Visualization in R with ggplot2

Visualization in R with ggplot2

This course will teach you ggplot as an implementation of the grammar of graphics in R. ggplot combines the advantages of base and lattice graphics while maintaining the ability to build up a plot step by step from multiple data sources.

This course will teach you ggplot as an implementation of the grammar of graphics in R. ggplot combines the advantages of base and lattice graphics while maintaining the ability to build up a plot step by step from multiple data sources.

$549 | Enroll Now
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Overview

In this course, participants will learn how to use the ggplot2 Project to make, format, label and adjust graphs using R. ggplot2 combines the advantages of base and lattice graphics while maintaining the ability to build up a plot step by step from multiple data sources. The ggplot2 Project, created by Hadley Wickham, is named after the term “Grammar of Graphics”, which was coined by Leland Wilkinson (creator of Systat) and won the 2006 John Chambers Award for Statistical Computing. This “grammar of graphics” is a system of describing and organizing the fundamental components of a graph and the process of creating a graph. Using ggplot2, participants will learn how to design and implement graphs in an efficient, elegant, and systematic manner, following principles of general good graphing practice.
CAP Credit Eligible
Expert Instructors
Teacher Assistant Support

Learning Outcomes

This course is ideal for statistical analysts who use R and need to create or modify graphs. In this online course, participants will learn how to use the ggplot2 Project to make, format, label, and adjust graphs using R.

  • Create basic plots (scatterplots, histograms, and barcharts)
  • Add extra variables with aesthetics (like color, shape, and size) or faceting
  • Display data in other forms (densityplots, boxplots, etc.) using geoms
  • Overcoming overplotting with drawing scatterplots of large datasets
  • Add group-wise summaries and transformations to your plots
  • Add labels, legends, and axes

Who Should Take This Course

Statistical analysts who use R and need to create or modify graphs.

Instructors

Dr. Randall Prium

Dr. Randall Pruim

Randall Pruim is chair of Mathematics and Statistics department at Calvin College as well as director of the Calvin€™s Integrated Science Research Institute (ISRI), which was founded in 2008 through a $1.1 million grant from the Howard Hughes Medical Institute. The ISRI organizes a number of initiatives to deepen integrated approaches to science research and education. Initiatives of the ISRI have led to grants from the NSF to provide scholarships to students who will combine computation with one of the traditional sciences like biology, chemistry, or physics and to renovate laboratory space to create the Integrated Science Research Lab, which combines both wet and in silico lab ...

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Course Syllabus

Week 1

Introduction to ggplot2

  • How to create basic plots (scatterplots, histograms, and barcharts) using qplot()
  • Setting vs. mapping
  • How to add extra variables with aesthetics (like color, shape, and size) or faceting

Week 2

Digging Deeper into ggplot2

  • The ggplot system: geoms, stats, and more
  • How to display data in other forms (densityplots, boxplots, etc.) using geoms
  • Techniques for overcoming overplotting with drawing scatterplots of large datasets

Week 3

Data Manipulation for Plotting, dplyr

  • Group-wise summaries and transformations to add extra information to your plots
  • How to visualize time series

Week 4

Polishing Your Plots

  • Tweaking your plots for maximum presentation impact
  • Introduction to color theory
  • Labels, legends, and axes
  • Using and adjusting the plot themes

Class Dates

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

2023

No classes scheduled at this time.

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Prerequisites

While ggplot2 is a mini-language specifically tailored for producing graphics, you will need some familiarity with data handling in R before taking this course.

The courses listed below are prerequisites for enrollment in this course:

R Programming - Introduction Part 1

Introduction to R Programming

This course provides an easy introduction to programming in R.
Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 14, 2022, May 13, 2022

What Our Students Say​

Peter Blenis

Dr. Pruim was an outstanding instructor, extremely knowledgeable, respectful and prompt.  This course has given me a strong foundation for continued learning

Peter Blenis
Professor Emeritus at University of Alberta

This course has been a life/work changer for me. Within 2 weeks of taking this class, I was able to produce far more informative plots than I ever had before. I'm inspired to take more classes and learn as much as I can about this wonderful new world. Regarding Statistics.com in general - based on my experience with this course that is, this is a class act online program. A friend recommended it several years ago, but I opted for free MOOCs instead, which haven't worked for me. The small size of the classes, the attention by the instructor, and the assignments bode well for learning. All in all - a great course!

Susan Kamp
Senior Scientist at Navarro Research & Engineering (contractor to U.S. Department of Energy)

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

R Programming - Introduction Part 1

Introduction to R Programming

This course provides an easy introduction to programming in R.
Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 14, 2022, May 13, 2022
Mapping in R Course

R Programming – Introduction Part 2

This course is a continuation of the introduction to R programming.
Topic: Data Science, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Nov 12, 2021, Mar 11, 2022, Jul 8, 2022, Nov 11, 2022

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of guided exercises in writing code for producing graphs.

In addition to assigned readings, this course also has discussion tasks, and supplemental readings available online.

Course Text

The instructor has prepared materials for use in the course. There is also a suggested text for review and reference –   ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham.  The text may be purchased here.

Software

Participants should be familiar with and have access to R, and should download and install the ggplot2 Project before the course starts.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

On the two courses “R Graphics” and “Visualization in R with ggplot2:” Visualization in R with ggplot2 is more about the use of the ggplot2 package to easily produce high quality plots. R Graphics covers the the core R graphics functions and the lattice package for producing plots and also looks at lower-level tools for customizing plots. We acknowledge the contributions of Hadley Wickham, who pioneered this and other courses at Statistics.com.

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Visualization in R with ggplot2
$549 | Enroll Now
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