Data Literacy: Introduction to R

.title[
# Data Literacy: Introduction to R
]
.subtitle[
## 101 Starting with R
]
.author[
### Veronika Batzdorfer
]
.date[
### 2025-11-07
]

---

---

## The whole research cycle with `R`

.center[
<img src="data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/795c039ba2520455d833b4034befc8cf360a70ba/558a5/diagrams/data-science-explore.png" width="70%" style="display: block; margin: auto;" />
]
.small[
Source: [*R for Data Science*](http://r4ds.had.co.nz/)
]
--
.small[
- **Import**: read in formats (e.g., .csv, .xls, .sav, .dta)
- **Tidy**: clean data (1 row = 1 case, 1 column = 1 variable), rename & recode variables
- **Transform**: prepare data for analysis (e.g., by aggregating and/or filtering)
- **Visualize**: explore/analyze data
- **Model**: analyze the data by creating models (e.g, linear regression model) 
- **Communicate**: present the results (to others)
]
Source: [*Packages*](https://github.com/qinwf/awesome-R#data-packages)

---

## My jou`R`ney

.pull-left[
<img src="data:image/png;base64,#../img/interest.png" width="150%" style="display: block; margin: auto;" />
]

- ITZ (Institut für Technikzukünfte), working at the EU project SoMe4Dem (Social Media for Democracy)

- Passionate about R, data visualization, and social science methods

- Curious about how technology shapes democracy

<br>

Contact:<br>

[veronika.batzdorfer@kit.edu](mailto:veronika.batzdorfer@kit.edu) |
[KIT website](https://sociology.itz.kit.edu/21_138.php)

]
]

---

## Your JouRney

- What's your JouRney?

- What’s one problem or dataset you’d love to analyze someday?

---

## Workshop Structure & Materials

- The workshop consists of a combination of lectures and hands-on exercises

- Slides & materials (and your *future submissions*) are available at

---

## Course schedule - Day 1

<table class="table" style="color: black; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Day </th>
   <th style="text-align:left;"> Time </th>
   <th style="text-align:left;"> Topic </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 09:00 - 10:00 </td>
   <td style="text-align:left;font-weight: bold;"> Onboarding &amp; Getting Started with R </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 10:00 - 10:10 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 10:10 - 12:00 </td>
   <td style="text-align:left;font-weight: bold;"> Data Types &amp; Loading </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 12:00 - 13:00 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Lunch Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 13:00 - 14:45 </td>
   <td style="text-align:left;font-weight: bold;"> Data Wrangling </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 14:45 - 15:00 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 15:00 - 16:00 </td>
   <td style="text-align:left;font-weight: bold;"> Data Workflows &amp; Github </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 16:00 - 16:10 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 16:10 - 17:00 </td>
   <td style="text-align:left;font-weight: bold;"> Tidy Tuesday -- Group Work </td>
  </tr>
</tbody>
</table>

---

## Course schedule - Day 2

<table class="table" style="color: black; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Day </th>
   <th style="text-align:left;"> Time </th>
   <th style="text-align:left;"> Topic </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 09:00 - 10:45 </td>
   <td style="text-align:left;font-weight: bold;"> Data Wrangling &amp; Exploratory Analyses </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 10:45 - 11:00 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 11:00 - 12:00 </td>
   <td style="text-align:left;font-weight: bold;"> Data Visualization </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 12:00 - 13:00 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Lunch Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 13:00 - 14:45 </td>
   <td style="text-align:left;font-weight: bold;"> Reproducible Reporting with R Markdown </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;color: gray !important;"> 14:45 - 15:00 </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 15:00 - 16:00 </td>
   <td style="text-align:left;font-weight: bold;"> Group exploration </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 16:00 - 16:15 </td>
   <td style="text-align:left;font-weight: bold;"> Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: darkgreen !important;"> Friday </td>
   <td style="text-align:left;color: darkgreen !important;"> 16:15 - 17:00 </td>
   <td style="text-align:left;font-weight: bold;"> Group pitches and Wrap-up </td>
  </tr>
</tbody>
</table>

