R

R Fundamentals: Parts 1-4

January 29, 2024, 9:00am
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.

R Fundamentals: Parts 1-4

January 8, 2024, 10:00am
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.

R Data Wrangling and Manipulation: Parts 1-2

February 12, 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 Machine Learning with tidymodels: Parts 1-2

February 27, 2024, 10:00am
Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. During this two part workshop, we will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

Reine Ngnonsse

IUSE Undergraduate Advisory Board
Genetics and Plant Biology

Reine Ngnonsse, an enthusiast for math and technology, delved into tutoring math at a community college through the EOPs program. At UC Berkeley, while pursuing Genetics and Plant Biology, She explored R programming in a CRISPR project. As an intern at Health Career Connection, Reine expanded coding skills in Python, R, and Tableau, igniting a passion for programming. With exposure to Python and Javascript, she can't wait to merge mathematical prowess with coding finesse for innovative solutions.

Addison Pickrell

IUSE Undergraduate Advisory Board
Mathematics
Sociology

Addison is an aspiring mathematician and social scientist (Class of '27). He loves collecting books he'll never read, is an open-source and open-access advocate, and an aspiring community organizer and systems disrupter. Ask me about community-based participatory action research (CBPAR), critical pedagogy, applied mathematics, and social science.

Larissa Benjamin

Doctor of Public Health Student
Public Health

Larissa Benjamin is a second year Doctor of Public Health (DrPH) student at UC Berkeley. Her research uses a mixed-methods approach to exploring the structural determinants of cardiovascular disease inequities in the rural Southeastern United States, also called the “Stroke Belt.” She is particularly curious about how regional history, geography, and structural racism shape inequitable rural neighborhood risk environments. Larissa earned a BS in Evolutionary Anthropology and English from University of Michigan, and an MPH at UC Berkeley in Health and Social Behavior with a...

Elaine Luo

Instructor
Graduate School of Education

Elaine (Hua) Luo is a PhD candidate in the Graduate School of Education, School Psychology PhD program. Her research interests focus on adolescents’ identity development and well-being under the transactional influence of entities in their socio-ecological systems. In her research, Elaine has utilized not only quantitative but also qualitative and mixed methods to study her research topics of interest. Before coming to Berkeley, Elaine earned her Master’s in Human Development and Psychology from Harvard Graduate School of Education and her Bachelor of Art in Education Sciences from...

Finley Golightly

D-Lab Staff
Applied Mathematics

Finley joined D-Lab as full-time staff launching their career in Data Science after graduating with a Bachelor's degree in Applied Math from UC Berkeley.

They have been with D-Lab since Fall 2020, formerly as part of the UTech Management team before joining as full-time staff in Fall 2023. They love the learning environment of D-Lab and their favorite part of the job is their co-workers! In their free time, they enjoy reading, boxing, listening to music, and playing Dungeons & Dragons. Feel free to stop by the front desk to ask them any questions or...

Paul Salamanca

Instructor
Sociology

I am a PhD student in sociology. I study imperialism, race, and gender, with a historical focus on the colonial Philippines. In my free time, I like to cook and bake.