Getting started

Most of the works in this repository, especially the R scripts, should be directly reproducible. You’ll need git, R, quarto, and more conveniently RStudio IDE installed and running well in your system. You simply need to fork/clone this repository using RStudio by following this tutorial, start right away from Step 2. Using terminal in linux/MacOS, you can issue the following command:

quarto tools install tinytex

This command will install tinytex in your path, which is required to compile quarto documents as latex/pdf. Afterwards, in your RStudio command line, you can copy paste the following code to setup your working directory:

install.packages("renv") # Only need to run this step if `renv` is not installed

This step will install renv package, which will help you set up the R environment. Please note that renv helps tracking, versioning, and updating packages I used throughout the analysis.

renv::restore()

This step will read renv.lock file and install required packages to your local machine. When all packages loaded properly (make sure there’s no error at all), you have to restart your R session. At this point, you need to export the data as data.csv and place it within the data/raw directory. The directory structure must look like this:

data
├── ...
├── raw
│   ├── data.csv
│   └── gbd.csv
└── ...

Then, you should be able to proceed with:

targets::tar_make()

This step will read _targets.R file, where I systematically draft all of the analysis steps. Once it’s done running, you will find the rendered document (either in html or pdf) inside the draft directory.

What’s this all about?

This is the functional pipeline for conducting statistical analysis. The complete flow can be viewed in the following mermaid diagram:

renv 1.0.0 was loaded from project library, but this project is configured to use renv 1.0.7. - Use renv::record("[email protected]") to record renv 1.0.0 in the lockfile. - Use renv::restore(packages = "renv") to install renv 1.0.7 into the project library.

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