Subset rows using their positions

A wrapper around dplyr::bind_rows(), dplyr::slice_min(), dplyr::slice_max()

Usage

slice_min_max(
  .data,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE,
  each = TRUE,
  ascending = TRUE
)

Arguments

.data

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

order_by

<data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.

...

Arguments are passed on to methods.

n, prop

Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows.

A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.

by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in arrange()), so they will only be included if there are insufficient non-missing values to reach n/prop.

each

If FALSE, n and prop passed to dplyr::slice_min() and dplyr::slice_max() will be divided by 2. (will use ceiling() if n is)

ascending

Return the output in ascending order. (min on top)

Value

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

Each row may appear 0, 1, or many times in the output.
A minmax column is added to show which is min, which is max.
Groups are not modified.
Data frame attributes are preserved.

Examples

# in the presence of ties.
mtcars |> dplyr::slice_min(cyl, n = 1)
#>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
# Use with_ties = FALSE to return exactly n matches
mtcars |> dplyr::slice_min(cyl, n = 1, with_ties = FALSE)
#>             mpg cyl disp hp drat   wt  qsec vs am gear carb
#> Datsun 710 22.8   4  108 93 3.85 2.32 18.61  1  1    4    1
# Use each = FALSE to have n divided in each place
mtcars |> slice_min_max(cyl, n = 2)
#>                     minmax  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Datsun 710             min 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Merc 240D              min 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230               min 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Fiat 128               min 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic            min 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla         min 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Toyota Corona          min 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Fiat X1-9              min 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2          min 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa           min 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Volvo 142E             min 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Hornet Sportabout      max 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Duster 360             max 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 450SE             max 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL             max 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 450SLC            max 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Cadillac Fleetwood     max 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental    max 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial      max 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Dodge Challenger       max 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin            max 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Camaro Z28             max 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird       max 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Ford Pantera L         max 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Maserati Bora          max 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
# Using each = TRUE (to retun n = 2, for min, n = 2 for max)
mtcars |> slice_min_max(cyl, each = TRUE, n = 2)
#>                     minmax  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Datsun 710             min 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Merc 240D              min 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230               min 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Fiat 128               min 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic            min 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla         min 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Toyota Corona          min 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Fiat X1-9              min 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2          min 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa           min 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Volvo 142E             min 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Hornet Sportabout      max 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Duster 360             max 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 450SE             max 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL             max 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 450SLC            max 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Cadillac Fleetwood     max 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental    max 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial      max 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Dodge Challenger       max 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin            max 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Camaro Z28             max 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird       max 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Ford Pantera L         max 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Maserati Bora          max 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

Usage

Arguments

Value

See also

Examples