Introductory R for Novices: Introduction to Data Visualization
When: January 6, 2026 - January 15, 2026Share
About this Course
We will use R on Biowulf for this course to avoid issues with R and package installations. To use R on Biowulf, you must have a NIH HPC account. If you do not have a NIH HPC (Biowulf) account, this course can be taken using a local R installation. However, we will not be able to troubleshoot package installation issues during class. Additionally, because we will use packages belonging to the tidyverse, you will need to install these packages using `install.packages("tidyverse")` prior to the first lesson if you are not using R on Biowulf.
Description
In this lesson, attendees will learn the basics of ggplot2 to create simple, pretty, and effective figures with R.
In this lesson, attendees will learn the basics of ggplot2 to create simple, pretty, and effective figures with R.
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Where
OnlineDescription
In this lesson, attendees will continue learning how to create publishable figures with ggplot2. Topics will include statistical transformations, coordinate systems, and themes.
In this lesson, attendees will continue learning how to create publishable figures with ggplot2. Topics will include statistical transformations, coordinate systems, and themes.
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Where
OnlineDescription
In this lesson, attendees and instructor will work together to craft a publishable volcano plot using the skills previously learned.
In this lesson, attendees and instructor will work together to craft a publishable volcano plot using the skills previously learned.
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Where
OnlineDescription
This lesson introduces general recommendations and tips to consider when creating effective and reproducible visualizations. Additional topics to be discussed include multi-figure panels, complementary or related R packages, and the use of ggplot2 in functions.
This lesson introduces general recommendations and tips to consider when creating effective and reproducible visualizations. Additional topics to be discussed include multi-figure panels, complementary or related R packages, and the use of ggplot2 in functions.