NOAA Storm Database analysis for U.S. healt and economic consequences due to severe weather events
Corrado Lanera
17 giugno 2015
Synopsis
Most 10 complete sentences.
In this report we aim to find the sever weather events that had the worst consequences on population healt and economic in the United State from 1950 to 2011. We based our analysis on the National Oceanic and Atmospheric Administration (NOAA) Storm Database. The results of the analysis clearly identify the tornado as the most harmful event wrt population healt and the flood as the one with the greatest economic consequences. To perform the research we used the statistical program language R.
Data Processing
Preamble
To perform the analysis we use R with the
package: dplyr. Full code are reported below whit each
explicit output. Morover the code to produce the present document is
written in R Markdown and can be found inside mine
GitHub repository [4].
Final configuration of the system is:
sessionInfo() # provide principal info about configuration of the system## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_India.utf8 LC_CTYPE=English_India.utf8
## [3] LC_MONETARY=English_India.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_India.utf8
##
## time zone: Asia/Calcutta
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.34 R6_2.5.1 fastmap_1.1.1 xfun_0.41
## [5] cachem_1.0.8 knitr_1.45 htmltools_0.5.7 rmarkdown_2.25
## [9] lifecycle_1.0.4 cli_3.6.2 sass_0.4.8 jquerylib_0.1.4
## [13] compiler_4.3.2 rstudioapi_0.15.0 tools_4.3.2 evaluate_0.23
## [17] bslib_0.6.1 yaml_2.3.8 rlang_1.1.3 jsonlite_1.8.8Loading & processing the Raw Data
Describes (in words and code) how the data were loaded into R and processed for analysis. In particular, your analysis must start from the raw CSV file containing the data. You cannot do any preprocessing outside the document. If preprocessing is time-consuming you may consider using the cache = TRUE option for certain code chunks.
From NOAA Storm Database [1] we obtain data on 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. We obtain file from 1950 up to November 2011. Some description of the data can be found in [2] and [3].
Data are stored in the standard comma-separated-value format, compress with bzip2 algorithm.
library(ggplot2)
if(!file.exists("stormData.csv.bz2")) {
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
destfile = "stormData.csv.bz2", method = "curl")
}Reading & selecting data
After loading data we have created the table to visualize information in a useful way Next we convert names in compatibility names both for R and for applications which do not allow underline in names.
dsNOAA <- read.csv(bzfile("stormData.csv.bz2"), sep=",", header=T)
head(dsNOAA)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 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
## 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
## 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 6tidyNOAA <- dsNOAA[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]
head(tidyNOAA)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0tidyNOAA$PROPDMGNUM = 0
## fill in the data with correct units
tidyNOAA[tidyNOAA$PROPDMGEXP == "H", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "H", ]$PROPDMG * 10^2
tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMG * 10^3
tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMG * 10^6
tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMG * 10^9
head(tidyNOAA, 100)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.00 K 0
