nplyr (version 0.2.0)

nest_filter: Subset rows in nested data frames using column values.

Description

nest_filter() is used to subset nested data frames, 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 [.

nest_filter() subsets the rows within .nest_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.

Usage

nest_filter(.data, .nest_data, ..., .preserve = FALSE)

Value

An object of the same type as .data. Each object in the column .nest_data

will also be of the same type as the input. Each object in .nest_data 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.

Arguments

.data

A data frame, data frame extension (e.g., a tibble), or a lazy data frame (e.g., from dbplyr or dtplyr).

.nest_data

A list-column containing data frames

...

Expressions that return a logical value, and are defined in terms of the variables in .nest_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 .nest_data 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

nest_filter() is largely a wrapper for dplyr::filter() and maintains the functionality of filter() within each nested data frame. For more information on filter(), please refer to the documentation in dplyr.

See Also

Other single table verbs: nest_arrange(), nest_mutate(), nest_rename(), nest_select(), nest_slice(), nest_summarise()

Examples

gm_nest <- gapminder::gapminder %>% tidyr::nest(country_data = -continent)

# apply a filter
gm_nest %>%
  nest_filter(country_data, year > 1972)

# apply multiple filters
gm_nest %>%
  nest_filter(country_data, year > 1972, pop < 10000000)
  
# apply a filter on grouped data
gm_nest %>%
  nest_group_by(country_data, country) %>%
  nest_filter(country_data, pop > mean(pop))