---

## Requirements

| **Requirement** | **Description** |
|------------------|------------------|
| **Active participation** | Engage actively in discussions and coding sessions. |
| **Create your own group challenge** | Develop your **own group challenge** (2–3 people) using [Tidy Tuesday data](https://github.com/rfordatascience/tidytuesday). |
| **Submission** |**R Markdown (.Rmd)** and the **HTML** file to the **GitHub repository** under the folder `submissions_wise25`. |
| **Documentation** | Document the **entire research cycle**: clear research question → data wrangling → exploratory analysis → visualization. |

---
## Submission

- Submit by **31st January 2026** on the Github Repo and by Email

---
## TidyTuesday Examples

- [David Robinson](https://www.rscreencasts.com/?ref=reddit)

- Get the data directly with [tidytuesdayR](https://dslc-io.github.io/tidytuesdayR/)

---
## Why use `R`?

- it is **free** and **open-source**
--

- it is **modular** through the use of packages
  - You want to do X with `R`? There's ~~an app~~ a package for that!
  - The universe of packages for `R` keeps on expanding
--

- it can be used for all steps in the research process: data collection, processing, exploration, analysis, and reporting/publishing
--

- it offers extremely powerful and versatile options for **data visualization**
--

- it has a **great community** with groups like the [*R Consortium*](https://www.r-consortium.org/), [*rOpenSci*](https://ropensci.org/), and many local [`R` user groups](https://jumpingrivers.github.io/meetingsR/r-user-groups.html) 
  
--

- it is becoming **increasingly popular** in [academic publications](http://r4stats.com/articles/popularity/), [programming communities like Stack Overflow](https://stackoverflow.blog/2017/10/10/impressive-growth-r/)

---

## Fun with `R`

You can use `R` to...

- [create memes](https://github.com/sctyner/memer)
  - there's also a whole [genre of `R` memes](https://github.com/favstats/rstatsmemes)
--

- statistical models like [Bayesian: `Stan`](https://mc-stan.org/tools/index.html)
--

- query Large Language Models e.g. [`tidychatmodels`](https://albert-rapp.de/posts/20_tidychatmodels/20_tidychatmodels) or local models [httr2](https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/LLMs_2_ollama.md)

---

## The versatility of `R`

.small[
- statistical analysis & machine learning (e.g., with [tidymodels](https://www.tidymodels.org/))
- text mining and natural language processing (e.g., with [quanteda](https://quanteda.io/))
- collecting data, for example:
	- MediaWiki API [WikipediR](https://r-packages.io/packages/WikipediR)
	- web scraping with [rvest](https://rvest.tidyverse.org/)
- all sorts of visualizations, including:
	- animated plots with [gganimate](https://gganimate.com/)
	- interactive visualizations with [plotly](https://plotly.com/r/)
	- (interactive) maps with [tmap](https://r-tmap.github.io/tmap/)
- interactive web applications with [Shiny](https://shiny.rstudio.com/)
- various sorts of publications, such as...
  - reproducible reports and publications with [R Markdown](https://rmarkdown.rstudio.com/)
  - websites with [blogdown](https://bookdown.org/yihui/blogdown/)
  - books with [bookdown](https://bookdown.org/)
  - presentations with [xaringan](https://github.com/yihui/xaringan)

]
---

## Installing `R`

You can download `R` via the [`R` POSIT-Project website](https://posit.co/download/rstudio-desktop/). The exact installation process depends on your OS. The *R Cookbook* provides a [detailed explanation of the installation process for *Windows*, *macOS*, and *Linux/Unix*](https://rc2e.com/gettingstarted#recipe-id001).

If you want or need to update your version of `R`, you can do this the same way as for the first-time installation. If you use *Windows*, you can also use the [`installr` package](https://github.com/talgalili/installr) to update `R`.

---

## Graphical user interfaces (GUI) for `R`

`R` comes with a basic GUI (on *Windows* you can access it by opening the `Rgui.exe` file).

