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

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

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

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

Arguments

.tbl

A tbl object.

.funs

List of function calls generated by funs(), or a character vector of function names, or simply a function.

Bare formulas are passed to rlang::as_function() to create purrr-style lambda functions. 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.

...

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

.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.

Examples

df <- as_tibble(mtcars) 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 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ... with 22 more rows
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
# You can supply a function that will be applied before taking the # ordering of the variables. The variables of the sorted tibble # keep their original values. 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_all(df, funs(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