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# Created by use_targets().
# Follow the comments below to fill in this target script.
# Then follow the manual to check and run the pipeline:
# https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline
# Load packages required to define the pipeline:
library(targets)
# library(tarchetypes) # Load other packages as needed.
# Set target options:
tar_option_set(
packages = c("tibble") # packages that your targets need to run
# format = "qs", # Optionally set the default storage format. qs is fast.
#
# For distributed computing in tar_make(), supply a {crew} controller
# as discussed at https://books.ropensci.org/targets/crew.html.
# Choose a controller that suits your needs. For example, the following
# sets a controller with 2 workers which will run as local R processes:
#
# controller = crew::crew_controller_local(workers = 2)
#
# Alternatively, if you want workers to run on a high-performance computing
# cluster, select a controller from the {crew.cluster} package. The following
# example is a controller for Sun Grid Engine (SGE).
#
# controller = crew.cluster::crew_controller_sge(
# workers = 50,
# # Many clusters install R as an environment module, and you can load it
# # with the script_lines argument. To select a specific verison of R,
# # you may need to include a version string, e.g. "module load R/4.3.0".
# # Check with your system administrator if you are unsure.
# script_lines = "module load R"
# )
#
# Set other options as needed.
)
# tar_make_clustermq() is an older (pre-{crew}) way to do distributed computing
# in {targets}, and its configuration for your machine is below.
CLUSTERMQ
# tar_make_future() is an older (pre-{crew}) way to do distributed computing
# in {targets}, and its configuration for your machine is below.
FUTURE
# Run the R scripts in the R/ folder with your custom functions:
tar_source()
# source("other_functions.R") # Source other scripts as needed.
# Replace the target list below with your own:
list(
tar_target(
name = data,
command = tibble(x = rnorm(100), y = rnorm(100))
# format = "feather" # efficient storage for large data frames
),
tar_target(
name = model,
command = coefficients(lm(y ~ x, data = data))
)
)
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