Reproducible Research 2 : Peer Graded
Dhaval Sawlani
January 24, 2017
Summary
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern. 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, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
Data Pre-processing
We will load the data of the NOAA’s database by the following R code.
require(ggplot2)
require(dplyr)
require(gridExtra)## Loading required package: gridExtra##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinerequire(cowplot)## Loading required package: cowplot##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggplot2':
##
## ggsaver <- read.csv("repdata%2Fdata%2FStormData.csv")Now, we will look at the summary of the dataset by the calling the str() function
str(r)## '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 "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ 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",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ 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 "","- 1 N Albion",..: 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 "","- .5 NNW",..: 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 "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ 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/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...The following dataset contains 902297 observations and 37 variables. We will not require all these variables. Only a handful of variables will be enough to answer the questions. These variables will be
1.EVTYPE: Type of Event i.e. Tornado, Hurricane, Floods etc.
2.FATALITIES: Harm to human health
3.INJURIES: Harm to human health
4.PROPDMG: Property damage in USD
5.PROPDMGEXP: Magnitude of Property damage in USD(Billions, Millions, etc)
6.CROPDMG: Crop damage in USD
7.CROPDMGEXP: Magnitude of crop damage in USD(Billions, Millions, etc)
We will use select() function of dplyr package to select all these variables from our main dataset.
s <- select(r, EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)
str(s)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ 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 "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ 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 ...What are the number of FATALITIES caused by different types of Events?
To calculate the number of fatalities of the different events we will use FATALITIS column of the selected dataset. The following R code will give us the Events which caused the maximum fatalities.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(RColorBrewer)
cols <- brewer.pal(8, "Set2")
pal <- colorRampPalette(cols)
gp <- group_by(s, EVTYPE)
gp_fatalities <- summarise(gp, `No. of Deaths` = sum(FATALITIES)) %>% arrange(desc(`No. of Deaths`)) %>% top_n(10)## Selecting by No. of Deathsgp_fatalities## # A tibble: 10 × 2
## EVTYPE `No. of Deaths`
## <fctr> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224g <- ggplot(gp_fatalities, aes(EVTYPE, `No. of Deaths`))
g + geom_bar(stat = "identity", fill = pal(10)) + coord_flip() +xlab("Event Type") + theme_minimal() + ggtitle("FATALITIES caused by Different types of Events")
What Weather Events caused the most Severe injuries?
The following will show the Top 10 Events that caused the most severe injuries to the population
cols <- brewer.pal(5, "Accent")
pal <- colorRampPalette(cols)
gp <- group_by(s, EVTYPE)
gp_injuries <- summarise(gp, `No. of Injuries` = sum(INJURIES)) %>% arrange(desc(`No. of Injuries`)) %>% top_n(10)## Selecting by No. of Injuriesgp_injuries## # A tibble: 10 × 2
## EVTYPE `No. of Injuries`
## <fctr> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361j <- ggplot(gp_injuries, aes(EVTYPE, `No. of Injuries`))
j + geom_bar(stat = "identity", fill = pal(10)) + coord_flip() +xlab("Event Type") + theme_minimal() + ggtitle("INJURIES caused by Different types of Events")
What are the Events that caused the Greatest Property Damage?
The Economic damage in the dataset is summed upto the Property damage and the crop damage. Now we will convert the PROPDMGEXP and CROPDMGEXP to their related numbers. For example B|b is 9 etc.
