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 explicit splicing.

.vars

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

.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 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 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 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 2 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 3 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 4 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 5 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 6 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 7 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 8 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 9 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 10 15.8 8 351.0 264 4.22 3.170 14.50 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 1.835 19.90 1 1 4 1 #> 2 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 3 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 4 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 5 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 6 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 7 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 9 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 10 21.5 4 120.1 97 3.70 2.465 20.01 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 1.835 19.90 1 1 4 1 #> 2 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 3 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 4 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 5 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 6 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 7 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 9 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 10 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> # ... with 22 more rows