Welcome

UO R Bootcamp 2025
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# Hello 👋

###  Welcome to the 8th annual UO R Bootcamp!

### Ian

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<img src="images/dom.png" width="100%" />
### Dominik

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### Sophia

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### Neha

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# Schedule 📆

#### **Day 1: Wednesday, 9/24, 9 AM - 12 PM PDT**
.smaller-font[
+ Basics of R, RStudio, & R Markdown
+ Data Types & Structures
+ Functions, Packages, & Debugging]

#### **Day 2: Thursday, 9/25, 9 AM - 12 PM PDT**
.smaller-font[
+ Piping
+ Introduction to the Tidyverse
+ Importing Data & Project-Oriented Workflows
+ Data Wrangling with `{dplyr}`]

#### **Day 3: Friday, 9/26, 9 AM - 1 PM PDT**
.smaller-font[+ Data Tidying with `{tidyr}`
+ Data Visualization with `{ggplot2}`
+ R Tips & Tricks]

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# Logistics

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### Slack (#rbootcamp-2025)
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### Google drive
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### GitHub
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# A word of encouragement

R has a substantial learning curve, but...

+ It's absolutely worth it! 
  
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+  *Everyone* goes through this
  
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> “There is no way of going from knowing nothing about a subject to knowing something about a subject without going through a period of great frustration and much suckiness.”

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-Hadley Wickham, *Chief Scientist at RStudio*]]]

<img src="images/hadley.jpg" width="25%" />
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# What are R & RStudio?

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<img src="images/r_logo.png" width="40%" />
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**R** is a programming language designed for statistics and data science
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**RStudio** is an integrated development environment (IDE) that provides an interface to R.

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# What are R & RStudio?

#Why should you invest the time to learn R?

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# R is open source

+ R and RStudio are free to download

+ You can easily save and share your code

+ Anyone can contribute (including you!)
 
--

+ Innovations spread quickly

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## R is powerful & flexible

+ You can use R for more than data analysis, including:

+ creating websites (including this one!)
  
  + slideshows (including this one!)
  
  + creating reproducible documents (including documents you will create in this bootcamp!)
  
  + books (e.g., [Hands-On Machine Learning with R](https://bradleyboehmke.github.io/HOML/))
  
  + web applications (e.g., [Monte Carlo Power Analysis for Indirect Effects](https://schoemanna.shinyapps.io/mc_power_med/))
  
  + entire APA-formatted manuscripts (e.g., [papaja](https://github.com/crsh/papaja))
--

+ In R, it is never *if* but *how*...

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## Learning to code in R is a useful, transferable skill

+ R is used across many industries, especially in UX & data science

+ It is easier to learn a new programming language when you already know one

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## Using R can reduce errors and enhance reproducibility & transparency

+ Generate publication-quality figures & tables within R, reducing copy-and-paste errors

+ Create detailed and fully-documented scripts showing every step between raw data & stats

+ You can use R to automate reporting of your analyses (for HW or publication), reducing all too common errors in reported statistics (see [Nuijten et al.](https://link.springer.com/article/10.3758/s13428-015-0664-2))

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## R is efficient

+ It saves you time in the long run

+ Scripts make re-using past work or using others' work as a starting point much easier

+ Typing scripts is much faster than clicking through menus, *especially* after you get the hang of keyboard shortcuts

+ It runs faster and is less bloated than GUI-based statistical software (e.g., SPSS)

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## R is fun 🥳

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# Q & A

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# Let's get started!

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# Can't an LLM do this for me? 🤖

.pull-center[
- What is an LLM doing for you right now?
- What could you be missing out on by **using** it?
- What could you be missing out on by **not using** it?
]