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Course Description

Bundle Registration! Save a bundle and receive 30% off the individual course rate when you register for both BIOF 501 and BIOF 539!

After practicing syntax, scripting, and data analysis in R, dive into visualization with this course! Effective data visualization is more complicated than it might seem. When you really want to fine-tune your figures, programming languages like R are the tool for the job. In this course, we will learn how to apply two popular visualization packages, ggplot2 and plotly, to the task, and cover important things to consider when creating different types of visualizations. You will also be working on your own data visualization project throughout, leaving you with a complete report or app demonstrating your analysis of a specific dataset in stunning, interactive visuals.

The course will be structured around examples from papers that you will analyze, replicate, and improve. Additional reading material, lectures, and activities will guide your own analyses and their application toward your own project. Roughly 50% of time will be spent analyzing, understanding, and critiquing figures, with the other 50% spent working on your own visualizations in R. The final grade will be composed of discussions/text responses, weekly assignments, and a course project. The course project will be split into parts so that each week involves working toward a final product.

Course Outline

This course will build upon the basics of R programming by diving into applications of R in data visualization. Students will learn concepts of data visualization and the factors involved in choosing visualization methods, and practice implementing these visualizations in R using a variety of different types of data. The course will focus on two popular visualization packages, ggplot2 and plotly, providing detail on the syntax, structure, and strengths and weaknesses of each package. Practical applications include elevating figures in manuscripts and presentations, more creatively illustrating results and hypotheses through interactive visuals, and incorporating effective visualization into reports, apps, and websites.  Additionally, students will complete a project on a dataset of their choice throughout the course, incorporating their visualization into a report or application by the final week.

BIOF 539 is well suited for professionals with a working understanding of R who want to improve their figures and other visualizations. 

Learner Outcomes

When you complete the course successfully, you will be able to:

  • Identify and justify/explain most suitable data visualization methods and choices for a given dataset
  • Write and document scripts to load, clean, summarize, and plot a dataset in R
  • Implement static and interactive figures using ggplot2 and plotly
  • Build a report with embedded visualizations using R Markdown

Microcredential(s)

This course applied toward to Bioinformatics Endeavor digital badge. 

Textbook Information

There is no textbook for purchase required for this course.

Prerequisites

BIOF 501 or equivalent introduction to R course. Prior to taking this course, you should be comfortable with using a text editor or IDE of your choice to write R code, basic syntax and capabilities of R and understanding the basics of commonly used statistical methods.

Additional Information

Readings and examples will be from openly available publications. This course requires access to a computer with R installed. A full list of packages and auxiliary software will be provided prior to the course.

Refund
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Scholarship and Funding

Are you a self-funded student? FAES offers scholarship options. Click here for more information and to apply. 

Looking for resources to help you acquire funding for your continued education? Click here for our funding justification guide. 

Photo Release

By registering for this event, you agree to allow FAES to take photographs of you during the event and to use these photos for promotional purposes, including on our website, social media, and marketing materials, without further compensation. You understand that you have no right to review or approve the final use of these images.

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To Register Click on "Add to Cart"
Section Title
Data Visualization in R
Total Cost (Includes $75 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
Instructor(s)
  • Roshni Bhatt
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