R

R Geospatial Fundamentals: Parts 1-3

October 14, 2024, 2:00pm
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 vector data, which encodes location as points, lines and polygons.

R Machine Learning with tidymodels: Parts 1-2

October 14, 2024, 1:00pm
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.

R Fundamentals: Parts 1-4

October 8, 2024, 5:00pm
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.

R Fundamentals: Parts 1-4

September 17, 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 Data Wrangling and Manipulation: Parts 1-2

October 1, 2024, 1:00pm
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.

Theo Snow

Availability: By appointment only

Consulting Areas: Python, R, SQL, SAS, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Visualization, Geospatial Data, Maps & Spatial Analysis, Machine Learning, Mixed Methods, Qualitative methods, Surveys, Sampling & Interviews, Regression Analysis, Means Tests, Software Output Interpretation, Other, Excel, Git or Github, RStudio, RStudio Cloud, SAS, Tableau

Sakina Dhorajiwala

Availability: By appointment only

Consulting Areas: Python, R, Stata, LaTeX, Data Manipulation and Cleaning, Data Visualization, Mixed Methods, Qualitative Methods, Surveys, Sampling & Interviews, Regression Analysis, Excel, Git or Github, RStudio

Manish Kumar

Availability: By appointment only

Consulting Areas: Python, R, Javascript, C, C++, APIs, Databases & SQL, Data Manipulation and Cleaning, Digital Humanities, Software Tools, Git or Github, MATLAB, RStudio

Karla Palos Castellanos

Availability: By appointment only

Consulting Areas: Python, R, SQL, Stata, Git or Github, RStudio

Emma Lasky

Availability: By appointment only

Consulting Areas: Python Programming, R Programming, Data Manipulation and Cleaning, Data Science, Data Sources, Data Visualization, Geospatial Data, Maps & Spatial Analysis, Mixed Methods, Regression Analysis, ArcGIS Desktop, Online or Pro, Excel, Git or Github, QGIS, RStudio, RStudio Cloud