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R Modules


"I don't know anything about R. I want to start from the beginning!"

R Fundamentals

The absolute basics

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.

"I know the basics of R. I want to learn more about manipulating and visualizing data."

R Data Wrangling and Manipulation

Working with data

This workshop will introduce packages in R (notably dplyr and tidyr) that make data wrangling and manipulation much easier.

R Data Visualization

Visualizing with R

This workshop will provide an introduction to graphics in R with ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data.

"I know a fair bit about R and data wrangling. I want to learn more advanced topics like Machine Learning."

R Text Analysis

Preprocessing, topic modeling, word embeddings and more

In this workshop, we'll focus fundamental approaches to applying computational and data visualization methods to text in R. We'll cover a "tidy data" approach to natural language processing, which incorporates tidyverse data transformations,tidytext, tidymodels and a host of other text related packages.

R Machine Learning with tidymodels

Using R for ML

This workshop offers an introduction to machine learning algorithms by making use of the tidymodels package.

R Deep Learning

Working with Keras in R

This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.

R Geospatial Fundamentals

Working with spatial data in R

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.