Data Visualization with R

Ends today! Save 40% on your access to 10,000+ programs and make a real impact in your career. Save now.

Data Visualization with R

This course is part of multiple programs.

Instructors: Yiwen Li +3 more

32,728 already enrolled

Included with

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Create bar charts, histograms, pie charts, scatter plots, line graphs, box plots, and maps using R and related packages.
Design customized charts and plots using annotations, axis titles, text labels, themes, and faceting.
Create maps using the Leaflet package for R.
Create interactive dashboards using the Shiny package for R.

Skills you'll gain

Category: Interactive Data Visualization
Category: Statistical Visualization
Category: Box Plots
Category: Data Presentation
Category: Geospatial Information and Technology
Category: Data Visualization
Category: Data Visualization Software
Category: Dashboard Creation
Category: Scatter Plots
Category: Graphing
Category: Plot (Graphics)
Category: Data Analysis
Category: Data Science

Tools you'll learn

Category: R (Software)
Category: Leaflet (Software)
Category: Shiny (R Package)
Category: Dashboard
Category: R Programming
Category: Ggplot2
Category: Rmarkdown

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments¹

AI Graded see disclaimer

Taught in English

Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

In this course, you will learn 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 data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to 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. Watch the videos, work through the labs, and watch your data science skill grow. Good luck! 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. Note: The pre-requisite for this course is basic R programming skills.

Data without a way to convey the story behind it to yourself or others is just numbers on a page. You can observe and tell the story of your data in a more impactful way through visualization. In this module, you will learn the basics of data visualization using R, including the fundamental components that are shared by all charts and plots, and how to bring those components to life using the ggplot2 package for R. You will also learn how to create three common chart types, including bar, histogram, and pie charts, from the qualitative and quantitative data.

What's included

5 videos3 readings4 assignments1 app item1 plugin

5 videosTotal 21 minutes

Introduction to Data Visualization4 minutes
Using ggplot25 minutes
Bar Charts4 minutes
Histograms4 minutes
Pie Charts5 minutes

3 readingsTotal 9 minutes

Course Introduction3 minutes
Summary & Highlights 3 minutes
Summary & Highlights 3 minutes

4 assignmentsTotal 24 minutes

Practice Quiz6 minutes
Practice Quiz6 minutes
Graded Quiz6 minutes
Graded Quiz6 minutes

1 app itemTotal 20 minutes

Hands-on Lab: Apply Basic Chart Learning20 minutes

1 pluginTotal 15 minutes

Cheat Sheet: Basic Plots15 minutes

In this module, you will take your data visualization skills to the next level! You will learn how to create three plot types, including scatter plots, line, plots, and box plots, using the ggplot2 library and then customize the visualizations using annotations and customized axis titles and text labels. You will also learn about faceting, a way to visualize each level of a discrete or categorical variable, and different ways to work with themes. Finally, you will learn about a unique chart type called a map that you can create using geolocation data and the Leaflet library.

What's included

6 videos2 readings4 assignments2 app items3 plugins

6 videosTotal 32 minutes

Scatter Plots4 minutes
Line Plots6 minutes
Box Plots5 minutes
Customize Plots6 minutes
Themes and Faceting6 minutes
v2.2.3 - Maps with Leaflet4 minutes

2 readingsTotal 10 minutes

Summary & Highlights 5 minutes
Summary & Highlights 5 minutes

4 assignmentsTotal 28 minutes

Practice Quiz6 minutes
Practice Quiz8 minutes
Graded Quiz6 minutes
Graded Quiz8 minutes

2 app itemsTotal 75 minutes

Hands-on Lab: Apply Basic Plot Learning30 minutes
Hands-on Lab: Apply Maps Learning45 minutes

3 pluginsTotal 45 minutes

Cheat Sheet - Basic Plots15 minutes
Cheat Sheet - Customization and Maps15 minutes
Cheat Sheet - Customization and Maps using Leaflet15 minutes

Your data tells a story. You have built the charts and plots that show important relationships between variables, identify outliers and anomalies, and see the trends that can help you predict what the future might bring. Now you want to put these insightful data visualizations at the fingertips of your stakeholders and make it easy to interact with and explore the data. You need a dashboard! In this module, you will learn why dashboards are important and then build interactive dashboards using the Shiny package for R. You will learn how Shiny dashboards are structured into user interface and server components and then build out these components and develop the logic to make them work together. You will also learn how to deploy your dashboards and provide a way to generate informative reports with R Markdown.

What's included

6 videos2 readings4 assignments2 ungraded labs5 plugins

6 videosTotal 32 minutes

Introduction to Dashboards & Shiny5 minutes
Shiny Basics6 minutes
Create a Simple Shiny App6 minutes
Explore Shiny Components5 minutes
Deploy the Shiny App5 minutes
Reports in rmd6 minutes

2 readingsTotal 15 minutes

Summary & Highlights 10 minutes
Summary & Highlights 5 minutes

4 assignmentsTotal 28 minutes

Practice Quiz8 minutes
Practice Quiz6 minutes
Graded Quiz8 minutes
Graded Quiz6 minutes

2 ungraded labsTotal 120 minutes

Hands-on Lab: Create a Shiny App (using Coursera Labs)60 minutes
Hands on Lab: Shiny Components (using Coursera Labs)60 minutes

5 pluginsTotal 75 minutes

(Optional) Lab: Getting started with Posit Cloud15 minutes
(Optional) Lab: Create Shiny App on Posit Cloud15 minutes
Cheat Sheet - DashBoard15 minutes
(Optional) Hands on Lab: Shiny Components on Posit Cloud15 minutes
Cheat Sheet - Dashboard Application15 minutes

What's included

3 readings1 assignment1 peer review1 app item1 ungraded lab2 plugins

3 readingsTotal 9 minutes

Final Project Overview – Dashboard with Shiny using Census Data 5 minutes
Congratulations and Next Steps2 minutes
Credits and Acknowledgments2 minutes

1 assignmentTotal 50 minutes

Final Exam 50 minutes

1 peer reviewTotal 30 minutes

Option 2 - Peer Graded: Final Project - Submission and Evaluation30 minutes

1 app itemTotal 20 minutes

Option 1 - AI Graded: Final Project - Submission and Evaluation20 minutes

1 ungraded labTotal 60 minutes

Final Lab: Dashboard with Shiny using Census Data (on Coursera Labs)60 minutes

2 pluginsTotal 20 minutes

Reading: Final Project Submission Guidelines and Deliverables5 minutes
(Optional) Final Lab: Dashboard with Shiny using Census Data (on Posit Cloud)15 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Yiwen Li

IBM

2 Courses53,921 learners

Offered by

IBM

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.