Storm Data Database Analysis
JOHN JAIRO PRADO PIÑERES
14/9/2020
STORM DATA DATABASE ANALYSIS
1. SYNOPSIS
Storm Data is an official publication of the National Oceanic and Atmospheric Administration (NOAA) that documents the occurrence of storms and other major weather events.
Depending on the intensity it can cause death, injury, significant property damage and / or business interruption.
Analyze rare and unusual weather phenomena that generate media attention, such as snow. gusts in South Florida or coastal San Diego among other major weather events.
This project analyzes severe weather events that can cause economic and public health problems.
2. Data Processing
2.1 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]>
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.
We load the libraries that we are going to use
packages <- c('markdown', #Is a plain-text formatting syntax that can be converted to 'XHTML' or other formats.
'knitr', #Tool for generating dynamic reports in R.
'dplyr', #For data manipulation.
'lubridate', #To work with date-times and time-spans.
'ggplot2', #For graphics
'sqldf', #configure and transparently import a database
'lattice', #Data visualization
'Hmisc' #Useful functions for data analysis,
#high-level graphing, impute missing values and import and annotate data sets
)
installed <- packages %in% installed.packages()
if(sum(installed == F) > 0) {
install.packages(packages[!installed])
}
lapply(packages,require,character.only = T)Loading and preprocessing the data.
setwd('F:/1. PROYECTOS DE TRABAJO/RStudio/5. Reproducible Research/Activity 2/')
getwd()## [1] "F:/1. PROYECTOS DE TRABAJO/RStudio/5. Reproducible Research/Activity 2"df <- read.csv('repdata_data_StormData.csv')
Sys.setlocale('LC_TIME', 'English')## [1] "English_United States.1252"General exploratory analysis and data type.
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, uniondim(df)## [1] 902297 37head(df)## 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 6glimpse(df)## Observations: 902,297
## Variables: 37
## $ STATE__ <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ BGN_DATE <fct> 4/18/1950 0:00:00, 4/18/1950 0:00:00, 2/20/1951 0:00:00,...
## $ BGN_TIME <fct> 0130, 0145, 1600, 0900, 1500, 2000, 0100, 0900, 2000, 20...
## $ TIME_ZONE <fct> CST, CST, CST, CST, CST, CST, CST, CST, CST, CST, CST, C...
## $ COUNTY <dbl> 97, 3, 57, 89, 43, 77, 9, 123, 125, 57, 43, 9, 73, 49, 1...
## $ COUNTYNAME <fct> MOBILE, BALDWIN, FAYETTE, MADISON, CULLMAN, LAUDERDALE, ...
## $ STATE <fct> AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, ...
## $ EVTYPE <fct> TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TO...
## $ BGN_RANGE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ BGN_AZI <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ BGN_LOCATI <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ END_DATE <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ END_TIME <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ COUNTY_END <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ COUNTYENDN <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ END_RANGE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ END_AZI <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ END_LOCATI <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ LENGTH <dbl> 14.0, 2.0, 0.1, 0.0, 0.0, 1.5, 1.5, 0.0, 3.3, 2.3, 1.3, ...
## $ WIDTH <dbl> 100, 150, 123, 100, 150, 177, 33, 33, 100, 100, 400, 400...
## $ F <int> 3, 2, 2, 2, 2, 2, 2, 1, 3, 3, 1, 1, 3, 3, 3, 4, 1, 1, 1,...
## $ MAG <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0,...
## $ INJURIES <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, ...
## $ PROPDMG <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2....
## $ PROPDMGEXP <fct> K, K, K, K, K, K, K, K, K, K, M, M, K, K, K, K, K, K, K,...
## $ CROPDMG <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ CROPDMGEXP <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ WFO <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ STATEOFFIC <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ ZONENAMES <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ LATITUDE <dbl> 3040, 3042, 3340, 3458, 3412, 3450, 3405, 3255, 3334, 33...
