Shiny Applications

NSF Office of International Science Award Browser Project Highlight: The Shiny application NSF-OISE-AwardBrowser was written in R and relies on a subset of NSF award data that is loaded into the app at launch. The subset includes awards from the Office of International Science and Engineering (OISE) from 2010-2020. The goal is to visualize the distribution of awards by country and region. The user can choose among several options: 1) selecting a specific year or ticking a box to see a time series animation across the years selected for the app (2010-2020); 2) choosing whether to see a linear model with a straight regression line through the data or a loess model which fits a curved line; 3) toggle the standard error off and on around the line; 4) choose among four color-blind friendly color schemes or four color schemes inspired by the director Wes Anderson

Indicators of Democracy Interactive Explorer Project Highlight: The Shiny application Women Demographic Outcomes was written in R and relies on a subset of vdem data that loaded into the app at launch. The subset includes indicators for women’s empowerment and demographic outcomes, such as life expectancy and mortality rates. The goal is to visualize the relationship between women’s empowerment and demographic outcomes. The data demonstrate that empowering women results in better outcomes–e.g., longer life, lower mortality. The user can choose among several options: 1) selecting a specific year or ticking a box to see a time series animation across the years selected for the app (1996-2019); 2) choosing whether to see a linear model with a straight regression line through the data or a loess model which fits a curved line; 3) toggle the standard error off and on around the line; 4) choose among four color-blind friendly color schemes or four color schemes inspired by the director Wes Anderson (muted palettes).

Next Word Prediction Using Natural Language Programming Project Highlight: The Shiny application Next Word was written in R relies on a language model created from a small media corpus to predict the next word in a sentence. The UX presents a box to enter a sentence that is dynamically reaction–you can add additional words to the original text and get new predictions. Users will see a graph of 10 possible words with the probability of their being next. If there is no word, an empty graph will display. Each set of results includes meta data on where the prediction was made. The model relies on ngrams ranging from 2-4 (word combinations of 2, 3, or 4) as found in the corpus. The output includes a summary of which ngram was used to predict and the total possibilities and matches found.

Calculator for Speed Time and Stopped Distance Project Highlight: This Shiny application SpeedStopTimes was written in R and predicts car speed based on a stopping time entered by using a slider (20-120 mph). The output is a plot of the data that is used to make the prediction (the regression line can be shown or hidden using a checkbox).