---

## Integrated development interfaces (IDE) for `R`

Using an IDE provides several advantages, such as:

- syntax highlighting
  
  - auto-completion
  
  - better overview of files, libraries, created objects/output
  
---

## *RStudio*

*RStudio/POSIT* is the most widely used IDE<sup>1</sup> for `R`.

- easy integration with version control via `Git` (see [*Happy Git and GitHub for the useR*](https://happygitwithr.com/))
- interfaces to [`Python` via the `reticulate` package](https://rstudio.github.io/reticulate/) and [`SQL`, e.g., via the `dbplyr` package](https://irene.rbind.io/post/using-sql-in-rstudio/)

.small[
[1] Another popular option is [*Visual Studio*](https://visualstudio.microsoft.com/) from *Microsoft* (for which an [`R` extension](https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) is available).
]

---

## *RStudio* interface

.smaller[
*Note*: You will only see the `Git` tab if you have [`Git`](https://git-scm.com/) installed and set up. 
]

---

## The `R` console in *RStudio*

The console is the interactive input-output window of *RStudio*. You can execute commands here. Typically, the output will also be displayed here.

---

## The `R` console in *RStudio*

If you see the <span style="background-color: yellow;">**`>`**</span> in the console, it means ready to receive commands.

If you see a <span style="background-color: yellow;">**`+`**</span> at the beginning of the console input line, this means that the command is incomplete. A common reason for this is a missing `)` or `"`.

Once you have executed at least one command in the console you can cycle through previous ones using <span style="background-color: yellow;">↑</span> and <span style="background-color: yellow;">↓</span> on your keyboard.

---

## `R` as a calculator

The simplest thing you can do with the `R` console is to use it as a calculator.

``` r
3+2
```

```
## [1] 5
```

``` r
2^3
```

```
## [1] 8
```

``` r
1/3
```

```
## [1] 0.3333333
```

*Note:* In the console, you won't see the `##` in the output.
The `[1]` before the result indicates that this is the first output value of the command (more complex commands can have more than one output value).

---

## `R` as a calculator

``` r
100^3
```

```
## [1] 1e+06
```

``` r
1/2500
```

```
## [1] 4e-04
```

.small[
For printing very small and very large numbers, [`R` uses scientific notation](https://www.datakwery.com/post/2020-07-11-scientific-notation-in-r/). If you want to avoid this (e.g., for the console output), you can use the command `options(scipen=10)`. *Note*: You may have to use a higher number and this setting will only be active for the current session.
]

``` r
options(scipen=10)
100^3
```

```
## [1] 1000000
```

``` r
1/2500
```

```
## [1] 0.0004
```

---

## Objects in `R`

`R` is <span style="background-color: yellow;">object-oriented</span>.

``` r
x <- 10
y <- "This is a character string"

x
```

```
## [1] 10
```

``` r
y
```

```
## [1] "This is a character string"
```

---

## `R` objects in *RStudio*

Once one or more objects have been assigned values they appear in the `Environment` tab.

---

## `R` workspace

The `Environment` tab in *RStudio* shows the content of your current working environment (also called <span style="background-color: yellow;">workspace</span>) which includes any used-defined objects. The contents of the current environment are stored in the working memory (RAM) of your computer until you exit `R` (or *RStudio*).

*Note*: The fact that `R` objects are stored in your computer's RAM can become problematic if you work with "big data". However, there are solutions for working with larger-than-RAM data in `R` (such as [`disk.frame`](https://diskframe.com/)).

---

## `R`s memory use

The `Environment` tab includes a small icon that displays the system's overall memory use and the amount of RAM used by `R`

---

## `R`s memory use

*Memory Usage Report* to get more information about current working memory use.

---

## The two workhorses 🐴 of `R`: Functions and packages 📦

If you want to do anything in `R`, you need to use functions, and functions are provided through packages.

