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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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