class: title-slide, center, middle # Welcome ## Introduction and Orientation --- class: split-four # Hello π ### Welcome to the 5th annual UO R Bootcamp! -- ## <center>The Team</center> .column[.content[.center[ <br><br><br><br><br><br><br><br><br><br><br><br><br> <img src="images/cam.png" width="75%" /> ### Cameron ]]] .column[.content[.center[ <br><br><br><br><br><br><br><br><br><br><br><br><br> <img src="images/sarah.png" width="75%" /> ### Sarah D. ]]] .column[.content[.center[ <br><br><br><br><br><br><br><br><br><br><br><br><br> <img src="images/dominik.png" width="75%" /> ### Dominik ]]] .column[.content[.center[ <br><br><br><br><br><br><br><br><br><br><br><br><br> <img src="images/mcdougald2.png" width="75%" /> ### Sarah M. ]]] --- # Schedule π #### **Day 1: Tuesday, 9/20, 9 AM - 11 AM PDT** + Basics of R, RStudio, & R Markdown + Data Types & Structures -- #### **Day 2: Thursday, 9/22, 9 AM - 2 PM PDT** + Functions, Packages, & Debugging + Piping + Introduction to the Tidyverse + Importing Data & Project-Oriented Workflows + Data Wrangling with {dplyr} -- #### **Day 3: Friday, 9/23, 9 AM - 1 PM PDT** + Data Tidying with {tidyr} + Data Visualization with {ggplot2} + R Tips & Tricks --- class: split-two # Logistics .column[.content[.center[ <br><br><br><br><br> # [
](https://uodatascience.slack.com/) ### Slack <br> (#bootcamp2022) ]]] .column[.content[.center[ <br><br><br><br><br> # [
](https://github.com/uopsych/summeR-bootcamp-2022) ### GitHub ]]] --- # A word of encouragement + R has a substantial learning curve, but... -- + 1) It's absolutely worth it! (I promise) -- + 2) *Everyone* goes through this -- > βThere 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.β -- .right[-Hadley Wickham, <br>Chief Scientist at RStudio] .right[ <img src="images/hadley.jpg" width="25%" /> ] --- # A word of encouragement + R has a substantial learning curve, but... + 1) It's absolutely worth it! (I promise) + 2) *Everyone* goes through this <img src="00-slides_files/figure-html/unnamed-chunk-6-1.png" width="720" style="display: block; margin: auto;" /> --- .footnote[Artwork by [@allison_horst](https://twitter.com/allison_horst)] .pull-left[.center[ <img src="images/breakr.gif" width="60%" /> ]] .pull-right[.center[ <img src="images/stormyr.gif" width="60%" /> ]] -- .center[ # π + πͺ ... ] -- .pull-left[.center[ <img src="images/heartyr.gif" width="60%" /> ]] .pull-right[.center[ <img src="images/rainbowr.gif" width="60%" /> ]] --- # What are R & RStudio? -- .pull-left[ <br> .center[ <img src="images/r_logo.png" width="40%" /> ] <br><br> **R** is a programming language designed for statistics and data science ] -- .pull-right[ .center[ <img src="images/rstudio_logo.png" width="1365" /> ] **RStudio** is an integrated development environment (IDE) that provides an interface to R. RStudio also *currently* refers to the [company](https://rstudio.com/about/) that develops RStudio. ] --- # What are R & RStudio? <img src="images/engine_dashboard.png" width="2021" /> .footnote[Image from [*Modern Dive*](https://moderndive.netlify.app/1-1-r-rstudio.html)] --- # Why use R? ## It's open source -- + It's free! -- + It's easier to share your data & code -- + Innovations spread quickly -- + *You* can contribute! --- # Why use R? ## It's 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*... --- # Why use R? ## It's 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 --- # Why use R? ## Reduce errors, 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)) --- # Why use R? ## It's 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) --- # Why use R? ## It's fun π₯³ --- class: inverse, center, middle # Q & A
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