[Superseded]

Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb. See vignette("colwise") for details.

The scoped variants of mutate() and transmute() make it easy to apply the same transformation to multiple variables. There are three variants:

  • _all affects every variable

  • _at affects variables selected with a character vector or vars()

  • _if affects variables selected with a predicate function:

mutate_all(.tbl, .funs, ...)

mutate_if(.tbl, .predicate, .funs, ...)

mutate_at(.tbl, .vars, .funs, ..., .cols = NULL)

transmute_all(.tbl, .funs, ...)

transmute_if(.tbl, .predicate, .funs, ...)

transmute_at(.tbl, .vars, .funs, ..., .cols = NULL)

Arguments

.tbl

A tbl object.

.funs

A function fun, a quosure style lambda ~ fun(.) or a list of either form.

...

Additional arguments for the function calls in .funs. These are evaluated only once, with tidy dots support.

.predicate

A predicate function to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected. This argument is passed to rlang::as_function() and thus supports quosure-style lambda functions and strings representing function names.

.vars

A list of columns generated by vars(), a character vector of column names, a numeric vector of column positions, or NULL.

.cols

This argument has been renamed to .vars to fit dplyr's terminology and is deprecated.

Value

A data frame. By default, the newly created columns have the shortest names needed to uniquely identify the output. To force inclusion of a name, even when not needed, name the input (see examples for details).

Grouping variables

If applied on a grouped tibble, these operations are not applied to the grouping variables. The behaviour depends on whether the selection is implicit (all and if selections) or explicit (at selections).

  • Grouping variables covered by explicit selections in mutate_at() and transmute_at() are always an error. Add -group_cols() to the vars() selection to avoid this:

    data %>% mutate_at(vars(-group_cols(), ...), myoperation)
    

    Or remove group_vars() from the character vector of column names:

    nms <- setdiff(nms, group_vars(data))
    data %>% mutate_at(vars, myoperation)
    
  • Grouping variables covered by implicit selections are ignored by mutate_all(), transmute_all(), mutate_if(), and transmute_if().

Naming

The names of the new columns are derived from the names of the input variables and the names of the functions.

  • if there is only one unnamed function (i.e. if .funs is an unnamed list of length one), the names of the input variables are used to name the new columns;

  • for _at functions, if there is only one unnamed variable (i.e., if .vars is of the form vars(a_single_column)) and .funs has length greater than one, the names of the functions are used to name the new columns;

  • otherwise, the new names are created by concatenating the names of the input variables and the names of the functions, separated with an underscore "_".

The .funs argument can be a named or unnamed list. If a function is unnamed and the name cannot be derived automatically, a name of the form "fn#" is used. Similarly, vars() accepts named and unnamed arguments. If a variable in .vars is named, a new column by that name will be created.

Name collisions in the new columns are disambiguated using a unique suffix.

Life cycle

The functions are maturing, because the naming scheme and the disambiguation algorithm are subject to change in dplyr 0.9.0.

See also

Examples

iris <- as_tibble(iris)

# All variants can be passed functions and additional arguments,
# purrr-style. The _at() variants directly support strings. Here
# we'll scale the variables `height` and `mass`:
scale2 <- function(x, na.rm = FALSE) (x - mean(x, na.rm = na.rm)) / sd(x, na.rm)
starwars %>% mutate_at(c("height", "mass"), scale2)
#> # A tibble: 87 x 14
#>    name    height  mass hair_color  skin_color eye_color birth_year sex   gender
#>    <chr>    <dbl> <dbl> <chr>       <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke S…     NA    NA blond       fair       blue            19   male  mascu…
#>  2 C-3PO       NA    NA NA          gold       yellow         112   none  mascu…
#>  3 R2-D2       NA    NA NA          white, bl… red             33   none  mascu…
#>  4 Darth …     NA    NA none        white      yellow          41.9 male  mascu…
#>  5 Leia O…     NA    NA brown       light      brown           19   fema… femin…
#>  6 Owen L…     NA    NA brown, grey light      blue            52   male  mascu…
#>  7 Beru W…     NA    NA brown       light      blue            47   fema… femin…
#>  8 R5-D4       NA    NA NA          white, red red             NA   none  mascu…
#>  9 Biggs …     NA    NA black       light      brown           24   male  mascu…
#> 10 Obi-Wa…     NA    NA auburn, wh… fair       blue-gray       57   male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
# ->
starwars %>% mutate(across(c("height", "mass"), scale2))
#> # A tibble: 87 x 14
#>    name    height  mass hair_color  skin_color eye_color birth_year sex   gender
#>    <chr>    <dbl> <dbl> <chr>       <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke S…     NA    NA blond       fair       blue            19   male  mascu…
#>  2 C-3PO       NA    NA NA          gold       yellow         112   none  mascu…
#>  3 R2-D2       NA    NA NA          white, bl… red             33   none  mascu…
#>  4 Darth …     NA    NA none        white      yellow          41.9 male  mascu…
#>  5 Leia O…     NA    NA brown       light      brown           19   fema… femin…
#>  6 Owen L…     NA    NA brown, grey light      blue            52   male  mascu…
#>  7 Beru W…     NA    NA brown       light      blue            47   fema… femin…
#>  8 R5-D4       NA    NA NA          white, red red             NA   none  mascu…
#>  9 Biggs …     NA    NA black       light      brown           24   male  mascu…
#> 10 Obi-Wa…     NA    NA auburn, wh… fair       blue-gray       57   male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

