[Superseded]

Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb.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

These scoped variants of arrange() sort a data frame by a selection of variables. Like arrange(), you can modify the variables before ordering with the .funs argument.

arrange_all(.tbl, .funs = list(), ..., .by_group = FALSE)

arrange_at(.tbl, .vars, .funs = list(), ..., .by_group = FALSE)

arrange_if(.tbl, .predicate, .funs = list(), ..., .by_group = FALSE)

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.

.by_group

If TRUE, will sort first by grouping variable. Applies to grouped data frames only.

.vars

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

.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.

Grouping variables

The grouping variables that are part of the selection participate in the sorting of the data frame.

Examples

df <- as_tibble(mtcars)
arrange_all(df)
#> # A tibble: 32 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  10.4     8  460    215  3     5.42  17.8     0     0     3     4
#>  2  10.4     8  472    205  2.93  5.25  18.0     0     0     3     4
#>  3  13.3     8  350    245  3.73  3.84  15.4     0     0     3     4
#>  4  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  5  14.7     8  440    230  3.23  5.34  17.4     0     0     3     4
#>  6  15       8  301    335  3.54  3.57  14.6     0     1     5     8
#>  7  15.2     8  276.   180  3.07  3.78  18       0     0     3     3
#>  8  15.2     8  304    150  3.15  3.44  17.3     0     0     3     2
#>  9  15.5     8  318    150  2.76  3.52  16.9     0     0     3     2
#> 10  15.8     8  351    264  4.22  3.17  14.5     0     1     5     4
#> # … with 22 more rows
# ->
arrange(df, across())
#> # A tibble: 32 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  10.4     8  460    215  3     5.42  17.8     0     0     3     4
#>  2  10.4     8  472    205  2.93  5.25  18.0     0     0     3     4
#>  3  13.3     8  350    245  3.73  3.84  15.4     0     0     3     4
#>  4  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  5  14.7     8  440    230  3.23  5.34  17.4     0     0     3     4
#>  6  15       8  301    335  3.54  3.57  14.6     0     1     5     8
#>  7  15.2     8  276.   180  3.07  3.78  18       0     0     3     3
#>  8  15.2     8  304    150  3.15  3.44  17.3     0     0     3     2
#>  9  15.5     8  318    150  2.76  3.52  16.9     0     0     3     2
#> 10  15.8     8  351    264  4.22  3.17  14.5     0     1     5     4
#> # … with 22 more rows

arrange_all(df, desc)
#> # A tibble: 32 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
#>  2  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
#>  3  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2
#>  4  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
#>  5  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
#>  6  26       4 120.     91  4.43  2.14  16.7     0     1     5     2
#>  7  24.4     4 147.     62  3.69  3.19  20       1     0     4     2
#>  8  22.8     4 141.     95  3.92  3.15  22.9     1     0     4     2
#>  9  22.8     4 108      93  3.85  2.32  18.6     1     1     4     1
#> 10  21.5     4 120.     97  3.7   2.46  20.0     1     0     3     1
#> # … with 22 more rows
# ->
arrange(df, across(everything(), desc))
#> # A tibble: 32 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
#>  2  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
#>  3  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2
#>  4  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
#>  5  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
#>  6  26       4 120.     91  4.43  2.14  16.7     0     1     5     2
#>  7  24.4     4 147.     62  3.69  3.19  20       1     0     4     2
#>  8  22.8     4 141.     95  3.92  3.15  22.9     1     0     4     2
#>  9  22.8     4 108      93  3.85  2.32  18.6     1     1     4     1
#> 10  21.5     4 120.     97  3.7   2.46  20.0     1     0     3     1
#> # … with 22 more rows