{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

Synopsis

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities.
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States.
This project attemps to answer two questions:
1) Across the United States, which types of events (as indicated in the “EVTYPE” variable) are most harmful with respect to population health?
2) Across the United States, which types of events have the greatest economic consequences?

Data Processing

The data (“repdata_data_StormData.csv.bz2”) was provided as part of the assignment documents and is included in the Github repo (https://github.com/GaryFH/RepDataStormStudy). The data is “read” in, converted to a tbl_df and stored as “d1” with the following code:
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.3.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.3
c1<-read.csv("repdata_data_StormData.csv.bz2",stringsAsFactors = FALSE)
d1<-tbl_df(c1)

The data is initially examined with the following code:

d1
## # A tibble: 902,297 × 37
##    STATE__           BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
##      <dbl>              <chr>    <chr>     <chr>  <dbl>      <chr> <chr>
## 1        1  4/18/1950 0:00:00     0130       CST     97     MOBILE    AL
## 2        1  4/18/1950 0:00:00     0145       CST      3    BALDWIN    AL
## 3        1  2/20/1951 0:00:00     1600       CST     57    FAYETTE    AL
## 4        1   6/8/1951 0:00:00     0900       CST     89    MADISON    AL
## 5        1 11/15/1951 0:00:00     1500       CST     43    CULLMAN    AL
## 6        1 11/15/1951 0:00:00     2000       CST     77 LAUDERDALE    AL
## 7        1 11/16/1951 0:00:00     0100       CST      9     BLOUNT    AL
## 8        1  1/22/1952 0:00:00     0900       CST    123 TALLAPOOSA    AL
## 9        1  2/13/1952 0:00:00     2000       CST    125 TUSCALOOSA    AL
## 10       1  2/13/1952 0:00:00     2000       CST     57    FAYETTE    AL
## # ... with 902,287 more rows, and 30 more variables: EVTYPE <chr>,
## #   BGN_RANGE <dbl>, BGN_AZI <chr>, BGN_LOCATI <chr>, END_DATE <chr>,
## #   END_TIME <chr>, COUNTY_END <dbl>, COUNTYENDN <lgl>, END_RANGE <dbl>,
## #   END_AZI <chr>, END_LOCATI <chr>, LENGTH <dbl>, WIDTH <dbl>, F <int>,
## #   MAG <dbl>, FATALITIES <dbl>, INJURIES <dbl>, PROPDMG <dbl>,
## #   PROPDMGEXP <chr>, CROPDMG <dbl>, CROPDMGEXP <chr>, WFO <chr>,
## #   STATEOFFIC <chr>, ZONENAMES <chr>, LATITUDE <dbl>, LONGITUDE <dbl>,
## #   LATITUDE_E <dbl>, LONGITUDE_ <dbl>, REMARKS <chr>, REFNUM <dbl>

There are 985 different types of weather events in the data - future investigations may want to look and combining some of these events (i.e all heat related events, Hurricane&Hurricane/Typhoon and/or flood&storm-surge, related events) to see additional patterns. The following code produces a plot that shows the top eight weather events based on fatalities.

d2<-group_by(d1,EVTYPE)
d3<-select(d2,EVTYPE,FATALITIES,INJURIES)
d4<-d3##d4<-filter(d3,FATALITIES,!is.na(FATALITIES))
d5<-summarise(d4,FATALITIES=sum(FATALITIES),INJURIES=sum(INJURIES))
d6<-arrange(d5,desc(FATALITIES))
d7<-head(d6,8)
d7
## # A tibble: 8 × 3
##           EVTYPE FATALITIES INJURIES
##            <chr>      <dbl>    <dbl>
## 1        TORNADO       5633    91346
## 2 EXCESSIVE HEAT       1903     6525
## 3    FLASH FLOOD        978     1777
## 4           HEAT        937     2100
## 5      LIGHTNING        816     5230
## 6      TSTM WIND        504     6957
## 7          FLOOD        470     6789
## 8    RIP CURRENT        368      232
g<-ggplot(d7,aes(x=reorder(EVTYPE,-FATALITIES),y=FATALITIES))
plotfatal<-g+geom_col(fill="yellow")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+geom_text(aes(label=FATALITIES), vjust=1.5, color="black")+labs(x="Weather Event",title="Eight most Deadly Weather Events US 1950-2011")
plotfatal

