Visualizing Data in the Tidyverse
Master data visualization in R using ggplot2 and tidyverse tools. Learn to create effective plots, customize graphics, and build compelling data stories.
Master data visualization in R using ggplot2 and tidyverse tools. Learn to create effective plots, customize graphics, and build compelling data stories.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Tidyverse Skills for Data Science in R Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
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What you'll learn
Create effective data visualizations using ggplot2
Customize plots with colors, labels, and themes
Build informative data summary tables
Develop animated data visualizations
Apply best practices in data visualization design
Skills you'll gain
This course includes:
8.5 Hours PreRecorded video
6 quizzes, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 10 modules in this course
This comprehensive course teaches data visualization using the tidyverse ecosystem in R, with a focus on the ggplot2 package. Students learn to create and customize various types of plots, build effective data summary tables, and develop data animations for visual storytelling. The curriculum covers plot types, aesthetic mappings, theme customization, and advanced visualization techniques while emphasizing best practices in data communication.
About This Course
Module 1 · 22 Minutes to complete
Plot Types
Module 2 · 1 Hours to complete
Making Good Plots
Module 3 · 55 Minutes to complete
Plot Generation Process
Module 4 · 5 Minutes to complete
ggplot2 Basics
Module 5 · 2 Hours to complete
ggplot2: Customization
Module 6 · 2 Hours to complete
Tables
Module 7 · 1 Hours to complete
ggplot2: Extensions
Module 8 · 3 Hours to complete
Case Studies
Module 9 · 2 Hours to complete
Project: Visualizing Data in the Tidyverse
Module 10 · 2 Hours to complete
Fee Structure
Instructors
Pioneering Data Science Education and Computational Biology Innovation
Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.
Professor of Biostatistics at Johns Hopkins University
Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and serves as a Co-Editor of the Simply Statistics blog. He earned his PhD in Statistics from the University of California, Los Angeles, and is recognized for his research in air pollution, health risk assessment, and statistical methods for environmental data. In 2016, he received the Mortimer Spiegelman Award from the American Public Health Association, honoring his significant contributions to health statistics. Dr. Peng developed the Statistical Programming course at Johns Hopkins to equip students with essential computational tools for data analysis. Additionally, he is a national leader in promoting reproducible research practices and serves as the Reproducible Research editor for the journal Biostatistics. His interdisciplinary research has been published in prestigious journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. He has authored over a dozen software packages that implement statistical methods for environmental studies and reproducible research, and he regularly conducts workshops and tutorials on statistical computing and data analysis.
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4.7 course rating
20 ratings
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