a$)$ Use left$\_$join$()$ to create a dataset containing all storms with fatalities appended. Notice that there

may be multiple fatalities per storm. Name this dataset detailsWfatalities.

b$)$ Use dplyr methods to calculate the number of fatalities per storm using the detailsWfatalities.

Summarize this variable to determine the spread$/$variability of number of fatalities per storm. What do

you notice? Does the spread of data in this dataset display well in a histogram?

c$)$ Use dplyr to select only the state and the event type from the details dataset. Summarize the event

types in a table or dataset, perhaps using summarise$().$ Then, only keep the $10$ storm types that are

most common in this data set, and provide a visualization of their frequency using ggplot$2.$ You will

probably want to rotate the axis labels on the $x$ axis $-$ this can be accomplished through theme$()$ and

appropriate use of searching google.

d$)$ We will now work with tornadoes only. Use filter$()$ to include only events of type tornado from the

original dataset details, and then assign this result to a dataset named tornadoes.

e$)$ Use dplyr methods to create a summarized dataset that includes number of tornadoes by state and

year in long format.

f$)$ Using the dataset created in e$),$ use the spread$()$ function to create a wide version of this dataset, with

columns $1996$ through $2017$ added to the new data. Name this dataset numTornadosSpread.

g$)$ Using dplyr$,$ determine how many tornadoes had at least one fatality. You may need to use a join, or

use a dataset you already created to complete this task.