filter: Keep or drop rows that match a condition
In dplyr: A Grammar of Data Manipulation

filter

R Documentation

Keep or drop rows that match a condition

Description

These functions are used to subset a data frame, applying the expressions in ... to determine which rows should be kept (for filter()) or dropped ( for filter_out()).

Multiple conditions can be supplied separated by a comma. These will be combined with the & operator. To combine comma separated conditions using | instead, wrap them in when_any().

Both filter() and filter_out() treat NA like FALSE. This subtle behavior can impact how you write your conditions when missing values are involved. See the section on ⁠Missing values⁠ for important details and examples.

Usage

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

filter_out(.data, ..., .by = NULL, .preserve = 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.
`...`	<`data-masking`> Expressions that return a logical vector, defined in terms of the variables in `.data`. If multiple expressions are included, they are combined with the `&` operator. To combine expressions using `\|` instead, wrap them in `when_any()`. Only rows for which all expressions evaluate to `TRUE` are kept (for `filter()`) or dropped (for `filter_out()`).
`.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.
`.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.

Value

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

Rows are a subset of the input, but appear in the same order.
Columns are not modified.
The number of groups may be reduced (if .preserve is not TRUE).
Data frame attributes are preserved.

Missing values

Both filter() and filter_out() treat NA like FALSE. This results in the following behavior:

filter() drops both NA and FALSE.
filter_out() keeps both NA and FALSE.

This means that ⁠filter(data, <conditions>) + filter_out(data, <conditions>)⁠ captures every row within data exactly once.

The NA handling of these functions has been designed to match your intent. When your intent is to keep rows, use filter(). When your intent is to drop rows, use filter_out().

For example, if your goal with this cars data is to "drop rows where the class is suv", then you might write this in one of two ways:

cars <- tibble(class = c("suv", NA, "coupe"))
cars
#> # A tibble: 3 x 1
#>   class
#>   <chr>
#> 1 suv  
#> 2 <NA> 
#> 3 coupe

cars |> filter(class != "suv")
#> # A tibble: 1 x 1
#>   class
#>   <chr>
#> 1 coupe

cars |> filter_out(class == "suv")
#> # A tibble: 2 x 1
#>   class
#>   <chr>
#> 1 <NA> 
#> 2 coupe

Note how filter() drops the NA rows even though our goal was only to drop "suv" rows, but filter_out() matches our intuition.

To generate the correct result with filter(), you'd need to use:

cars |> filter(class != "suv" | is.na(class))
#> # A tibble: 2 x 1
#>   class
#>   <chr>
#> 1 <NA> 
#> 2 coupe

This quickly gets unwieldy when multiple conditions are involved.

In general, if you find yourself:

Using "negative" operators like != or !
Adding in NA handling like ⁠| is.na(col)⁠ or ⁠& !is.na(col)⁠

then you should consider if swapping to the other filtering variant would make your conditions simpler.

Comparison to base subsetting

Base subsetting with [ doesn't treat NA like TRUE or FALSE. Instead, it generates a fully missing row, which is different from how both filter() and filter_out() work.

cars <- tibble(class = c("suv", NA, "coupe"), mpg = c(10, 12, 14))
cars
#> # A tibble: 3 x 2
#>   class   mpg
#>   <chr> <dbl>
#> 1 suv      10
#> 2 <NA>     12
#> 3 coupe    14

cars[cars$class == "suv",]
#> # A tibble: 2 x 2
#>   class   mpg
#>   <chr> <dbl>
#> 1 suv      10
#> 2 <NA>     NA

cars |> filter(class == "suv")
#> # A tibble: 1 x 2
#>   class   mpg
#>   <chr> <dbl>
#> 1 suv      10

Useful filter functions

There are many functions and operators that are useful when constructing the expressions used to filter the data:

==, >, >= etc
&, |, !, xor()
is.na()
between(), near()
when_any(), when_all()

Grouped tibbles

Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:

starwars |> filter(mass > mean(mass, na.rm = TRUE))

With the grouped equivalent:

starwars |> filter(mass > mean(mass, na.rm = TRUE), .by = gender)

In the ungrouped version, filter() compares the value of mass in each row to the global average (taken over the whole data set), keeping only the rows with mass greater than this global average. In contrast, the grouped version calculates the average mass separately for each gender group, and keeps rows with mass greater than the relevant within-gender average.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("filter")}.

Examples

# Filtering for one criterion
filter(starwars, species == "Human")

# Filtering for multiple criteria within a single logical expression
filter(starwars, hair_color == "none" & eye_color == "black")
filter(starwars, hair_color == "none" | eye_color == "black")

# Multiple comma separated expressions are combined using `&`
starwars |> filter(hair_color == "none", eye_color == "black")

# To combine comma separated expressions using `|` instead, use `when_any()`
starwars |> filter(when_any(hair_color == "none", eye_color == "black"))

# Filtering out to drop rows
filter_out(starwars, hair_color == "none")

# When filtering out, it can be useful to first interactively filter for the
# rows you want to drop, just to double check that you've written the
# conditions correctly. Then, just change `filter()` to `filter_out()`.
filter(starwars, mass > 1000, eye_color == "orange")
filter_out(starwars, mass > 1000, eye_color == "orange")

# The filtering operation may yield different results on grouped
# tibbles because the expressions are computed within groups.
#
# The following keeps rows where `mass` is greater than the
# global average:
starwars |> filter(mass > mean(mass, na.rm = TRUE))

# Whereas this keeps rows with `mass` greater than the per `gender`
# average:
starwars |> filter(mass > mean(mass, na.rm = TRUE), .by = gender)

# If you find yourself trying to use a `filter()` to drop rows, then
# you should consider if switching to `filter_out()` can simplify your
# conditions. For example, to drop blond individuals, you might try:
starwars |> filter(hair_color != "blond")

# But this also drops rows with an `NA` hair color! To retain those:
starwars |> filter(hair_color != "blond" | is.na(hair_color))

# But explicit `NA` handling like this can quickly get unwieldy, especially
# with multiple conditions. Since your intent was to specify rows to drop
# rather than rows to keep, use `filter_out()`. This also removes the need
# for any explicit `NA` handling.
starwars |> filter_out(hair_color == "blond")

# To refer to column names that are stored as strings, use the `.data`
# pronoun:
vars <- c("mass", "height")
cond <- c(80, 150)
starwars |>
  filter(
    .data[[vars[[1]]]] > cond[[1]],
    .data[[vars[[2]]]] > cond[[2]]
  )
# Learn more in ?rlang::args_data_masking

dplyr documentation built on Feb. 3, 2026, 9:08 a.m.