Reproducible Research Project
Nsovo Ntuli
2023-10-23
Exploring the NOAA Storm Database: US Severe Weather impacts on Health and Economics
ASSIGNMENT OVERVIEW
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.
Synopsis
this assignment focuses on analyzing severe weather events in the U.S., with an emphasis on their impact on public health and the economy,The NOAA 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.
##Questions: This data analysis must address the following questions :
Question 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?
Question 2: Across the United States, which types of events have the greatest economic consequences?
##Data Processing and Retrieval
Install the needed packages and load the libraries
library("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("knitr")library(rmarkdown)library(lattice)download the NAOO file for analyisis
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
local_file <- "R"download.file(url, local_file, mode = "wb") storm <- read.csv("storm")
head(storm,10)## 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
## 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
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 TORNADO 0 0 NA
## 2 TORNADO 0 0 NA
## 3 TORNADO 0 0 NA
## 4 TORNADO 0 0 NA
## 5 TORNADO 0 0 NA
## 6 TORNADO 0 0 NA
## 7 TORNADO 0 0 NA
## 8 TORNADO 0 0 NA
## 9 TORNADO 0 0 NA
## 10 TORNADO 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## 7 0 1.5 33 2 0 0 1 2.5
## 8 0 0.0 33 1 0 0 0 2.5
## 9 0 3.3 100 3 0 1 14 25.0
## 10 0 2.3 100 3 0 0 0 25.0
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## 7 K 0 3405 8631
## 8 K 0 3255 8558
## 9 K 0 3334 8740
## 10 K 0 3336 8738
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6
## 7 0 0 7
## 8 0 0 8
## 9 3336 8738 9
## 10 3337 8737 10##data processing
summary(storm)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## names(storm)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"##question 1 Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
1.1 Variable selection (reducing the data set to only needed columns and variables)
variables <- c("EVTYPE","FATALITIES","INJURIES","PROPDMG", "PROPDMGEXP","CROPDMG","CROPDMGEXP")
storm_data <- storm[variables]
dim(storm_data)## [1] 902297 7names(storm_data)## [1] "EVTYPE" "FATALITIES" "INJURIES" "PROPDMG" "PROPDMGEXP"
## [6] "CROPDMG" "CROPDMGEXP"1.2 Reviewing events that cause the most fatalities ( The Top-10 Fatalities by Weather Event )
## Procedure = aggregate the top 10 fatalities by the event type and sort the output in descending order
Fatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm_data, FUN = sum)
Top10_Fatalities <- Fatalities[order(-Fatalities$FATALITIES), ][1:10, ]
Top10_Fatalities ## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 2241.3 Reviewing events that cause the most injuries ( The Top-10 Injuries by Weather Event )
## Procedure = aggregate the top 10 injuries by the event type and sort the output in descending order
Injuries <- aggregate(INJURIES ~ EVTYPE, data = storm_data, FUN = sum)
Top10_Injuries <- Injuries[order(-Injuries$INJURIES), ][1:10, ]
Top10_Injuries ## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 13611.4 Plot of Top 10 Fatalities & Injuries for Weather Event Types ( Population Health Impact
## Proecedure = plot graphs showing the top 10 fatalities and injuries
par(mfrow=c(1,2),mar=c(10,3,3,2))
barplot(Top10_Fatalities$FATALITIES,names.arg=Top10_Fatalities$EVTYPE,las=2,col="skyblue",ylab="fatalities",main="Top 10 fatalities")
barplot(Top10_Injuries$INJURIES,names.arg=Top10_Injuries$EVTYPE,las=2,col="skyblue",ylab="injuries",main="Top 10 Injuries")
Question 2
Across the United States, which types of events have the greatest economic consequences?
unique(storm_data$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"We will need to first calculate the actual damange by converting exponent columns in numeric data
Property damage exponents for each level listed out & assigned those values for the property exponent data.
Invalid data was excluded by assigning the value as ‘0’.
Then, the property damage value was calculated by multiplying the property damage and property exponent value.
