Events

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. Prerequisites: None 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 Visualization 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 using ggplot. Part 4: Control Structures In the final part, we will cover how to use programming control structures such as functions, for-loops, and if-else statements to make more readable and re-usable code. Workshop Materials: https://github.com/dlab-berkeley/R-Fundamentals Software Requirements: Installation Instructions for R and RStudio

This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application. The complete Python Fundamentals series has 6 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walkthrough interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. Parts 1-3 are intended for the complete beginner in Python. We will go over the basics of Python in Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas: Part 1: Introduction to Jupyter and Python, Variables Part 2: Data Types and Structures Part 3: Introduction to Pandas After completing parts 1-3, you will be able to do basic operations in Python. You will know how to navigate Jupyter Notebooks, how to work with common data types and structures, methods, and basic operations in Pandas. You will have the minimum requirements to continue to other D-Lab workshops such as Python Data Wrangling or Python Data Visualization. Prerequisites: None Workshop Materials: https://github.com/dlab-berkeley/Python-Fundamentals Software Requirements: Installation Instructions for Python Anaconda