Impact of Weather Events in the US

Synopsis

This analysis report is done as a part of the Coursera’s Reproducible Research course held by John Hopkins University. The goal of the report is to is to explore U.S. National Oceanic and Atmospheric Administration’s (storm_data) storm database (https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2) and answer the following questions:

Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?

The storm_data storm 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. The database covers the time period between 1950 and 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

The following packages are needed to perform analysis.

# Needed libraries
library(dplyr)
library(knitr)
library(ggplot2)
library(gridExtra)
library(grid)

The data is downloaded from the following URL: https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2

# Download the data
url_storm_data <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url_storm_data, destfile="StormData.csv.bz2")

# Read the raw data
storm_data <- read.csv(bzfile("StormData.csv.bz2"))

head(storm_data)

##   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              6

We can see that the data frame has total 37 variables and most of them are not relevant for this analysis. The following code will create a subset of the data, which contains only needeed variables.

# Subsetting the dataset
storm_data <- storm_data[ , c('EVTYPE', 'FATALITIES', 'INJURIES', 'PROPDMG', 'PROPDMGEXP', 
                         'CROPDMG', 'CROPDMGEXP')]
                         
# Exploring the crop exponent data
unique(storm_data$CROPDMGEXP)

## [1]   M K m B ? 0 k 2
## Levels:  ? 0 2 B k K m M

To be able to calculate economic consequences, we need to first extract the exponent value of PROPDMGEXP and CROPDMGEXP columns.

where: - H,h = hundreds = 100 - K,k = kilos = thousands = 1,000 - M,m = millions = 1,000,000 - B,b = billions = 1,000,000,000

# Ddd columns for property and crop damage totals
storm_data$TOTPROPDMG <- 0
storm_data$TOTCROPDMG <- 0

numbers <- as.character(1:9)

# Calculating the property damage totals
storm_data$TOTPROPDMG[which(storm_data$PROPDMGEXP %in% numbers)] <- 
  storm_data$PROPDMG[which(storm_data$PROPDMGEXP %in% numbers)] * 1
storm_data$TOTPROPDMG[which(storm_data$PROPDMGEXP == "H")] <- 
  storm_data$PROPDMG[which(storm_data$PROPDMGEXP == "H")] * 100
storm_data$TOTPROPDMG[which(storm_data$PROPDMGEXP == "K")] <- 
  storm_data$PROPDMG[which(storm_data$PROPDMGEXP == "K")] * 1000
storm_data$TOTPROPDMG[which(storm_data$PROPDMGEXP == "M")] <- 
  storm_data$PROPDMG[which(storm_data$PROPDMGEXP == "M")] * 1000000
storm_data$TOTPROPDMG[which(storm_data$PROPDMGEXP == "B")] <- 
  storm_data$PROPDMG[which(storm_data$PROPDMGEXP == "B")] * 1000000000

# Calculating the crop damage totals
storm_data$TOTCROPDMG[which(storm_data$CROPDMGEXP %in% numbers)] <- 
  storm_data$CROPDMG[which(storm_data$CROPDMGEXP %in% numbers)] * 1
storm_data$TOTCROPDMG[which(storm_data$CROPDMGEXP == "H")] <- 
  storm_data$CROPDMG[which(storm_data$CROPDMGEXP == "H")] * 100
storm_data$TOTCROPDMG[which(storm_data$CROPDMGEXP == "K")] <- 
  storm_data$CROPDMG[which(storm_data$CROPDMGEXP == "K")] * 1000
storm_data$TOTCROPDMG[which(storm_data$CROPDMGEXP == "M")] <-
  storm_data$CROPDMG[which(storm_data$CROPDMGEXP == "M")] * 1000000
storm_data$TOTCROPDMG[which(storm_data$CROPDMGEXP == "B")] <- 
  storm_data$CROPDMG[which(storm_data$CROPDMGEXP == "B")] * 1000000000

