filter {tidybulk} | R Documentation

## Subset rows using column values

### Description

'filter()' retains the rows where the conditions you provide a 'TRUE'. Note that, unlike base subsetting with '[', rows where the condition evaluates to 'NA' are dropped.

Most data operations are done on groups defined by variables. 'group_by()' takes an existing tbl and converts it into a grouped tbl where operations are performed "by group". 'ungroup()' removes grouping.

### Usage

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

    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.
`...` | In 'group_by()', variables or computations to group by. In 'ungroup()', variables to remove from the grouping.
`.preserve` | when 'FALSE' (the default), the grouping structure is recalculated based on the resulting data, otherwise it is kept as is.

### Details

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 type as '.data'.

* 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.

A [grouped data frame][grouped_df()], unless the combination of '...' and 'add' yields a non empty set of grouping columns, a regular (ungrouped) data frame otherwise.

### 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:

The former keeps rows with 'mass' greater than the global average whereas the latter keeps rows with 'mass' greater than the 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:

These function are **generic**s, 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:

### See Also

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

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

Other grouping functions: `group_by()`

### Examples

    # Learn more in ?dplyr_tidy_eval
    `%>%` = magrittr::`%>%`
    by_cyl <- mtcars %>% group_by(cyl)

    # grouping doesn't change how the data looks (apart from listing
    # how it's grouped):
    by_cyl

    # It changes how it acts with the other dplyr verbs:
    by_cyl %>% summarise(
      disp = mean(disp),
      hp = mean(hp)
    )
    by_cyl %>% filter(disp == max(disp))

    # Each call to summarise() removes a layer of grouping
    `%>%` = magrittr::`%>%`
    by_vs_am <- mtcars %>% group_by(vs, am)
    by_vs <- by_vs_am %>% summarise(n = n())
    by_vs
    by_vs %>% summarise(n = sum(n))

    # To removing grouping, use ungroup
    by_vs %>%
      ungroup() %>%
      summarise(n = sum(n))

    # You can group by expressions: this is just short-hand for
    # a mutate() followed by a group_by()
    mtcars %>% group_by(vsam = vs + am)

    # when factors are involved, groups can be empty
    tbl <- tibble(
      x = 1:10,
      y = factor(rep(c("a", "c"), each  = 5), levels = c("a", "b", "c"))
    )

[Package tidybulk version 0.99.23 Index]