Data visualization for researchers

by Jaume Nualart

This seminar is organized by Joëlle Vandermensbrugghe (Graduate Research Office) and it is addressed to PhD students, mainly

Introduction

what are we going to do? Why data visualization

Visualization Types:

• Duke University Libraries
• Periodic table of visualization methods

D3 Gallery

Maps

• 23 maps and charts on language
• The dude map: How Americans refer to their bros

Bad datavis!

• viz.wtf
• Horrible data visualizations

3 levels of tools

• Graphic visualization software: Tableau public, Google chart, The 36 best tools for data visualization
• SAAS tools: Many Eyes, Dashboard, Plotly
• Inforgraphics:
• Infographi Creator
• 10 free tools for creating infographics
• Pikto Chart
• mind maps: Examples google images, ,
• Create timelines
• Coding:
• Easy coding: HighCharts7 or Chart.js or Google Charts API
• Coding tools (free license): d3js, RaphaelJS, R, etc...

Data scientist skills

• Modern data scientist

Functions of data visualization:

• Purposes of a visualization [from: "Data visualization: a successful design process" by Andy Kirk 2012]
• Main three goals:
  • To explain the data
  • To explore the data
  • To exhibit the data

Dealing with design

• Dealing with colors: Agave, online paletton, The 28 best tools for choosing a colour scheme, etc.
• Dealing with icons: Glyphicons, etc.
• Dealing with fonts: google fonts, etc.

Several concepts

• visualization & exploration
• diagrams & infographies
• visual truth & lies
• interaction & animation
• storytelling:
• Alberto Cairo and his "Functional Art"
• Daily rituals
• World's deadliest animals
• Genres of Storytelling

Preparing your data visualizations for 2nd session

Describe your data

Describe the intention of your data

Conversation about best visualization options

Describe tasks to be done during the week to prepare the data.

2nd session:
practice & practice Feb 28st 2015

Next week we are going to put all this theoretical in practice

• Defining goals of the visualization:
• Task oriented: do it solve a task or several? Describe them
• Storitelling oriented: which is the history and context of your discurse?
• Tool oriented: to be used in specific contexts. To use for a specific dataset that will be shown multiple times.
• what else?
• 2.1 Defining the data
• Dataset description: number of records, data-type, fields, distinct values of fields, rage,...
• 2.1 Choice of tools
• Explore: Area, Tableau public,
• D3 visualizations: D3 Gallery
• Test code: jsfiddle
• 2.2 Data formatting
• Formating data tools: desktop software (google refine, tableau public, etc), spreadsheets, scripts
• Data insights: mining, patterns, trends, irregularities
• 2.3 Building visualizations
• see 2,1
• 2.4 Publishing visualizations
• Online, interective, animated images
• Static images
• Printing
• Reuse of visualization elements integrated in other projects