Difference between revisions of "Course Resources"

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* [https://shiny.rstudio.com/tutorial/ Learn Shiny]
 
* [https://shiny.rstudio.com/tutorial/ Learn Shiny]
 
* [http://shiny.rstudio.com/reference/shiny/latest/ Function reference]
 
* [http://shiny.rstudio.com/reference/shiny/latest/ Function reference]
* [https://shiny.rstudio.com/images/shiny-cheatsheet.pdf The Shiny Cheat sheet]  
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* [https://shiny.rstudio.com/images/shiny-cheatsheet.pdf The Shiny Cheat sheet]
 +
* Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard (2020) [https://engineering-shiny.org/ Engineering Production-Grade Shiny Apps]  
 
* [https://bookdown.org/tpemartin/shiny_intro/shiny-part-i.html Shiny 入門]
 
* [https://bookdown.org/tpemartin/shiny_intro/shiny-part-i.html Shiny 入門]
  

Revision as of 22:58, 25 December 2020

Vaa logo.jpg ISSS608 Visual Analytics and Applications

About

Weekly Session

DataViz Makeover

Assignment

Visual Analytics Project

Resources

 


Data Visualisation Desinger

In this course, students will be exposed to and gain hands-on experience on several generic visual analytics toolkit and specialised data visualisation applications. Below are a list of the core software tools for this course.

Desktop Data Visualisation Designer

Tableau

  • Tableau home page [1]
  • Training and Tutorials [2]
  • Visual Gallery [3]
  • Blogs that inspired
    • The Information Lab [4]
    • DataRemixed [5]

JMP Pro

  • JMP home page [6]
  • Discovering JMP [7]
  • JMP Learning Library [8]
  • JMP® for Students 1: Navigation and Use [9]

QlikView and/or Qlik Sense (Optional)

  • Qlik home page [10]
  • QlikView home page [11]
  • Qlik Sense home page [12]

Power BI (Optional)

  • Power BI homepage [13]
  • Guided Learning [14]
  • Power BI Documentation [15]


Online Data Visualisation Designer

Flourish


Specialised Data Visualisation Tools

Interactive Exploratory Data Analysis

High-dimensional Data Visualisation

  • Treemaps [18]
  • Hierarchical Clustering Explorer [19]

Time-series Data Visualisation

Graph Visualisation

Gephi

Cytoscape


Getting Started with R

R Packages for Data Visualisation

ggplot2

ggplot2 Core

ggplots Extension

  • ggVis
  • ggmap
  • ggtern, an extension to ggplot2 specifically for the plotting of ternary diagrams [33]
  • ggExtra, a collection of functions and layers to enhance ggplot2. The main function is ggMarginal, which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots. [34]
  • ggthemes, some extra themes, geoms, and scales for 'ggplot2'. Provides 'ggplot2' themes and scales that replicate the look of plots by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata', 'Excel', and 'The Wall Street Journal', among others. Provides 'geoms' for Tufte's box plot and range frame. [35]
  • ggigraph lets R users to make ggplot interactive. [36]
  • GGally extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks. [37]
  • sjPlot-package, Data Visualization for Statistics in Social Science [38]
  • ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the plots themselves and targeted primarily at behavioral sciences community to provide a one-line code to produce information-rich plots.


Interactive Data Visualisation with R

plotly R


Other R graphics packages

  • corrplot [39]. A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.
  • corrgram [40] calculates correlation of variables and displays the results graphically. Included panel functions can display points, shading, ellipses, and correlation values with confidence intervals. [41]
  • vcd, Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. [42]
  • tmap [43] offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.


Web-based Visual Analytics Development tool in R

Getting Started


Shiny Applications


github

R Markdown

blogdown