Group a data frame to iterate over subsets of rows.
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.
tar_group(x)
x |
Grouped data frame from |
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.
A data frame with a special tar_group column that
targets will use to find subsets of your data frame.
Other utilities:
tar_active(),
tar_call(),
tar_cancel(),
tar_definition(),
tar_envir(),
tar_name(),
tar_path(),
tar_seed(),
tar_store()
if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
# 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_dir() runs code from a temporary directory.
tar_script({
library(dplyr)
list(
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).
})
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