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This guide walks you through setting up and running the inappropriate acid suppressor agent use meta-analysis pipeline from scratch. By the end, you will have a fully reproduced analysis — including pooled prevalence estimates, subgroup analyses, meta-regression models, and a rendered Quarto report — on your local machine.

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Prerequisites

Before you begin, make sure you have the following installed:
  • git — for cloning the repository
  • R 4.x — the analysis runtime (download)
  • Quarto — for rendering the report (download)
  • RStudio (optional) — recommended IDE for interactive use (download)
  • tinytex — required to compile the report to PDF (installed in step 2 below)

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Setup and run

1

Clone the repository

Clone the project from GitHub to your local machine:
git clone https://github.com/namakala/inappropriate-acid-suppressor-agent-use
cd inappropriate-acid-suppressor-agent-use
Alternatively, you can fork the repository and clone your fork, or open it directly as a new project in RStudio.
2

Install tinytex

Install tinytex so Quarto can compile the report to PDF:
quarto tools install tinytex
3

Install renv

If you do not already have the renv package installed, run the following in your R console:
install.packages("renv")
4

Restore the R environment

Restore all required packages from the lockfile:
renv::restore()
This reads renv.lock and installs every package at the exact version used in the original analysis.
After renv::restore() completes without errors, restart your R session before continuing. This ensures all restored packages are loaded correctly into the new session.
5

Prepare your data

Export the study-level dataset as data.csv and place it in the data/raw/ directory. The directory structure must look like this:
data
├── raw
│   └── data.csv
└── ...
The pipeline expects columns including Author, Sample_size, Inappropriate_indication, Prevalence, Year, Continent, Setting, JBI_Classification, and use_guideline. See the Introduction for the full variable reference.
6

Run the pipeline

Execute the full analysis pipeline:
targets::tar_make()
This reads _targets.R, which defines every step in the analysis — from reading and cleaning the data, through meta-analysis, subgroup analyses, meta-regression, and publication bias assessment — and executes them in the correct order. When the pipeline finishes, the rendered report is written to docs/report.pdf (or docs/report.html).

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Check pipeline status

To visualize the dependency graph of all pipeline targets and see which steps are up to date, run: 
targets::tar_visnetwork()
This opens an interactive DAG in your browser. Nodes shaded in green are up to date; nodes in orange are outdated or have not yet been run.
When debugging interactively in an R session, use targets::tar_make(callr_function = NULL) to run the pipeline in the current R process. This lets you inspect objects and errors directly without spawning a separate callr subprocess.

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What to expect

After a successful run, you will find:
  • docs/report.pdf (or docs/report.html) — the fully rendered analysis report
  • docs/figures/ — individual forest plots and funnel plots for the overall and subgroup analyses
  • Cached target objects stored by targets in _targets/ — re-running tar_make() will only recompute steps whose inputs have changed

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