Helpers that return the same value

They all share the *_identity suffix, they are silent in non-interactive sessions. They are very handy to create clickable hyperlinks that do not modify the current state of the analysis.

They are inspired by pillar::glimpse(), tibble::view().

Look at the original functions for the other parameters.

Usage

names_identity(x, nrows = NULL, extra_msg = NULL)

count_identity(
  x,
  ...,
  sort = TRUE,
  name = NULL,
  nrows = NULL,
  extra_msg = NULL
)

mutate_identity(
  x,
  ...,
  .keep = NULL,
  .before = NULL,
  nrows = NULL,
  extra_msg = NULL
)

slice_identity(
  x,
  ...,
  .by = NULL,
  .preserve = FALSE,
  nrows = NULL,
  extra_msg = NULL
)

slice_min_identity(
  x,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = TRUE,
  nrows = NULL,
  extra_msg = NULL
)

slice_max_identity(
  x,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = TRUE,
  nrows = NULL,
  extra_msg = NULL
)

arrange_identity(x, ..., .by_group = FALSE, nrows = NULL, extra_msg = NULL)

distinct_identity(
  x,
  ...,
  .keep_all = FALSE,
  .arrange = FALSE,
  nrows = NULL,
  extra_msg = NULL
)

filter_identity(x, ..., .by = NULL, nrows = NULL, extra_msg = NULL)

slice_sample_identity(
  x,
  ...,
  n,
  prop,
  by = NULL,
  weight_by = NULL,
  replace = FALSE,
  nrows = NULL,
  extra_msg = NULL
)

filter_if_any_identity(
  x,
  ...,
  .by = NULL,
  .keep_new_var = FALSE,
  nrows = NULL,
  extra_msg = NULL
)

slice_min_max_identity(
  x,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE,
  each = TRUE,
  ascending = TRUE,
  nrows = NULL,
  extra_msg = NULL
)

slice_group_sample_identity(
  x,
  group_var = NULL,
  n_groups = 1,
  nrows = NULL,
  extra_msg = NULL
)

Arguments

x

The main object (a data.frame, but some functions accept a vector.) (aka .data in some dplyr functions, but naming it x throughout.)

nrows

Number of rows to print.

extra_msg

A character vector of observations that will print to console, notes taken related to the transformation.

name, sort, .keep_all, .by, by, n_groups, group_var, ..., n, prop, with_ties, order_by, .keep, .before, each, na_rm, weight_by, replace, .by_group, .keep_new_var, .preserve, ascending

Check original functions.

.arrange

Should arrange output?

Value

x, the original input is (invisibly) returned. (allowing the *_identity() functions to be used in a pipeline) will print extra_msg to the console in interactive sessions.

Use cases / advantages

• Like many other reuseme functions, they are most useful in interactive sessions

• print the result in interactive sessions (quiet in non-interactive.)

• Create runnable hyperlinks (In August 2023, RStudio forbids runnable hyperlinks of base functions, or non-package functions. (i.e. that don't have ::))

• Use in pipelines to explore the data

• Use rlang::is_interactive() over base::interactive() as it's easier to control and test with options(rlang_interactive)

• Use the original functions for your final results.

• count_identity() also prints percentages.

• slice_identity() can be useful to resolve many-to-many warnings from dplyr join functions.

Caution

• Don't put those at the end of a pipeline

• Don't name the first argument, to avoid conflicts in case a column in the data is named x.

• Some functions have small tweaks

  • mutate_identity() only prints the distinct values, and uses .keep = "used", .before = 0, unless specified to improve the display.

  • count_identity() is a wrapper of count_pct() (itself a wrapper of dplyr::count()),

  • count_identity() may fail if there is already a variable named n.

  • slice_min/max_identity() relocates the target column at the beginning.

  • filter_identity() prints a short message if no rows are returned.

See also

• dplyr::distinct()

• dplyr::filter()

• dplyr::slice()

• dplyr::mutate()

• dplyr::arrange()

• count_pct()

• slice_min_max()

• slice_group_sample()

Examples

withr::local_options(rlang_interactive = TRUE)
# Workflow to explore mtcars
mtcars |>
  filter_identity(mpg > 21, extra_msg = c("Wow, these rows are very interesting.")) |>
  count_identity(
    vs,
    extra_msg = c(
      "Woo, there are 14 obs with vs = 1, 18 obs with vs = 0",
      "The split is 56%-43%"
    )
  ) |>
  dplyr::filter(disp > 150) # after all, I need only disp > 150
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 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
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    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
#> 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
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8