11 5.5 6
#12: 12 5.5 7
#13: 13 5.5 8
#14: 14 5.5 9
#15: 15 5.5 10
#16: 16 5.5 11
#17: 17 5.5 12
#18: 18 5.5 13
#19: 19 5.5 14
#20: 20 5.5 15
Or we can use na.aggregate directly on the dataset.
na.aggregate(df)
