Health and Economic Impact of Weather Events

Synopsis

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Data Processing

setwd("~/GitHub/RepData_PeerAssignment2")
storm_data<-read.csv("repdata-data-StormData.csv.bz2")
names(storm_data)<-tolower(names(storm_data))

data1<-data.frame(cbind(evtype=storm_data$evtype, 
                fatalities=storm_data$fatalities,
            injuries=storm_data$injuries, propdmg=storm_data$propdmg,
              cropdmg=storm_data$cropdmg))

Loading the data:

Questions to be Addressed

Question 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

Question 2: Across the United States, which types of events have the greatest economic consequences?

There should be a section titled Data Processing which describe(in words and code) how the data were loaded into R and processed for analysis. In particular, your analysis must start from the raw CSV file containing the data. You cannot do any preprocessing outside the document. If preprocessing is time-consuming you may consider using the cache = TRUE option for certain code chunks.

Results

There should be a section titled Results in which your results are presented.

You may have other sections in your analysis, but Data Processing and Results are required.

The analysis document must have at least one figure containing a plot.

Your analyis must have no more than three figures. Figures may have multiple plots in them (i.e. panel plots), but there cannot be more than three figures total.

You must show all your code for the work in your analysis document. This may make the document a bit verbose, but that is okay. In general, you should ensure that echo = TRUE for every code chunk (this is the default setting in knitr).