filter {dplyr} R Documentation

## Subset rows using column values

### Description

The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [.

### Usage

filter(.data, ..., .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 value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. .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.

### Details

The filter() function is used to subset the rows of .data, applying the expressions in ... to the column values to determine which rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and ungroup()). However, dplyr is not yet smart enough to optimise the filtering operation on grouped datasets that do not need grouped calculations. For this reason, filtering is often considerably faster on ungrouped data.

### 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.

### 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()

### 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 %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))


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: no methods found.

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

### Examples

# Filtering by one criterion
filter(starwars, species == "Human")
filter(starwars, mass > 1000)

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

# When multiple expressions are used, they are combined using &
filter(starwars, hair_color == "none", eye_color == "black")

# The filtering operation may yield different results on grouped
# tibbles because the expressions are computed within groups.
#
# The following filters 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 gender
# average:
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))

# 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]]
)