# You can pass additional arguments to the function:
starwars %>% mutate_at(c("height", "mass"), scale2, na.rm = TRUE)
#> # A tibble: 87 x 14
#>    name   height    mass hair_color skin_color eye_color birth_year sex   gender
#>    <chr>   <dbl>   <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke… -0.0678 -0.120  blond      fair       blue            19   male  mascu…
#>  2 C-3PO -0.212  -0.132  NA         gold       yellow         112   none  mascu…
#>  3 R2-D2 -2.25   -0.385  NA         white, bl… red             33   none  mascu…
#>  4 Dart…  0.795   0.228  none       white      yellow          41.9 male  mascu…
#>  5 Leia… -0.701  -0.285  brown      light      brown           19   fema… femin…
#>  6 Owen…  0.105   0.134  brown, gr… light      blue            52   male  mascu…
#>  7 Beru… -0.269  -0.132  brown      light      blue            47   fema… femin…
#>  8 R5-D4 -2.22   -0.385  NA         white, red red             NA   none  mascu…
#>  9 Bigg…  0.249  -0.0786 black      light      brown           24   male  mascu…
#> 10 Obi-…  0.220  -0.120  auburn, w… fair       blue-gray       57   male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
starwars %>% mutate_at(c("height", "mass"), ~scale2(., na.rm = TRUE))
#> # A tibble: 87 x 14
#>    name   height    mass hair_color skin_color eye_color birth_year sex   gender
#>    <chr>   <dbl>   <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke… -0.0678 -0.120  blond      fair       blue            19   male  mascu…
#>  2 C-3PO -0.212  -0.132  NA         gold       yellow         112   none  mascu…
#>  3 R2-D2 -2.25   -0.385  NA         white, bl… red             33   none  mascu…
#>  4 Dart…  0.795   0.228  none       white      yellow          41.9 male  mascu…
#>  5 Leia… -0.701  -0.285  brown      light      brown           19   fema… femin…
#>  6 Owen…  0.105   0.134  brown, gr… light      blue            52   male  mascu…
#>  7 Beru… -0.269  -0.132  brown      light      blue            47   fema… femin…
#>  8 R5-D4 -2.22   -0.385  NA         white, red red             NA   none  mascu…
#>  9 Bigg…  0.249  -0.0786 black      light      brown           24   male  mascu…
#> 10 Obi-…  0.220  -0.120  auburn, w… fair       blue-gray       57   male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
# ->
starwars %>% mutate(across(c("height", "mass"), ~ scale2(.x, na.rm = TRUE)))
#> # A tibble: 87 x 14
#>    name   height    mass hair_color skin_color eye_color birth_year sex   gender
#>    <chr>   <dbl>   <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke… -0.0678 -0.120  blond      fair       blue            19   male  mascu…
#>  2 C-3PO -0.212  -0.132  NA         gold       yellow         112   none  mascu…
#>  3 R2-D2 -2.25   -0.385  NA         white, bl… red             33   none  mascu…
#>  4 Dart…  0.795   0.228  none       white      yellow          41.9 male  mascu…
#>  5 Leia… -0.701  -0.285  brown      light      brown           19   fema… femin…
#>  6 Owen…  0.105   0.134  brown, gr… light      blue            52   male  mascu…
#>  7 Beru… -0.269  -0.132  brown      light      blue            47   fema… femin…
#>  8 R5-D4 -2.22   -0.385  NA         white, red red             NA   none  mascu…
#>  9 Bigg…  0.249  -0.0786 black      light      brown           24   male  mascu…
#> 10 Obi-…  0.220  -0.120  auburn, w… fair       blue-gray       57   male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

