Analysis of Storm Data looking for impacts regarding population health and damage caused

Synopsis

In this analysis we use the Storm data to determine which events are most harmful to the population of the US and which events cause the most damage.

Importing data

download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2','project_data', method = 'curl')

project_data <- read.csv('project_data.csv')

head(project_data)

##   STATE__           BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 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
##    EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO         0                                               0
## 2 TORNADO         0                                               0
## 3 TORNADO         0                                               0
## 4 TORNADO         0                                               0
## 5 TORNADO         0                                               0
## 6 TORNADO         0                                               0
##   COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1         NA         0                      14.0   100 3   0          0
## 2         NA         0                       2.0   150 2   0          0
## 3         NA         0                       0.1   123 2   0          0
## 4         NA         0                       0.0   100 2   0          0
## 5         NA         0                       0.0   150 2   0          0
## 6         NA         0                       1.5   177 2   0          0
##   INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1       15    25.0          K       0                                    
## 2        0     2.5          K       0                                    
## 3        2    25.0          K       0                                    
## 4        2     2.5          K       0                                    
## 5        2     2.5          K       0                                    
## 6        6     2.5          K       0                                    
##   LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1     3040      8812       3051       8806              1
## 2     3042      8755          0          0              2
## 3     3340      8742          0          0              3
## 4     3458      8626          0          0              4
## 5     3412      8642          0          0              5
## 6     3450      8748          0          0              6

dim(project_data)

## [1] 902297     37

str(project_data)

## 'data.frame':    902297 obs. of  37 variables:
##  $ STATE__   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_DATE  : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
##  $ BGN_TIME  : Factor w/ 3608 levels "000","0000","0001",..: 152 167 2645 1563 2524 3126 122 1563 3126 3126 ...
##  $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ COUNTY    : num  97 3 57 89 43 77 9 123 125 57 ...
##  $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
##  $ STATE     : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 826 826 826 826 826 826 826 826 826 826 ...
##  $ BGN_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BGN_AZI   : Factor w/ 35 levels "","  N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_LOCATI: Factor w/ 54429 levels ""," Christiansburg",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_DATE  : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_TIME  : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ COUNTY_END: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ COUNTYENDN: logi  NA NA NA NA NA NA ...
##  $ END_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ END_AZI   : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_LOCATI: Factor w/ 34506 levels ""," CANTON"," TULIA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LENGTH    : num  14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
##  $ WIDTH     : num  100 150 123 100 150 177 33 33 100 100 ...
##  $ F         : int  3 2 2 2 2 2 2 1 3 3 ...
##  $ MAG       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ FATALITIES: num  0 0 0 0 0 0 0 0 1 0 ...
##  $ INJURIES  : num  15 0 2 2 2 6 1 0 14 0 ...
##  $ PROPDMG   : num  25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
##  $ PROPDMGEXP: Factor w/ 19 levels "","+","-","0",..: 16 16 16 16 16 16 16 16 16 16 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","0","2","?",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WFO       : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ ZONENAMES : Factor w/ 25112 levels "","                                                                                                                               "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LATITUDE  : num  3040 3042 3340 3458 3412 ...
##  $ LONGITUDE : num  8812 8755 8742 8626 8642 ...
##  $ LATITUDE_E: num  3051 0 0 0 0 ...
##  $ LONGITUDE_: num  8806 0 0 0 0 ...
##  $ REMARKS   : Factor w/ 436781 levels "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ REFNUM    : num  1 2 3 4 5 6 7 8 9 10 ...

Data Processing

Dataset to analyse injuries

inj_data <- aggregate(INJURIES ~ EVTYPE, project_data,sum)
attach(inj_data)
inj_data <- inj_data[order(-INJURIES),]
inj_data <- inj_data[1:10,]

Dataset to analyse fatalities

fat_data <- aggregate(FATALITIES ~ EVTYPE, project_data,sum)
attach(fat_data)

## The following object is masked from inj_data:
## 
##     EVTYPE

fat_data <- fat_data[order(-FATALITIES),]
fat_data <- fat_data[1:10,]

Dataset to analyse damage

dmg_data <- aggregate(PROPDMG ~ EVTYPE, project_data,sum)
attach(dmg_data)

## The following object is masked from fat_data:
## 
##     EVTYPE

## The following object is masked from inj_data:
## 
##     EVTYPE

dmg_data <- dmg_data[order(-PROPDMG),]
dmg_data <- dmg_data[1:10,]

Results

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

ggplot(aes(x = EVTYPE, y = INJURIES), data = inj_data) + 
        geom_bar(stat = 'identity', aes(fill = EVTYPE), position = position_dodge(width = 0.5)) +
        ggtitle('Top 10 Event with most injuries') +
        geom_text(aes(label = INJURIES), vjust = -0.5) +
        #ylab('Number of Injuries') +
        xlab('Event')+
        labs(fill = 'Event') +
        theme(axis.text.x = element_text(angle = 75, hjust = 1),
              plot.title = element_text(hjust = 0.5), axis.text.y=element_blank(),
              axis.ticks.y =element_blank(), axis.title.y=element_blank())

ggplot(aes(x = EVTYPE, y = FATALITIES), data = fat_data) + 
        geom_bar(stat = 'identity', aes(fill = EVTYPE), position = position_dodge(width = 0.5)) +
        ggtitle('Top 10 Event with most Fatalities') +
        geom_text(aes(label = FATALITIES), vjust = -0.5) +
        ylab('Number of Fatalities') +
        xlab('Event')+
        labs(fill = 'Event') +
        theme(axis.text.x = element_text(angle = 25, hjust = 1), plot.title = element_text(hjust = 0.5),
              axis.text.y=element_blank(),
              axis.ticks.y =element_blank(), axis.title.y=element_blank())

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

ggplot(aes(x = EVTYPE, y = PROPDMG), data = dmg_data) + 
        geom_bar(stat = 'identity', aes(fill = EVTYPE), position = position_dodge(width = 0.5)) +
        ggtitle('Top 10 Event with most Damage') +
        geom_text(aes(label = PROPDMG), vjust = -0.5) +
        ylab('Damage Amount in USD') +
        xlab('Event')+
        labs(fill = 'Event') +
        theme(axis.text.x = element_text(angle = 25, hjust = 1), plot.title = element_text(hjust = 0.5),
              axis.text.y=element_blank(),
              axis.ticks.y =element_blank(), axis.title.y=element_blank())

Course Project 2

Analysis of Storm Data looking for impacts regarding population health and damage caused

Synopsis

Importing data

Data Processing

Dataset to analyse injuries

Dataset to analyse fatalities

Dataset to analyse damage

Results