Synopsis

This analysis identifies the adverse weather events with most harmful impact on public health and the econonomy in US communities and municipalities. The data [Storm Data] from this analysis is taken from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database from Year 1950 to November 2011.Supporting documentations are National Weather Service Storm Data Documentation and National Climatic Data Center Storm Events FAQ.
Public health impact are identified by summing up number of fatalities in the FATALITIES field and injuries in the INJURIES field for an event as identified by the EVTYPE in the NOAA storm database. Economic impact are identified by summing up the cost of crops damaged in the CROPDMG field and the property damaged in the PROPDMG field for an event type as identified by the EVTYPE in the NOAA storm database.
This analysis identifies these events in order of most impactful on public health and economic problem by providing a tables and bar charts.

Data Processing

The data file is downloaded using R from the URL location and saved as StormData.csv.bz2

The file is read into the data frame StormData

#Download file 
download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",mode="wb", destfile = "stormData.csv.bz2")
##Read file
StormData <- read.csv(bzfile("stormData.csv.bz2"))

The required R libraries are loaded

## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Two additional variables are created and appended to the data frame
1. totalHumanImpact - the sum of number of injuries and fatalities. This represents the total Population impact.
2. totalEconomicImpact - the total cost of property and crops damaged. This represents the economic impact.

StormData <- tbl_df(StormData) %>%
           mutate(totalHumanImpact = INJURIES + FATALITIES, totalEconomicImpact = PROPDMG + CROPDMG )

The populationHealthByEVType is created. It contains the total population impacted by each event ordered by most impactful at the top and the least impactful at the bottom.

populationHealthByEVTYPE  <- group_by(StormData, EVTYPE)%>%
                 summarize(count=sum(totalHumanImpact)) %>%
                 arrange(desc(count))
economicImpactByEVTYPE  <- group_by(StormData, EVTYPE)%>%
        summarize(cost=sum(totalEconomicImpact)) %>%
        arrange(desc(cost))

Results

First 30 events with most impactful impact on population health

Table of Top 30 events with population health most impacted

data.frame(populationHealthByEVTYPE[1:30,])
##                EVTYPE count
## 1             TORNADO 96979
## 2      EXCESSIVE HEAT  8428
## 3           TSTM WIND  7461
## 4               FLOOD  7259
## 5           LIGHTNING  6046
## 6                HEAT  3037
## 7         FLASH FLOOD  2755
## 8           ICE STORM  2064
## 9   THUNDERSTORM WIND  1621
## 10       WINTER STORM  1527
## 11          HIGH WIND  1385
## 12               HAIL  1376
## 13  HURRICANE/TYPHOON  1339
## 14         HEAVY SNOW  1148
## 15           WILDFIRE   986
## 16 THUNDERSTORM WINDS   972
## 17           BLIZZARD   906
## 18                FOG   796
## 19        RIP CURRENT   600
## 20   WILD/FOREST FIRE   557
## 21       RIP CURRENTS   501
## 22          HEAT WAVE   481
## 23         DUST STORM   462
## 24     WINTER WEATHER   431
## 25     TROPICAL STORM   398
## 26          AVALANCHE   394
## 27       EXTREME COLD   391
## 28        STRONG WIND   383
## 29          DENSE FOG   360
## 30         HEAVY RAIN   349

Bar Chart

qplot(EVTYPE, data=populationHealthByEVTYPE[1:5,] , geom="bar", weight=count,xlab="Event Type[EVTYPE]", ylab="Total Population Impacted",main = "Bar Chart of Top 5 Events Most Impacting the Population Health")

First 30 events with most impactful economic impact

Table of Top 30 events with economic impacted

data.frame(economicImpactByEVTYPE [1:30,])
##                  EVTYPE       cost
## 1               TORNADO 3312276.68
## 2           FLASH FLOOD 1599325.05
## 3             TSTM WIND 1445168.21
## 4                  HAIL 1268289.66
## 5                 FLOOD 1067976.36
## 6     THUNDERSTORM WIND  943635.62
## 7             LIGHTNING  606932.39
## 8    THUNDERSTORM WINDS  464978.11
## 9             HIGH WIND  342014.77
## 10         WINTER STORM  134699.58
## 11           HEAVY SNOW  124417.71
## 12             WILDFIRE   88823.54
## 13            ICE STORM   67689.62
## 14          STRONG WIND   64610.71
## 15           HEAVY RAIN   61964.94
## 16           HIGH WINDS   57384.60
## 17       TROPICAL STORM   54322.80
## 18     WILD/FOREST FIRE   43534.49
## 19              DROUGHT   37997.67
## 20       FLASH FLOODING   33623.20
## 21 URBAN/SML STREAM FLD   28845.74
## 22             BLIZZARD   25490.48
## 23            HURRICANE   20852.99
## 24    FLOOD/FLASH FLOOD   20580.95
## 25          STORM SURGE   19398.49
## 26            LANDSLIDE   18998.94
## 27          RIVER FLOOD   17345.70
## 28          URBAN FLOOD   14216.50
## 29     LAKE-EFFECT SNOW   14141.00
## 30         EXTREME COLD   13778.68

Bar Chart

qplot(EVTYPE, data=economicImpactByEVTYPE[1:5,] , geom="bar", weight=cost,xlab="Event Type[EVTYPE]", ylab="Total Economic Impact",main = "Bar Chart of Top 5 Events Most Economic Impact")