data visualization tools · - presentation of solid data about immunization risks, impact of...

Data Visualization Tools

Taxiarchis Botsis, MSc, MPS, PhD

Assistant Professor of Oncology & Health Sciences InformaticsThe Sidney Kimmel Comprehensive Cancer Center

EPC Journal Club - FEbruarY 18, 2020

botsislab.com EPC Journal Club - FEbruarY 18, 2020

Outline

Introduction

Tool Categories

The Right Tool(s)

Examples - Data - Goals - Audience - Design - Implementation

Key Points


Introduction

Data OR Information Visualizations OR Infographics represent Data/Information in a graphical and compelling form

A visual story combines multiple media (photographs, illustrations, video, animation, 3D models, and other graphics) with text and/or audio to create powerful stories for selected target audiences

They may be found in: - interactive or static e-format - physical forms


Tool Categories

1. Interactive nonprogrammatic: Tableau, Google Sheets, Infogram, ChartBlocks - small learning curve for beginners

2. Programmatic: R, D3.js, Chart.js, Python - large learning curve for beginners, some may take years to master

3. Image manipulation or illustration: Adobe Photoshop, Illustrator, Inkscape - average learning curve for beginners

?Would you ever consider hardware tools for building visualizations?


The Right Tool(s)

According to Wilke*:

“the best visualization tool is the one that allows you to make the figures you need”

Tool selection is related to the overall goals and actual tasks:• Reproduce visualizations• Perform data and visualization exploration• Tweak the visual output

*Claus O. Wilke, Fundamentals of Data Visualization, O’Reilly 2019


The Right Tool(s)

Reproducible visualizations: same data, similar visual output vs. Repeatable visualizations: same data, exact same output

• Reproducibility may be supported by interactive nonprogrammatic tools unless many steps are needed to develop the visualization

• Repeatability requires the use of programmatic tools -- a script will always generate the same output

Reproducibility


The Right Tool(s)

Exploration: quickly try multiple visualizations for a dataset vs. Production: generate an aesthetically appealing and efficient final visualization

• Exploration can be only supported by interactive nonprogrammatic tools

• Production includes many steps and may rely on: - interactive or programmatic tools OR - image manipulation or illustration tools OR - a combination of all the above, especially if complex information visualizations or visual stories are built

Exploration


The Right Tool(s)

Tweaking: rework the overall design vs. Prototyping: quickly generate a basic visual with all context

• Tweaking can be supported by any tool

• Prototyping can be also supported by any tool

Tweaking


Examples

Data: Adverse Event (AE) reports submitted to the Food and Drug Administration (FDA)

Audience: Safety Evaluators at the FDA

Goal: Improve the rigor of pharmacovigilance through efficient visualizations: - Summary presentation of major AEs per drug product - Focus on reports with serious outcomes, such as death and hospitalizations

Design: Interactive visualization with the following functionalities: - Easily select a drug product and view a summary of the AE information - Focus on either an AE or a serious outcome or both

Implementation: D3.js (many iterations with end users, completed in several months)

Bubble Plot


Examples Bubble Plot

botsislab.com/#/ae-bubbles

Interactive

http://botsislab.com/#/ae-bubbles


Examples

Data: Cervical cancer data per US State and Nationwide over the period 2010-2015 according to the US Cancer Statistics by the Centers for Disease Control and Prevention

Audience: Public

Goal: Inform the public about cervical cancer incidence rates: - Presentation of rates for each race per US State and Nationwide - Summarize the major finding(s) and observations

Design: Static visualization with adequate context, no complexity and familiar elements

Implementation: Tableau, Adobe Illustrator (completed in days)

Tile Grid Map


Examples Tile Grid Map

Static+Source

+Title

Data Source: US Cancer Statistics (https://www.cdc.gov/cancer/npcr/uscs/download_data.htm)

Washington

Oregon

California

Idaho

Nevada

Utah

Arizona

AlaskaHawaii

Montana

Wyoming

Colorado

New Mexico

North Dakota

South Dakota

Nebraska

Kansas

Oklahoma

Texas

Minnesota

Iowa

Missouri

Arkansas

Louisiana

Wisconsin

Illinois

Indiana

Kentucky

Tennessee

Mississippi

Michigan

Ohio

West Virginia

North Carolina

Alabama Georgia

South Carolina

Virginia

Pennsylvania

New York

New Jersey

D.C.

Florida

Vermont

Massachusetts

Connecticut

Maryland

Maine

Rhode Island

Delaware

New Hampshire

Is it a Privilege to Be Asian? Cervival Cancer Rates Lowest Among All RacesAsians or Pacic Islanders had the lowest cervival cancer incidence crude rates among all races in the period 2010-2015 nationwide . Hispanics appear to suffer the most in many states. No solid data for American Indians or Alaska Natives.

No Data

rida

20102011201220132014

IncidenceCrude Rate

3020

2015

10

American Indian/Alaska Native RaceAsian/Pacic Islander Race

Black RaceHispanic Race

White Race

botsislab.com/#/viz/cervical_cancer_map

http://botsislab.com/#/viz/cervical_cancer_map


Examples

Data: Mainly -> Post-market vaccine reports, rotavirus mortality rates, vaccine morbidity

Audience: Public

Goal: Inform the public about vaccine safety: - Presentation of solid data about immunization risks, impact of vaccines, etc. - Be objective and build a data-driven visualization

Design: A compound figure with static multiples, no complexity, familiar elements

Implementation: Tableau, Ms Excel, Adobe Stock, Adobe Illustrator, Adobe Photoshop (completed in ~2 weeks)

Visual Story


Examples Visual Story

Static+Sources

+Title


Examples

Data: Survival lung cancer data*

Audience: Lung cancer patients (and physicians)

Goal: Inform patients about immunotherapy benefits: - Clear presentation of complex concepts and recent findings - Improve communication between patients and physicians

Design: A 3D construction with an opportunity to physically interact with it

Implementation: Hardware tools and various materials (about 4 months)

*Anagnostou et al., Nature Cancer 2020

3D Survival Box

https://www.nature.com/articles/s43018-019-0008-8


Examples 3D Survival Box


Examples 3D Survival Box

3D Physical

botsislab.com/#/viz/lung_cancer_3d

http://botsislab.com/#/viz/lung_cancer_3d


Key Points

• Prior to selecting any tool: - Evaluate data, target audience, and overall goals - Design the information visualization

• Select the appropriate tool(s) depending on the type of visualization and final product

• Use the tool(s) that you feel most comfortable with

• Think out of the...box

QUESTIONS?

THANK YOU!

data visualization tools · - presentation of solid data about immunization risks, impact of...

Documents