Subset rows using their positions

Source: R/slice.R

slice() lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:

  • slice_head() and slice_tail() select the first or last rows.

  • slice_sample() randomly selects rows.

  • slice_min() and slice_max() select rows with the smallest or largest values of a variable.

If .data is a grouped_df, the operation will be performed on each group, so that (e.g.) slice_head(df, n = 5) will select the first five rows in each group.

slice(.data, ..., .by = NULL, .preserve = FALSE)

slice_head(.data, ..., n, prop, by = NULL)

slice_tail(.data, ..., n, prop, by = NULL)

slice_min(
  .data,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE
)

slice_max(
  .data,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE
)

slice_sample(.data, ..., n, prop, by = NULL, weight_by = NULL, replace = FALSE)

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.

...

For slice(): <data-masking> Integer row values.

Provide either positive values to keep, or negative values to drop. The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored.

For slice_*(), these arguments are passed on to methods.

.by, by

[Experimental]

<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.

.preserve

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

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.

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.

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.

weight_by

<data-masking> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

replace

Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.

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.

  • Columns are not modified.

  • Groups are not modified.

  • Data frame attributes are preserved.

Details

Slice does not work with relational databases because they have no intrinsic notion of row order. If you want to perform the equivalent operation, use filter() and row_number().

Methods

These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

  • slice(): dplyr (data.frame) .

  • slice_head(): dplyr (data.frame) .

  • slice_tail(): dplyr (data.frame) .

  • slice_min(): dplyr (data.frame) .

  • slice_max(): dplyr (data.frame) .

  • slice_sample(): dplyr (data.frame) .

See also

Other single table verbs: arrange(), filter(), mutate(), reframe(), rename(), select(), summarise()

Examples

# Similar to head(mtcars, 1):
mtcars %>% slice(1L)
#>           mpg cyl disp  hp drat   wt  qsec vs am gear carb
#> Mazda RX4  21   6  160 110  3.9 2.62 16.46  0  1    4    4
# Similar to tail(mtcars, 1):
mtcars %>% slice(n())
#>             mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Volvo 142E 21.4   4  121 109 4.11 2.78 18.6  1  1    4    2
mtcars %>% slice(5:n())
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 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 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
#> 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
#> 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
#> 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
#> 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
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
# Rows can be dropped with negative indices:
slice(mtcars, -(1:4))
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 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 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
#> 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
#> 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
#> 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
#> 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
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

# First and last rows based on existing order
mtcars %>% slice_head(n = 5)
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
mtcars %>% slice_tail(n = 5)
#>                 mpg cyl  disp  hp drat    wt qsec vs am gear carb
#> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
#> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
#> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
#> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
#> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2

# Rows with minimum and maximum values of a variable
mtcars %>% slice_min(mpg, n = 5)
#>                      mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
#> Camaro Z28          13.3   8  350 245 3.73 3.840 15.41  0  0    3    4
#> Duster 360          14.3   8  360 245 3.21 3.570 15.84  0  0    3    4
#> Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4
mtcars %>% slice_max(mpg, n = 5)
#>                 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
#> 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
#> Lotus Europa   30.4   4 95.1 113 3.77 1.513 16.90  1  1    5    2
#> Fiat X1-9      27.3   4 79.0  66 4.08 1.935 18.90  1  1    4    1

# slice_min() and slice_max() may return more rows than requested
# in the presence of ties.
mtcars %>% 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 %>% 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
# Or use additional variables to break the tie:
mtcars %>% slice_min(tibble(cyl, mpg), n = 1)
#>             mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Volvo 142E 21.4   4  121 109 4.11 2.78 18.6  1  1    4    2

# slice_sample() allows you to random select with or without replacement
mtcars %>% slice_sample(n = 5)
#>                   mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Camaro Z28       13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Fiat X1-9        27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Merc 230         22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
mtcars %>% slice_sample(n = 5, replace = TRUE)
#>                       mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Hornet Sportabout    18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Ford Pantera L       15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Dodge Challenger...3 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> Lotus Europa         30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Dodge Challenger...5 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2

# you can optionally weight by a variable - this code weights by the
# physical weight of the cars, so heavy cars are more likely to get
# selected
mtcars %>% slice_sample(weight_by = wt, n = 5)
#>                     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
#> Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Merc 240D          24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Honda Civic        30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Pontiac Firebird   19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2

# Group wise operation ----------------------------------------
df <- tibble(
  group = rep(c("a", "b", "c"), c(1, 2, 4)),
  x = runif(7)
)

# All slice helpers operate per group, silently truncating to the group
# size, so the following code works without error
df %>% group_by(group) %>% slice_head(n = 2)
#> # A tibble: 5 × 2
#> # Groups:   group [3]
#>   group      x
#>   <chr>  <dbl>
#> 1 a     0.138 
#> 2 b     0.0566
#> 3 b     0.546 
#> 4 c     0.467 
#> 5 c     0.896 

# When specifying the proportion of rows to include non-integer sizes
# are rounded down, so group a gets 0 rows
df %>% group_by(group) %>% slice_head(prop = 0.5)
#> # A tibble: 3 × 2
#> # Groups:   group [2]
#>   group      x
#>   <chr>  <dbl>
#> 1 b     0.0566
#> 2 c     0.467 
#> 3 c     0.896 

# Filter equivalents --------------------------------------------
# slice() expressions can often be written to use `filter()` and
# `row_number()`, which can also be translated to SQL. For many databases,
# you'll need to supply an explicit variable to use to compute the row number.
filter(mtcars, row_number() == 1L)
#>           mpg cyl disp  hp drat   wt  qsec vs am gear carb
#> Mazda RX4  21   6  160 110  3.9 2.62 16.46  0  1    4    4
filter(mtcars, row_number() == n())
#>             mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Volvo 142E 21.4   4  121 109 4.11 2.78 18.6  1  1    4    2
filter(mtcars, between(row_number(), 5, n()))
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 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 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
#> 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
#> 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
#> 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
#> 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
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2