dlab-berkeley / r-push-ins

D-Lab's 4.5 hour "push-in" introduction to R, providing a brief survey of foundational R concepts and operations.

D-Lab's R Push-Ins

This repository contains the materials for D-Lab's R Push-Ins. No prior experience with R is required.

Push-In Goals

Our Push-Ins are lightweight modules aimed to provide a brief introduction to a topic. Our R Push-Ins are divided into three parts, each designed to cover roughly 45 minutes. The topics include:

• Part 1: Introduction to R, navigating RStudio, variable assignment, data types and coercion, and data structures.
• Part 2: Working with data frames in R.
• Part 3: Data visualization using R and ggplot2.

Prior experience with R is not required.

Installation Instructions

We will use RStudio to go through the workshop materials, which requires the installation of both the R language and the RStudio software. Complete the following steps:

1. Download R: Follow the links according to the operating system you are running. Download the package, and install R onto your computer. You should install the most recent version (at least version 4.1).
2. Download RStudio: Install RStudio Desktop. This should be free. Do this after you have already installed R. The D-Lab strongly recommends an RStudio edition of 2022.02.0+443 "Prairie Trillium" or higher.

Some individuals with older operating systems may run into odd issues. If you are running into issues with the installation of RStudio, you may need to install a specific version of RStudio. Please check this link if this applies to you.

3. Download these workshop materials:

• Click the green "Code" button in the top right of the repository information.
• Click "Download Zip".
• Extract this file to a folder on your computer where you can easily access it (we recommend Desktop).

4. Optional: if you're familiar with git, you can instead clone this repository by opening a terminal and entering git clone [email protected]:dlab-berkeley/R-Fundamentals.git.

Run the Code

Now that you have all the required software and materials, you need to run the code:

1. Launch the RStudio software.

2. Use the file navigator to find the R-Fundamentals folder you downloaded from Github. Open R-Fundamentals.Rproj by double clicking on the file. RStudio should open.

3. Open up the file corresponding to the part of the workshop you're attending (Part1.R, Part2.R, Part3.R, Part4.R) via the Files panel in RStudio.

4. Place your cursor on a given line and press "Command + Enter" (Mac) or "Control + Enter" (PC) to run an individual line of code.

5. The solutions folder contains the solutions to the challenge problems.

Is R not working on your laptop?

If you do not have R installed and the materials loaded on your workshop by the time it starts, we strongly recommend using the UC Berkeley Datahub to run the materials for these lessons. You can access the DataHub by clicking this button:

Some users may have to click the link twice if the materials do not load initially.

The DataHub downloads this repository, along with any necessary packages, and allows you to run the materials in an RStudio instance on UC Berkeley's servers. No installation is needed from your end - you only need an internet browser and a CalNet ID to log in. By using the DataHub, you can save your work and come back to it at any time. When you want to return to your saved work, go straight to DataHub, sign in, and you click on the R-Fundamentals folder.

If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud, by clicking this button:

By using this button, however, you cannot save your work.

Additional Resources

Check out the following online resources to learn more about R:

• Software Carpentry
• Quick-R
• UCLA idre
• R-bloggers
• R Markdown: The Definitive Guide
• The tidyverse style guide
• Quick Intro to Parallel Computing in R

as well as the following books:

• Bookdown Featured Books
• Kearns GJ. 2010. Introduction to Probability and Statistics in R
• Wickham H. 2014. Advanced R
• R for Data Science
• Lander J. 2013. R for everyone: Advanced analytics and graphics
• Matloff N. 2011. The art of R programming: A tour of statistical software design
• Brunsdon C, Comber L. 2015. An Introduction to R for Spatial Analysis and Mapping
• James G, Witten D, Hastie T, Tibshirani R. 2013. An Introduction to Statistical Learning: With Applications in R, 7th edition

About the UC Berkeley D-Lab

D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels, and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops such as R Fundamentals, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.

Visit the D-Lab homepage to learn more about us. View our calendar for upcoming events, and also learn about how to utilize our consulting and data services.

Other D-Lab R workshops

Here are other R workshops offered by the D-Lab:

Basic Competency

• R Data Wrangling
• R Graphics with ggplot2
• R Functional Programming
• Geospatial Fundamentals in R with sf
• Census Data in R

Intermediate/Advanced Competency

• Unsupervised Learning in R
• R Machine Learning with tidymodels
• Introduction to Deep Learning in R
• Fairness and Bias in Machine Learning
• R Package Development

Contributors

• Pratik Sachdeva
• Alex Stephenson
• Avery Richards

r-push-ins's Issues

Thoughts on squishing R-vis materials into 1.5 hours of workshop

When pruning material from the 3 hour Data visualization workshop, the first thing I removed was the base-R plotting examples. This may cause issues with teaching if the module is ran before data-wrangling in a module sequence, (since ggplot2 layering is so much like dplyr pipes), but may be less of a stretch for students if they're exposed to dplyr first.

I regrettably removed heatmap and viridis examples.

added pacman and pload() automation to prevent library loading kerfuffles in situ.

Going to compose introduction narrative, explicitly referencing 3 hour visualization workshop as source material and learning resource to cover the missing parts.

Considering reducing challenges from 5 to 3.

Personalization

Opening this issue as a way for discussion.

Push-in Goals

My understanding is that Push-ins are short lessons that can be taught in person and are not replacements for R workshops in general. The shortness of time and that they are used as an invitation suggest that any work needs to be compressed.

Proposal 1

Introductory push-ins could be around doing a basic task. Through completion of the task, students get acquainted with aspects of the R language that point to further workshops. This is more limited in the sense that it doesn't teach the language from first principles.

Upside of this proposal

1. If done correctly, there is clear value for students who actually follow along and apply. They learn how to do something that is at least a reasonable facsimile of the work they do.

2. A mini project is generally more fun to work with than learning general data structures and types.

Downsides

1. Initial start up costs for introductions because of increased personalization and a new approach.

2. Potentially higher maintenance costs.

Typos in Introduction

There are some typos in the comments in the Intro workshop file. This issue exists as a reminder to fix them in copy/edit.