Course Summary
This course covers the basics of data visualization in R, including how to create different types of plots and how to customize them to better communicate insights from your data.Key Learning Points
- Learn how to create basic plots like histograms, scatterplots, and box plots
- Explore advanced techniques like faceting, adding annotations, and creating interactive plots
- Get hands-on experience with real-world datasets and practical applications
Job Positions & Salaries of people who have taken this course might have
- USA: $60,000 - $100,000
- India: ₹5,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $60,000 - $100,000
- India: ₹5,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $70,000 - $120,000
- India: ₹6,00,000 - ₹15,00,000
- Spain: €30,000 - €50,000
- USA: $60,000 - $100,000
- India: ₹5,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $70,000 - $120,000
- India: ₹6,00,000 - ₹15,00,000
- Spain: €30,000 - €50,000
- USA: $90,000 - $150,000
- India: ₹8,00,000 - ₹20,00,000
- Spain: €40,000 - €70,000
Related Topics for further study
Learning Outcomes
- Create a variety of basic and advanced plots in R
- Customize plots to better communicate insights from your data
- Apply these skills to real-world datasets and practical applications
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of R programming
- Familiarity with data analysis and statistics
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on assignments
Similar Courses
- Data Visualization with Python
- Data Visualization with Tableau
- Applied Data Visualization with ggplot2
Related Education Paths
Notable People in This Field
- Chief Scientist at RStudio
- Data Visualization Pioneer
Related Books
Description
The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks. This course provides you with the skills for creating those new visualization building blocks. We focus on the ggplot2 framework and describe how to use and extend the system to suit the specific needs of your organization or team. Upon completing this course, learners will be able to build the tools needed to visualize a wide variety of data types and will have the fundamentals needed to address new data types as they come about.
Outline
- Welcome to Building Data Visualization Tools
- Welcome to Building Data Visualization Tools
- Textbook: Mastering Software Development in R
- Syllabus
- Plotting with ggplot2
- Introduction
- Initializing a ggplot object
- Plot aesthetics
- Creating a basic ggplot plot
- Geoms
- Using multiple geoms
- Constant aesthetics
- Example plots
- Extensions of ggplot2
- Introduction
- Guidelines for good plots
- Scales and color
- To find out more
- Plotting with ggplot2
- Mapping and interactive plots
- Introduction
- Basics of Mapping
- ggmap, Google Maps API
- Mapping US counties and states
- More advanced mapping– Spatial objects
- Where to find more on mapping with R
- Overview of htmlWidgets
- plotly package
- Creating your own widget
- Mapping and interactive plots
- The grid Package
- Introduction
- Overview of grid graphics
- Grobs
- Viewports
- Grid graphics coordinate systems
- The gridExtra package
- Where to find more about grid graphics
- Basics of grid graphics
- Building New Graphical Elements
- Introduction
- Why Build a New Theme?
- Default Theme
- Building a New Theme
- Summary
- Introduction
- Building a Geom
- Example: An Automatic Transparency Geom
- Building a Stat
- Example: Normal Confidence Intervals
- Combining Geoms and Stats
- Summary
Summary of User Reviews
Discover the art of data visualization using R with the R Data Visualization course on Coursera. This course has received high praise from its users for its comprehensive and engaging curriculum, making it a top choice for anyone looking to improve their data visualization skills.Key Aspect Users Liked About This Course
Many users have praised the course for its practical approach, providing hands-on exercises and real-world examples that help students apply their newfound knowledge in a meaningful way.Pros from User Reviews
- Comprehensive and engaging curriculum
- Practical approach with hands-on exercises
- Real-world examples to help with application
- Great course for beginners and intermediate users
- Excellent instructor and support staff
Cons from User Reviews
- Some users found the course too basic for advanced users
- Lack of interaction with other students
- No certificate of completion provided for the free version
- Some technical issues with the platform
- Limited feedback on assignments
Roger D. Peng, PhD
3.9 Raiting
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