---

## Functions

- a function takes an <span style="background-color: yellow;">input <span> -> does something with it -> produces some sort of <span style="background-color: yellow;">output <span>.

- <span style="background-color: orange">**arguments**</span> in parentheses (e.g. a value or object) as a single argument

``` r
sqrt(9)
```

```
## [1] 3
```

``` r
x <- 9
sqrt(x)
```

```
## [1] 3
```

---

## Functions

The output of a function can be assigned to an object.

``` r
x <- sqrt(9)
x
```

```
## [1] 3
```

---

## Functions

Most functions in `R` have more than one argument.

``` r
y <- "This is a character string"

gsub(pattern = "i", replacement = "X", y)
```

```
## [1] "ThXs Xs a character strXng"
```

``` r
# replace the character i with the character X in the object y (in this case a character string)
```

*Note*: Technically, functions are also objects in `R`.

---

## Functions

If you want to know how to use a function, you can consult its help file. You can do that via the `?` command followed by the function name:

``` r
?gsub
```

In *RStudio*, this will open a file in the `Help` tab.

---

## Functions

Functions can have required and **optional** arguments. You can easily identify required and optional arguments in the `Usage` section of the help file for a function: If the argument is in the format `argument = value` it is optional. If only the argument name is provided `function(argument_1)`, this means that this argument is required.

---

## Functions

Function arguments: (a) <span style="background-color: yellow;"> specified order</span> or (b) by <span style="background-color: yellow;" > referencing </span> them by name (then the order can change).

``` r
y <- "This is a character string"

gsub("i", "X", y)
```

```
## [1] "ThXs Xs a character strXng"
```

``` r
gsub(y, replacement = "X", pattern = "i")
```

```
## [1] "ThXs Xs a character strXng"
```

Typing the argument names is more work but it increases the comprehensibility of your code for human readers.

---

## Functions

For the <span style="background-color: orange;" >"inner workings" </span> of a function (or maybe use code from existing functions for writing your own functions), you can print the function body:

``` r
gsub
```

```
## function (pattern, replacement, x, ignore.case = FALSE, perl = FALSE, 
##     fixed = FALSE, useBytes = FALSE) 
## {
##     if (is.factor(x) && length(levels(x)) < length(x)) {
##         gsub(pattern, replacement, levels(x), ignore.case, perl, 
##             fixed, useBytes)[x]
##     }
##     else {
##         if (!is.character(x)) 
##             x <- as.character(x)
##         .Internal(gsub(as.character(pattern), as.character(replacement), 
##             x, ignore.case, perl, fixed, useBytes))
##     }
## }
## <bytecode: 0x000002a7c3c84740>
## <environment: namespace:base>
```

---

# [Exercise](https://rawcdn.githack.com/nika-akin/r-intro/9d05476f895e390df08662eecbefd4137f67acf4/exercises/Exercise_1_1_1_First_Steps.html) time 🏋️‍♀️💪🏃🚴

## [Solutions](https://rawcdn.githack.com/nika-akin/r-intro/9d05476f895e390df08662eecbefd4137f67acf4/solutions/Exercise_1_1_1_First_Steps.html)

---

## `R` packages

They essentially are collections of functions (and sometimes also data sets).

The basic `R` system as well as a huge number of additional packages that extend its functionalities are available via [*The Comprehensive R Archive Network* (CRAN)](https://cran.r-project.org/).

>CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R ([CRAN website](https://cran.r-project.org/)).

---

## `base R`

When we talk about <span style="background-color: yellow;" >`base R` </span> we refer to the  packages that come with a new installation of `R` via *CRAN*.

---

## Finding packages

*CRAN* provides an [alphabetically sorted list with all available packages](https://cran.r-project.org/web/packages/available_packages_by_name.html). You can search for your keywords of interest in that list, but that is not the most convenient option.

