filter {dplyr} | R Documentation

## Return rows with matching conditions

### Description

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

### Usage

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

### 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 condition evaluates to `TRUE` are kept. The arguments in `...` 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.
`.preserve` | when `FALSE` (the default), the grouping structure is recalculated based on the resulting data, otherwise it is kept as is.

### 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.

### Value

An object of the same class as `.data`.

### Useful filter functions

- `==`, `>`, `>=` 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))

The former keeps rows with `mass` greater than the global average whereas the latter keeps rows with `mass` greater than the gender average.

It is valid to use grouping variables in filter expressions.

When applied on a grouped tibble, `filter()` automatically rearranges the tibble by groups for performance reasons.

### 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.

### See Also

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

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

### Examples

    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")

    # 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))

    # Refer to column names stored as strings with the `.data` pronoun:
    vars <- c("mass", "height")
    cond <- c(80, 150)
    starwars %>%
      filter(
        .data[[vars[[1]]]] > cond[[1]],
        .data[[vars[[2]]]] > cond[[2]]
      )

    # For more complex cases, knowledge of tidy evaluation and the
    # unquote operator `!!` is required. See https://tidyeval.tidyverse.org/
    #
    # One useful and simple tidy eval technique is to use `!!` to bypass
    # the data frame and its columns. Here is how to filter the columns
    # `mass` and `height` relative to objects of the same names:
    mass <- 80
    height <- 150
    filter(starwars, mass > !!mass, height > !!height)

[Package dplyr version 0.8.0.1 Index]