A Reproducible Research Project using NOAA Storm Data

Synopsis:

This analysis uses Storm Data that is collected and documented by the National Oceanic and Atmospheric Administration (NOAA). The data includes: (a.) The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce;(b.) Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area; and (c.) Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

The 2 goals of the analysis are:

(1) To determine the types of events that are most harmful with respect to population health across the U.S.

(2) To determine the types of events with the greatest economic consequences across the U.S.

Data processing:

- Make sure these packages are installed: dplyr, ggplot2 and gridExtra

- Download & read the NOAA Storm Data file

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, union

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.3.3

library(gridExtra)

## Warning: package 'gridExtra' was built under R version 3.3.3

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(grid)

file_url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
file_name <- "repdata_data_StormData.csv.bz2"
download.file(file_url, file_name, method = "curl")

stormdata <- read.csv(file_name, header=TRUE, sep = ",")
str(stormdata)

## '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/ 436781 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 ...

head(stormdata, 3)

##   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
##    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
##   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
##   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                                    
##   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

Results:

Determine the types of events that are most harmful with respect to population health.

- Fatalities and Injuries are used to determine harmful impact.

- Aggregate the two variables using the summarize() function.

- Sort the results in descending total fatalities and injuries.

- Due to the large number of event types, limit results to the top 10 event types based on total impact.

# create a dataframe that will be modified
mydata <- stormdata

mydata$fatalities_injuries <- mydata$FATALITIES + mydata$INJURIES

Harmfulevents <- mydata %>% group_by(EVTYPE) %>% summarize(sum(FATALITIES,na.rm=TRUE), sum(INJURIES,na.rm=TRUE),sum(fatalities_injuries,na.rm=TRUE))

colnames(Harmfulevents)<-c("EventType","Total_fatalities", "Total_injuries", "Total_fatalities_injuries")

sorted_Harmfulevents <- arrange(Harmfulevents, -Total_fatalities_injuries)
head(sorted_Harmfulevents,10)

## # A tibble: 10 × 4
##            EventType Total_fatalities Total_injuries
##               <fctr>            <dbl>          <dbl>
## 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
## # ... with 1 more variables: Total_fatalities_injuries <dbl>

Determine the types of events with the greatest economic consequences.

- Property and Crop damage measures are used to determine the economic impact.

- Aggregate the two variables using the summarize() function.

- Sort the results in descending total property and cost damage costs.

- Due to the large number of event types, limit results to the top 10 events types based on total costs.

mydata$TOTALDMG <- mydata$PROPDMG + mydata$CROPDMG

Damage_events <- mydata %>% group_by(EVTYPE) %>% summarize(sum(PROPDMG,na.rm=TRUE), sum(CROPDMG,na.rm=TRUE),sum(TOTALDMG,na.rm=TRUE))

colnames(Damage_events)<-c("EventType","Total_PropertyDamage", "Total_CropDamage", "Total_Damage")

sorted_Damage_events <- arrange(Damage_events, -Total_Damage)
head(sorted_Damage_events,10)

## # A tibble: 10 × 4
##             EventType Total_PropertyDamage Total_CropDamage Total_Damage
##                <fctr>                <dbl>            <dbl>        <dbl>
## 1             TORNADO            3212258.2        100018.52    3312276.7
## 2         FLASH FLOOD            1420124.6        179200.46    1599325.1
## 3           TSTM WIND            1335965.6        109202.60    1445168.2
## 4                HAIL             688693.4        579596.28    1268289.7
## 5               FLOOD             899938.5        168037.88    1067976.4
## 6   THUNDERSTORM WIND             876844.2         66791.45     943635.6
## 7           LIGHTNING             603351.8          3580.61     606932.4
## 8  THUNDERSTORM WINDS             446293.2         18684.93     464978.1
## 9           HIGH WIND             324731.6         17283.21     342014.8
## 10       WINTER STORM             132720.6          1978.99     134699.6

Analysis:

Use ggplot() to display results showing the events that are most harmful with respect to population health.

