README.qmd

---
format: gfm
knitr:
  opts_chunk:
    echo: false
    comments: "|"
---

```{r init}

pkgs <- c("targets", "magrittr")
pkgs_load <- sapply(pkgs, library, character.only = TRUE)

```

# Getting started

Most of the works in this repository, especially the `R` scripts, should be directly reproducible. You'll need [`git`](https://git-scm.com/downloads), [`R`](https://www.r-project.org/), [`quarto`](https://quarto.org/docs/download/), and more conveniently [RStudio IDE](https://posit.co/downloads/) 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`](https://book.cds101.com/using-rstudio-server-to-clone-a-github-repo-as-a-new-project.html#step---2). Using terminal in linux/MacOS, you can issue the following command:

```{.bash}
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:

```{.r}
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.

```{.r}
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:

```{.bash}
data
├── ...
├── raw
│   ├── data.csv
│   └── gbd.csv
└── ...
```

Then, you should be able to proceed with:

```{.r}
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:

```{r, results = "asis"}

cat("```mermaid", tar_mermaid(), "```", sep = "\n")

```