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Summarise each group to one row

Source: R/step-subset-summarise.R
summarise.dtplyr_step.Rd

This is a method for the dplyr dplyr::summarise() generic. It is translated to the j argument of [.data.table.

Usage

# S3 method for class 'dtplyr_step'
summarise(.data, ..., .by = NULL, .groups = NULL)

Arguments

.data

A lazy_dt().

...

<data-masking> Name-value pairs of summary functions. The name will be the name of the variable in the result.

The value can be:

  • A vector of length 1, e.g. min(x), n(), or sum(is.na(y)).

  • A data frame with 1 row, to add multiple columns from a single expression.

.by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

.groups

[Experimental] Grouping structure of the result.

  • "drop_last": drops the last level of grouping. This was the only supported option before version 1.0.0.

  • "drop": All levels of grouping are dropped.

  • "keep": Same grouping structure as .data.

  • "rowwise": Each row is its own group.

When .groups is not specified, it is set to "drop_last" for a grouped data frame, and "keep" for a rowwise data frame. In addition, a message informs you of how the result will be grouped unless the result is ungrouped, the option "dplyr.summarise.inform" is set to FALSE, or when summarise() is called from a function in a package.

Examples

library(dplyr, warn.conflicts = FALSE)

dt <- lazy_dt(mtcars)

dt %>%
  group_by(cyl) %>%
  summarise(vs = mean(vs))
#> Source: local data table [3 x 2]
#> Call:   `_DT38`[, .(vs = mean(vs)), keyby = .(cyl)]
#> 
#>     cyl    vs
#>   <dbl> <dbl>
#> 1     4 0.909
#> 2     6 0.571
#> 3     8 0    
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

dt %>%
  group_by(cyl) %>%
  summarise(across(disp:wt, mean))
#> Source: local data table [3 x 5]
#> Call:   `_DT38`[, .(disp = mean(disp), hp = mean(hp), drat = mean(drat), 
#>     wt = mean(wt)), keyby = .(cyl)]
#> 
#>     cyl  disp    hp  drat    wt
#>   <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4  105.  82.6  4.07  2.29
#> 2     6  183. 122.   3.59  3.12
#> 3     8  353. 209.   3.23  4.00
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results