dplyr (version 0.7.1)

filter: Return rows with matching conditions

Description

Use filter() find rows/cases where conditions are true. Unlike base subsetting, rows where the condition evaluates to NA are dropped.

Usage

filter(.data, ...)

Arguments

.data

A tbl. All main verbs are S3 generics and provide methods for tbl_df(), dtplyr::tbl_dt() and dbplyr::tbl_dbi().

...

Logical predicates defined in terms of the variables in .data. Multiple conditions are combined with &. Only rows where the conditon evalutes to TRUE are kept.

These arguments are automatically quoted and evaluated in the context of the data frame. They support unquoting and splicing. See vignette("programming") for an introduction to these concepts.

Value

An object of the same class as .data.

Useful filter functions

• ==, >, >= etc

• &, |, !, xor()

• is.na()

• between(), near()

Tidy data

When applied to a data frame, row names are silently dropped. To preserve, convert to an explicit variable with tibble::rownames_to_column().

Scoped filtering

The three scoped variants (filter_all(), filter_if() and filter_at()) make it easy to apply a filtering condition to a selection of variables.

Details

Note that dplyr is not yet smart enough to optimise filtering optimisation on grouped datasets that don't need grouped calculations. For this reason, filtering is often considerably faster on ungroup()ed data.

See Also

filter_all(), filter_if() and filter_at().

Other single table verbs: arrange, mutate, select, slice, summarise

Examples

# NOT RUN {
filter(starwars, species == "Human")
filter(starwars, mass > 1000)

# Multiple criteria
filter(starwars, hair_color == "none" & eye_color == "black")
filter(starwars, hair_color == "none" | eye_color == "black")

# Multiple arguments are equivalent to and
filter(starwars, hair_color == "none", eye_color == "black")
# }