Reproducible Research Peer Assessment 2: NOAA Storm Data Analysis

Synopsis

This is the code to completement Reproducible Research Peer Assessment 2.

The assignment 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.

The data for this assignment can be found at: Storm Data

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

National Climatic Data Center Storm Events FAQ

Questions to be answered

This data analysis will address the following questions:

Across the United States, which types of events are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?

Data Processing

Load the data

storm_data <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))[, 1:28]
names(storm_data) <- tolower(names(storm_data))
names(storm_data)

##  [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"

head(storm_data, 5)

##   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
##    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
##   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
##   injuries propdmg propdmgexp cropdmg cropdmgexp
## 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

Analysis 1: Across the United States, which types of events are most harmful with respect to population health?

Process fatalities and injuries data

harm_data <- storm_data[, c("evtype", "fatalities", "injuries")]
fatalities_summary <- aggregate(fatalities ~ evtype, harm_data, sum)
injuries_summary <- aggregate(injuries ~ evtype, harm_data, sum)

select top 8 event types which are most harmful with respect to population health

top_8_evtype_fatalities <- fatalities_summary[with(fatalities_summary, order(-fatalities)), 
    ][1:8, ]
top_8_evtype_injuries <- injuries_summary[with(injuries_summary, order(-injuries)), 
    ][1:8, ]

Analysis 2: Across the United States, which types of events have the greatest economic consequences?

Process prop impact

storm_data$prop_impact <- storm_data$propdmg * 1e-09
storm_data[grepl("K|k", storm_data$propdmgexp), ]$prop_impact <- storm_data[grepl("K|k", 
    storm_data$propdmgexp), ]$propdmg * 1e-06
storm_data[grepl("M|m", storm_data$propdmgexp), ]$prop_impact <- storm_data[grepl("M|m", 
    storm_data$propdmgexp), ]$propdmg * 0.001
storm_data[grepl("B|b", storm_data$propdmgexp), ]$prop_impact <- storm_data[grepl("B|b", 
    storm_data$propdmgexp), ]$propdmg

Process crop impacts

storm_data$crop_impact <- storm_data$cropdmg * 1e-09
storm_data[grepl("K|k", storm_data$cropdmgexp), ]$crop_impact <- storm_data[grepl("K|k", 
    storm_data$cropdmgexp), ]$cropdmg * 1e-06
storm_data[grepl("M|m", storm_data$cropdmgexp), ]$crop_impact <- storm_data[grepl("M|m", 
    storm_data$cropdmgexp), ]$cropdmg * 0.001
storm_data[grepl("B|b", storm_data$cropdmgexp), ]$crop_impact <- storm_data[grepl("B|b", 
    storm_data$cropdmgexp), ]$cropdmg

Combine impact above together and select top 8 event types which have greatest economic consequences

storm_data$economic_impact <- storm_data$prop_impact + storm_data$crop_impact
economic_impact_summary <- aggregate(economic_impact ~ evtype, storm_data, sum)
top_8_evtype_economic_impact <- economic_impact_summary[with(economic_impact_summary, 
    order(-economic_impact)), ][1:8, ]

Results

Question 1: Across the United States, which types of events are most harmful with respect to population health?

Fatalities

pie(top_8_evtype_fatalities$fatalities, labels = top_8_evtype_fatalities$evtype)

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Injuries

pie(top_8_evtype_injuries$injuries, labels = top_8_evtype_injuries$evtype)

plot of chunk unnamed-chunk-8

Question 2: Across the United States, which types of events have the greatest economic consequences?

pie(top_8_evtype_economic_impact$economic_impact, labels = paste(top_8_evtype_economic_impact$evtype, 
    "=", top_8_evtype_economic_impact$economic_impact, " billions of USD"))

plot of chunk unnamed-chunk-9