# You can also supply selection helpers to _at() functions but you have
# to quote them with vars():
iris %>% mutate_at(vars(matches("Sepal")), log)
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1         1.63        1.25          1.4         0.2 setosa 
#>  2         1.59        1.10          1.4         0.2 setosa 
#>  3         1.55        1.16          1.3         0.2 setosa 
#>  4         1.53        1.13          1.5         0.2 setosa 
#>  5         1.61        1.28          1.4         0.2 setosa 
#>  6         1.69        1.36          1.7         0.4 setosa 
#>  7         1.53        1.22          1.4         0.3 setosa 
#>  8         1.61        1.22          1.5         0.2 setosa 
#>  9         1.48        1.06          1.4         0.2 setosa 
#> 10         1.59        1.13          1.5         0.1 setosa 
#> # … with 140 more rows
iris %>% mutate(across(matches("Sepal"), log))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1         1.63        1.25          1.4         0.2 setosa 
#>  2         1.59        1.10          1.4         0.2 setosa 
#>  3         1.55        1.16          1.3         0.2 setosa 
#>  4         1.53        1.13          1.5         0.2 setosa 
#>  5         1.61        1.28          1.4         0.2 setosa 
#>  6         1.69        1.36          1.7         0.4 setosa 
#>  7         1.53        1.22          1.4         0.3 setosa 
#>  8         1.61        1.22          1.5         0.2 setosa 
#>  9         1.48        1.06          1.4         0.2 setosa 
#> 10         1.59        1.13          1.5         0.1 setosa 
#> # … with 140 more rows

# The _if() variants apply a predicate function (a function that
# returns TRUE or FALSE) to determine the relevant subset of
# columns. Here we divide all the numeric columns by 100:
starwars %>% mutate_if(is.numeric, scale2, na.rm = TRUE)
#> # A tibble: 87 x 14
#>    name   height    mass hair_color skin_color eye_color birth_year sex   gender
#>    <chr>   <dbl>   <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke… -0.0678 -0.120  blond      fair       blue          -0.443 male  mascu…
#>  2 C-3PO -0.212  -0.132  NA         gold       yellow         0.158 none  mascu…
#>  3 R2-D2 -2.25   -0.385  NA         white, bl… red           -0.353 none  mascu…
#>  4 Dart…  0.795   0.228  none       white      yellow        -0.295 male  mascu…
#>  5 Leia… -0.701  -0.285  brown      light      brown         -0.443 fema… femin…
#>  6 Owen…  0.105   0.134  brown, gr… light      blue          -0.230 male  mascu…
#>  7 Beru… -0.269  -0.132  brown      light      blue          -0.262 fema… femin…
#>  8 R5-D4 -2.22   -0.385  NA         white, red red           NA     none  mascu…
#>  9 Bigg…  0.249  -0.0786 black      light      brown         -0.411 male  mascu…
#> 10 Obi-…  0.220  -0.120  auburn, w… fair       blue-gray     -0.198 male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
starwars %>% mutate(across(where(is.numeric), ~ scale2(.x, na.rm = TRUE)))
#> # A tibble: 87 x 14
#>    name   height    mass hair_color skin_color eye_color birth_year sex   gender
#>    <chr>   <dbl>   <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#>  1 Luke… -0.0678 -0.120  blond      fair       blue          -0.443 male  mascu…
#>  2 C-3PO -0.212  -0.132  NA         gold       yellow         0.158 none  mascu…
#>  3 R2-D2 -2.25   -0.385  NA         white, bl… red           -0.353 none  mascu…
#>  4 Dart…  0.795   0.228  none       white      yellow        -0.295 male  mascu…
#>  5 Leia… -0.701  -0.285  brown      light      brown         -0.443 fema… femin…
#>  6 Owen…  0.105   0.134  brown, gr… light      blue          -0.230 male  mascu…
#>  7 Beru… -0.269  -0.132  brown      light      blue          -0.262 fema… femin…
#>  8 R5-D4 -2.22   -0.385  NA         white, red red           NA     none  mascu…
#>  9 Bigg…  0.249  -0.0786 black      light      brown         -0.411 male  mascu…
#> 10 Obi-…  0.220  -0.120  auburn, w… fair       blue-gray     -0.198 male  mascu…
#> # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

