View source: R/nest_summarise.R
| nest_summarise | R Documentation |
nest_summarise() creates a new set of nested data frames. Each will have
one (or more) rows for each combination of grouping variables; if there are
no grouping variables, the output will have a single row summarising all
observations in .nest_data. Each nested data frame will contain one column
for each grouping variable and one column for each of the summary statistics
that you have specified.
nest_summarise() and nest_summarize() are synonyms.
nest_summarise(.data, .nest_data, ..., .groups = NULL)
nest_summarize(.data, .nest_data, ..., .groups = NULL)
nest_summarise() is largely a wrapper for dplyr::summarise() and
maintains the functionality of summarise() within each nested data frame.
For more information on summarise(), please refer to the documentation in
dplyr.
An object of the same type as .data. Each object in the column .nest_data
will usually be of the same type as the input. Each object in .nest_data has
the following properties:
The rows come from the underlying group_keys()
The columns are a combination of the grouping keys and the summary expressions that you provide.
The grouping structure is controlled by the .groups argument, the output
may be another grouped_df, a tibble, or a rowwise data frame.
Data frame attributes are not preserved, because nest_summarise()
fundamentally creates a new data frame for each object in .nest_data.
Other single table verbs:
nest_arrange(),
nest_filter(),
nest_mutate(),
nest_rename(),
nest_select(),
nest_slice()
gm_nest <- gapminder::gapminder %>% tidyr::nest(country_data = -continent)
# a summary applied to an ungrouped tbl returns a single row
gm_nest %>%
nest_summarise(
country_data,
n = dplyr::n(),
median_pop = median(pop)
)
# usually, you'll want to group first
gm_nest %>%
nest_group_by(country_data, country) %>%
nest_summarise(
country_data,
n = dplyr::n(),
median_pop = median(pop)
)
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