Software Environment information sessionInfo() ## R version 4.0.2 (2020-06-22) ## Platform: x86_64-apple-darwin17.0 (64-bit) ## Running under: macOS 10.16 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base
## ## loaded via a namespace (and not attached): ## [1] compiler_4.0.2 magrittr_1.5 tools_4.0.2 htmltools_0.5.0 ## [5] yaml_2.2.1 stringi_1.4.6 rmarkdown_2.3 knitr_1.29
## [9] stringr_1.4.0 xfun_0.16 digest_0.6.25 rlang_0.4.7
## [13] evaluate_0.14 Loading packages library(R.utils) # load bz2 file library(data.table) library(dplyr) library(ggplot2) library(tidyr) Download and read bz2 file if (!file.exists(“stormdata.csv.bz2”)) { url <- “https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2” download.file(url, “stormdata.csv.bz2”) bunzip2(“stormdata.csv.bz2”, “stormdata.csv”, remove=FALSE) }

storm <- data.table::fread(“stormdata.csv”, fill=TRUE, header=TRUE) head(storm) ## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE ## 1: 1.00 4/18/1950 0:00:00 0130 CST 97 MOBILE AL ## 2: 1.00 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL ## 3: 1.00 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL ## 4: 1.00 6/8/1951 0:00:00 0900 CST 89 MADISON AL ## 5: 1.00 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL ## 6: 1.00 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE 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 ## 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 6 Look at column names at the data,

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” we see there’s over 37 of variables. However, for the purpose of this analysis, we won’t been needing all the columns, so I’ll be using dplyr to subset them and lowercase them

storm2 <- storm %>% select(c(“EVTYPE”,“FATALITIES”,“INJURIES”,“PROPDMG”,“PROPDMGEXP”,“CROPDMG”,“CROPDMGEXP”)) %>% rename_all(tolower) str(storm2) ## Classes ‘data.table’ and ‘data.frame’: 902297 obs. of 7 variables: ## $ evtype : chr “TORNADO” “TORNADO” “TORNADO” “TORNADO” … ## $ 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: chr “K” “K” “K” “K” … ## $ cropdmg : num 0 0 0 0 0 0 0 0 0 0 … ## $ cropdmgexp: chr “” “” “” “” … ## - attr(*, “.internal.selfref”)= Based on the information above, the data table now has 902,297 rows and 7 columns. Below is a brief description of each variable.

evtype : storm event type fatalities: amount of fatalities per event injuries : amount of injuries per event propdmg : property damage amount propdmgexp: property damage in exponents cropdmg : crop damage amount cropdmgexp: crop damage in exponents Data Processing Processing data for population health analysis length(unique(storm$EVTYPE)) ## [1] 985 First I select columns I need for the bar plot, group it by event type and calculate sum of both fatalities and injuries. Then, arrange it in descending order and slice the first 10 rows, then gather it and turning it into categorical variables for creating a grouped bar plot.

pop_health <- storm2 %>% select(evtype, fatalities, injuries) %>% group_by(evtype) %>% summarize(fatalities = sum(fatalities), injuries = sum(injuries), .groups=‘drop’) %>% arrange(desc(fatalities), desc(injuries)) %>% slice(1:10) %>% gather(key = type, value = value, fatalities, injuries) Processing data for economic consequences analysis the variable PROPDMGEXP is regarding property damage expenses, so it can be utilized to denote the events with greatest economic consequences

unique(storm2\(propdmgexp) ## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8" unique(storm2\)cropdmgexp) ## [1] “” “M” “K” “m” “B” “?” “0” “k” “2” The values for the exponents for property and crop damage costs are messy, so I created a function to deal with that, and to calculate the cost with their respective exponent values (but in millions).

create function to calculate cost

cost <- function(x) { if (x == “H”) 1E-4 else if (x == “K”) 1E-3 else if (x == “M”) 1 else if (x == “B”) 1E3 else 1-6 } Aside from the function to calculate cost, the methodology is pretty much the same for the rest of the manipulation.

economic <- storm2 %>% select(“evtype”, “propdmg”, “propdmgexp”, “cropdmg”, “cropdmgexp”) %>% mutate(prop_dmg = propdmgsapply(propdmgexp, FUN = cost), crop_dmg = cropdmgsapply(cropdmgexp, FUN = cost), .keep=“unused”) %>% group_by(evtype) %>% summarize(property = sum(prop_dmg), crop = sum(crop_dmg), .groups=‘drop’) %>% arrange(desc(property), desc(crop)) %>% slice(1:10) %>% gather(key = type, value = value, property, crop) Results With the data processed and ready for creating plots, we can now answer both questions.

  1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? ggplot(data=pop_health, aes(reorder(evtype, -value), value, fill=type)) + geom_bar(position = “dodge”, stat=“identity”) + labs(x=“Event Type”, y=“Count”) + theme_bw() + theme(axis.text.x = element_text(angle = 20, vjust=0.7)) + ggtitle(“Total Number of Fatalities and Injuries of top 10 storm event types”) + scale_fill_manual(values=c(“red”, “pink”))

Based on the bar plot, it’s evident that tornadoes have the highest impact on the popoulation health, since it causes the most fatalities and injuries.

  1. Across the United States, which types of events have the greatest economic consequences? ggplot(data=economic, aes(reorder(evtype, -value), value, fill=type)) + geom_bar(position = “dodge”, stat=“identity”) + labs(x=“Event Type”, y=“Count (millions)”) + theme_bw() + theme(axis.text.x = element_text(angle = 25, vjust=0.5)) + ggtitle(“Total Cost of Property and Crop Damage by top 10 storm event types”) + scale_fill_manual(values=c(“darkgreen”, “grey”))

From the bar plot, Floods and Hurricanes/Typhoons have highest property and crop damage costs, thus resulting in the biggest economic consequences.