Data are the foundations of the social and biological sciences. Familiarizing yourself with a programming language can help you better understand the roles that data play in your field. Learn to develop and train your data skills at the free D-Lab R workshops!
The R Fundamentals 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, import, export, manipulate, and visualize data, and learn to write shorthand functions of your code. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.
Each of the four parts is divided into a lecture-style format interrupted by short breaks, challenge problems, and discussions. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. No prior experience or background knowledge is required, except we recommend that you enroll in D-Lab’s Programming FUN!damentals workshop if you are unfamiliar with the command line.
Intermediate workshops teach data subsetting, reshaping, and visualization, creating markdown and interactive .HTML files, dimension reduction techniques, and function writing. Advanced workshops emphasize machine learning algorithm creation and implementation in the semi-canned ‘caret’, ‘SuperLearner’, and ‘H2O’ software libraries. Intermediate and advanced workshops require completion of R Fundamentals Parts 1 through 4 or equivalent knowledge.
Part 2: Subsetting and Reshaping
Students will 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 to search for and subset missing data, merge data frames, and will be introduced to the ‘dplyr’ and ‘tidyr’ packages. Terms like subset, bracket notation, and logical vectors will be defined or reintroduced in Part 2.
Knowledge requirements: R Fundamentals: Part 1 or equivalent.
Registration note: To participate in multiple parts of this series, please be sure to register for each day separately.