# mutate_if() is particularly useful for transforming variables from
# one type to another
iris %>% mutate_if(is.factor, as.character)
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            1.4         0.2 setosa 
#>  3          4.7         3.2          1.3         0.2 setosa 
#>  4          4.6         3.1          1.5         0.2 setosa 
#>  5          5           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # … with 140 more rows
iris %>% mutate_if(is.double, as.integer)
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <int>       <int>        <int>       <int> <fct>  
#>  1            5           3            1           0 setosa 
#>  2            4           3            1           0 setosa 
#>  3            4           3            1           0 setosa 
#>  4            4           3            1           0 setosa 
#>  5            5           3            1           0 setosa 
#>  6            5           3            1           0 setosa 
#>  7            4           3            1           0 setosa 
#>  8            5           3            1           0 setosa 
#>  9            4           2            1           0 setosa 
#> 10            4           3            1           0 setosa 
#> # … with 140 more rows
# ->
iris %>% mutate(across(where(is.factor), as.character))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            1.4         0.2 setosa 
#>  3          4.7         3.2          1.3         0.2 setosa 
#>  4          4.6         3.1          1.5         0.2 setosa 
#>  5          5           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # … with 140 more rows
iris %>% mutate(across(where(is.double), as.integer))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <int>       <int>        <int>       <int> <fct>  
#>  1            5           3            1           0 setosa 
#>  2            4           3            1           0 setosa 
#>  3            4           3            1           0 setosa 
#>  4            4           3            1           0 setosa 
#>  5            5           3            1           0 setosa 
#>  6            5           3            1           0 setosa 
#>  7            4           3            1           0 setosa 
#>  8            5           3            1           0 setosa 
#>  9            4           2            1           0 setosa 
#> 10            4           3            1           0 setosa 
#> # … with 140 more rows

# Multiple transformations ----------------------------------------

# If you want to apply multiple transformations, pass a list of
# functions. When there are multiple functions, they create new
# variables instead of modifying the variables in place:
iris %>% mutate_if(is.numeric, list(scale2, log))
#> # A tibble: 150 x 13
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_fn1
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>              <dbl>
#>  1          5.1         3.5          1.4         0.2 setosa            -0.898
#>  2          4.9         3            1.4         0.2 setosa            -1.14 
#>  3          4.7         3.2          1.3         0.2 setosa            -1.38 
#>  4          4.6         3.1          1.5         0.2 setosa            -1.50 
#>  5          5           3.6          1.4         0.2 setosa            -1.02 
#>  6          5.4         3.9          1.7         0.4 setosa            -0.535
#>  7          4.6         3.4          1.4         0.3 setosa            -1.50 
#>  8          5           3.4          1.5         0.2 setosa            -1.02 
#>  9          4.4         2.9          1.4         0.2 setosa            -1.74 
#> 10          4.9         3.1          1.5         0.1 setosa            -1.14 
#> # … with 140 more rows, and 7 more variables: Sepal.Width_fn1 <dbl>,
#> #   Petal.Length_fn1 <dbl>, Petal.Width_fn1 <dbl>, Sepal.Length_fn2 <dbl>,
#> #   Sepal.Width_fn2 <dbl>, Petal.Length_fn2 <dbl>, Petal.Width_fn2 <dbl>
iris %>% mutate_if(is.numeric, list(~scale2(.), ~log(.)))
#> # A tibble: 150 x 13
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_scale2
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>                 <dbl>
#>  1          5.1         3.5          1.4         0.2 setosa               -0.898
#>  2          4.9         3            1.4         0.2 setosa               -1.14 
#>  3          4.7         3.2          1.3         0.2 setosa               -1.38 
#>  4          4.6         3.1          1.5         0.2 setosa               -1.50 
#>  5          5           3.6          1.4         0.2 setosa               -1.02 
#>  6          5.4         3.9          1.7         0.4 setosa               -0.535
#>  7          4.6         3.4          1.4         0.3 setosa               -1.50 
#>  8          5           3.4          1.5         0.2 setosa               -1.02 
#>  9          4.4         2.9          1.4         0.2 setosa               -1.74 
#> 10          4.9         3.1          1.5         0.1 setosa               -1.14 
#> # … with 140 more rows, and 7 more variables: Sepal.Width_scale2 <dbl>,
#> #   Petal.Length_scale2 <dbl>, Petal.Width_scale2 <dbl>,
#> #   Sepal.Length_log <dbl>, Sepal.Width_log <dbl>, Petal.Length_log <dbl>,
#> #   Petal.Width_log <dbl>
iris %>% mutate_if(is.numeric, list(scale = scale2, log = log))
#> # A tibble: 150 x 13
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_scale
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>                <dbl>
#>  1          5.1         3.5          1.4         0.2 setosa              -0.898
#>  2          4.9         3            1.4         0.2 setosa              -1.14 
#>  3          4.7         3.2          1.3         0.2 setosa              -1.38 
#>  4          4.6         3.1          1.5         0.2 setosa              -1.50 
#>  5          5           3.6          1.4         0.2 setosa              -1.02 
#>  6          5.4         3.9          1.7         0.4 setosa              -0.535
#>  7          4.6         3.4          1.4         0.3 setosa              -1.50 
#>  8          5           3.4          1.5         0.2 setosa              -1.02 
#>  9          4.4         2.9          1.4         0.2 setosa              -1.74 
#> 10          4.9         3.1          1.5         0.1 setosa              -1.14 
#> # … with 140 more rows, and 7 more variables: Sepal.Width_scale <dbl>,
#> #   Petal.Length_scale <dbl>, Petal.Width_scale <dbl>, Sepal.Length_log <dbl>,
#> #   Sepal.Width_log <dbl>, Petal.Length_log <dbl>, Petal.Width_log <dbl>
# ->
iris %>%
  as_tibble() %>%
  mutate(across(where(is.numeric), list(scale = scale2, log = log)))
#> # A tibble: 150 x 13
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_scale
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>                <dbl>
#>  1          5.1         3.5          1.4         0.2 setosa              -0.898
#>  2          4.9         3            1.4         0.2 setosa              -1.14 
#>  3          4.7         3.2          1.3         0.2 setosa              -1.38 
#>  4          4.6         3.1          1.5         0.2 setosa              -1.50 
#>  5          5           3.6          1.4         0.2 setosa              -1.02 
#>  6          5.4         3.9          1.7         0.4 setosa              -0.535
#>  7          4.6         3.4          1.4         0.3 setosa              -1.50 
#>  8          5           3.4          1.5         0.2 setosa              -1.02 
#>  9          4.4         2.9          1.4         0.2 setosa              -1.74 
#> 10          4.9         3.1          1.5         0.1 setosa              -1.14 
#> # … with 140 more rows, and 7 more variables: Sepal.Length_log <dbl>,
#> #   Sepal.Width_scale <dbl>, Sepal.Width_log <dbl>, Petal.Length_scale <dbl>,
#> #   Petal.Length_log <dbl>, Petal.Width_scale <dbl>, Petal.Width_log <dbl>

