Introduction to Data Visualization in R
What you'll learn
- Data Visualization in R using ggplot2
- general computer literacy
- R and RStudio Desktop need to be installed on your computer
This introductory course will quickly get you up to speed creating quality graphics in R using the ggplot2 package.
In this course, we'll cover:
Getting started with R and R Studio
Getting started with R Markdown Documents
How to import data using:
csv and text files
creating data "on the fly"
The basics of ggplot2:
multiple graphs (facets)
Adding titles, subtitles, axis labels, and captions
What you'll learn:
After completing this course, you'll have the tools you need to start creating professional graphics in R using ggplot2.
You'll apply the "grammar of graphics" as implemented in ggplot2 to build graphics layer-by-layer and understand how to customize your graphics using facets and color.
Are there any course requirements or prerequisites?
You will need to have R and RStudio Desktop installed on your computer (Mac or PC) as well as an internet connection to download and install packages within RStudio Desktop. An overview of installing R and RStudio is part of the course.
Who is this course for?
If you currently create multiple data visualizations in spreadsheets, you've probably wondered how you could improve your work or how you could more efficiently work with large datasets. Or, if you have to recreate graphics repeatedly, you might be looking for a tool to make your work more reproducible.
Learning R and ggplot2 will allow you to move beyond spreadsheets and use a professional tool that is open source.
Who this course is for:
- Students and professionals interested in data visualization and data science.
Developing new skills is critical to personal and professional development.
I hold a Ph.D. in Chemistry; however, I developed my understanding and appreciation for the power of statistical analysis and data visualization over a 15+ year career in Silicon Valley.
Over the last 5+ years, I've returned to academics, focusing on graduate education and the skills needed for STEM professionals to succeed in their careers, including developing a course on Applied Statistical Techniques using R.
I am fortunate that my career has provided continuous opportunities to develop new skills, and today— with platforms like Udemy—I look forward to sharing this learning.