This course covers the very basics of R and Rstudio. This course covers the different types of object in R, saving and loading data, and a very basic introduction to plotting using base R functions.

This course is meant for those who have absolutely no prior experience with R, need a refresher on how to install R/Rstudio, or are struggling with some of the core concepts. We will cover:

What is R and Rstudio?

How to install R and Rstudio

The layout of Rstudio What is a function?

What is a package?

How to add comments to your scripts

Simple Data Manipulation in R This course will focus on simple data manipulation in base R via subsetting and combining datasets. Subsetting allows us to filter and extract data via index numbers, filters, or some other criteria. Combining involves adding new information from another dataset using cbind(), rbind(), and merge.

This course focuses on the ever useful tidyverse package! We will cover: What is tidyverse? What is a data pipeline? How can we use tidyverse to clean and manipulate our data?

This course provides a straightforward toolkit for cleaning data using the tidyverse package. In this course, we cover data best practices, how to identify potential issues easily, and how to create pipelines to make cleaning our data even easier. Through this course, students can work with an example dataset that includes intentional typos. Or work with your own dataset that needs to be cleaned!

This course covers critical functions for the R programming language centered around optimizing code, and creating custom scripts. The lessons in this course are broadly applicable to all coding languages. Mastery of these concepts elevates R novices into advanced R users, capable of creating advanced data pipelines.

This course focuses entirely on the ggplot2, a powerful package that allows us to visualize our data and results. This course covers the very basics of ggplot2, how to use facet wrapping for advanced visualizations, and the common arguments used in ggplot functions.