Exploratory Data Analysis of Tropical Storms in R by Scott Stoltzman.
From the post:
The disastrous impact of recent hurricanes, Harvey and Irma, generated a large influx of data within the online community. I was curious about the history of hurricanes and tropical storms so I found a data set on data.world and started some basic Exploratory data analysis (EDA).
EDA is crucial to starting any project. Through EDA you can start to identify errors & inconsistencies in your data, find interesting patterns, see correlations and start to develop hypotheses to test. For most people, basic spreadsheets and charts are handy and provide a great place to start. They are an easy-to-use method to manipulate and visualize your data quickly. Data scientists may cringe at the idea of using a graphical user interface (GUI) to kick-off the EDA process but those tools are very effective and efficient when used properly. However, if you’re reading this, you’re probably trying to take EDA to the next level. The best way to learn is to get your hands dirty, let’s get started.
The original source of the data was can be found at DHS.gov.
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Great walk through on exploratory data analysis.
Everyone talks about the weather but did you know there is a forty (40) year climate lag between cause and effect?
The human impact on the environment today, won’t be felt for another forty (40) years.
Can to predict the impact of a hurricane in 2057?
Some other data/analysis resources on hurricanes, Climate Prediction Center, Hurricane Forecast Computer Models, National Hurricane Center.
PS: Is a Category 6 Hurricane Possible? by Brian Donegan is an interesting discussion on going beyond category 5 for hurricanes. For reference on speeds, see: Fujita Scale (tornadoes).