R

R Geospatial Fundamentals: Raster Data

December 3, 2021, 12:00pm
Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. This workshop focuses on fundamental operations for reading, writing, manipulating and mapping raster data, which typically represents geographic information in a grid of regular sized cells.

R Geospatial Fundamentals: Raster Data

March 16, 2022, 10:00am
Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. This workshop focuses on fundamental operations for reading, writing, manipulating and mapping raster data, which typically represents geographic information in a grid of regular sized cells.

R Data Wrangling and Manipulation: Parts 1-2

March 19, 2024, 9:00am
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.

R Fundamentals: Parts 1-4

January 10, 2023, 2:00pm
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.

Skyler Yumeng Chen

Data Science for Social Justice Fellow 2024
Haas School of Business

Skyler is a Ph.D. student in Behavioral Marketing at the Haas School of Business. Her research centers on consumer behavior and judgment and decision-making, with a keen interest in both experimental methods and data science techniques. She holds a B.A. in Economics and a B.S. in Data Science from New York University Shanghai.

Tracy Burnett

Data Science for Social Justice Fellow 2024
Department of Environmental Science, Policy, and Management

Tracy uses qualitative methods founded in complexity theory and hierarchy theory to model the interlinked scales of coupled social-ecological systems. She conducted the majority of her research among nomads in Amdo, Tibet. She works to develop both theoretical and technological tools that support linguistic diversity and cultural resilience.

Taylor Galdi

Data Science for Social Justice Fellow 2024
Law (JSP)
Sociology
Social Psychology

Taylor is a dual JD/Ph.D. student in Berkeley Law's Jurisprudence and Social Policy Program. Broadly, she is interested in studying courts, social movements and social change, and the legal profession.

Kai Hsu

Data Science for Social Justice Fellow 2024
City and Regional Planning

Kai is a PhD candidate in UC Berkeley's Department of City and Regional Planning, whose research interests lie at the intersection of transportation planning, environmental exposure, and health equity. He holds a BS in Urban Planning from National Cheng Kung University, Taiwan, and a MSc in Transport from Imperial College London and University College London.

His current research projects look at the association between heat exposure and road safety risks, with a particular focus on food-delivery motorcyclists.

Propensity Score Matching for Causal Inference: Creating Data Visualizations to Assess Covariate Balance in R

June 10, 2024
Sharon Green
by Sharon Green. Although some people consider randomized experiments the gold standard, in many cases, it would be highly unethical to assign individuals to harmful exposures to measure their effects. Modern causal inference techniques help scientists to estimate treatment effects using observational data. In particular, propensity score matching helps scientists estimate causal effects using observational data by matching individuals so that the “treatment” and “control” groups are balanced on measured covariates. After implementing propensity score matching, data visualizations make it easier to assess the quality of the matches before estimating effects. This blog post is a tutorial for implementing propensity score matching and creating data visualizations to assess covariate balance–that is, visually assessing whether the matched individuals are balanced with respect to measured covariates.

Introduction to Propensity Score Matching with MatchIt

April 1, 2024
Alex Ramiller
by Alex Ramiller. When working with observational (i.e. non-experimental) data, it is often challenging to establish the existence of causal relationships between interventions and outcomes. Propensity Score Matching (PSM) provides a powerful tool for causal inference with observational data, enabling the creation of comparable groups that allow us to directly measure the impact of an intervention. This blog post introduces MatchIt – a software package that provides all of the necessary tools for conducting Propensity Score Matching in R – and provides step-by-step instructions on how to conduct and evaluate matches.