Two helpful resources for finding `R` packages:

- [CRAN Task Views](https://cran.r-project.org/) provide curated lists of recommended packages for specific tasks/areas/topics
  
  - [METACRAN](https://www.r-pkg.org/) allows you to search and browse all packages on *CRAN*

---

## Installing packages from *CRAN* in `R`

Installing packages from *CRAN* in `R` is very straightforward.

``` r
# Install a package
install.packages("correlation") # single or double quotation marks

# Install multiple packages at once
install.packages(c("correlation", "effectsize"))
```

`R` packages are installed in specific directories on your computer.

**NB**: If you have multiple versions of `R` installed, there are directories for each version. 
To find where packages are installed on your machine for the version of `R` you are currently using you can use the following command:

``` r
.libPaths()
```

---

## Loading packages

Once installed, you need to load it to be able to use the functions (and/or datasets) it contains in your `R` session.

``` r
library(correlation) # no quotation marks needed
```

**NB**: While you only need to install packages once, you need to load them at the start of each session to be able to use them.

---

## Other sources for `R` packages

Another important source of `R` packages, especially those that are still in early development, is [*GitHub*](https://github.com/). To be able to install packages hosted on *GitHub* you need to use functions from the [`devtools`](https://devtools.r-lib.org/) or the [`remotes`](https://remotes.r-lib.org/) package (which you need to install first as they do not come with `base R`).

``` r
# Option 1
library(devtools)
install_github("Felixmil/rollR") # last part of the GitHub URL (user name + repository name)

# Option 2
library(remotes)
install_github("Felixmil/rollR") # last part of the GitHub URL (user name + repository name)
```
]

*Note*: To be able to install packages from *GitHub* on *Windows* machines, you will need to install [`Rtools`](https://cran.r-project.org/bin/windows/Rtools/) first (make sure to choose the correct version!).

---

## Packages about packages

There are a few packages that facilitate the installation and loading of `R` packages (from various sources). Two popular ones are:

- [easypackages](https://cran.r-project.org/web/packages/easypackages/index.html)

- [pacman](https://github.com/trinker/pacman)

---

## Installed packages

You can get information about the packages you have installed on your system with the following function:

``` r
installed.packages()
```

---

## Managing packages with the GUI

You can also use the `Packages` tab in the *RStudio* GUI to install, load, update, and uninstall packages.

---

## `R` scripts

While the console is useful for trying things out, you should not use it for your actual data analysis. For this you should use `R` scripts that allow you to store and document your code. `R` scripts are similar to syntax files for *SPSS* or do-files for *Stata*. `R` scripts have the file extension 
<span style="background-color: yellow;" >  
`.R`</span>.

---

## `R` scripts

`File` -> `New File` -> `R Script`

---

## *RStudio* interface: Scripts

When you open or create a script in *RStudio* this will be displayed in a fourth pane (which will have multiple tabs if you open/create more than one `R` script or other types of source files).

---

## Working with `R` scripts

You can write your code in an `R` script just like you do in the console.

If you want to execute a single command from your script in *RStudio*, you can do so by placing your cursor somewhere in command (or directly after it) and clicking the `Run` button in the menu or by using the keyboard shortcut <kbd>Ctrl + Return</kbd> (*Windows* & *Linux*)/<kbd>Cmd + Enter</kbd> (*Mac*). This also works if you select multiple lines of code/commands.

You can also run all commands in your script by selecting `Run all` from the dropdown menu next to the `Run` button or via the keyboard shortcut <kbd>Ctrl + Alt + R</kbd> (*Windows* & *Linux*)/<kbd>Cmd + Option + R</kbd> (*Mac*).

---

## Working with `R` scripts

You can save your script in *RStudio* via `File` -> `Save` or `Save As...`, by clicking the small blue floppy disk icon, or through the keyboard shortcut <kbd>Ctrl + S</kbd> (*Windows* & *Linux*)/<kbd>Cmd + S</kbd> (*Mac*).

---

## Commenting `R` scripts

To properly document your code (for your future self as well as other people who may use your code) it is good practice to use comments. In `R` scripts, you can create a comment by starting a line with a `#`.

``` r
# this is a comment
library(tidyverse)
```

---

## Working directory

The working directory is where `R` will look for and save files by default.

