targets (version 0.0.0.9000)

tar_group: Group a data frame to iterate over subsets of rows.

Description

Like dplyr::group_by(), but for patterns. tar_group() allows you to map or cross over subsets of data frames. Requires iteration = "group" on the target. See the example.

Usage

tar_group(x)

Arguments

x

Grouped data frame from dplyr::group_by()

Value

A data frame with a special tar_group column that targets will use to find subsets of your data frame.

Details

The goal of tar_group() is to post-process the return value of a data frame target to allow downstream targets to branch over subsets of rows. It takes the groups defined by dplyr::group_by() and translates that information into a special tar_group is a column. tar_group is a vector of positive integers from 1 to the number of groups. Rows with the same integer in tar_group belong to the same group, and branches are arranged in increasing order with respect to the integers in tar_group. The assignment of tar_group integers to group levels depends on the orderings inside the grouping variables and not the order of rows in the dataset. dplyr::group_keys() on the grouped data frame shows how the grouping variables correspond to the integers in the tar_group column.

Examples

# NOT RUN {
# The tar_group() function simply creates
# a tar_group column to partition the rows
# of a data frame.
data.frame(
  x = seq_len(6),
  id = rep(letters[seq_len(3)], each = 2)
) %>%
  dplyr::group_by(id) %>%
  tar_group()
# We use tar_group() below to branch over
# subsets of a data frame defined with dplyr::group_by().
tar_dir({
tar_script({
library(dplyr)
tar_pipeline(
  tar_target(
    data,
    data.frame(
      x = seq_len(6),
      id = rep(letters[seq_len(3)], each = 2)
    ) %>%
      group_by(id) %>%
      tar_group(),
    iteration = "group"
  ),
  tar_target(
    sums,
    sum(data$x),
    pattern = map(data),
    iteration = "vector"
  )
)
})
tar_make()
tar_read(sums) # Should be c(3, 7, 11).
})
# }