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Comprehensive Graphics Visualizations with R

Learn the important features of the base, ggplot and lattice ("trellis") graphical capabilities in R
(73 reviews)
2,025 students
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Platform: Udemy
Video: 8h 31m
Language: English
Next start: On Demand

Table of contents


Comprehensive Graphics with R is a thorough, comprehensive overview of each of three major graphics approaches in R: base, lattice, and ggplot. The course also demonstrates the use of the R Commander interface to create a variety of 2D and 3D graphics. Most of the course is engaged in live, “hands-on” demonstrations of creating a wide range of 2D and 3D plots and graphs using extensive scripts and data sets, all provided with the course materials. Adequate documentation including slides, exercises and exercise solutions are also provided. The course demonstrates (and uses) two of the most popular ‘front-ends’ to the R Console: R Commander and RStudio. We begin by exploring the range of graphics output available using both the R Commander and RStudio GUI interfaces to the R Console. The course then follows with a more in-depth examination of the graphics capabilities for each of the three main graphics systems, base, lattice, and ggplot.
This course is a ‘must see’ for anyone who will use R and wishes to get the most out of the stunning variety of graphical charts, plots, and even animations that are available. The R software was designed from the outset to be particularly strong in visualization and graphical capabilities. However, if you are unaware of the full range of these capabilities you are missing opportunities to apply this wide variety of rich, powerful graphics to your own work and research projects. Accordingly, this course is specifically designed to comprehensively demonstrate and explain the broad range of graphical outputs that are available with R.

You will learn

✓ Learn how to create a wide range of elegant and stunning graphical visualizations using the base, lattice and ggplot graphics systems in R.
✓ Learn to use the R Commander interface to create a variety of 2D and 3D graphics using different data sets.
✓ Learn to apply all of these graphical capabilities to your own data by practicing with all of the supplied course videos R scripts and by completiing each of the five sets of exercises – one for each section of the course.


• Students will need to install the R console software, RStudio and the R Commander R packages. They are each free and instructions are provided with the course materials.

This course is for

• Anyone who uses R software for any reason will benefit from this course, including beginning, intermediate and advanced R users.
• Anyone who wants to learn R will benefit from this course.
• The course is especially appropriate for graduate students and faculty who wish to learn a graphics software alternative to SPSS or SAS, as well as working data analytics professionals and other quantitative professionals.
Associate Professor of Information Systems
Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master’s and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.
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Platform: Udemy
Video: 8h 31m
Language: English
Next start: On Demand

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