NOAA Storm Analysis
Felix An. Gar. Agui.
15/6/2020
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
Question 1 : Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
For this question we need to load the data and clean it,so we run:
library(ggplot2)
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, unionNOAA<-read.csv("repdata_data_StormData.csv", header = TRUE, sep = ",")
head(NOAA)## 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 6Then we need to Subset (NOAA) storm database:
tNOAA <- NOAA[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]
head(tNOAA)## 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 0Let’s see the structure of the data selected:
str(tNOAA)## '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 "" "" "" "" ...To calculate the economic damage the following variables (without unit) must be used:
- PROPDMG : Amount of property damage,
- CROPDMG : Amount of crop damage
PROPDMGEXP and CROPDMGEXP: Unit expressed in power of 10 of the above variables (H,K,M B means Hundreds, Thousands, Millions and Billions respectively). So we need to convert H, K, M, B units to calculate Property Damage and clean all the data por those variables. Let’s start with PROPDMG:
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "K"] <- 10^3
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "M"] <- 10^6
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == ""] <- 1
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "B"] <- 10^9
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "m"] <- 10^6
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "0"] <- 1
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "5"] <- 10^5
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "6"] <- 10^6
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "4"] <- 10^4
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "2"] <- 10^2
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "3"] <- 10^3
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "h"] <- 10^2
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "7"] <- 10^7
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "H"] <- 10^2
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "1"] <- 10^1
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "8"] <- 10^8
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "+"] <- 0
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "-"] <- 0
tNOAA$PROPDMGEXP[tNOAA$PROPDMGEXP == "?"] <- 0
tNOAA$PROPDMGEXP <- as.numeric(tNOAA$PROPDMGEXP)
tNOAA$PROPDMG <- tNOAA$PROPDMG*tNOAA$PROPDMGEXP
head(tNOAA)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25000 1000 0
## 2 TORNADO 0 0 2500 1000 0
## 3 TORNADO 0 2 25000 1000 0
## 4 TORNADO 0 2 2500 1000 0
## 5 TORNADO 0 2 2500 1000 0
## 6 TORNADO 0 6 2500 1000 0Now continuing with CROPDMG variable, we will do the same as we did with the previous variable:
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "M"] = 10^6
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "K"] = 10^3
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "m"] = 10^6
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "B"] = 10^9
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "0"] = 1
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "k"] = 10^3
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "2"] = 10^2
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "M"] = 10^6
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == ""] = 1
tNOAA$CROPDMGEXP[tNOAA$CROPDMGEXP == "?"] = 0
tNOAA$CROPDMGEXP <- as.numeric(tNOAA$CROPDMGEXP)
tNOAA$CROPDMG <- tNOAA$CROPDMG*tNOAA$CROPDMGEXP
head(tNOAA)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25000 1000 0 1
## 2 TORNADO 0 0 2500 1000 0 1
## 3 TORNADO 0 2 25000 1000 0 1
## 4 TORNADO 0 2 2500 1000 0 1
## 5 TORNADO 0 2 2500 1000 0 1
## 6 TORNADO 0 6 2500 1000 0 1Next we need to create new variables that sums up and ranked the most harmful weather type:
fatalities_10 <- tNOAA %>% group_by(EVTYPE) %>% summarize(FATALITIES= sum(FATALITIES)) %>%
top_n(10) %>% arrange(desc(FATALITIES))## `summarise()` ungrouping output (override with `.groups` argument)## Selecting by FATALITIESinjuries_10 <- tNOAA %>% group_by(EVTYPE) %>% summarize(INJURIES= sum(INJURIES)) %>%
top_n(10) %>% arrange(desc(INJURIES))## `summarise()` ungrouping output (override with `.groups` argument)## Selecting by INJURIESprop_dam10 <- tNOAA %>% group_by(EVTYPE) %>% summarize(PROPDMG= sum(PROPDMG)) %>%
top_n(10) %>% arrange(desc(PROPDMG))## `summarise()` ungrouping output (override with `.groups` argument)## Selecting by PROPDMGcrop_dam10 <- tNOAA %>% group_by(EVTYPE) %>% summarize(CROPDMG= sum(CROPDMG)) %>%
top_n(10) %>% arrange(desc(CROPDMG))## `summarise()` ungrouping output (override with `.groups` argument)## Selecting by CROPDMGfatalities_10; injuries_10; prop_dam10; crop_dam10## # A tibble: 10 x 2
## EVTYPE FATALITIES
## <chr> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224## # A tibble: 10 x 2
## EVTYPE INJURIES
## <chr> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361## # A tibble: 10 x 2
## EVTYPE PROPDMG
## <chr> <dbl>
## 1 FLOOD 144657709870
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56947381845
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16822678195
## 6 HAIL 15735270147
## 7 HURRICANE 11868319010
## 8 TROPICAL STORM 7703890550
## 9 WINTER STORM 6688497260
## 10 HIGH WIND 5270046260## # A tibble: 10 x 2
## EVTYPE CROPDMG
## <chr> <dbl>
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954473
## 6 HURRICANE 2741910000
## 7 HURRICANE/TYPHOON 2607872800
## 8 FLASH FLOOD 1421317100
## 9 EXTREME COLD 1292973000
## 10 FROST/FREEZE 1094086000Answer for question 1:
par(mfrow = c(1, 2), mar = c(10, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
fatalities_10 %>% ggplot(aes(x=reorder(EVTYPE,-FATALITIES), y =FATALITIES)) +
geom_bar(stat = "identity",position="dodge",fill ="blue" ,col = "black", alpha=0.5)+
theme_light() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type")+
ylab("Total Fatalities")+
ggtitle("Top 10 Fatalities by Weather Events")
injuries_10 %>% ggplot(aes(x=reorder(EVTYPE,-INJURIES), y =INJURIES)) +
geom_bar(stat = "identity",position="dodge",fill ="green" ,col = "black", alpha=0.5)+
theme_light() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type")+
ylab("Total Injuries")+
ggtitle("Top 10 Injuries by Weather Events")
dev.copy(png,file="PublicHealth_barplot1.png")## png
## 3dev.off()## png
## 2The weather event that causes the most harm to public health is Tornadoes. They have shown in the graphs above to be the largest cause of fatalities and injuries due to weather events in the United States.
Question 2 : Across the United States, which types of events have the greatest economic consequences?
Answer
For question 2 we just need to replace the previous commands for the variables of Economic Damage, we can present this information on the next plot:
par(mfrow = c(1, 2), mar = c(10, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
prop_dam10 %>% ggplot(aes(x=reorder(EVTYPE,-PROPDMG), y =PROPDMG)) +
geom_bar(stat = "identity",position="dodge",fill ="blue" ,col = "black", alpha=0.5)+
theme_light() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type")+
ylab("Total Property Damage")+
ggtitle("Top 10 Property Damage sources by Weather Events")
crop_dam10 %>% ggplot(aes(x=reorder(EVTYPE,-CROPDMG), y =CROPDMG)) +
geom_bar(stat = "identity",position="dodge",fill ="green" ,col = "black", alpha=0.5)+
theme_light() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type")+
ylab("Total Crop Damage")+
ggtitle("Top 10 Crop Damage sources by Weather Events")
dev.copy(png,file="EconomicDamage_barplot1.png")## png
## 3dev.off()## png
## 2Finally The events that have caused the most damage in the United states from an economic stand point are Flood, Drought and Hurricane, but for different reasons. For example the biggest risk to crops is a drougth event, whereas the biggest threat to properties are floods.