Assignment

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.

Questions

Your data analysis must address the following 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?

Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.

Description

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.

A data frame with 902297 observations on 37 variables.

Important variables:

EVTYPE - event type (TORNADO, TSTM WIND, HAIL, FREEZING RAIN, …)

FATALITIES - number of people died

INJURIES - number of people injuured

PROPDMG - amount of property damage (measured in money)

PROPDMGEXP - unit of damage (B,M,K,H,…)

CROPDMG - amount of corp damage (measured in money)

CROPDMGEXP - unit of damage (B,M,K,H,…)

library(plyr)
library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(doBy)

## Loading required package: survival

library("knitr")

Loading and preprocessing the data

allStormData=read.csv("~/Downloads/repdata-data-StormData.csv.bz2")
dim(allStormData)

## [1] 902297     37

Select the variables from dataset, which we will be used later.

stormData = allStormData[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
summary(stormData)

##                EVTYPE         FATALITIES          INJURIES        
##  HAIL             :288661   Min.   :  0.0000   Min.   :   0.0000  
##  TSTM WIND        :219940   1st Qu.:  0.0000   1st Qu.:   0.0000  
##  THUNDERSTORM WIND: 82563   Median :  0.0000   Median :   0.0000  
##  TORNADO          : 60652   Mean   :  0.0168   Mean   :   0.1557  
##  FLASH FLOOD      : 54277   3rd Qu.:  0.0000   3rd Qu.:   0.0000  
##  FLOOD            : 25326   Max.   :583.0000   Max.   :1700.0000  
##  (Other)          :170878                                         
##     PROPDMG          PROPDMGEXP        CROPDMG          CROPDMGEXP    
##  Min.   :   0.00          :465934   Min.   :  0.000          :618413  
##  1st Qu.:   0.00   K      :424665   1st Qu.:  0.000   K      :281832  
##  Median :   0.00   M      : 11330   Median :  0.000   M      :  1994  
##  Mean   :  12.06   0      :   216   Mean   :  1.527   k      :    21  
##  3rd Qu.:   0.50   B      :    40   3rd Qu.:  0.000   0      :    19  
##  Max.   :5000.00   5      :    28   Max.   :990.000   B      :     9  
##                    (Other):    84                     (Other):     9

# Pandoc tables
kable(head(stormData), format = "pandoc")

EVTYPE	INJURIES	PROPDMG	PROPDMGEXP
TORNADO	15	25.0	K
TORNADO	0	2.5	K
TORNADO	2	25.0	K
TORNADO	2	2.5	K
TORNADO	2	2.5	K
TORNADO	6	2.5	K

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

Select corrective data, which we will be used to see which event is the most harmful respect to population health.

sub<-subset(stormData,
           select=c("EVTYPE", "FATALITIES", "INJURIES"), 
           ((!is.na(FATALITIES)) & (!is.na(INJURIES)) &
           ((FATALITIES > 0) |  (INJURIES > 0)) ))
summary(sub)

##                EVTYPE       FATALITIES          INJURIES       
##  TORNADO          :7928   Min.   :  0.0000   Min.   :   0.000  
##  LIGHTNING        :3305   1st Qu.:  0.0000   1st Qu.:   1.000  
##  TSTM WIND        :2930   Median :  0.0000   Median :   1.000  
##  FLASH FLOOD      : 931   Mean   :  0.6906   Mean   :   6.408  
##  THUNDERSTORM WIND: 682   3rd Qu.:  1.0000   3rd Qu.:   3.000  
##  EXCESSIVE HEAT   : 678   Max.   :583.0000   Max.   :1700.000  
##  (Other)          :5475

#Group data by variable “EVTYPE” and calculate harmful
sub=summaryBy(FATALITIES+INJURIES~EVTYPE, data=sub, FUN=sum) 

# Order the number of FATALITIES.sum + INJURIES.sum by function arrange, using decending method
sub = arrange(sub, desc(FATALITIES.sum + INJURIES.sum))

#top 10 events which are most harmful
sub=sub[1:10,]
sub

##               EVTYPE FATALITIES.sum INJURIES.sum
## 1            TORNADO           5633        91346
## 2     EXCESSIVE HEAT           1903         6525
## 3          TSTM WIND            504         6957
## 4              FLOOD            470         6789
## 5          LIGHTNING            816         5230
## 6               HEAT            937         2100
## 7        FLASH FLOOD            978         1777
## 8          ICE STORM             89         1975
## 9  THUNDERSTORM WIND            133         1488
## 10      WINTER STORM            206         1321