## $ LONGITUDE <dbl> 8812, 8755, 8742, 8626, 8642, 8748, 8631, 8558, 8740, 87...
## $ LATITUDE_E <dbl> 3051, 0, 0, 0, 0, 0, 0, 0, 3336, 3337, 3402, 3404, 0, 34...
## $ LONGITUDE_ <dbl> 8806, 0, 0, 0, 0, 0, 0, 0, 8738, 8737, 8644, 8640, 0, 85...
## $ REMARKS <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ REFNUM <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1...sort(names(df))## [1] "BGN_AZI" "BGN_DATE" "BGN_LOCATI" "BGN_RANGE" "BGN_TIME"
## [6] "COUNTY" "COUNTY_END" "COUNTYENDN" "COUNTYNAME" "CROPDMG"
## [11] "CROPDMGEXP" "END_AZI" "END_DATE" "END_LOCATI" "END_RANGE"
## [16] "END_TIME" "EVTYPE" "F" "FATALITIES" "INJURIES"
## [21] "LATITUDE" "LATITUDE_E" "LENGTH" "LONGITUDE" "LONGITUDE_"
## [26] "MAG" "PROPDMG" "PROPDMGEXP" "REFNUM" "REMARKS"
## [31] "STATE" "STATE__" "STATEOFFIC" "TIME_ZONE" "WFO"
## [36] "WIDTH" "ZONENAMES"Basic statistics
summary(df)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588294##Results
##Questions
Your data analysis must address the following questions:
1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Variable selection (reducing the data set to only needed columns and variables).
df2 <- df[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP",
"CROPDMG", "CROPDMGEXP")]General exploratory analysis and data type.
library(dplyr)
dim(df2)## [1] 902297 7head(df2)## 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 0glimpse(df2)## Observations: 902,297
## Variables: 7
## $ EVTYPE <fct> TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TO...
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0,...
## $ INJURIES <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, ...
## $ PROPDMG <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2....
## $ PROPDMGEXP <fct> K, K, K, K, K, K, K, K, K, K, M, M, K, K, K, K, K, K, K,...
## $ CROPDMG <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ CROPDMGEXP <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,sort(names(df2))## [1] "CROPDMG" "CROPDMGEXP" "EVTYPE" "FATALITIES" "INJURIES"
## [6] "PROPDMG" "PROPDMGEXP"Basic statistics
summary(df2)## EVTYPE FATALITIES INJURIES
## HAIL :288661 Min. : 0.0000 Min. : 0.0000
## TSTM WIND :219940 1st Qu.: 0.0000 1st Qu.: 0.0000
## THUNDERSTORM WIND: 82563 Median : 0.0000 Median : 0.0000
## TORNADO : 60652 Mean : 0.0168 Mean : 0.1557
## FLASH FLOOD : 54277 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## FLOOD : 25326 Max. :583.0000 Max. :1700.0000
## (Other) :170878
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9Reviewing events that cause the most fatalities (The Top-10 Fatalities by Weather Event).
Procedure = aggregate the top 10 fatalities by the event type and sort the output in descending order.
Fatalities <- aggregate(FATALITIES ~ EVTYPE, data = df2, FUN = sum)
Top10_Fatalities <- Fatalities[order(-Fatalities$FATALITIES), ][1:10, ]
Top10_Fatalities## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224Reviewing events that cause the most injuries (The Top-10 Injuries by Weather Event).
Procedure = aggregate the top 10 injuries by the event type and sort the output in descending order.
Injuries <- aggregate(INJURIES ~ EVTYPE, data = df2, FUN = sum)
Top10_Injuries <- Injuries[order(-Injuries$INJURIES), ][1:10, ]
Top10_Injuries ## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 1361Plot of Top 10 Fatalities & Injuries for Weather Event Types ( Population Health Impact ).
Proecedure = plot graphs showing the top 10 fatalities and injuries.
par(mfrow=c(1,2),mar=c(11,5,5,2))
barplot(Top10_Fatalities$FATALITIES,names.arg=Top10_Fatalities$EVTYPE,las=2,col='violet',ylab='fatalities',main='Top 10 fatalities')
barplot(Top10_Injuries$INJURIES,names.arg=Top10_Injuries$EVTYPE,las=2,col='violet',ylab='injuries',main='Top 10 Injuries')
Figure 1: The weather event responsbile for the highest fatalities and injuries is the ‘Tornado’.
2. Across the United States, which types of events have the greatest economic consequences?
An analysis of the weather events responsible for the greatest economic consequences.
Hypothesis: Economic consequences means damages. The two significant types of damage typically caused by weather events include ‘properties and crops’.
Data Exploration & Findings …
As a result, let’s convert the exponent columns into numeric data for the calculation of total property and crop damages encountered.
Defining & Calcuating [ Property Damage ].
Property damage exponents for each level listed out & assigned those values for the property exponent data.
Invalid data was excluded by assigning the value as ‘0’.
