Data Literacy: Introduction to R

class: center, middle, inverse, title-slide

.title[
# Data Literacy: Introduction to R
]
.subtitle[
## Workflow & Github
]
.author[
### Veronika Batzdorfer
]
.date[
### 2025-05-23
]

---

layout: true

---

## Space of names ♠️

Packages are developed (and maintained) by different people and teams and - according to [METACRAN](https://www.r-pkg.org/) - there are currently more than 18,400 packages available on *CRAN*. Also, there are clear limits for creativity in naming functions. Hence, different packages can use the same function names. This can create what is called a .highlight["namespace conflict"].

---

## Masking 😷

If you load a package and some of its functions have the same names as those from packages you loaded before in your current `R` session, it "masks" these functions.

``` r
library(dplyr)
```

---

## Avoiding/resolving namespace conflicts

The order in which packages are loaded (in a session) matters as the masking of functions happens consecutively. You can, however, still access masked functions.

``` r
stats::filter() # package_name::function_name()
```

This is also a way of accessing functions from an installed package without loading it with the `library()` command.

You can also unload a package, but this generally is/should not be the preferred option.

``` r
detach("package:dplyr", unload = TRUE)
```

---

## Avoiding/resolving namespace conflicts

A helpful tool for detecting as well as resolving namespace conflicts is the [`conflicted` package](https://www.tidyverse.org/blog/2018/06/conflicted/).

---

## Updating packages

You can update all packages you have installed with the following command:

``` r
update.packages()
```

If you want to update individual packages, the easiest way is to install them again (the usual way).

Another option for updating specific packages is the following:

``` r
update.packages(oldPkgs = c("correlation", "effectsize"))
```

---

## Uninstalling packages

If you want to uninstall one or more packages, you can do so as follows:

``` r
# uninstall one package
remove.packages("correlation")

# uninstall multiple packages
remove.packages(c("correlation", "effectsize"))
```

---

## Advanced commenting of `R` scripts

A neat trick in *RStudio* is that if you end a comment preceding a block of code with `####` (or more `#` or `----` or more dashes), you create a section in your script that can be collapsed and expanded in the *RStudio* script pane. This also creates a small interactive table of contents for the script at the bottom of the script pane.

You can also use the keyboard shortcut <kbd>Ctrl + Shift + R</kbd> (*Windows* & *Linux*)/<kbd>Cmd + Shift + R</kbd> (*Mac*) to insert code sections.

---

## Advanced commenting of `R` scripts

---

## Advanced commenting of `R` scripts

---

## *RStudio* projects

*RStudio* projects are helpful tool for developing a [project-oriented workflow](https://rstats.wtf/project-oriented-workflow.html) that can enhance reproducibility.

You can create a project via the *RStudio* menu: `File` -> `New Project`. *RStudio* projects are associated with `.Rproj` files that contain some specific settings for the project. If you double-click on a `.Rproj` file, this opens a new instance of *RStudio* with the working directory and file browser set to the location of that file (the repository/folder for this workshop contains an `.Rproj` file, if you want to try this out).

Explaining *RStudio* projects in detail is beyond the scope of this course, but there are good tutorials available, e.g., on the [*RStudio* support site](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects) or in the [respective chapter in *What They Forgot to Teach You About R*](https://rstats.wtf/project-oriented-workflow.html#rstudio-projectsl).

---

## Customization options in *RStudio*

*RStudio* offers a wide range of [customization options](https://support.rstudio.com/hc/en-us/articles/200549016-Customizing-RStudio). In the following, we will briefly discuss two of them:

- pane layout

- appearance

---

## *RStudio* pane layout

You can adjust the size, minimize, and maximize the different panes in *RStudio* .
You can also customize the pane layout in *RStudio* by via `Tools` -> `Global Options` -> `Pane Layout`.

---

## `R` workspace

In addition to the `Environment` tab in *RStudio*, you can view the content of your workspace via the .highlight[`ls()`] command.

You can remove objects from your workspace with the `rm()` function. Note, however, that this cannot be undone, so you have to create the object again in case you need it (again).

Unless you change the default settings in *RStudio* as suggested in the "Getting Started" session, `R` will save the workspace in a file called `.RData` in your current working directory and restore the workspace when you restart it.

---

## Package documentation

Packages on *CRAN* include (PDF) Reference Manuals (see the [*CRAN* website for the `usethis` package](https://cran.r-project.org/web/packages/usethis/index.html) for an example) that include descriptions of all functions included in the package.

Some packages also provide their own websites with detailed additional documentation (the [`quanteda` package for text analysis](https://quanteda.io/) is a good example here).

If packages are hosted on *GitHub*, the associated repository normally also includes some documentation for the package. If you encounter an issue while using a package that is hosted on *GitHub*, you can also check out the `Issues` section in the *GitHub* repository for that package.

---

## Coding styles

- line length (see the ["sacred 80 column rule"]( https://www.emacswiki.org/emacs/EightyColumnRule))
- indentation
- variable/object naming
- comments
- assignment rules

`R` is pretty flexible in styling (compared, e.g., to `Python`, which has strict rules for indentation) which makes it even more necessary to think about these conventions.

---

## Starting with Git & GitHub

- .highlight[Git]: version control in code/ projects (history with snapshots (commits))

- .highlight[Github]: platform for hosting Git repositories and collaboration

---

## Why use Github with R?

- to track and merge changes in R scripts or projects with multiple collaborators

- maintain version history/ backup

- early publishing share work (RMarkdown)

---

[cheetsheet](https://education.github.com/git-cheat-sheet-education.pdf)
[documentation](https://docs.github.com/de)

.small[[Source](https://github.com/gesis-css-python/materials/blob/main/1-css/1-2-workflow.ipynb)

]

---

## Setup

- [create a GitHub account](https://github.com/)

- [GitHub Desktop](https://desktop.github.com): userfriendly interface to manage repositories

- [install Git](https://git-scm.com/)

- (in case doesnt work straight away: Configure Git: go to **Tools** > **Global Options** > **Git/SVN** and make sure that the box Git executable points to your Git executable)

---

## Integration with R

##### Step 1: Clone a Repository with GitHub Desktop

- Open **GitHub Desktop**.

- Click "File" > "Clone Repository".

- Choose a GitHub repo (or paste URL), select local path, click "Clone".
--

##### Step 2: Open the Project in RStudio

- In RStudio: File > Open Project, navigate to the cloned folder .highlight[(.Rproj file)].

- Now you're working in an RStudio Project linked to GitHub.
    
---

## Workflow Overview

- Pull – Get the latest changes from GitHub.

``` r
git pull
```

- Edit and Save your R scripts or R Markdown.
--

- Commit – Save changes with a message (e.g., "added analysis plot").

``` r
git commit -m "version control- go for launch"
```

- Push – Send your changes back to GitHub.

``` r
git push
```

---

## Workflow in RStudio

(Use Git tab in RStudio or GitHub Desktop for commit/push)

---

## Tips

- Use .highlight[.gitignore] to avoid pushing large data files.

- Commit often with meaningful messages.

- Use branches for experimenting without breaking the main code.