dplyr (version 1.0.10)

slice: Subset rows using their positions


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 highest or lowest 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, ..., .preserve = FALSE)

slice_head(.data, ..., n, prop)

slice_tail(.data, ..., n, prop)

slice_min(.data, order_by, ..., n, prop, with_ties = TRUE)

slice_max(.data, order_by, ..., n, prop, with_ties = TRUE)

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


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.



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_helpers(), these arguments are passed on to methods.


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 a negative value of n or prop is provided, the specified number or proportion of rows will be removed.

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. If the proportion of a group size does not yield an integer number of rows, the absolute value of prop*nrow(.data) is rounded down.


Variable or function of variables to order by.


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.


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.


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


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:::methods_rd("slice").

  • slice_head(): dplyr:::methods_rd("slice_head").

  • slice_tail(): dplyr:::methods_rd("slice_tail").

  • slice_min(): dplyr:::methods_rd("slice_min").

  • slice_max(): dplyr:::methods_rd("slice_max").

  • slice_sample(): dplyr:::methods_rd("slice_sample").


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().

See Also

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


Run this code
mtcars %>% slice(1L)
# Similar to tail(mtcars, 1):
mtcars %>% slice(n())
mtcars %>% slice(5:n())
# Rows can be dropped with negative indices:
slice(mtcars, -(1:4))

# First and last rows based on existing order
mtcars %>% slice_head(n = 5)
mtcars %>% slice_tail(n = 5)

# Rows with minimum and maximum values of a variable
mtcars %>% slice_min(mpg, n = 5)
mtcars %>% slice_max(mpg, n = 5)

# slice_min() and slice_max() may return more rows than requested
# in the presence of ties. Use with_ties = FALSE to suppress
mtcars %>% slice_min(cyl, n = 1)
mtcars %>% slice_min(cyl, n = 1, with_ties = FALSE)

# slice_sample() allows you to random select with or without replacement
mtcars %>% slice_sample(n = 5)
mtcars %>% slice_sample(n = 5, replace = TRUE)

# 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)

# 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)

# 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)

# 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)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

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