Arrange the 3 plots in one grid

- Figure1:

x <-ggplot(head(sorted_Harmfulevents,5), aes(x=reorder(EventType,Total_fatalities_injuries), y=Total_fatalities_injuries))
x1 <- x + geom_bar(stat="identity",color="white", fill="navyblue") + 
    xlab("Event Types") + ylab("Total Fatalities & Injuries") +
    ggtitle("Top 5 Combined Fatalities & Injuries by Event Type") +
    theme(plot.title = element_text(size=10, face="bold",margin = margin(10, 0, 10, 0)))+
    theme(axis.text.y=element_text(size=6)) +
    theme(axis.text.x=element_text(size=6)) +
    coord_flip() 

y <-ggplot(head(sorted_Harmfulevents,5), aes(x=reorder(EventType,Total_fatalities), y=Total_fatalities))
y1 <- y + geom_bar(stat="identity",color="white", fill="dodgerblue2") + 
    xlab("Event Types") + ylab("Total Fatalities") +
    ggtitle("Top 5 Fatalities by Event Type") +
    theme(plot.title = element_text(size=10, face="bold",margin = margin(10, 0, 10, 0)))+
    theme(axis.text.y=element_text(size=6)) +
    theme(axis.text.x=element_text(size=6)) +
    coord_flip()

z <-ggplot(head(sorted_Harmfulevents,5), aes(x=reorder(EventType,Total_injuries), y=Total_injuries))
z1 <- z + geom_bar(stat="identity",color="white", fill="cadetblue") + 
    xlab("Event Types") + ylab("Total Injuries") +
    ggtitle("Top 5 Injuries by Event Type") +
    theme(plot.title = element_text(size=10, face="bold",margin = margin(10, 0, 10, 0)))+
    theme(axis.text.y=element_text(size=6)) +
    theme(axis.text.x=element_text(size=6)) +
    coord_flip()

grid.arrange (x1, y1, z1)

Use ggplot() to display results showing the events with the greatest economic consequences.

Arrange the 3 plots in one grid

- Figure2:

x <-ggplot(head(sorted_Damage_events,5), aes(x=reorder(EventType,Total_Damage), y=Total_Damage))
x2 <- x + geom_bar(stat="identity",color="white", fill="darkorange2") + 
    xlab("Event Types") + ylab("Total Property & Crop Damage") +
    ggtitle("Top 5 Event Types by Property & Crop Damage Costs") +
    theme(plot.title = element_text(size=10, face="bold",margin = margin(10, 0, 10, 0)))+
    theme(axis.text.y=element_text(size=6)) +
    theme(axis.text.x=element_text(size=6)) +
    coord_flip() 

y <-ggplot(head(sorted_Damage_events,5), aes(x=reorder(EventType,Total_PropertyDamage), y=Total_PropertyDamage))
y2 <- y + geom_bar(stat="identity",color="white", fill="goldenrod3") + 
    xlab("Event Types") + ylab("Total Property Damage") +
    ggtitle("Top 5 Event Types by Property Damage Cost") +
    theme(plot.title = element_text(size=10, face="bold",margin = margin(10, 0, 10, 0)))+
    theme(axis.text.y=element_text(size=6)) +
    theme(axis.text.x=element_text(size=6)) +
    coord_flip() 

z <-ggplot(head(sorted_Damage_events,5), aes(x=reorder(EventType,Total_CropDamage), y=Total_CropDamage))
z2 <- z + geom_bar(stat="identity",color="white", fill="khaki4") + 
    xlab("Event Types") + ylab("Total Crop Damage") +
    ggtitle("Top 5 Event Types by Crop Damage Cost") +
    theme(plot.title = element_text(size=10, face="bold",margin = margin(10, 0, 10, 0)))+
    theme(axis.text.y=element_text(size=6)) +
    theme(axis.text.x=element_text(size=6)) +
    coord_flip()

grid.arrange (x2, y2, z2)

- The most harmful event types were determined by quantifying the total fatalities and injuries collected across the U.S. by the NOAA.

- Tornadoes, excessive heat, TSTM wind, floods and lightning were responsible for causing the most harm with respect to population health.

- This was true for both fatalities and injuries documented in the storm data.

- Tornadoes disproportionally caused the most injuries and fatalities.

- The event types with the greatest economic consequences were determined by quantifying the total property and crop damage costs collected across the U.S.

- Tornadoes, flash floods, TSTM wind, hail and floods had the most economic impact.

- This was true for combined property and crop damage costs.

- Tornadoes disproportionally had the greatest economic impact on property damage, while hail had a similarly large impact on crop damage.

- These findings are helpful in the planning of the types and amounts of resources required by states and local county governments to mitigate any future property and crop damage, and loss of life.