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As you can see, the new column is in the right position, but the original one is still there! A: So far, there is no way to overwrite the original column with new values. Maybe I’m wrong here, but I didn’t see how you could accomplish this either way. The current solution I have seen on this website, that works for me, is mentioned by @levisto: library(dplyr) df %>% mutate(newColumn = list(firstColumn = “NA”, secondColumn = “NA”, thirdColumn = “NA”, fourthColumn = “NA”, fifthColumn = “NA”, sixthColumn = “NA”, seventhColumn = “NA”, eighthColumn = “NA”, ninthColumn = “NA”, tenthColumn = “NA”, eleventhColumn = “NA”, twelfthColumn = “NA”)) %>% select(-newColumn) As far as I’m concerned, this solution requires a little bit more work, since we need to assign a new name in the new columns. The good side is that the original columns don’t get affected. A: You could do something like this (note that I just used a condition because this answer is not reproducible. For a reproducible solution see my previous answer): df % mutate(tmp = ifelse(x > 0.5, y, x)) df[, c(“x”, “y”)]

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