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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?

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  • $\begingroup$ 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 $\endgroup$ – Sadiaz Nov 23 at 9:42
  • $\begingroup$ I want to replace by the column mean. $\endgroup$ – toric_actions Nov 24 at 11:43
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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.

1) If you want to replace the NAs per column one by one, you could try this:

df <- sapply(df, function(x)ifelse(is.na(x), mean(x, na.rm=TRUE), x))

2) If you want to replace all NAs in one go, you could try this:

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