In fact, using any of the dplyr functions is very straightforward, because they are quite well designed. #> disambiguation algorithm are subject to change in dplyr 0.9.0.# All variants can be passed functions and additional arguments,# purrr-style.
3.9.1 factor levels; 3.10 Exercises. By default, the newly created columns have the shortest #> brown, gr… light blue 52 male mascu…
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> white, bl… red 33 none mascu… Andrea. The _at() variants directly support strings. #> name height mass hair_color skin_color eye_color birth_year sex gender #> 4.9 3.1 1.5 0.1 setosa summarise_all.Rd.
Thanks. #> blond fair blue 19 male mascu… step_mutate_at creates a specification of a recipe step that will modify the selected variables using a common function. and I want the dataset to look like thisBut, instead of computing all these ratios line by line, I want to create a look to do this all at once.
Hot Network Questions How can I use an 'align*' environment inside a 'tabular' environment? _at affects variables selected with a character vector or vars() #> # … with 77 more rows, and 5 more variables: homeworld # You can pass additional arguments to the function: #> 1.59 1.13 1.5 0.1 setosa names needed to uniquely identify the output.
Should the step be skipped when the Like all of the dplyr functions, it is designed to do one thing. In Stata, I would doSince I answered this question, I have realized that some SO users have been checking this answer. #> 5.4 3.9 1.7 0.4 setosa 1.69 _if affects variables selected with a predicate function:
#> 4.9 3 1.4 0.2 setosa 1.59
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#> blond fair blue 19 male mascu… input variables and the names of the functions.otherwise, the new names are created by
#> brown light blue 47 fema… femin… #> brown light brown 19 fema… femin… #> 1.48 1.06 1.4 0.2 setosa
#> 5 3.4 1.5 0.2 setosa 1.61
#> 5.1 3.5 1.4 0.2 setosa #> gold yellow 112 none mascu…
#> 5 3 1 0 setosa
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Mutate multiple columns Source: R/mutate_at.R. #> Like all of the dplyr functions, it is designed to do one thing. The optionsinside mutate are almost endless: pretty much anything that you can doto normal vectors, can be done inside a mutate() function.Anything inside mutate can either be a new column (by giving mutate anew column name), or can replace the current column (by keeping the samecolumn name).One of the simplest options is a calculation base… #> 4.9 3.1 1.5 0.1 setosa 1.59 #> # The _if() variants apply a predicate function (a function that# returns TRUE or FALSE) to determine the relevant subset of# columns. #> 5 3 1 0 setosa
#> 1.61 1.28 1.4 0.2 setosa 8. Here we divide all the numeric columns by 100:
#> 4.6 3.1 1.5 0.2 setosa #> 1.53 1.13 1.5 0.2 setosa
#> 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_fn1
#> 5 3 1 0 setosa Give it a name to instead create new variables: (see For model terms created by this step, what analysis role should
The downside is you overwrite cash, bond, and loans.If you prefer your expected outcome, I think some typing is necessary.
#> white, bl… red 33 none mascu… #> black light brown 24 male mascu…
Just try this and see if it yields what you need.This should get you started in the right direction.Thanks for contributing an answer to Stack Overflow!
even when not needed, name the input (see examples for details).
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r mutate multiple columns