Assigning values for the property exponent storm_data
storm_data$PROPEXP[storm_data$PROPDMGEXP == "K"] <- 1000
storm_data$PROPEXP[storm_data$PROPDMGEXP == "M"] <- 1e+06
storm_data$PROPEXP[storm_data$PROPDMGEXP == ""] <- 1
storm_data$PROPEXP[storm_data$PROPDMGEXP == "B"] <- 1e+09
storm_data$PROPEXP[storm_data$PROPDMGEXP == "m"] <- 1e+06
storm_data$PROPEXP[storm_data$PROPDMGEXP == "0"] <- 1
storm_data$PROPEXP[storm_data$PROPDMGEXP == "5"] <- 1e+05
storm_data$PROPEXP[storm_data$PROPDMGEXP == "6"] <- 1e+06
storm_data$PROPEXP[storm_data$PROPDMGEXP == "4"] <- 10000
storm_data$PROPEXP[storm_data$PROPDMGEXP == "2"] <- 100
storm_data$PROPEXP[storm_data$PROPDMGEXP == "3"] <- 1000
storm_data$PROPEXP[storm_data$PROPDMGEXP == "h"] <- 100
storm_data$PROPEXP[storm_data$PROPDMGEXP == "7"] <- 1e+07
storm_data$PROPEXP[storm_data$PROPDMGEXP == "H"] <- 100
storm_data$PROPEXP[storm_data$PROPDMGEXP == "1"] <- 10
storm_data$PROPEXP[storm_data$PROPDMGEXP == "8"] <- 1e+08Assigning ‘0’ to invalid exponent strmdata
storm_data$PROPEXP[storm_data$PROPDMGEXP == "+"] <- 0
storm_data$PROPEXP[storm_data$PROPDMGEXP == "-"] <- 0
storm_data$PROPEXP[storm_data$PROPDMGEXP == "?"] <- 0Calculating the property damage value
storm_data$PROPDMGVAL <- storm_data$PROPDMG * storm_data$PROPEXPCalculating the crop damage
unique(storm_data$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"Assigning values for the crop exponent storm_data
storm_data$CROPEXP[storm_data$CROPDMGEXP == "M"] <- 1e+06
storm_data$CROPEXP[storm_data$CROPDMGEXP == "K"] <- 1000
storm_data$CROPEXP[storm_data$CROPDMGEXP == "m"] <- 1e+06
storm_data$CROPEXP[storm_data$CROPDMGEXP == "B"] <- 1e+09
storm_data$CROPEXP[storm_data$CROPDMGEXP == "0"] <- 1
storm_data$CROPEXP[storm_data$CROPDMGEXP == "k"] <- 1000
storm_data$CROPEXP[storm_data$CROPDMGEXP == "2"] <- 100
storm_data$CROPEXP[storm_data$CROPDMGEXP == ""] <- 1calculating the crop damage
storm_data$CROPDMGVAL <- storm_data$CROPDMG * storm_data$CROPEXP#2.4 Property Damage Summary
Procedure = aggregate the property damage by the event type and sort the output it in descending order
prop <- aggregate(PROPDMGVAL~EVTYPE,data=storm_data,FUN=sum,na.rm=TRUE)
prop <- prop[with(prop,order(-PROPDMGVAL)),]
prop <- head(prop,10)
print(prop)## EVTYPE PROPDMGVAL
## 170 FLOOD 144657709807
## 411 HURRICANE/TYPHOON 69305840000
## 834 TORNADO 56947380617
## 670 STORM SURGE 43323536000
## 153 FLASH FLOOD 16822673979
## 244 HAIL 15735267513
## 402 HURRICANE 11868319010
## 848 TROPICAL STORM 7703890550
## 972 WINTER STORM 6688497251
## 359 HIGH WIND 5270046260#2.5 Crop Damage Summary
Procedure = aggregate the crop damage by the event type and sort the output it in descending order
crop <- aggregate(CROPDMGVAL~EVTYPE,data=storm_data,FUN=sum,na.rm=TRUE)
crop <- crop[with(crop,order(-CROPDMGVAL)),]
crop <- head(crop,10)
print(crop)## EVTYPE CROPDMGVAL
## 95 DROUGHT 13972566000
## 169 FLOOD 5661968450
## 589 RIVER FLOOD 5029459000
## 426 ICE STORM 5022113500
## 243 HAIL 3025954473
## 401 HURRICANE 2741910000
## 410 HURRICANE/TYPHOON 2607872800
## 153 FLASH FLOOD 1421317100
## 140 EXTREME COLD 1292973000
## 211 FROST/FREEZE 10940860002.6 Plot of Top 10 Property & Crop damages by Weather Event Types ( Economic Consequences )
##plot the graph showing the top 10 property and crop damages
par(mfrow=c(1,2),mar=c(11,3,3,2))
barplot(prop$PROPDMGVAL/(10^9),names.arg=prop$EVTYPE,las=2,col="skyblue",ylab="Prop.damage(billions)",main="Top10 Prop.Damages")
barplot(crop$CROPDMGVAL/(10^9),names.arg=crop$EVTYPE,las=2,col="skyblue",ylab="Crop damage(billions)",main="Top10 Crop.Damages")
Analysis Results
Floods caused a huge damage on both crop farming and property where else the frost/freeze caused the least damage in the crops farming and the high winds caused the least damage on the properties.