## 2 TORNADO 0 0 2.50 K 0
## 3 TORNADO 0 2 25.00 K 0
## 4 TORNADO 0 2 2.50 K 0
## 5 TORNADO 0 2 2.50 K 0
## 6 TORNADO 0 6 2.50 K 0
## 7 TORNADO 0 1 2.50 K 0
## 8 TORNADO 0 0 2.50 K 0
## 9 TORNADO 1 14 25.00 K 0
## 10 TORNADO 0 0 25.00 K 0
## 11 TORNADO 0 3 2.50 M 0
## 12 TORNADO 0 3 2.50 M 0
## 13 TORNADO 1 26 250.00 K 0
## 14 TORNADO 0 12 0.00 K 0
## 15 TORNADO 0 6 25.00 K 0
## 16 TORNADO 4 50 25.00 K 0
## 17 TORNADO 0 2 25.00 K 0
## 18 TORNADO 0 0 25.00 K 0
## 19 TORNADO 0 0 25.00 K 0
## 20 TORNADO 0 0 25.00 K 0
## 21 TORNADO 0 0 25.00 K 0
## 22 TORNADO 0 0 2.50 K 0
## 23 TORNADO 0 0 2.50 K 0
## 24 TORNADO 0 1 25.00 K 0
## 25 TORNADO 0 1 25.00 K 0
## 26 TORNADO 1 8 25.00 K 0
## 27 TORNADO 0 2 25.00 K 0
## 28 TORNADO 0 1 25.00 K 0
## 29 TORNADO 0 6 25.00 K 0
## 30 TORNADO 0 2 2.50 K 0
## 31 TORNADO 0 0 2.50 K 0
## 32 TORNADO 0 12 2.50 K 0
## 33 TORNADO 0 0 25.00 K 0
## 34 TORNADO 6 195 2.50 M 0
## 35 TORNADO 0 2 25.00 K 0
## 36 TORNADO 7 12 250.00 K 0
## 37 TORNADO 0 0 2.50 K 0
## 38 TORNADO 2 3 25.00 K 0
## 39 TORNADO 0 2 2.50 K 0
## 40 TORNADO 0 0 25.00 K 0
## 41 TORNADO 0 0 2.50 K 0
## 42 TORNADO 0 1 25.00 K 0
## 43 TORNADO 0 0 2.50 K 0
## 44 TORNADO 0 0 25.00 K 0
## 45 TORNADO 0 0 25.00 K 0
## 46 TORNADO 0 0 0.03 K 0
## 47 TORNADO 0 1 25.00 K 0
## 48 TORNADO 0 4 250.00 K 0
## 49 TORNADO 0 26 250.00 K 0
## 50 TORNADO 0 3 2.50 K 0
## 51 TORNADO 0 2 2.50 K 0
## 52 TORNADO 0 0 25.00 K 0
## 53 TORNADO 0 1 25.00 K 0
## 54 TSTM WIND 0 0 0.00 0
## 55 HAIL 0 0 0.00 0
## 56 HAIL 0 0 0.00 0
## 57 TSTM WIND 0 0 0.00 0
## 58 HAIL 0 0 0.00 0
## 59 TSTM WIND 0 0 0.00 0
## 60 TSTM WIND 0 0 0.00 0
## 61 HAIL 0 0 0.00 0
## 62 HAIL 0 0 0.00 0
## 63 HAIL 0 0 0.00 0
## 64 TSTM WIND 0 0 0.00 0
## 65 TSTM WIND 0 0 0.00 0
## 66 TSTM WIND 0 0 0.00 0
## 67 HAIL 0 0 0.00 0
## 68 TORNADO 0 1 25.00 K 0
## 69 TSTM WIND 0 0 0.00 0
## 70 TORNADO 5 20 2.50 M 0
## 71 TSTM WIND 0 0 0.00 0
## 72 TSTM WIND 0 0 0.00 0
## 73 TSTM WIND 0 0 0.00 0
## 74 HAIL 0 0 0.00 0
## 75 TSTM WIND 0 0 0.00 0
## 76 TORNADO 0 0 2.50 K 0
## 77 TORNADO 0 0 2.50 K 0
## 78 TORNADO 0 0 25.00 K 0
## 79 TORNADO 0 0 2.50 M 0
## 80 TORNADO 0 5 2.50 M 0
## 81 TSTM WIND 0 0 0.00 0
## 82 HAIL 0 0 0.00 0
## 83 TSTM WIND 0 0 0.00 0
## 84 TSTM WIND 0 0 0.00 0
## 85 TSTM WIND 0 0 0.00 0
## 86 TSTM WIND 0 0 0.00 0
## 87 TSTM WIND 0 0 0.00 0
## 88 HAIL 0 0 0.00 0
## 89 HAIL 0 0 0.00 0
## 90 TORNADO 0 0 25.00 K 0
## 91 TSTM WIND 0 0 0.00 0
## 92 HAIL 0 0 0.00 0
## 93 TSTM WIND 0 0 0.00 0
## 94 TORNADO 25 200 2.50 M 0
## 95 TSTM WIND 0 0 0.00 0
## 96 TSTM WIND 0 0 0.00 0
## 97 TSTM WIND 0 0 0.00 0
## 98 TSTM WIND 0 0 0.00 0
## 99 TORNADO 0 2 25.00 K 0
## 100 HAIL 0 0 0.00 0
## PROPDMGNUM
## 1 25000
## 2 2500
## 3 25000
## 4 2500
## 5 2500
## 6 2500
## 7 2500
## 8 2500
## 9 25000
## 10 25000
## 11 2500000
## 12 2500000
## 13 250000
## 14 0
## 15 25000
## 16 25000
## 17 25000
## 18 25000
## 19 25000
## 20 25000
## 21 25000
## 22 2500
## 23 2500
## 24 25000
## 25 25000
## 26 25000
## 27 25000
## 28 25000
## 29 25000
## 30 2500
## 31 2500
## 32 2500
## 33 25000
## 34 2500000
## 35 25000
## 36 250000
## 37 2500
## 38 25000
## 39 2500
## 40 25000
## 41 2500
## 42 25000
## 43 2500
## 44 25000
## 45 25000
## 46 30
## 47 25000
## 48 250000
## 49 250000
## 50 2500
## 51 2500
## 52 25000
## 53 25000
## 54 0
## 55 0
## 56 0
## 57 0
## 58 0
## 59 0
## 60 0
## 61 0
## 62 0
## 63 0
## 64 0
## 65 0
## 66 0
## 67 0
## 68 25000
## 69 0
## 70 2500000
## 71 0
## 72 0
## 73 0
## 74 0
## 75 0
## 76 2500
## 77 2500
## 78 25000
## 79 2500000
## 80 2500000
## 81 0
## 82 0
## 83 0
## 84 0
## 85 0
## 86 0
## 87 0
## 88 0
## 89 0
## 90 25000
## 91 0
## 92 0
## 93 0
## 94 2500000
## 95 0
## 96 0
## 97 0
## 98 0
## 99 25000
## 100 0tidyNOAA$CROPDMGNUM = 0
## assign correct values based on parameters
tidyNOAA[tidyNOAA$CROPDMGEXP == "H", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "H", ]$CROPDMG * 10^2
tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMG * 10^3
tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMG * 10^6
tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMG * 10^9The variables we are interested in are the type of
event (EVTYPE), fatalities
(FATALITIES) and injuries
(INJURIES) and those describing the ammount of
damage (all fields including DMG). Here we extract
those variables and print a sample of ten cases to watch them
togheter.
Results
At least one figure containing a plot.
Figures may have multiple plots in them (i.e. panel plots), but there cannot be more than three figures total.
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
In order to find the most harmful event with respect to population healt we firstly select only those variable concerning the type of event, fatalities and injuries. Next we consider the total ammount of both wrt the type of event. Next we consider the sum of fatalities and injuries and rank each events according to the number of fatalities, injuries and the sum of them. Finally we show the first ten events wrt to rank of total injuries and fatalities.
fatalities <- aggregate(FATALITIES ~ EVTYPE, data=tidyNOAA, sum)
fatalities <- fatalities[order(-fatalities$FATALITIES), ][1:10, ]
fatalities$EVTYPE <- factor(fatalities$EVTYPE, levels = fatalities$EVTYPE)
ggplot(fatalities, aes(x = EVTYPE, y = FATALITIES)) +
geom_bar(stat = "identity", fill = "red", las = 3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event") + ylab("Fatalities") + ggtitle("Number of fatalities by top 10 Weather Events")## Warning in geom_bar(stat = "identity", fill = "red", las = 3): Ignoring unknown
## parameters: `las`
It is clear that in all considered cases (only fatalities, only injuries or the sum of both) tornado is the most harmful event wrt population healt.
Across the United States, which types of events have the greatest economic consequences?
As above, in order to find the event with the greatest economic consequences we firstly select only the variable of the dataset concerning the type of event, and the ones which report the damages ammount. More in detail we compute separately the property damage and the crop damage computing the explicit ammount of damage wrt the events. Next we rank the events wrt each of type of damage and for the total ammount of them. Finally we show the first ten events wrt to rank of total economic consequences.
damages <- aggregate(PROPDMGNUM + CROPDMGNUM ~ EVTYPE, data=tidyNOAA, sum)
names(damages) = c("EVTYPE", "TOTALDAMAGE")
damages <- damages[order(-damages$TOTALDAMAGE), ][1:10, ]
damages$EVTYPE <- factor(damages$EVTYPE, levels = damages$EVTYPE)
ggplot(damages, aes(x = EVTYPE, y = TOTALDAMAGE)) +
geom_bar(stat = "identity", fill = "green", las = 3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Damages ($)") + ggtitle("Property & Crop Damages by top 10 Weather Events")## Warning in geom_bar(stat = "identity", fill = "green", las = 3): Ignoring
## unknown parameters: `las`
As in the case of population healt it is one events that clearly is the worst one. In this case it is not the tornado (which reach the 3rd position) but the Flood is the events with the greatest economic consequences.