# up-shift EVTYPE column
storm_data$EVTYPE <- toupper(storm_data$EVTYPE)

Results

Which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

# Aggregating the data by event and summarizing injuries
injury_data <- storm_data %>%
  group_by(EVTYPE) %>%
  summarize(INJURIES = sum(INJURIES)) %>%
  arrange(desc(INJURIES)) 

# Aggregating the data by event and summarizing fatalies  
fatal_data <- storm_data %>%
  group_by(EVTYPE) %>%
  summarize(FATALITIES = sum(FATALITIES)) %>%
  arrange(desc(FATALITIES)) 

# Creates a barplot containing top 10 injuries & fatalities 
plot1 <- ggplot(data=head(injury_data,10), aes(x=reorder(EVTYPE, INJURIES), y=INJURIES)) +
   geom_bar(fill="olivedrab",stat="identity") + coord_flip() +
    ylab("Total number of injuries") + xlab("Event type") +
    ggtitle("The most harmful events with respect to population health") +
    theme(legend.position="none")

plot2 <- ggplot(data=head(fatal_data,10), aes(x=reorder(EVTYPE, FATALITIES), y=FATALITIES)) +
    geom_bar(fill="red4",stat="identity") + coord_flip() +
    ylab("Total number of fatalities") + xlab("Event type") +
    theme(legend.position="none")

grid.arrange(plot1, plot2, nrow =2)

We can very clearly see from the resulting graph that TORNADO and EXCESSIVE HEAT events cause most of fatalities. TORNADO events are also causing most of the injuries. These are the most harmful weather events with respect to population health.

Across the United States, which types of events have the greatest economic consequences?

# Aggregating the data by event and summarizing property damage totals
prop_data <- storm_data %>%
  group_by(EVTYPE) %>%
  summarize(TOTPROPDMG = sum(TOTPROPDMG)) %>%
  arrange(desc(TOTPROPDMG)) 

# Convert values to millions
prop_data$TOTPROPDMG <- prop_data$TOTPROPDMG/1e6

# Creates a table containing top 10 property damagies
kable(head(prop_data, n=10),
      row.name= FALSE, col.names = c("Event Type","Cost of Damage"),
      caption = "Greatest property damagies in millions",
      format = "html",
      table.attr = "border=\"2\"")

Greatest property damagies in millions
Event Type	Cost of Damage
FLOOD	144657.710
HURRICANE/TYPHOON	69305.840
TORNADO	56925.661
STORM SURGE	43323.536
FLASH FLOOD	16140.812
HAIL	15727.367
HURRICANE	11868.319
TROPICAL STORM	7703.891
WINTER STORM	6688.497
HIGH WIND	5270.046

From the table above we can see that FLOOD, HURRICANE/TYPHOON and TORNADO events are causing most of the poperty damagies.

# Aggregating the data by event and summarizing crop damage totals 
crop_data <- storm_data %>%
  group_by(EVTYPE) %>%
  summarize(TOTCROPDMG = sum(TOTCROPDMG)) %>%
  arrange(desc(TOTCROPDMG)) 

# Convert values to millions
crop_data$TOTCROPDMG <- crop_data$TOTCROPDMG/1e6

# Creates a table containing top 10 crop damagies
kable(head(crop_data, n=10),
      row.name= FALSE, col.names = c("Event Type","Cost of Damage"),
      caption = "Greatest crop damagies in millions",
      format = "html",
      table.attr = "border=\"2\"")

Greatest crop damagies in millions
Event Type	Cost of Damage
DROUGHT	13972.566
FLOOD	5661.968
RIVER FLOOD	5029.459
ICE STORM	5022.114
HAIL	3025.537
HURRICANE	2741.910
HURRICANE/TYPHOON	2607.873
FLASH FLOOD	1421.317
EXTREME COLD	1312.973
FROST/FREEZE	1094.186

While DROUGHT and FLOOD are causing most of the crop damagies.

Course Project 2

Eeva Rasi

20th October 2017

Impact of Weather Events in the US

Synopsis

Data Processing

Results