Lesson04

ISSS608 Visual Analytics and Applications

About

Weekly Session

Assignment

Visual Analytics Project

Course Resources

Readings

Core Readings

• An Introduction to Visual Multivariate Analysis
• The scatter-plot matrix: a great tool
• A Graphical Display of Large Correlation Matrices
• Corrgrams: Exploratory Displays for Correlation Matrices
• GPLOM: The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data
• Ternary Plot
• Multivariate Data Glyphs
• Parallel Coordinates

• Wikipedia: Heatmap
• Mosaic Plots
• Turning a Table into a Tree: Growing Parallel Sets into a Purposeful Project
• TableLens: A Clear Window for Viewing Multivariate Data

Optional Readings

• Multidimensional Detective
• Parallel Coordinates
• Multivariate Analysis Using Parallel Coordinates
• Understanding Area Based Plots: Mosaic Plots
• Visualizing Defect Percentages with Parallel Sets
• Parallel Sets vs. Mosaic Plots (Take I)
• Discovery Exhibition: Parallel Sets

R Methods

Ternary Plot

• R packages for creating ternary plot
  • Ternary, which employs the native plot functions;
  • Plotly, which uses a bespoke suite of plotting functions;
  • vcd, which contains the function ternaryplot;
  • ggtern, an extension to ggplot2.

Heatmap

• Using R to draw a Heatmap from Microarray Data
• R packages for creating heatmap
  • heatmap() [R base function, stats package]: Draws a simple heatmap.
  • heatmap.2() [gplots R package]: Draws an enhanced heatmap compared to the R base function.
  • pheatmap() [pheatmap R package]: Draws pretty heatmaps and provides more control to change the appearance of heatmaps.
  • Heatmap() [ComplexHeatmap R/Bioconductor package]: Draws, annotates and arranges complex heatmaps (very useful for genomic data analysis)
  • Superheatmap
  • d3heatmap() [d3heatmap R package]: Draws an interactive/clickable heatmap