Reproducible Research Project 2: Analysis of Weather Information to Assess Impacts
Francisco Chávez
2025-02-15
Introduction
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This 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.
Data
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
Storm Data [47Mb] https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf National Climatic Data Center Storm Events FAQ https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Data Processing
Dta Load
First we load the data as a table, and transform the date columns, in case we need to do some plotting.:
storm_data = read.csv('repdata_data_StormData.csv.bz2')
storm_data$BGN_DATE = as.Date(storm_data$BGN_DATE, format = '%m/%d/%Y %H:%M:%S')
storm_data$END_DATE = as.Date(storm_data$END_DATE, format = '%m/%d/%Y %H:%M:%S')Subsetting the data
We will only need a few columns to defin the effects of each event. First the type of event, and then the human and economic impact the event had.
relevant_columns <- c('EVTYPE', 'FATALITIES', 'INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')
storm_data <- storm_data[, relevant_columns]
summary(storm_data)## EVTYPE FATALITIES INJURIES PROPDMG
## Length:902297 Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Mode :character Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
## PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000str(storm_data)## '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 "" "" "" "" ...Calibrating the numbers
We also have the order of magnitude the events had a,dn that is done through the GEXP variables, so we need to convert the damages both property and crop, using the scale listed.
PROPDM_keys <- c("\"\"" = 10^0, "-" = 10^0, "+" = 10^0, "0" = 10^0, "1" = 10^1, "2" = 10^2, "3" = 10^3, "4" = 10^4, "5" = 10^5,
"6" = 10^6, "7" = 10^7, "8" = 10^8, "9" = 10^9, "H" = 10^2, "K" = 10^3, "M" = 10^6, "B" = 10^9)
CROPDMG_keys <- c("\"\"" = 10^0, "?" = 10^0, "0" = 10^0, "K" = 10^3, "M" = 10^6, "B" = 10^9)
storm_data$PROPDMGEXP = PROPDM_keys[as.character(storm_data$PROPDMGEXP)]
storm_data$PROPDMGEXP[is.na(storm_data$PROPDMGEXP)] <- 0
storm_data$CROPDMGEXP = CROPDMG_keys[as.character(storm_data$CROPDMGEXP)]
storm_data$CROPDMGEXP[is.na(storm_data$CROPDMGEXP)] <- 0
storm_data$PROPDMG_DLLS = storm_data$PROPDMG * storm_data$PROPDMGEXP
storm_data$CROPDMG_DLLS = storm_data$CROPDMG * storm_data$CROPDMGEXP Summarization
So with the right order of magnitude for the actual variables, we now generate a couple of tables with the human and economic total effects:
total_human <- storm_data %>% group_by(EVTYPE) %>% summarise(total_fatalities = sum(FATALITIES), total_injuries = sum(INJURIES), total=total_fatalities+total_injuries) %>% arrange(desc(total))
total_dlls <- storm_data %>% group_by(EVTYPE) %>% summarise(total_prop_damage = sum(PROPDMG_DLLS), total_crop_damage = sum(CROPDMG_DLLS), total=total_prop_damage+total_crop_damage) %>% arrange(desc(total))Results
Population Health
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Ranking
First we generate the top-10 events for each type of human damange, fatalities or injuries, and if we look at them side by side, the general human impact mostly resembles the same order for fatalities and injuries:
total_human %>% arrange(desc(total_fatalities)) %>% head(10)## # A tibble: 10 × 4
## EVTYPE total_fatalities total_injuries total
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 FLASH FLOOD 978 1777 2755
## 4 HEAT 937 2100 3037
## 5 LIGHTNING 816 5230 6046
## 6 TSTM WIND 504 6957 7461
## 7 FLOOD 470 6789 7259
## 8 RIP CURRENT 368 232 600
## 9 HIGH WIND 248 1137 1385
## 10 AVALANCHE 224 170 394There’s a slight variation in order:
total_human %>% arrange(desc(total_injuries)) %>% head(10)## # A tibble: 10 × 4
## EVTYPE total_fatalities total_injuries total
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 TSTM WIND 504 6957 7461
## 3 FLOOD 470 6789 7259
## 4 EXCESSIVE HEAT 1903 6525 8428
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 ICE STORM 89 1975 2064
## 8 FLASH FLOOD 978 1777 2755
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 HAIL 15 1361 1376Summary
But in general and by far, tornados have the highest human impact of all:
health_impact <- melt(data.table(head(total_human,10)), id.vars = "EVTYPE", variable.name = "Impact")
ggplot(health_impact, aes(x = reorder(EVTYPE, -value), y = value)) +
geom_bar(stat = "identity", aes(fill = Impact), position = "dodge") +
ylab("Total Human Impact") +
xlab("Event Type") +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Top 10 most impactful US Weather Events on Humans") +
theme(plot.title = element_text(hjust = 0.5))
Economic Impact
Across the United States, which types of events have the greatest economic consequences?
Ranking
If we look at crop and property damage separately, we can see that property damage is mostly caused by flooding.
total_dlls %>% arrange(desc(total_prop_damage)) %>% head(10)## # A tibble: 10 × 4
## EVTYPE total_prop_damage total_crop_damage total
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709800 5661968450 150319678250
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56935880674. 414953270 57350833944.
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 FLASH FLOOD 16822673772. 1421317100 18243990872.
## 6 HAIL 15730367456. 3025537470 18755904926.
## 7 HURRICANE 11868319010 2741910000 14610229010
## 8 TROPICAL STORM 7703890550 678346000 8382236550
## 9 WINTER STORM 6688497251 26944000 6715441251
## 10 HIGH WIND 5270046295 638571300 5908617595While crop damange happens mostly with drought and flood events, but even that number is nowhere close as the property damage that has been caused by all events.
total_dlls %>% arrange(desc(total_crop_damage)) %>% head(10)## # A tibble: 10 × 4
## EVTYPE total_prop_damage total_crop_damage total
## <chr> <dbl> <dbl> <dbl>
## 1 DROUGHT 1046106000 13972566000 15018672000
## 2 FLOOD 144657709800 5661968450 150319678250
## 3 RIVER FLOOD 5118945500 5029459000 10148404500
## 4 ICE STORM 3944927860 5022113500 8967041360
## 5 HAIL 15730367456. 3025537470 18755904926.
## 6 HURRICANE 11868319010 2741910000 14610229010
## 7 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 8 FLASH FLOOD 16822673772. 1421317100 18243990872.
## 9 EXTREME COLD 67737400 1292973000 1360710400
## 10 FROST/FREEZE 9480000 1094086000 1103566000Summary
When looking at the total economic impact, flooding is the root causes for property and drop damage:
eonomic_impact <- melt(data.table(head(total_dlls,10)), id.vars = "EVTYPE", variable.name = "Impact")
ggplot(eonomic_impact, aes(x = reorder(EVTYPE, -value), y = value)) +
geom_bar(stat = "identity", aes(fill = Impact), position = "dodge") +
ylab("Total Economic Impact") +
xlab("Event Type") +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Top 10 most impactful US Weather Events on the Economy") +
theme(plot.title = element_text(hjust = 0.5))