# When there's only one function in the list, it modifies existing
# variables in place. Give it a name to instead create new variables:
iris %>% mutate_if(is.numeric, list(scale2))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1       -0.898      1.02          -1.34       -1.31 setosa 
#>  2       -1.14      -0.132         -1.34       -1.31 setosa 
#>  3       -1.38       0.327         -1.39       -1.31 setosa 
#>  4       -1.50       0.0979        -1.28       -1.31 setosa 
#>  5       -1.02       1.25          -1.34       -1.31 setosa 
#>  6       -0.535      1.93          -1.17       -1.05 setosa 
#>  7       -1.50       0.786         -1.34       -1.18 setosa 
#>  8       -1.02       0.786         -1.28       -1.31 setosa 
#>  9       -1.74      -0.361         -1.34       -1.31 setosa 
#> 10       -1.14       0.0979        -1.28       -1.44 setosa 
#> # … with 140 more rows
iris %>% mutate_if(is.numeric, list(scale = scale2))
#> # A tibble: 150 x 9
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_scale
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>                <dbl>
#>  1          5.1         3.5          1.4         0.2 setosa              -0.898
#>  2          4.9         3            1.4         0.2 setosa              -1.14 
#>  3          4.7         3.2          1.3         0.2 setosa              -1.38 
#>  4          4.6         3.1          1.5         0.2 setosa              -1.50 
#>  5          5           3.6          1.4         0.2 setosa              -1.02 
#>  6          5.4         3.9          1.7         0.4 setosa              -0.535
#>  7          4.6         3.4          1.4         0.3 setosa              -1.50 
#>  8          5           3.4          1.5         0.2 setosa              -1.02 
#>  9          4.4         2.9          1.4         0.2 setosa              -1.74 
#> 10          4.9         3.1          1.5         0.1 setosa              -1.14 
#> # … with 140 more rows, and 3 more variables: Sepal.Width_scale <dbl>,
#> #   Petal.Length_scale <dbl>, Petal.Width_scale <dbl>