Data visualization based on US baby names data - Popular names over time - Popularity can switch between girls & boys - We keep making Percivals, just not many

network visualization example primarily using the ggraph library with a simple social network

Travel map as an animated gif plotting a world map from ggmap with font awesome icons for travel locations

Example uses the Anscombe data to illustrate the importance of data visualization

application takes words and phrases and predicts the next word

The Capstone project will apply data science in the area of natural language processing to predict the next word based on input of one or more words. The course provides a starting set of training data from HC Corpora using the English language sources from blogs, news articles and Twitter messages. The purpose of this report is to describe the exploratory analysis that's been done and my goals goals for the eventual app and algorithm. 1. Show that the data has been downloaded 2. Provide summary statistics 3. Report findings from data exploration so far 4. Get feedback on your plans for creating a prediction algorithm and Shiny app

Short presentation (made using RPres in RStudio) to describe a shiny app that plots a map of my travels. The app itself is at http://jrwalker.shinyapps.io/myTravelMap/

Johns Hopkins Data Science Machine Learning course project

From the 1974 Motor Trend dataset data(mtcars) we select a linear model to answer two questions: 1) Is an automatic or manual transmission better for MPG? We find that manual transmission is better and that the difference using this dataset is statistically significant. The parsimonious model for mpg uses engine weight, quarter mile time and automatic vs manual transmission as regressors. 2) Quantify the MPG difference between automatic and manual transmissions. Our model shows that changing from automatic to manual transmission improves mileage by about 2.9 mpg.

In this report we look at data from the National Oceanographic & Atmospheric Administration (NOAA) storm data this data analysis intends to answer these questions: 1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? 2. Across the United States, which types of events have the greatest economic consequences? To answer these questions we standardized and summarized the data to compare the impact of the various storm event types. We found that excessive heat and tornadoes are most harmful to human life and that hurricanes and storm tides have the greatest economic impact.