Guides

01. Setting up an R environment

In this guide, we will explore options for setting up an R environment. We will discuss local, remote, and virtual environments. Each have their own advantages and shortcomings. The best option for you will depend on your needs and preferences.

02. Installing and managing R packages

In this guide, we will cover how to install and manage R packages. We will discuss two primary methods for installing packages: using the RStudio IDE interface and using the R console. We will also cover how to attach and detach packages in an R session, and how to manage packages by listing, updating, and removing them.

03. Working with the interactive R programming lessons

In this guide, we provide an overview of the interactive R programming lessons, explain how to access the lessons, use the lessons, and remove the lessons.

04. Setting up Git and GitHub

In this guide, we will cover the basics of setting up Git and GitHub. We will also cover the basics of using Git and GitHub to manage a project. This guide is intended for beginners who are new to Git and GitHub. It is also intended for those who are new to using Git and GitHub with R.

05. Creating reproducible examples

Helping you help yourself

In this guide, we will explore how to create reproducible examples using {reprex}. Reproducible examples are essential for effective communication and collaboration among data scientists and statisticians.

06. Identifying data and data sources

This guide will outline some key data and dataset resources that will be usefull for your own text analysis projects. This guide is not exhaustive, but it will provide you with a good starting point for you to start to explore different types of data and data sources.

07. Web scraping with R

This guide will provide you with an overview of web scraping and how you can use R to scrape data from the web using packages such as {rvest}. Web scraping is a technique used to extract data from websites. It is a powerful tool that can be used to collect data for research, analysis, and visualization. In this guide, you will learn how to use R to scrape data from websites and save it in a format that can be used for further analysis.