#> 1.61 1.22 1.5 0.2 setosa even when not needed, name the input (see examples for details).If applied on a grouped tibble, these operations are Grouping variables covered by explicit selections in #> name height mass hair_color skin_color eye_color birth_year sex gender #> auburn, w… fair blue-gray 57 male mascu… #> brown light blue 47 fema… femin…

#> The .funs argument accepts an anonymous function defined on the fly inside a call to list(). #> 4 3 1 0 setosa Probably 10x or 100x faster.

#> name height mass hair_color skin_color eye_color birth_year sex gender I always … Note that these lambda prevent hybrid evaluation from happening and it is thus more efficient to supply functions like mean() directly rather than in a lambda-formula. #> 5.1 3.5 1.4 0.2 setosa - 4.9 3 1.4 0.2 setosa - 4.7 3.2 1.3 0.2 setosa - 4.6 3.1 1.5 0.2 setosa - 5 3.6 1.4 0.2 setosa - 5.4 3.9 1.7 0.4 setosa - 4.6 3.4 1.4 0.3 setosa - 5 3.4 1.5 0.2 setosa - 4.4 2.9 1.4 0.2 setosa - 4.9 3.1 1.5 0.1 setosa -# … with 140 more rows, and 7 more variables: Sepal.Width_scale2 #> 1.69 1.36 1.7 0.4 setosa The _at() variants directly support strings. Then .funs would need to be a list of the same length (or a list of lists if you wanted to do multiple functions on that variable set).That would achieve parity with what is possible with the unstopped version of mutate whilst still allowing scoping.Apologies if there is a much better way to do this that I haven't seen!Usually, when some task involves lists, there is a good chance it can be done with purrr package. #> 4.6 3.4 1.4 0.3 setosa #> 1.61 1.22 1.5 0.2 setosa #> 4.6 3.1 1.5 0.2 setosa

Successfully merging a pull request may close this issue. If you believe you have found a related problem, please file a new issue (with reprex) and link to this issue. #> Dart… 0.795 0.228 none white yellow - Owen… 0.105 0.134 brown, gr… light blue -# … with 77 more rows, and 5 more variables: homeworld _at affects variables selected with a character vector or vars()_if affects variables selected with a predicate function:A predicate function to be applied to the columns Here #> 5.1 3.5 1.4 0.2 setosa - 4.9 3 1.4 0.2 setosa - 4.7 3.2 1.3 0.2 setosa - 4.6 3.1 1.5 0.2 setosa - 5 3.6 1.4 0.2 setosa - 5.4 3.9 1.7 0.4 setosa - 4.6 3.4 1.4 0.3 setosa - 5 3.4 1.5 0.2 setosa - 4.4 2.9 1.4 0.2 setosa - 4.9 3.1 1.5 0.1 setosa -# … with 140 more rows, and 3 more variables: Sepal.Width_scale I have a data frame with 8 columns and 40 rows. Stack Overflow for Teams is a private, secure spot for you and

#> Dart… 0.795 0.228 none white yellow 41.9 male mascu… #> Owen… 0.105 0.134 brown, gr… light blue 52 male mascu… Then .funs would need to be a list of the same length (or a list of lists if you wanted to do multiple functions on that variable set). #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species

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