The following code shows the top eight weather events based on injuries.

g2<-ggplot(d7,aes(x=reorder(EVTYPE,-INJURIES),y=INJURIES))
plotinjury<-g2+geom_col(fill="magenta")+labs(x="Weather Event",title="Eight most Injuries due to Weather Events US 1950-2011")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+ geom_text(aes(label=INJURIES), vjust=1.5, color="black")
plotinjury

The original dataset showing damage amounts in dollars are stored in two variables (PROPDMG & CROPDMG) the values in these columns need to be multiplied by factors that vary per observation - the code below adds two new columns(property damage multiplier - “pdmltplyr” & crop damage multiplier - “cdmltplyr”) that make the character codes found in the dataset into numeric factors for each observation.

dd2<-group_by(d1,EVTYPE)
dd25<-mutate(dd2, pdmultplyr = 
        ifelse(PROPDMGEXP == "h" | PROPDMGEXP == "H", 100,
        ifelse(PROPDMGEXP == "k" | PROPDMGEXP == "K", 1000,
        ifelse(PROPDMGEXP == "m" | PROPDMGEXP == "M", 1000000,
        ifelse(PROPDMGEXP == "b" | PROPDMGEXP == "B", 1000000000, 0 )))))
dd26<-mutate(dd25, cdmultplyr=
        ifelse(CROPDMGEXP == "h" | CROPDMGEXP == "H", 100,
        ifelse(CROPDMGEXP == "k" | CROPDMGEXP == "K", 1000,
        ifelse(CROPDMGEXP == "m" | CROPDMGEXP == "M", 1000000,
        ifelse(CROPDMGEXP == "b" | CROPDMGEXP == "B", 1000000000, 0 )))))
dd3<-select(dd26,EVTYPE,PROPDMG:CROPDMGEXP,pdmultplyr,cdmultplyr)

The resulting “improved” dataframe can now be used to answer the question - which weather events caused the most damage? The following code applys:

dd35<-mutate(dd3,TotalDamage=(PROPDMG*pdmultplyr)+(CROPDMG*cdmultplyr))

dd4<-filter(dd35,TotalDamage,!is.na(TotalDamage))

dd5<-summarise(dd4,TotalDamage=sum(TotalDamage)/1000000000)
dd6<-arrange(dd5,desc(TotalDamage))
dd7<-head(dd6,8)
dd7
## # A tibble: 8 × 2
##              EVTYPE TotalDamage
##               <chr>       <dbl>
## 1             FLOOD   150.31968
## 2 HURRICANE/TYPHOON    71.91371
## 3           TORNADO    57.35211
## 4       STORM SURGE    43.32354
## 5              HAIL    18.75822
## 6       FLASH FLOOD    17.56213
## 7           DROUGHT    15.01867
## 8         HURRICANE    14.61023
g3<-ggplot(dd7,aes(x=reorder(EVTYPE,-TotalDamage),y=TotalDamage))
plotdamage<-g3+geom_col(fill="orange")+labs(x="Weather Event",title="Eight highest damages due to Weather Events US 1950-2011")+ theme(axis.text.x = element_text(angle = 90, hjust = 1))+geom_text(aes(label=TotalDamage), vjust=1.5)+labs(x="Weather event type",y="Total Property damage in billions",title="Top eight most expensive weather events")
plotdamage

Results

Clearly Tornados are the most dangerous weather events per this data set - Tornadoes have the most injuries and most fatalities by far.

Tornados also rank 3rd under most costly weather events behind both Hurricans/Typhoons(#2) and Floods(#1). There are 985 different types of weather events in the data - future investigations may want to look and combining some of these events (i.e all heat related events, Hurricane&Hurricane/Typhoon and/or flood&storm-surge, related events) to see additional patterns.