Comments (1)
Hi, I'm not a dplyr
dev (or a tidyverse
dev at all), but I'm not sure what you expect here. across()
simply has to do more operations since it must evaluate the tidy selection passed in .cols
and there are probably other checks and steps that need to be done. Note that the across()
call with where()
is the slowest because it must evaluate the condition on all columns and retain only those where this condition is true.
Moreover, this timing difference barely scales with the number of rows and columns in the data (except for where()
that increases with the number of columns). On my machine, the difference is always 3-4ms. I don't think this overhead is important, but if it is in your case maybe you should consider alternative packages like data.table
that are built for performance.
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Related Issues (20)
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