Draw barplots

barplot(sub$INJURIES.sum, 
        names.arg = sub$EVTYPE, 
        main = "Fatalities and Injuries",
        ylab = "count of population health damages",
        cex.axis = 0.7,cex.names = 0.7,
        las = 2,  col="red")
barplot(sub$FATALITIES.sum,
        cex.axis = 0.7,cex.names = 0.7,
        las = 2,col="blue",add=T)

par(mfrow = c(1, 2))
barplot(sub$FATALITIES.sum, 
        names.arg = sub$EVTYPE, 
        main = "Fatalities", 
        ylab = "fatalities", 
        cex.axis = 0.7, col="blue",
        cex.names = 0.7, las = 2)
barplot(sub$INJURIES.sum, names.arg = sub$EVTYPE, main = "Injuries", 
        ylab = "injuries", 
        cex.axis = 0.7, col="red",
        cex.names = 0.7, las = 2)

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

Select corrective data, which we will be used to see which event is the greatest respect to economic consequences.

sub=subset(stormData,
           select=c("EVTYPE","PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP"),
           (!is.na(PROPDMG)) & (!is.na(CROPDMG)) &
           ((PROPDMG > 0) | (CROPDMG > 0)))

Replace means of CROPDMGEXP and PROPDMGEXP by rules:

B -> 1,000,000,000

M,m -> 1,000,000

K,k -> 1,000

H,h -> 100

=,-,?, blank -> 0

1-8 -> 1

unique(sub$CROPDMGEXP)

## [1]   M K m B ? 0 k
## Levels:  ? 0 2 B k K m M

sub$CROPDMGEXP[sub$CROPDMGEXP == "?" | sub$CROPDMGEXP == ""]="0"
 
CROPDMGDol= mapvalues(sub$CROPDMGEXP,
                from=c("M","K","m","B","k","0"),
                to=c(1e6,1e3,1e6,1e9,1e3,0e0))

unique(sub$PROPDMGEXP)

##  [1] K M B m   + 0 5 6 4 h 2 7 3 H -
## Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M

sub$PROPDMGEXP[sub$PROPDMGEXP == "+" | 
            sub$PROPDMGEXP == "" |
            sub$PROPDMGEXP == "-"]="0"


PROPDMGDol = mapvalues(sub$PROPDMGEXP,
from=c("K","M", "B","m","5","6","4","2","3","h","7","H","1","8","0"), 
#to=c(1e3,1e6,1e9,1e6, 1e5,1e6,1e4,1e2,1e3,1e2,1e7,1e2, 1e1,1e8,0e0))
to=c(1e3,1e6,1e9,1e6, 1e1,1e1,1e1,1e1,1e1,1e2,1e1,1e2, 1e1,1e1,0e0))

#Create new columns with exact amounts for property and corp damage
sub$PROP=as.numeric(as.vector(PROPDMGDol))*sub$PROPDMG
sub$CROP=as.numeric(as.vector(CROPDMGDol))*sub$CROPDMG


#Group data by variable “EVTYPE” and calculate economic consequences.
sub=summaryBy(CROP + PROP ~ EVTYPE, data=sub, FUN=sum) 

# Order the number of PROP.sum + CROP.sum by function arrange, using decending method
sub <- arrange(sub, desc(PROP.sum + CROP.sum))

#top 10 events which have the greatest economic consequences
sub=sub[1:10,]
sub

##               EVTYPE    CROP.sum     PROP.sum
## 1              FLOOD  5661968450 144657709800
## 2  HURRICANE/TYPHOON  2607872800  69305840000
## 3            TORNADO   414953110  56937161502
## 4        STORM SURGE        5000  43323536000
## 5               HAIL  3025954450  15732267520
## 6        FLASH FLOOD  1421317100  16140812396
## 7            DROUGHT 13972566000   1046106000
## 8          HURRICANE  2741910000  11868319010
## 9        RIVER FLOOD  5029459000   5118945500
## 10         ICE STORM  5022113500   3944927810

Draw barplots

barplot(sub$PROP.sum, 
        names.arg = sub$EVTYPE, 
        main = "Property and crop damage",
        ylab = "Value of economic damages",
        cex.axis = 0.7,cex.names = 0.7,
        las = 2,  col="red")
barplot(sub$CROP.sum,
        cex.axis = 0.7,cex.names = 0.7,
        las = 2,col="blue",add=T)

par(mfrow = c(1, 2))
barplot(sub$CROP.sum, 
        names.arg = sub$EVTYPE, 
        main = "Crop damage", 
        ylab = "crop", 
        cex.axis = 0.7, col="blue",
        cex.names = 0.7, las = 2)
barplot(sub$PROP.sum, names.arg = sub$EVTYPE, 
        main = "Property damage", 
        ylab = "property", 
        cex.axis = 0.7, col="red",
        cex.names = 0.7, las = 2)

Conclusion

1.Type of event “TORNADO” is most harmful with respect to population health.

2.Type of event “FLOOD” has the greatest economic consequences.

Reproducible Research: Peer Assessment 2

Assignment

Questions

Description

Conclusion