A repository for useful data visualization and analysis tidbits.
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NYS Property Tax Revenue at Risk due to Chronic Flooding from Sea Level Rise
  • There is anecdotal information that municipalities (i.e., villages, towns, cities) are opposed to participating in voluntary buyouts (i.e., property acquisition) as a flood mitigation approach because of the perceived loss of property tax revenue that would occur.
  • It is unclear to what extent the property tax revenue of municipalities in New York state is at risk to flooding.
  • This analysis attempts to answer: If coastal municipalities in New York state were to offer voluntary buyouts to the properties most at-risk to chronic inundation, how would the municipal tax revenue be impacted?​​
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Cost-Effectiveness of Property Acquisition for Flood Mitigation
  • This analysis attempts to answer: Are property acquisitions a cost-effective flood mitigation strategy? Property acquisitions are also known as voluntary buyouts and are a component of managed retreat from flood prone areas.


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​Visualizing New York Harbor Water Quality
  • This web app (created in R Shiny) allows you to visualize NY Harbor water quality data collected by the New York City of Department of Environmental Protection.
  • Users can select parameters (oxygen, fecal coliform, enterococcus), sites, and time span (2012-present).
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Simulation models of aquatic vegetation 
  • This is a spatially- and temporally-explicit simulation model of interactions between floating plants and submerged plants in freshwater lakes that I have developed as part of my dissertation. 
  • These interactive apps are made with the R package shiny and the content is hosted on Amazon Web Services.
  • With the app, you can view the results of different parameterizations for simulation scenarios including different water body sizes and shapes, growing season lengths, and species compositions. (NOTE: Apps are no longer hosted on the web)
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Make a Twitter Bot with R 
  • Every wanted to make a Twitter Bot that automatically tweets out random thoughts?
  • Thanks to this tutorial from Automated Data Collection, it's never been easier. 
  • You can follow my creation: @TacoBot2000 which randomly tweets out names for new Mexican foods.
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Most Common Taxi Driver Names in NYC 
This tutorial uses data from NYC OpenData to show you how to:
  • download data directly from the web,
  • do some basic clean-up and processing of text strings, and
  • make some nice plots with the R package ggplot2.
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Machine Learning & National Lakes Assessment Data 
  • Two of my favorite things: lakes and statistics!
  • Can we predict the amount of chlorophyll a in a lake with other variables measured during a survey? 
  • We'll use data from the US EPA National Lakes Assessment and a machine learning algorithm, Random Forests.

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Data Visualization: Raw & Summary Data 
  • Don't hide your messy data behind tidy standard error bars!
  • Fess up about those sample sizes! 
  • Display both your raw data and summary statistics on the same plot.
  • A short demo that I made with R Markdown.
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Probability Distributions
  • Gamma, Beta, Poisson???  Not everything is Normal! 
  • Ever wondered what all of those weird-sounding statistical distributions look like? 
  • Here is a shiny app where you take a random sample and look at some of the statistical properties of each probability distribution.
  • Code available on GitHub.