Programming Languages

Mapping Census Data with tidycensus

November 6, 2023
Alex Ramiller
by Alex Ramiller. The U.S. Census Bureau provides a rich source of publicly available data for a wide variety of research applications. However, the traditional process of downloading these data from the census website is slow, cumbersome, and inefficient. The R package “tidycensus” provides researchers with a tool to overcome these challenges, enabling a streamlined process to quickly downloading numerous datasets directly from the census API (Application Programming Interface). This blog post provides a basic workflow for the use of the tidycensus package, from installing the package and identifying variables to efficiently downloading and mapping census data.

Using Forest Plots to Report Regression Estimates: A Useful Data Visualization Technique

October 17, 2023
Sharon Green
by Sharon Green. Regression models help us understand relationships between two or more variables. In many cases, results are summarized in tables that present coefficients, standard errors, and p-values. Reading these can be a slog. Figures such as forest plots can help us communicate results more effectively and may lead to a better understanding of the data. This blog post is a tutorial on two different approaches to creating high-quality and reproducible forest plots, one using ggplot2 and one using the forestplot package.

Using Artificial Intelligence to Help Write Code

February 28, 2023
Daniel Tan
by Daniel Tan. ChatGPT is a natural language processing model that has applications in a wide variety of research settings. It is a chatbot-style tool that was created by OpenAI using a deep learning model that allows it to generate human-like responses to a wide variety of questions and prompts spanning a multitude of topics. Because it has been trained on a large body of text, ChatGPT is a particularly useful tool for programming. This post explores ways to use ChatGPT to help write code in Stata, a statistical software package that is widely used in academic and policy research.

A Brief Introduction to Cloud Native Approaches for Big Data Analysis

March 20, 2023
Millie Chapman
by Millie Chapman. Satellites, smart phones, and other monitoring technologies are creating vast amounts of data about our earth every day. These data hold promise to provide global insights on everything from biodiversity patterns to human activity at increasingly fine spatial and temporal resolution. But leveraging this information often requires us to work with data that is too big to fit in our computer's "working memory" (RAM) or even to download to our computer's hard drive. In this post, I walk through tools, terms, and examples to get started with cloud native workflows. These workflows allow us to remotely access and query large data from online resources or web services, all while skipping the download step!

James Hall

Consultant
Department of Statistics

James Hall is a graduate student in the Statistics MA program at University of California, Berkeley. He is a husband and father to three awesome kids. Originally from Baltimore, MD, James earned his bachelors in Mathematics at the United States Military Academy at West Point, NY in 2011, and served as a U.S. Army officer. He’s served as a leader at multiple levels within large organizations with a professional focus on visualizing and communicating complex analysis to decision makers. James’ experience and coursework give him expertise in navigating different statistical methods,...

Hikari Murayama

Senior Data Science Fellow, Senior Instructor
Digital Health Social Justice
Energy and Resources Group

Hikari is a graduate student in the Energy and Resource Group. Her research interests involve utilizing remote sensing and geospatial analysis to address pressing problems at the intersection of humans and climate. She recently served as a Data Science for Social Good Fellow at the University of Washington eScience Institute in the summer of 2020. She is experienced and happy to help in the areas of geospatial analysis, remote sensing, and other statistical analyses and methods. Hikari is devoted to helping community members realize their potential to conduct...

Cheng Ren

Senior Data Science Fellow
School of Social Welfare

Cheng Ren is a D-Lab Senior Data Science Fellow and a Ph.D. student at the School of Social Welfare. His research interests are community engagement and assessment, nonprofit development, community database, computational social welfare, and data for social goods.

Reduce, Reuse, Recycle: Practical strategies for working with large datasets

October 12, 2022
Angela Liu

When the size of your datasets start to approach the size of your computer’s available memory, even the simplest data wrangling tasks can become frustrating. Suddenly, reading in a .csv or calculating a simple average becomes time-consuming or impossible. As students or researchers, accessing additional computing resources can be costly or is not always an available option. Here are some principles and strategies for reducing the overhead of your dataset while keeping the momentum going. The code mainly focuses on reading csv files - a very common data format - into Python...

Christopher Paciorek, Ph.D.

Research Computing Consultant, Adjunct Professor
Department of Statistics
Research IT

Chris Paciorek is an adjunct professor in the Department of Statistics, as well as the Statistical Computing Consultant in the Department's Statistical Computing Facility (SCF) and in the Econometrics Laboratory (EML) of the Economics Department. He is also a user support consultant for Berkeley Research Computing. He teaches and presents workshops on statistical computing topics, with a focus on R.

Ash Tan

Consultant
School of Information

Ash is a Masters of Information and Data Science student at the Berkeley School of Information. He currently studies data collection, analysis, and visualization, as well as research design and machine learning techniques. His interests include cognitive science, Wikipedia data, and privacy research.

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