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Saishruthi Swaminathan, Gabriela de Queiroz, Tiffany Zhu, and Yiwen Li

In this course, you will learn about the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another R data visualization package called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will learn about creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web.

Read more

In this course, you will learn about the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another R data visualization package called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will learn about creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web.

You will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course. Enroll in the course, watch the videos, work through the labs, and then watch your data science skills grow!

NOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course.

What's inside

Learning objectives

  • Create basic bar charts, histograms, pie charts, scatter plots, line plots, box plots, and maps using r and related packages, such as ggplot2.
  • Customize charts and plots using themes and faceting.
  • Create maps using the leaflet package for r.
  • Create interactive dashboards using the shiny package for r.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores a framework for data visualization that's an industry standard: Grammar of Graphics
Develops skills in data visualization that are foundational for data science
Teaches graphing techniques using the ggplot2 data visualization package for R, a tool popular for its ggplot2 package
Builds a strong foundation for learners in using the Leaflet package for R, a tool used to create maps
Demonstrates how to create interactive dashboards using the R Shiny package, a versatile tool for developing data apps
Provides hands-on practice through labs, reinforcing the concepts learned in each module

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Visualizing Data with R with these activities:
Create basic visualizations using R
Refresh your understanding of the basics of data visualizations in R to ensure you have a strong foundation for this course.
Browse courses on Visualizations
Show steps
  • Review basic ggplot2 syntax and chart types
  • Create a simple bar chart or histogram with R code
Compile a collection of data visualization resources
Build a curated repository of useful resources, including articles, tutorials, datasets, and tools related to data visualization.
Browse courses on Data Visualization
Show steps
  • Search and identify relevant resources
  • Organize and categorize the resources
Review the book 'ggplot2: Elegant Graphics for Data Analysis' by Hadley Wickham
Gain a deeper understanding of the principles and techniques of ggplot2 by reviewing this comprehensive book.
Show steps
  • Read and understand the concepts introduced in the book
  • Follow along with the code examples and exercises provided
15 other activities
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Show all 18 activities
Practice creating basic bar charts
Will help you gain proficiency and familiarity with how to create bar charts using R.
Show steps
  • Open ggplot2 in R
  • Create sample dataset
  • Create a bar chart using the sample dataset
Practice creating histograms
Will help you reinforce your understanding of creating histograms in R.
Show steps
  • Open ggplot2 in R
  • Create sample dataset
  • Create a histogram using the sample dataset
Practice creating pie charts
Will get you comfortable in creating pie charts using R and ggplot2.
Show steps
  • Open ggplot2 in R
  • Create sample dataset
  • Create a pie chart using the sample dataset
Practice creating line plots
Will aid you in practicing how to create line plots with R.
Show steps
  • Open ggplot2 in R
  • Create sample dataset
  • Create a line plot using the sample dataset
Explore the ggplot2 package
Delve deeper into the features and capabilities of ggplot2 through guided tutorials to enhance your data visualization skills.
Browse courses on Ggplot2
Show steps
  • Follow an online tutorial on using ggplot2
  • Create a more complex visualization using ggplot2
Join or start a peer group to discuss ggplot2 and data visualization
Connect with other learners and exchange knowledge by participating in peer-led discussions on ggplot2 and data visualization.
Browse courses on Data Visualization
Show steps
  • Join or create a peer group
  • Participate in discussions
  • Share your experiences and insights
Practice creating different chart types
Reinforce your understanding by practicing the creation of various chart types using R code.
Browse courses on Data Visualization
Show steps
  • Create a scatter plot
  • Create a line plot
  • Create a box plot
Complete coding challenges on platforms like LeetCode or HackerRank
Reinforce your understanding of ggplot2 and other data visualization techniques by solving coding challenges that require you to implement these concepts.
Browse courses on Data Visualization
Show steps
  • Choose a platform like LeetCode or HackerRank
  • Identify challenges related to data visualization
  • Solve the challenges using ggplot2 or other relevant packages
  • Review and analyze your solutions
Discuss visualization techniques with peers
Engage with fellow learners to exchange ideas and insights on visualization techniques, best practices, and common challenges.
Browse courses on Data Visualization
Show steps
  • Join a study group or online forum
  • Participate in discussions on visualization topics
Follow online tutorials on advanced ggplot2 techniques
Expand your knowledge of ggplot2 by exploring online tutorials that cover advanced topics such as faceting, themes, and interactive graphics.
Browse courses on Data Visualization
Show steps
  • Identify online tutorials on advanced ggplot2 techniques
  • Follow the tutorials and experiment with the code examples
  • Apply the learned techniques to your own data visualization projects
Volunteer as a mentor for aspiring data visualization learners
Strengthen your understanding of ggplot2 and data visualization by sharing your knowledge and mentoring others.
Browse courses on Data Visualization
Show steps
  • Join a mentoring program or find mentees directly
  • Provide guidance and support on ggplot2 and data visualization concepts
Develop a data visualization dashboard for a real-world dataset
Apply the concepts of ggplot2 and dashboard creation to a practical problem by designing and implementing a dashboard that visualizes and analyzes a dataset of your choice.
Browse courses on Data Visualization
Show steps
  • Gather and explore a real-world dataset
  • Design the dashboard layout and visualizations
  • Implement the dashboard using ggplot2 and Shiny
  • Test and refine the dashboard for usability
  • Deploy the dashboard and share it with others
Create a visualization dashboard using Shiny
Will allow you to apply your skills in creating interactive dashboards using Shiny, which can help you present your data in a more engaging and user-friendly way.
Browse courses on Shiny
Show steps
  • Design the layout and functionality of your dashboard
  • Create the Shiny app using R code
  • Deploy your dashboard on a web server
Develop a visualization for a dataset
Apply your skills to create a visualization that effectively communicates insights from a real-world dataset.
Browse courses on Data Visualization
Show steps
  • Choose a dataset of interest
  • Explore the data to identify key features
  • Design and create a visualization
Participate in data visualization competitions or hackathons
Challenge yourself and showcase your skills by participating in data visualization competitions or hackathons.
Browse courses on Data Visualization
Show steps
  • Identify relevant competitions or hackathons
  • Form a team or work individually
  • Develop a data visualization solution
  • Submit and present your solution

