Target Markdown
In targets: Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

Target Markdown is a powerful R Markdown interface for reproducible analysis pipelines, and the chapter at https://books.ropensci.org/targets/markdown.html walks through it in detail. This R Markdown report the example from the chapter. Try it out in both interactive and non-interactive modes, either by running the code chunks in different ways or setting the tar_interactive chunk option.

Packages

The example requires several R packages, and targets must be version 0.5.0.9000 or above.

install.packages(c("biglm", "dplyr", "ggplot2", "readr", "targets", "tidyr"))

Setup

If you are using old versions of targets (<= 0.7.0) and/or knitr (<= 1.33), you will need to load the targets package in the R Markdown document in order for Target Markdown code chunks to work.

library(targets)

Near the top of the document, you may also wish to remove the _targets_r directory previously written by non-interactive runs of the report. Otherwise, your pipeline may contain superfluous targets.

library(targets)
tar_unscript()

Globals

We first define some global options/functions common to all targets. The function below plots a histogram of ozone concentrations, and our histogram target will need it.

```{targets example-globals, tar_globals = TRUE} options(tidyverse.quiet = TRUE) tar_option_set(packages = c("biglm", "dplyr", "ggplot2", "readr", "tidyr")) create_plot <- function(data) { ggplot(data) + geom_histogram(aes(x = Ozone), bins = 12) + theme_gray(24) }

# Targets

Our first target borrows the `airquality` dataset built into base R.

```{targets raw-data}
tar_target(raw_data, airquality)

Our next targets preprocess the data, make a histogram, and fit a model.

```{targets downstream-targets} list( tar_target(data, raw_data %>% filter(!is.na(Ozone))), tar_target(hist, create_plot(data)) )

Set the `tar_simple` chunk option to `TRUE` to define a single target with the command in the code chunk. The chunk below only contains `biglm(Ozone ~ Wind + Temp, data)` in the source, but because `tar_simple` is `TRUE`, it is shorthand for `tar_target(name = fit, command = biglm(Ozone ~ Wind + Temp, data))`. All other arguments to `tar_target()` are set to their default values (configurable with `tar_option_set()`).

```{targets fit, tar_simple = TRUE}
biglm(Ozone ~ Wind + Temp, data)

Pipeline

If you ran all the {targets} chunks in non-interactive mode, then your R scripts are set up to run the pipeline.

tar_make()

Output

You can retrieve results from the _targets/ data store using tar_read() or tar_load().

library(biglm)
tar_read(fit)

tar_read(hist)

The targets dependency graph helps your readers understand the steps of your pipeline at a high level.

tar_visnetwork()

At this point, you can go back and run {targets} chunks in interactive mode without interfering with the code or data of the non-interactive pipeline.