Location: Remote via Zoom. Link will be sent on the morning of the event.
- CZ Biohub: Please use this form to register as CZ Biohub participant.
- LBL: Please use this form to register as an LBL participant.
Date & Time: This workshop is a 4-part series running from 9am-12pm each day:
- Part 1: Monday, October 25
- Part 2: Tuesday, October 26
- Part 3: Monday, November 1
- Part 4: Tuesday, November 2
Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.
Each of the parts is divided into a lecture-style coding walkthrough interrupted by challenge problems, discussions of the solutions, and breaks. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language.
Part 1: Introduction
Learn how to navigate the R Studio environment. You will also learn how to store data, characteristics of basic data types and data, the importance of data frames (think Excel spreadsheets), and how to save your work.
Part 2: Subsetting and Reshaping
You will then be introduced to loading data from files and various ways to subset it with an emphasis on bracket notation. You will also learn how to use logical vectors, search for and subset missing data, and merge data frames.
Part 3: Data Exploration and Analysis
Students will be introduced to data exploration and analysis in R. You will learn how to summarize data and explore it with histograms, scatterplots, and boxplots. You will also be introduced to coding statistical data analysis via t-tests, analyses of variance, correlation, and linear regression.
Part 4: Data Importing and Visualization
In the final part, we will review data importing, more subsetting, and visualization. Students will then be given the majority of time to reproduce a workflow on two different datasets, ask questions, and dive in-depth to ggplot2.
Software Requirements: Installation Instructions for R and RStudio
Is RStudio Not working on your laptop?
Attend the workshop anyway, we can provide you with a cloud-based solution until you figure out the problems with your local installation.
Feedback: After completing the workshop, please provide us feedback using this form.
Questions? Email: email@example.com