Reproducible Research Peer Assignment 2
Nihar Satsangi
April 28, 2017
Title: NOAA Storm Data Analysis - Health Effects and Economic Consequences of Severe Weather Events
Synopsis
The basic goal of this assignment is to explore the NOAA Storm Database and address the following questions:
Q1) Across the United States, which types of events are most harmful with respect to population health?
Q2) Across the United States, which types of events have the greatest economic consequences?
After careful analysis, tornadoes were found to be the most hazardous event with respect to public health, and the greatest economic impact was caused by flash floods and droughts.
Data Processing
Setting up Global Options and Loading the Required Libraries:
knitr::opts_chunk$set(echo = TRUE)
library(plyr)
library(dplyr)
library(ggplot2)
library(grid)
library(gridExtra)Getting information of the environment of the analysis:
sessionInfo()## R version 3.3.1 (2016-06-21)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 14393)
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gridExtra_2.2.1 ggplot2_2.2.1 dplyr_0.5.0 plyr_1.8.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.7 digest_0.6.9 rprojroot_1.2 assertthat_0.1
## [5] R6_2.1.2 gtable_0.2.0 DBI_0.5-1 backports_1.0.5
## [9] magrittr_1.5 scales_0.4.1 evaluate_0.10 stringi_1.1.5
## [13] lazyeval_0.2.0 rmarkdown_1.4 tools_3.3.1 stringr_1.2.0
## [17] munsell_0.4.3 yaml_2.1.14 colorspace_1.3-2 htmltools_0.3.5
## [21] knitr_1.15.1 tibble_1.2Loading and Preprocessing the Data:
stormdata <- read.csv("NOAAStormData.csv.")
head(stormdata)## 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
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6Before the data is processed for analysis, the storm data need some preprocessing. Event types don’t have a specific format. For instance, there are events with types TORNAO, TORNADO and tornado which obviously refer to the same type of event.
# number of unique items in the column EVTYPE
length(unique(stormdata$EVTYPE))## [1] 985# Convert all letters to lowercase to be able to process homogeneously
event_types <- tolower(stormdata$EVTYPE)
# replace all punct. characters with a space
event_types <- gsub("[[:blank:][:punct:]+]", " ", event_types)
length(unique(event_types))## [1] 874# update the storm frame
stormdata$EVTYPE <- event_typesData Analysis:
To find the event types that are most harmful to population health, the number of casualties are aggregated by the event type.
casualties <- ddply(stormdata, .(EVTYPE), summarize,
fatalities = sum(FATALITIES),
injuries = sum(INJURIES))
# Find events that caused most death and injury
fatal_events <- head(casualties[order(casualties$fatalities, decreasing = T), ], 10)
injury_events <- head(casualties[order(casualties$injuries, decreasing = T), ], 10)The property damage is represented with two fields, a number PROPDMG in dollars and the exponent PROPDMGEXP. Similarly, the crop damage is represented using two fields, CROPDMG and CROPDMGEXP. To find the event types that are most harmful to the economy, the property and crop damage for each event are calculated.
exp_transform <- function(e) {
# h -> hundred, k -> thousand, m -> million, b -> billion
if (e %in% c('h', 'H'))
return(2)
else if (e %in% c('k', 'K'))
return(3)
else if (e %in% c('m', 'M'))
return(6)
else if (e %in% c('b', 'B'))
return(9)
else if (!is.na(as.numeric(e))) # if a digit
return(as.numeric(e))
else if (e %in% c('', '-', '?', '+'))
return(0)
else {
stop("Invalid exponent value.")
}
}prop_dmg_exp <- sapply(stormdata$PROPDMGEXP, FUN=exp_transform)
stormdata$prop_dmg <- stormdata$PROPDMG * (10 ** prop_dmg_exp)
crop_dmg_exp <- sapply(stormdata$CROPDMGEXP, FUN=exp_transform)
stormdata$crop_dmg <- stormdata$CROPDMG * (10 ** crop_dmg_exp)# Compute the economic loss by event type
econ_loss <- ddply(stormdata, .(EVTYPE), summarize,
prop_dmg = sum(prop_dmg),
crop_dmg = sum(crop_dmg))
# filter out events that caused no economic loss
econ_loss <- econ_loss[(econ_loss$prop_dmg > 0 | econ_loss$crop_dmg > 0), ]
prop_dmg_events <- head(econ_loss[order(econ_loss$prop_dmg, decreasing = T), ], 10)
crop_dmg_events <- head(econ_loss[order(econ_loss$crop_dmg, decreasing = T), ], 10)Results
Question 1: Across the United States, which types of events are most harmful with respect to population health?
Top 10 events that caused largest number of deaths are:
fatal_events[, c("EVTYPE", "fatalities")]## EVTYPE fatalities
## 741 tornado 5633
## 116 excessive heat 1903
## 138 flash flood 978
## 240 heat 937
## 410 lightning 816
## 762 tstm wind 504
## 154 flood 470
## 515 rip current 368
## 314 high wind 248
## 19 avalanche 224#Set the levels in order
p1 <- ggplot(data=fatal_events,
aes(x=reorder(EVTYPE, fatalities), y=fatalities, fill=fatalities)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Total number of fatalities") +
xlab("Event type") +
theme(legend.position="none")
p2 <- ggplot(data=injury_events,
aes(x=reorder(EVTYPE, injuries), y=injuries, fill=injuries)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Total number of injuries") +
xlab("Event type") +
theme(legend.position="none")
grid.arrange(p1, p2, ncol = 2,top = "Most Dangerous Weather Events for Public Health")
Question 2: Across the United States, which types of events have the greatest economic consequences?
Top 10 events that caused most property damage (in dollars) are as follows:
prop_dmg_events[, c("EVTYPE", "prop_dmg")]## EVTYPE prop_dmg
## 138 flash flood 6.820237e+13
## 697 thunderstorm winds 2.086532e+13
## 741 tornado 1.078951e+12
## 209 hail 3.157558e+11
## 410 lightning 1.729433e+11
## 154 flood 1.446577e+11
## 366 hurricane typhoon 6.930584e+10
## 166 flooding 5.920826e+10
## 585 storm surge 4.332354e+10
## 270 heavy snow 1.793259e+10Similarly, the events that caused biggest crop damage are
crop_dmg_events[, c("EVTYPE", "crop_dmg")]## EVTYPE crop_dmg
## 84 drought 13972566000
## 154 flood 5661968450
## 519 river flood 5029459000
## 382 ice storm 5022113500
## 209 hail 3025974480
## 357 hurricane 2741910000
## 366 hurricane typhoon 2607872800
## 138 flash flood 1421317100
## 125 extreme cold 1312973000
## 185 frost freeze 1094186000# Set the levels in order
p1 <- ggplot(data=prop_dmg_events,
aes(x=reorder(EVTYPE, prop_dmg), y=log10(prop_dmg), fill=prop_dmg )) +
geom_bar(stat="identity") +
coord_flip() +
xlab("Event type") +
ylab("Property damage in dollars (log-scale)") +
theme(legend.position="none")
p2 <- ggplot(data=crop_dmg_events,
aes(x=reorder(EVTYPE, crop_dmg), y=crop_dmg, fill=crop_dmg)) +
geom_bar(stat="identity") +
coord_flip() +
xlab("Event type") +
ylab("Crop damage in dollars") +
theme(legend.position="none")
grid.arrange(p1, p2, ncol = 2,top = "Most Hazardous Weather Events for US Economy")