Career center

Learners who complete Visualizing Data with R will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data analysts use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to collect, analyze, and interpret data to help organizations make informed decisions. This course helps data analysts build the skills to transform raw data into relevant and actionable insights.
Data Scientist
Data scientists use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to build and deploy machine learning and AI models to identify patterns and trends in data. These insights provide data scientists with valuable information they can use to solve complex problems and make informed decisions.
Data Engineer
Data engineers use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to design, build, and maintain data pipelines to ensure the data used by data scientists and other stakeholders is accurate, reliable, and accessible.
Statistician
Statisticians use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to collect, analyze, and interpret data to draw conclusions and make predictions about the future. This course provides statisticians with the tools they need to communicate complex data in clear and concise ways.
Machine Learning Engineer
Machine learning engineers use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to build and deploy machine learning models to solve real-world problems. This course helps machine learning engineers develop the skills they need to work with complex datasets and build models that are accurate and efficient.
Business Analyst
Business analysts use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to analyze data and identify trends and recommend solutions to business problems. This course, by teaching business analysts how to communicate insights from data in clear and compelling ways, helps them succeed in their chosen career field.
Software Engineer
Software engineers use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to design, build, and maintain software applications. This course, by providing software engineers with the skills to develop data visualization dashboards, ensures that the applications they build are both visually appealing and informative.
Data Journalist
Data journalists use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to communicate data-driven stories to the public. This course helps data journalists to create visually appealing and easy-to-understand data visualizations that inform and engage their audience.
UX Designer
UX designers use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to create user-friendly and visually appealing digital products. This course provides UX designers with the tools they need to understand how users interact with data and how to design interfaces that are both intuitive and efficient.
Product Manager
Product managers use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to manage the development and launch of new products. This course helps product managers to understand how to use data to make informed decisions about product features and marketing strategies.
Marketing Analyst
Marketing analysts use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to analyze marketing data and identify trends and recommend solutions to marketing problems. This course provides marketing analysts with the tools they need to communicate insights from data in clear and compelling ways.
Financial Analyst
Financial analysts use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to analyze financial data and make recommendations about investments. This course provides financial analysts with the tools they need to identify trends and make informed decisions about investments.
Actuary
Actuaries use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to analyze risk and uncertainty to make informed decisions about insurance and other financial products. This course provides actuaries with the tools they need to develop models that can predict future events and make informed decisions about risk.
Operations Research Analyst
Operations research analysts use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to analyze data and develop solutions to improve the efficiency of operations. This course provides operations research analysts with the tools they need to identify bottlenecks and develop solutions that can improve productivity.
Epidemiologist
Epidemiologists use skills and knowledge gained from courses, such as \"Visualizing Data with R\", to investigate the causes of disease and develop strategies to prevent and control outbreaks. This course provides epidemiologists with the tools they need to analyze data and identify trends that can help them to identify the causes of disease and develop effective prevention strategies.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Visualizing Data with R.
Comprehensive introduction to the ggplot2 package, which powerful tool for creating visualizations in R. It provides a solid foundation for students who want to learn how to create effective data visualizations.
Introduces the plotly and shiny packages, which are used to create interactive visualizations in R. It valuable resource for students who want to learn how to create interactive dashboards and web applications.
Classic work on data visualization. It provides a comprehensive overview of the principles of data visualization and how to create effective visualizations.
Comprehensive introduction to R for data science. It covers a wide range of topics, including data manipulation, statistical analysis, and machine learning.
Concise guide to creating visualizations in R using the ggplot2 package. It useful resource for students who want to learn the basics of data visualization quickly.
Provides a broad overview of data science. It covers a wide range of topics, including data collection, data analysis, and data visualization. It useful resource for students who want to learn about the field of data science.
Comprehensive introduction to statistical analysis using the S-PLUS software. It covers a wide range of topics, including data manipulation, statistical analysis, and graphical techniques. It useful resource for students who want to learn about statistical analysis and how to use the S-PLUS software.
Provides a practical introduction to data science. It covers a wide range of topics, including data collection, data analysis, and data visualization. It useful resource for students who want to learn about the field of data science.
Comprehensive introduction to data mining using the R software. It covers a wide range of topics, including data mining techniques, data mining algorithms, and data mining applications. It useful resource for students who want to learn about data mining and how to use the R software.
Comprehensive introduction to machine learning using the R software. It covers a wide range of topics, including machine learning algorithms, machine learning techniques, and machine learning applications. It useful resource for students who want to learn about machine learning and how to use the R software.

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