Then, the property damage value was calculated by multiplying the property damage and property exponent value.
unique(df2$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Mdf2$PROPEXP[df2$PROPDMGEXP == "K"] <- 1000
df2$PROPEXP[df2$PROPDMGEXP == "M"] <- 1e+06
df2$PROPEXP[df2$PROPDMGEXP == ""] <- 1
df2$PROPEXP[df2$PROPDMGEXP == "B"] <- 1e+09
df2$PROPEXP[df2$PROPDMGEXP == "m"] <- 1e+06
df2$PROPEXP[df2$PROPDMGEXP == "0"] <- 1
df2$PROPEXP[df2$PROPDMGEXP == "5"] <- 1e+05
df2$PROPEXPP[df2$PROPDMGEXP == "6"] <- 1e+06
df2$PROPEXP[df2$PROPDMGEXP == "4"] <- 10000
df2$PROPEXP[df2$PROPDMGEXP == "2"] <- 100
df2$PROPEXP[df2$PROPDMGEXP == "3"] <- 1000
df2$PROPEXP[df2$PROPDMGEXP == "h"] <- 100
df2$PROPEXP[df2$PROPDMGEXP == "7"] <- 1e+07
df2$PROPEXP[df2$PROPDMGEXP == "H"] <- 100
df2$PROPEXP[df2$PROPDMGEXP == "1"] <- 10
df2$PROPEXP[df2$PROPDMGEXP == "8"] <- 1e+08
# Assigning '0' to invalid exponent strmdata
df2$PROPEXP[df2$PROPDMGEXP == "+"] <- 0
df2$PROPEXP[df2$PROPDMGEXP == "-"] <- 0
df2$PROPEXP[df2$PROPDMGEXP == "?"] <- 0
# Calculating the property damage value
df2$PROPDMGVAL <- df2$PROPDMG * df2$PROPEXPDefining & Calcuating [ Crop Damage ].
Crop damage exponents for each level listed out & assigned those values for the crop exponent data.
Invalid data was excluded by assigning the value as ‘0’.
Then, the crop damage value was calculated by multiplying the crop damage and crop exponent value.
unique(df2$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m M# Assigning values for the crop exponent strmdata
df2$CROPEXP[df2$CROPDMGEXP == "M"] <- 1e+06
df2$CROPEXP[df2$CROPDMGEXP == "K"] <- 1000
df2$CROPEXP[df2$CROPDMGEXP == "m"] <- 1e+06
df2$CROPEXP[df2$CROPDMGEXP == "B"] <- 1e+09
df2$CROPEXP[df2$CROPDMGEXP == "0"] <- 1
df2$CROPEXP[df2$CROPDMGEXP == "k"] <- 1000
df2$CROPEXP[df2$CROPDMGEXP == "2"] <- 100
df2$CROPEXP[df2$CROPDMGEXP == ""] <- 1
# Assigning '0' to invalid exponent strmdata
df2$CROPEXP[df2$CROPDMGEXP == "?"] <- 0
# calculating the crop damage
df2$CROPDMGVAL <- df2$CROPDMG * df2$CROPEXPProperty Damage Summary.
Procedure = aggregate the property damage by the event type and sort the output it in descending order.
prop <- aggregate(PROPDMGVAL~EVTYPE,data=df2,FUN=sum,na.rm=TRUE)
prop <- prop[with(prop,order(-PROPDMGVAL)),]
prop <- head(prop, 10)
prop## EVTYPE PROPDMGVAL
## 170 FLOOD 144657709807
## 411 HURRICANE/TYPHOON 69305840000
## 834 TORNADO 56947380617
## 670 STORM SURGE 43323536000
## 153 FLASH FLOOD 16822673979
## 244 HAIL 15735267513
## 402 HURRICANE 11868319010
## 848 TROPICAL STORM 7703890550
## 972 WINTER STORM 6688497251
## 359 HIGH WIND 5270046260Crop Damage Summary
Procedure = aggregate the crop damage by the event type and sort the output it in descending order.
crop <- aggregate(CROPDMGVAL~EVTYPE,data=df2,FUN=sum,na.rm=TRUE)
crop <- crop[with(crop,order(-CROPDMGVAL)),]
crop <- head(crop, 10)
crop## EVTYPE CROPDMGVAL
## 95 DROUGHT 13972566000
## 170 FLOOD 5661968450
## 590 RIVER FLOOD 5029459000
## 427 ICE STORM 5022113500
## 244 HAIL 3025954473
## 402 HURRICANE 2741910000
## 411 HURRICANE/TYPHOON 2607872800
## 153 FLASH FLOOD 1421317100
## 140 EXTREME COLD 1292973000
## 212 FROST/FREEZE 1094086000Plot of Top 10 Property & Crop damages by Weather Event Types (Economic Consequences).
plot the graph showing the top 10 property and crop damages.
par(mfrow=c(1,2),mar=c(11,4,4,2))
barplot(prop$PROPDMGVAL/(10^9),names.arg=prop$EVTYPE,las=2,col="blue",ylab="Prop.damage(billions)",main="Top10 Prop.Damages")
barplot(crop$CROPDMGVAL/(10^9),names.arg=crop$EVTYPE,las=2,col="blue",ylab="Crop damage(billions)",main="Top10 Crop.Damages")