##Convert the PROPDMGEXP to character class
s$PROPDMGEXP <- as.character(s$PROPDMGEXP)
s$PROPDMGEXP <- gsub("\\-|\\+|\\?", "0", s$PROPDMGEXP)
s$PROPDMGEXP <- gsub("B|b", "9", s$PROPDMGEXP)
s$PROPDMGEXP <- gsub("M|m", "6", s$PROPDMGEXP)
s$PROPDMGEXP <- gsub("K|k", "3", s$PROPDMGEXP)
s$PROPDMGEXP <- gsub("H|h", "2", s$PROPDMGEXP)
##Convert the PROPDMGEXP to numeric class
s$PROPDMGEXP <- as.numeric(s$PROPDMGEXP)
s$PROPDMGEXP[is.na(s$PROPDMGEXP)] = 0
s$PropertyDamage <- s$PROPDMG * 10^s$PROPDMGEXP
gp <- group_by(s, EVTYPE)
gp_propdamage <- summarise(gp, PropDamage = sum(PropertyDamage)) %>% arrange(desc(PropDamage))
gppdmg <- data.frame(gp_propdamage[1:10, ])
gppdmg## EVTYPE PropDamage
## 1 FLOOD 144657709807
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56947380677
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16822673979
## 6 HAIL 15735267513
## 7 HURRICANE 11868319010
## 8 TROPICAL STORM 7703890550
## 9 WINTER STORM 6688497251
## 10 HIGH WIND 5270046295In the same manner we will convert the CRPDMGEXP
##Convert the cROPDMGEXP to character class
s$CROPDMGEXP <- as.character(s$CROPDMGEXP)
s$CROPDMGEXP <- gsub("\\-|\\+|\\?", "0", s$CROPDMGEXP)
s$CROPDMGEXP <- gsub("B|b", "9", s$CROPDMGEXP)
s$CROPDMGEXP <- gsub("M|m", "6", s$CROPDMGEXP)
s$CROPDMGEXP <- gsub("K|k", "3", s$CROPDMGEXP)
s$CROPDMGEXP <- gsub("H|h", "2", s$CROPDMGEXP)
##Convert the CROPDMGEXP to numeric class
s$CROPDMGEXP <- as.numeric(s$CROPDMGEXP)
s$CROPDMGEXP[is.na(s$CROPDMGEXP)] = 0
s$CropDamage <- s$CROPDMG * 10^s$CROPDMGEXP
gp <- group_by(s, EVTYPE)
gp_cropdamage <- summarise(gp, CropDamage = sum(CropDamage)) %>% arrange(desc(CropDamage))
gpcdmg <- data.frame(gp_cropdamage[1:10, ])
gpcdmg## EVTYPE CropDamage
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954473
## 6 HURRICANE 2741910000
## 7 HURRICANE/TYPHOON 2607872800
## 8 FLASH FLOOD 1421317100
## 9 EXTREME COLD 1292973000
## 10 FROST/FREEZE 1094086000The Total Economic Damage caused by these Catastrophes can be given as follows
totdamage <- merge(gp_propdamage, gp_cropdamage)
totdamage$`Total Damage` <- totdamage$PropDamage + totdamage$CropDamage
EVTYPE <- totdamage$EVTYPE
`Total Damage` <- totdamage$`Total Damage`
TOTAL_df <- data.frame(EVTYPE, `Total Damage`)
gptdm <- arrange(TOTAL_df, desc(`Total Damage`))
gptdmg <- gptdm[1:10, ]
gptdmg## EVTYPE Total.Damage
## 1 FLOOD 150319678257
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57362333947
## 4 STORM SURGE 43323541000
## 5 HAIL 18761221986
## 6 FLASH FLOOD 18243991079
## 7 DROUGHT 15018672000
## 8 HURRICANE 14610229010
## 9 RIVER FLOOD 10148404500
## 10 ICE STORM 8967041360The Total, Property & Crop Damage incured by Different Events is shown in the following plots
library(ggplot2)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinecols <- brewer.pal(10, "Spectral")
pal <- colorRampPalette(cols)
prop <- ggplot(gppdmg, aes(EVTYPE, PropDamage)) + geom_bar(stat = "identity", fill = pal(10)) + xlab("Event Type") + theme(axis.text.x = element_text(angle = 90)) + ylab("Property Damage ($)") + ggtitle("Top 10 Events Causing Most Property Damage")
crop <- ggplot(gpcdmg, aes(EVTYPE, CropDamage)) + geom_bar(stat = "identity", fill = pal(10)) + xlab("Event Type") + theme(axis.text.x = element_text(angle = 90)) + ylab("Crop Damage ($)") + ggtitle("Top 10 Events Causing Most Crop Damage")
tot <- ggplot(gptdmg, aes(EVTYPE, Total.Damage)) + geom_bar(stat = "identity", fill = pal(10)) + xlab("Event Type") + theme(axis.text.x = element_text(angle = 90)) + ylab("Total Economic Damage ($)") + ggtitle("Top 10 Events Causing Most Economic Damage")
grid.arrange(prop ,crop ,tot, ncol = 3)
RESULTS
Hence we can say from the above Analysis that
1.TORNADO had the largest number of FATALITIES and INJURIES
2.FLOOD did the most Property Damage
3.Drought did the most Crop Damage
4.FLOOD did the most Economic Damage