# Best Practices R

## theme

If you like vscode theme, use [`https://github.com/anthonynorth/rscodeio`](https://github.com/anthonynorth/rscodeio)

## dev\_mode

`devtools::dev_mode` function switches your version of R into "development mode". This is useful to avoid clobbering the existing versions of CRAN packages that you need for other tasks. Calling dev\_mode() again will turn development mode off, and return you to your default library setup.

```r
# This will install the package in the folder C:/Rpackages
devtools::dev_mode(path="C:/Rpackages")
```

## Reload the package

```r
devtools::load_all()
```

or `Ctrl + Shift + L`

## Add or update script files

`.R` files defined in `tests\dev\` will be removed from the package and can be used to simulate interaction with the package. See [scripts.R](https://github.com/Open-Systems-Pharmacology/OSPSuite-R/wiki/tests/dev/scripts.R)

## Coding standards

Coding standards are described [here](https://dev.open-systems-pharmacology.org/r-development-resources/coding_standards_r)

## Useful literature

* [**Advanced R** by Hadley Wickham](https://adv-r.hadley.nz/)

## Examples of "good" packages

Examples of packages that can serve as inspiration:

* [tidyverse](https://github.com/tidyverse)
* [PKPDsim](https://github.com/InsightRX/PKPDsim)

## Useful shortcuts

* Show all shortcuts: `Alt+Shift+K`
* Reload package: `Cmd + Shift + L`
* Navigate to: `Ctrl + .`
* Generate Doc: `Ctrl + Shift + D`
* Run unit tests: `Ctrl + Shift + T`
* Navigate to implementation: `Mouse over + F2` or `CTRL + Mouse Click`
* Un-/Comment line/selection: `Ctrl + Shift + C`
* Multi-select: `CTRL+SHIFT+ALT+M`

## Profiling with R-Studio

Profiling of code can be done within R-Studio with the package `profvis`, a description of the process is given [here](https://support.rstudio.com/hc/en-us/articles/218221837-Profiling-with-RStudio). In short, pass the code to be profiled as argument to the function `profvis`:

```
profvis({
  data(diamonds, package = "ggplot2")

  plot(price ~ carat, data = diamonds)
  m <- lm(price ~ carat, data = diamonds)
  abline(m, col = "red")
})
```

## Graphics

* [Export to SVG](https://stackoverflow.com/questions/12226822/how-to-save-a-plot-made-with-ggplot2-as-svg)

## Snapshot testing

* `{ospsuite}` uses snapshots to test the behavior of plot functions. Read [Introduction to snapshot testing in R](https://github.com/IndrajeetPatil/intro-to-snapshot-testing/#/title-slide) for information on how to.
* Short summary:
  * The first time a test with snapshot is executed, it creates a snapshot file that will be considered **the truth**. Therefore it is important to check this file for its validity.
  * If the behavior of the tested function changes, the test will fail, as the new output will differ from the snapshot.
  * Run `snapshot_review()` to compare the new output with the snapshot.
  * If the new behavior is correct, accept the snapshot by calling `snapshot_accept()`.
* If build fails because of failing snapshot tests, **never** accept new snapshots without manual review.

## Setting up Linux environment for R development

As an example, a Hyper-V Virtual Machine under Windows 10 is used. Currently tested with Ubuntu 19.10

1. **Install Ubuntu**

* Download Ubuntu from <https://ubuntu.com/download/desktop>
* Tutorial: <https://www.youtube.com/watch?v=oyNjjzg-UXo> = >This is a very good intro to get ubuntu installed from scratch

2. **Install git**

* `sudo apt install git`

3. **Install nuget**

* `sudo apt install nuget`

4. **Install R**

* `sudo apt install r-base`

5. **Install R Studio**

* Download R studio from [here](https://www.rstudio.com/products/rstudio/download/#download)

6. **Install OSPSuite-R**

* [Prerequisites](https://github.com/Open-Systems-Pharmacology/rSharp?tab=readme-ov-file#ubuntu)
* [Main Package](https://github.com/Open-Systems-Pharmacology/OSPSuite-R#ospsuite-r-package)