Multivariate Data Visualization with R
Multivariate Data Visualization with R, available at $39.99, has an average rating of 4.05, with 32 lectures, based on 41 reviews, and has 2866 subscribers.
You will learn about Graphically depict visual 2D, 3D, 4D (and so on) relationships that exist in multivariate data sets. Understand how "trellis" graphic objects are different from other graphic objects in R. Understand how to apply the techniques of conditioning and paneling to present multivariate data relationships. Understand the nature of lattice panel functions and know how to create and modify them for brilliant multivariate graphics displays. Have a powerful visual toolset to visually present the results of multi-variable statistical model fitting. This course is ideal for individuals who are Anyone who uses R, or who wants to use R, for any sort of multivariate data analysis would benefit from taking this course. or The course is appropriate for students, scientists, or other quantitative-analysis professionals who want to display numerical information in plots and graphs. or To take advantage of the course, students will need to have a basic (introductory) level or ability to use R software. However, all of the graphic R scripts are provided with the course materials. It is particularly useful for Anyone who uses R, or who wants to use R, for any sort of multivariate data analysis would benefit from taking this course. or The course is appropriate for students, scientists, or other quantitative-analysis professionals who want to display numerical information in plots and graphs. or To take advantage of the course, students will need to have a basic (introductory) level or ability to use R software. However, all of the graphic R scripts are provided with the course materials.
Enroll now: Multivariate Data Visualization with R
Summary
Title: Multivariate Data Visualization with R
Price: $39.99
Average Rating: 4.05
Number of Lectures: 32
Number of Published Lectures: 32
Number of Curriculum Items: 32
Number of Published Curriculum Objects: 32
Original Price: $84.99
Quality Status: approved
Status: Live
What You Will Learn
- Graphically depict visual 2D, 3D, 4D (and so on) relationships that exist in multivariate data sets.
- Understand how "trellis" graphic objects are different from other graphic objects in R.
- Understand how to apply the techniques of conditioning and paneling to present multivariate data relationships.
- Understand the nature of lattice panel functions and know how to create and modify them for brilliant multivariate graphics displays.
- Have a powerful visual toolset to visually present the results of multi-variable statistical model fitting.
Who Should Attend
- Anyone who uses R, or who wants to use R, for any sort of multivariate data analysis would benefit from taking this course.
- The course is appropriate for students, scientists, or other quantitative-analysis professionals who want to display numerical information in plots and graphs.
- To take advantage of the course, students will need to have a basic (introductory) level or ability to use R software. However, all of the graphic R scripts are provided with the course materials.
Target Audiences
- Anyone who uses R, or who wants to use R, for any sort of multivariate data analysis would benefit from taking this course.
- The course is appropriate for students, scientists, or other quantitative-analysis professionals who want to display numerical information in plots and graphs.
- To take advantage of the course, students will need to have a basic (introductory) level or ability to use R software. However, all of the graphic R scripts are provided with the course materials.
It is often both useful and revealing to create visualizations, plots and graphs of the multivariate data that is the subject of one's research project. Often, both pre-analysis and post-analysis visualizations can help one understand “what is going on in the data" in a way that looking at numerical summaries of fitted model estimates cannot. The lattice package in R is uniquely designed to graphically depict relationships in multivariate data sets.
This course describes and demonstrates this creative approach for constructing and drawing grid-based multivariate graphic plots and figures using R. Lattice graphics are characterized as multi-variable (3, 4, 5 or more variables) plots that use conditioning and paneling. Consequently, it is a popular approach for, and a good fit to visually present the results of multi-variable statistical model fitting. The appearance of most of the plots, graphs and figures are determined by panel functions, rather than by the high-level graphics function calls themselves. Further, the user of lattice graphics has extensive and comprehensive control over many more of the details and features of the visual plots, far greater control that is afforded by the base graphics approach in R. The method is based on trellis graphics which were popularized in the S language developed by Bell Labs.
Course Curriculum
Chapter 1: Introduction to Lattice and to "Trellis" Graphics
Lecture 1: Introduction to Course
Lecture 2: Introduction to Lattice
Lecture 3: The Trellis Object
Lecture 4: Dimension and Physical Layout
Lecture 5: Scales and Axes
Lecture 6: Visualizing Univariate Distributions (part 1)
Lecture 7: Visualizing Univariate Distributions (part 2)
Lecture 8: Two-Sample QQ Plots
Lecture 9: Strip Plots
Chapter 2: Multiway Tables and Scatter Plots
Lecture 1: Multiway Tables
Lecture 2: Multipanel Dot Plots
Lecture 3: Scatter Plots and Extensions
Lecture 4: Shingles and Advanced Indexing
Lecture 5: More Scatter Plots (part 1)
Lecture 6: More Scatter Plots (part 2)
Lecture 7: Scatter Plot Matrices
Lecture 8: Parallel Coordinates Plot
Chapter 3: Trivariate, 3D, and Other Complex Displays
Lecture 1: Trivariate Displays
Lecture 2: 3D Scatter Plots (part 1)
Lecture 3: 3D Scatter Plots (part 2)
Lecture 4: 3D Panel Functions
Lecture 5: Visualizing 3D Surfaces
Lecture 6: More 3D Visualizations
Lecture 7: Visualizing Theoretical 3D Surfaces
Chapter 4: Finer Control Graphical Parameters and Other Settings
Lecture 1: Graphical Parameters and Other Settings
Lecture 2: Graphical Parameters Continued
Lecture 3: Plot Coordinates and Axis Annotation
Lecture 4: Labels and Legends
Lecture 5: Data Manipulation (part 1)
Lecture 6: Data Manipulation (part 2)
Lecture 7: Shingles and Related Utilities
Lecture 8: Ordering Categorical Variables
Instructors
-
Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 8 votes
- 4 stars: 16 votes
- 5 stars: 17 votes
Frequently Asked Questions
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