Data Science Resources

Resources for those that want to leverage data science tools for work in government and nonprofits.


CONTENTS:




Moneyball for Government



US Digital Services Overview

  • Inside Obama’s Stealth Startup [ link ]
  • Why I Joined the US Digital Services [ link ]
  • Five Examples of How Federal Agencies Use Big Data [ link ]

Predictive Analytics Models

  • Food Inspection Forecasting: case study on predictive analytics for food violations in Chicago[ link ] 
  • Optimizing Infrastructure Repair [ measurement ] [ model ] [ news ]
  • Pretrial Criminal Risk Assessment for Judges [ link ]
  • Predicting Fire Hazards [ link ] [ model ]
  • Why the Bronx Really Burned - Predictive Analytics Fail [ link ]
  • Use Machine Learning to Predict Infrastructure Failure [ link ]
  • Using Prediction to Prioritize Water Infrastructure Maintenance [ link ] 
  • Using RFIDs to Regulate Marijuana Distribution in Colorado [ link ]
  • Crowd-Sources Solutions [ about DrivenData ] [ current competitions ]
  • State and National Presidential Poll Aggregation [ link ]

Artificial Intelligence’s Impact on Government

  • AI to Transform Government [ link ]
  • AI for the American People [ link ]
  • Delivering Artificial Intelligence in Government: Challenges and Opportunities [ IBM report ]
  • Brookings Center Report on Automation [ link ]
  • Developing AI for Federal Government [ link ]

Open Innovation

  • The Data-Driven Justice Initiative [ link ]
  • Next Stage in the Open Data Movement [ link ]
  • Challenge.gov: Using Competitions to Spur Innovation [ link ]
  • Data for Democracy [ link ]

Examples of Good Local Government Portals

Data Science Vignettes

Some cool applications of open data + open source tools.

  • Spatial Analysis (GIS) for Urban Policy [ link ]
  • Network Analysis [ link ]
  • Child Welfare [ link ]
  • Social Media Data [ link ]
  • Moneyball for Infrastructure [ measurement ] [ model ]
  • Target Food Safety Inspection w Open Data [ link ]
  • Everything by David Smale [ link ]
  • Data Science for Fair Housing [ link ]

Open Science Replication Files

Examples of how to make your research transparent and extensible.

Suparna Chaudhry and Andrew Heiss [ paper1 ] [ paper2 ] [ paper3 ]




Methods

Visualization

Compendium of Clean Graphs in R: [ link ]
The Data Viz Project [ link ]
Gallery of ggplot geoms [ link ]
Creating More Effective Graphs [ book ] [ gallery
Data + Design: Ebook On Data [pdf ]
An Economist’s Guide to Visualizing Data [ pdf ]
Visuals for Teaching Statistics [ link ] [ link ]
Bl.ocks.org Graphics Gallery [ link ]
Help Me Viz Graphics Gallery [ link ]
What Makes a Map Beautiful? [ link ]
Tableau: Which chart or graph is right for you. [ link ]
Flowing Data [ link ]
Graphics in R Tutorial: [ FlowingData ]
ChartsNThings: A Blog by the NYT Graphics Dept [ link ]
Data Viz Syllabus by Quealy & Carter [ link
Junk Charts: Blog on Making Graphics Better [ link ]
Primer on Making Great Graphs in R [ download ]
10 Tips for Making R Graphics Look Good [ link ]
Data USA [ link ]
CityBike Data Visualized [ link ]
Arms Sales Visualized [ link ]
Pedestrian & Routes in US Cities Visualized [ link ] & Europe [ link ]
Winners of Infographic Awards [ link ]
Visual Essays [ link ]

Bad Graphs

How to Display Data Badly [ link ]
Clowns [ link ]
Worst of 2017 [ link ]
More Worst [ link ]
Calling Bullshit [ Misleading Axes ] [ Proportional Ink ]
Label Your Axes [ link ]
Pie Charts [ link ] [ link ]
Foreign Aid as Missile Attacks [ link ]

Dashboard Design

R Shiny Showcase [ link ]
R Shiny Widgets Gallery [ link ]
Nonprofit Dashboard Design [ webinar ] [ slides ]
Tableau: 6 Best Practices of Effective Dashboards [ download ]

Dashboard Examples

Pittsburgh Building Permits [ link ]
Government Performance in Chattanooga [link ]
Fundraising Dashboard in R [ link ]
DataUSA [ link ]
Census Reporter [ link ]
Teacher Dashboard on Student Performance [ link ]
Vehicle collisions in Edinburgh [ link ]
Traffic accidents in London [ link ]
Life Expectancy Charts [ link ] [ link ]
Rise of Inequality [ link ]
World Development Indicators [ link ]
Demographics in Catalonia, Spain [ link ]
Tableau Gallery [ link ]

Text Analysis

Quanteda [ link ]
Who Wrote the Anonymous Op-Ed? [ link ] [ link ]

Network Analysis

tidygraph & ggraph [ vignette using characters from Wes Anderson films ]
igraph tutorial by Katherine Ognyanova [ link ]




Style Guides

Style guides are the hand-writing of the coding world. Some people have really nice code style, some people have really sloppy code that is hard to read. Consider the readibility of this:

y<-cut(rank(x),breaks=seq(from=1,to=100,by=10),labels=paste("X",1:10,sep=""))

Versus:

y <- cut( rank( x ), breaks=seq( from=1, to=100, by=10 ), labels=paste( "X", 1:10, sep="" ) )

Do yourself and all of your future collaborators a favor and try to develop a consistent coding style. There are two popular style guides for R:

Think of these suggestions as good habits that will make your life easier and will improve your ability to collaborate with others. And remember, your most important collaborator is yourself two months from now!