grouped_calculations: Grouped Calculations

In nathaneastwood/sparkplugs: Common Wrappers and Missing Functionality from 'sparklyr' and 'dplyr'

Description

Each of these functions first group the data using dplyr::group_by() and then:

• mutate_groups(): Apply calculations using dplyr::mutate().

• transmute_groups(): Apply calculations using dplyr::transmute().

• summarise_groups(): Summarise the data by applying calculations using dplyr::summarise().

• arrange_groups(): Order the data using dplyr::arrange() with .by_group = TRUE.

The respective output is ungrouped.

Usage

mutate_groups(.data, .groups, ...)

summarise_groups(.data, .groups, ...)

summarize_groups(.data, .groups, ...)

transmute_groups(.data, .groups, ...)

arrange_groups(.data, .groups, ...)

Arguments

.data	A tbl_spark or a data.frame.
.groups	character(n). The columns to group by.
...	Arguments to pass onto the respective function.

Value

A tbl_spark or a data.frame depending on the input, .data.

Examples

mtcars %>%
  mutate_groups(.groups = c("am", "cyl"), avgMpg = mean(mpg))
mtcars %>%
  summarise_groups(.groups = c("am", "cyl"), avgMpg = mean(mpg))

# Additional arguments can still be passed to the dplyr functions
mtcars %>%
  mutate_groups(.groups = "am", avgMpg = mean(mpg), .before = mpg)

nathaneastwood/sparkplugs documentation built on Feb. 28, 2021, 4:57 p.m.