Where are we?

``` r
getwd()
```

In *RStudio*, the current working directory is also displayed at the top of the `Console` tab.

---

## Setting the working directory via the *RStudio* GUI
`Session` -> `Set Working Directory` which provides different options:
- "To Project Directory": can be used if you have an `.Rproj` file 
- "To Source File Location": sets the working directory to the location of the currently active source file (typically an `R` script) 
- "To FilesPane Location": sets the working directory to the directory that is currently visible in the `Files` tab
- "Choose Directory": opens a file browser window that lets you choose a directory

---

## Setting the working directory using functions

Using functions in scripts is better.

``` r
setwd("C:/Users/user/Documents/analysis")
```

---

## Interlude: File paths

`R` uses `Unix`-style file paths with <span style="background-color: orange;" >`/` <span>, while *Windows* uses `\` in file paths. However `\\` also works in `R` for both OS types.

There is a [Stackoverflow post](https://stackoverflow.com/questions/17605563/efficiently-convert-backslash-to-forward-slash-in-r) discussing several ways of dealing with that.

---

## Interlude: File paths

There are absolute (example: "C:/Users/user/Documents/example.R") and relative file paths (example: "./r-scripts/example.R").

[**Relative file**](https://martinctc.github.io/blog/rstudio-projects-and-working-directories-a-beginner's-guide/) paths are relative to the current working directory.

Common shorthand options for relative file paths are <span style="background-color: orange;" >`.`<span> for the current (working) directory, `..` for one folder level up (parent folder), and `~` for the home directory (which is the default working directory in `R`).

---

## General settings for *RStudio*

By default, `R` stores your workspace and command history when closing a session (and also restores the former upon startup). While this can be helpful, this creates files that you probably will not use, and can also be a barrier for adopting reproducible workflows.

To avoid that, there are some general settings in *RStudio* that you might want to change via `Tools` -> `Global Options` -> `General`.

---

## Recommended settings for *RStudio*

---

## Basic workflow and setup recommendations

- use `R` scripts to store your code

- save/export important output in appropriate file formats (more on that in the following session on *Data Import & Export*)

- (try to) use relative file paths in your scripts

- eventually consider adopting a [project-oriented workflow](https://rstats.wtf/project-oriented-workflow.html)

---

## Troubleshooting 101

In case you get an error message or if your `R` session crashes, there are a couple of things you can do/try out:

- abort `R` process: via `Session` -> `Terminate R` in the *RStudio* menu or the stop sign icon in the upper right corner of the console
- Restart `R` (*RStudio*  menu: `Session` -> `Restart R`) or *RStudio*
- re-install packages

---

## Common sources of errors

- typos (**NB**: `R` is case-sensitive)
- missing or unmatched `(`, `'`, or `"` (often at the end of a command)
- incorrect file paths
  - `\` instead of `/` in file paths (e.g., when copied from the *Windows* explorer)
- packages not installed or loaded
- code (chunks) executed in the wrong order

<img src="data:image/png;base64,#https://github.com/allisonhorst/stats-illustrations/blob/main/rstats-artwork/breakr.gif?raw=true" width="20%" style="display: block; margin: auto;" />
.center[
<small><small>GIF by [Allison Horst](https://github.com/allisonhorst/stats-illustrations)</small></small>
]

---
## Some house keeping

``` r
testvar     # not ideal

01.test_var # not good

coxph_plot  # ok

# specify functions from packages directly

dplyr::mutate()
```

---

## Cheatsheets

*RStudio* offers a good collection of [cheatsheets for R](https://www.rstudio.com/resources/cheatsheets/).

The following ones are of particular interest for this workshop:

- [RStudio IDE Cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/rstudio-ide.pdf)

- [Data Import Cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-import.pdf)

- [Data Transformation with `dplyr` Cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-transformation.pdf)

- [Data Visualization with `ggplot2` Cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf)

- [R Markdown Cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/rmarkdown.pdf)