Course Summary
Learn the art of data visualization using R programming language through this course offered by Johns Hopkins University on Coursera.Key Learning Points
- Gain expertise in creating various types of charts, graphs, and maps using R packages like ggplot2, lattice, and leaflet.
- Explore different visualization techniques to convey complex data sets in clear and concise ways.
- Learn to use R Markdown and Shiny to produce interactive visualizations and reports.
Job Positions & Salaries of people who have taken this course might have
- USA: $65,000 - $110,000
- India: ₹4 - ₹8 lakhs
- Spain: €25,000 - €45,000
- USA: $65,000 - $110,000
- India: ₹4 - ₹8 lakhs
- Spain: €25,000 - €45,000
- USA: $90,000 - $150,000
- India: ₹6 - ₹20 lakhs
- Spain: €35,000 - €60,000
- USA: $65,000 - $110,000
- India: ₹4 - ₹8 lakhs
- Spain: €25,000 - €45,000
- USA: $90,000 - $150,000
- India: ₹6 - ₹20 lakhs
- Spain: €35,000 - €60,000
- USA: $70,000 - $110,000
- India: ₹5 - ₹10 lakhs
- Spain: €25,000 - €45,000
Related Topics for further study
Learning Outcomes
- Ability to create various types of charts, graphs, and maps using R packages.
- Expertise in using R Markdown and Shiny to produce interactive visualizations and reports.
- Proficiency in conveying complex data sets in clear and concise ways.
Prerequisites or good to have knowledge before taking this course
- Prior programming experience in R is required.
- Basic knowledge of statistics is recommended.
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Interactive quizzes
Similar Courses
- Data Visualization with Tableau
- Data Visualization with Python
- Data Science Essentials
Related Education Paths
Notable People in This Field
- Nathan Yau
- Edward Tufte
Related Books
Description
Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance.
Outline
- Introduction to the Grammar of Graphics
- Welcome to the Course
- Getting Started with ggplot Part 1
- Getting Started with ggplot Part 2
- Distributions
- Note on Previewing Figures in R Studio
- Wickham et al, Chapters 1 and 2
- ggplot Cheat Sheet
- ggplot2 Overview and Reference
- R Graphics Cookbook - Scatter Plots
- Sample Data
- R Graphics Cookbook - Histograms
- R Graphics Cookbook - Box Plots
- R Graphics Cookbook - Making a Density Plot
- R Graphics Cookbook - Making a Violin Plot
- A Note About Peer Review Assignments
- ggplot2 Introduction and Scatter Plots
- Univariate Figures Quiz
- More Visualizations with ggplot
- Bar Plots Part 1
- Bar Plots Part 2
- Bar Plots Part 3
- Line Plots Part 1
- Line Plots Part 2
- Learning New Figures Part 1
- Learning New Figures Part 2
- Bar plots in the R Graph Gallery
- Cookbook for R - Bar and line graphs
- R Graphics Cookbook - Line Graphs
- R Graph Gallery
- Bar plots
- Line plots quiz
- ggplot Graphical Elements
- Annotations Part 1
- Annotations Part 2
- Colors, Legends, and Themes Part 1
- Colors, Legends, and Themes Part 2
- Inkscape Part 1
- Inkscape Part 2
- Wickham et al, Chapter 8
- Wickham et al, Chapter 10
- Wickham et al, Chapter 16
- ggplot2 Themes Documentation
- ggthemes Gallery
- Download Page for Inkscape
- Inkscape Tutorial Parts 1-3
- Inkscape Manual Quick Start Section
- Annotations Quiz
- Modifying Graphical Elements and Themes Quiz
- Vector Graphics
Summary of User Reviews
Discover the art of data visualization with JHU Data Visualization in R course on Coursera. Students highly recommend this course for its comprehensive tutorials and hands-on projects that help them understand complex data in a more visual way.Key Aspect Users Liked About This Course
The course offers a great learning experience with practical examples and real-world projects that help students learn the intricacies of data visualization.Pros from User Reviews
- Well-structured lectures that are easy to follow
- The assignments and projects provide a practical learning experience
- The instructors are knowledgeable and supportive
- The course content is up-to-date and relevant to the industry
- The course provides a good foundation for building a career in data visualization
Cons from User Reviews
- Some users found the course challenging at times
- The course requires a basic understanding of R programming
- The course may not be suitable for beginners with no prior programming experience
- Some users felt that the course could benefit from more interactive elements
- The course does not cover all aspects of data visualization
Collin Paschall
5 Raiting
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