Shiny Demo Meetup

Welcome to our Shiny Apps Demo day. Our 12-week data science bootcamp students will present the Shiny Interactive Apps that they build at 4th week of the program. These little interactive apps that are created in R are great ways of manipulating and visualizing data. This demo event has always been fun and a favorite of our community. Please Enjoy!

Presenters:

Amy Ma: How AirBnB is in NYC? (http://216.230.228.88:3838/bootcamp004_project/Project2-Shiny/amyma/)

Visiting NYC? Airbnb may be a good choice to book unique accommodations. To better explore its rental listings across New York City, Amy Ma designed this app to answer some questions: How many of the listings are for an entire home versus a room in an apartment? How many are controlled by the same host? Why tax Issue is serious in Airbnb NYC? Should you think twice before trusting an review? Results also provide suggestions for Airbnb, hosts and visitors.

Sricharan Maddineni: Why are Airports Important? (http://216.230.228.88:3838/bootcamp004_project/Project2-Shiny/Sri_New/)

Aviation infrastructure has been a bedrock of the United States economy and culture for many decades, and I've created an interactive map to show the connectedness of US airports as well as a motion chart which shows the evolution of airline passengers vs population for the world's countries over the last four decades.

Christopher Redino: Health Indicators and Geographic Factors (http://216.230.228.88:3838/bootcamp004_project/Project2-Shiny/chris_redino/)

The World Health Organization lists obesity as one of the leading causes of preventable death worldwide. Although genetics play a role in the likelihood of obesity in an individual, lifestyle (by choice or otherwise) is also an important factor that cannot be ignored. The New York City Department of Health and Mental Hygiene conducts an annual Community Health Survey (CHS) wherein they collect data on a variety of health factors from thousands of New Yorkers in all five boroughs. Some of this data is used in this app to visualize how large an effect our geography has on obesity rates, even on a scale as small as the span of a few miles between neighbourhoods in NYC.

Which geographic factors are causing this difference in rates is somewhat an open question: is the availability of fresh food more important, or the median income of residents in a particular neighbourhood? In addition to visually exploring the survey data, the app allows the user to explore the effect of different types of food vendors on the obesity rate of different neighborhoods.

Wendy Yu: Interactive data visualization of gender pay gap in the United States (http://216.230.228.88:3838/bootcamp004_project/Project2-Shiny/wendy_yu/)

Gender pay gap is a continuous problem in the United States. To date, women are still making about 20% less then their male counterparts. I collected data from United States Census Bureau and created an interactive app to explore changes of gender pay gap over the past decades.

Michael Todisco: MLB 2012 Attendance (http://216.230.228.88:3838/bootcamp004_project/Project2-Shiny/michael_todisco/)

This Shiny Application visualizes Major League Baseball attendance data for the 2012 season. A user can select any of the 30 teams in baseball and then interactively filter attendance data based on categories such as game time (day vs night), temperature, weather, promotion, and opponent's winning percentage. The results are displayed in four main graphs: 1) attendance vs opponent 2) attendance vs day of the week 3) attendance vs month 4) season trending attendance.

Brett Amdur: Understanding Regression Diagnostics: An Introduction (https://brettamdur.shinyapps.io/regDiagnostics/)

For his Shiny project, Brett created an educational tool that uses visuals and interactivity to help explain the diagnostic tests that assess the validity of a regression analysis. The tool allows users to choose from among several different several data sets (most of which come from the well known Anscombe's Quartet) to see how their diagnostic plots differ. The tool also allows users to make adjustments to the data to see how changes in the data can influence the diagnostics.