# Fill the missing values (NA) in various columns (independently of each other) using imputeTS package (in particular, na_kalman function)

A friend of mine has recently started working on R-studio and is interested in filling the NA values in different columns using the above-mentioned function. Also, since he intends to run a time series analysis for every column, what should be the correct approach?

• I think, the initial decision should be to consider: 1. Is imputation the right thing to do? Sometimes, replacing the missing values does not make sense. 2. If imputation is the right choice, what exactly do you want to achieve: a) replace by column mean, b) replace by row mean c) or replace by the mean of a given user based on other responses they have Nov 23 '19 at 9:42
• I want to replace by the column mean. Nov 24 '19 at 11:43

To replace by column means, an easy approach would be to use the base R function colMeans. Let's say you have a data frame df.
df <- sapply(df, function(x)ifelse(is.na(x), mean(x, na.rm=TRUE), x))

df <- ifelse(is.na(df), rep(colMeans(df, na.rm=TRUE), rep(nrow(df), ncol(df))), unlist(df))