Predicts the words you're typing in!

This report is an exploratory analysis on the Coursera-SwiftKey English data sets. There are 3 data sets containing snippets from blog posts, news articles and tweets. First, I show how the loading and preprocessing of the data sets were performed. Then, I examine the distribution of words per sentence and list the top 20 words with and without stop words for each data set, discussing some interesting findings. Lastly, I present my future plans for the prediction algorithm and Shiny application.

In this report we determine both the weather events most harmful to population health in terms of fatalities and injuries, and the weather events that have the greatest economic consequence in terms of property damage and crop damage, in the last 5 years ending 30 November 2011. Data was obtained from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. We found that tornadoes are the most harmful to population health in terms of both fatalities and injuries, followed by flash floods and rip currents in terms of fatalities, and thunderstorm wind and lightning in terms of injuries. As for economic consequences, in decreasing order tornadoes, floods and hail cause the greatest amounts of property damage, while floods, frost/freeze events and hail cause the greatest amounts of crop damage.