Help File Sections

R help files can contain diverse information: some are only a few lines long while others contain dozens of pages of information. Not all R help files include all possible sections, but there are a few core sections that tend to show up pretty consistently due to their general usefulness.

Description

R: Order rows using column values

Description

arrange() orders the rows of a data frame by the values of selected columns.

Unlike other dplyr verbs, arrange() largely ignores grouping; you need to explicitly mention grouping variables (or use .by_group = TRUE) in order to group by them, and functions of variables are evaluated once per data frame, not once per group.

As the name implies, the Description section provides a brief, human-readable description of the functions purpose.

Usage

R: Order rows using column values

Usage

arrange(.data, ..., .by_group = FALSE)

## S3 method for class 'data.frame'
arrange(.data, ..., .by_group = FALSE, .locale = NULL)

The Usage section shows the fully parameterized function call. In other words, how to call the function. In particular, this section provides several crucial pieces of information.

  • The name used to call the function
  • The name of all the function arguments
  • The order of the function arguments
  • Any default values defined for function arguments

Arguments

R: Order rows using column values

Arguments

The Arguments section briefly explains each function argument. As you might suspect, the arguments section will probably be the one you reference most frequently since the information contained herein explains the purpose or each function argument and what type of values you are able to specify for each argument.

Details

R: Order rows using column values

Details

Missing values

Unlike base sorting with sort(), NA are:

  • always sorted to the end for local data, even when wrapped with desc().

  • treated differently for remote data, depending on the backend.

The Details section expands on the information listed in the Arguments section to provide additional details of function arguments or the functions behavior

Value

R: Order rows using column values

Value

An object of the same type as .data. The output has the following properties:

  • All rows appear in the output, but (usually) in a different place.

  • Columns are not modified.

  • Groups are not modified.

  • Data frame attributes are preserved.

The Value section describes the type of object returned by the function.

Examples

R: Order rows using column values

Examples

arrange(mtcars, cyl, disp)
arrange(mtcars, desc(disp))

# grouped arrange ignores groups
by_cyl <- mtcars %>% group_by(cyl)
by_cyl %>% arrange(desc(wt))
# Unless you specifically ask:
by_cyl %>% arrange(desc(wt), .by_group = TRUE)

# use embracing when wrapping in a function;
# see ?rlang::args_data_masking for more details
tidy_eval_arrange <- function(.data, var) {
  .data %>%
    arrange({{ var }})
}
tidy_eval_arrange(mtcars, mpg)

# Use `across()` or `pick()` to select columns with tidy-select
iris %>% arrange(pick(starts_with("Sepal")))
iris %>% arrange(across(starts_with("Sepal"), desc))

The Examples section contains executable R code that demonstrates the function’s primary use-cases.

Practice

Run the first line of example code from the Examples section of the arrange() documentation shown above.

You can copy and paste the first line of code directly from the examples section into the editor window.

library(dplyr)

arrange(mtcars, cyl, disp)
mpg cyl disp hp drat wt qsec vs am gear carb
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
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