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RepAssi2.html
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<body>
<h1>MOST HARMFUL AND DESTRUCTIVE ATMOSPHERIC EVENTS IN THE US IN THE 1950-2011 PERIOD</h1>
<h1></h1>
<h2>SYNOPSYS</h2>
<h1></h1>
<p>This study aims to analyze the consequences of weather events as recorded by U.S. National Oceanic and Atmospheric Administration's (NOAA) from 1950 to 2011. The study will point out the weather events causing the highest damage to human properties and agriculture crops and the events causing the highest number of injuries and fatalities.<br/>
Injuries, fatalities, property and crop damages will be analyzed separately because it is author's belief that, for instance, injuries and fatalities cannot be summed for their different nature and meaning; additionally damages to properties may be of higher interest than damages to crops, or viceversa, depending on the use made of this document and its audience.<br/>
First aggregated data will be shown. Afterwards, results will be split by decades pointing out differences, which are probably due to different methods of gathering data over time. In the end, a comparison between events from 90s and events from 00s will be reported. It will be shown that while tornados have been the most harmful and floods have been the most destructive weather events taking the time period as a whole, if we only consider the last 2 decades, hot weather have become more relevant than tornados as major cause of fatalities while floods keeps on being the worst enemy for properties.<br/>
A code-visible data processing section is included for reader to be able to reproduce the study and validate it.</p>
<h2>DATA PROCESSING</h2>
<h1></h1>
<p>In this following section, it will be described the manipulation used to clean, reshape, classify and plot the data.</p>
<h3>READING DATA, COERSION AND SUMMARY</h3>
<h1></h1>
<pre><code class="r">
## Please install and load the R.utils in order to 'bunzip' the .bz2 file
## Make also sure to have either the StormData.csv.bz2 or StormData.csv
## stored in your working directory
## Unzip and load the source file
if ((!file.exists("StormData.csv")) & (!file.exists("StormData.csv.bz2"))) {
message("please make sure to download either the file StormData.csv.bz2, or StormData.csv in your working directory")
return()
}
if ((!file.exists("StormData.csv")) & (file.exists("StormData.csv.bz2"))) {
bunzip2("StormData.csv.bz2", "StormData.csv")
}
raw_data <- read.csv("StormData.csv")
## Subsets over the columns of interest, eliminating the rest in order to
## make the huge dataframe easier to handle
data <- raw_data[, c("STATE", "BGN_DATE", "EVTYPE", "FATALITIES", "INJURIES",
"PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
## Un-factiring the char variables
data$PROPDMGEXP <- as.character(data$PROPDMGEXP)
data$CROPDMGEXP <- as.character(data$CROPDMGEXP)
data$EVTYPE <- as.character(data$EVTYPE)
## Let's take a first look to the data frame
str(data)
</code></pre>
<pre><code>## 'data.frame': 902297 obs. of 9 variables:
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ 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 "" "" "" "" ...
</code></pre>
<h3>DAMAGES CALCULATION</h3>
<h1></h1>
<p>The general idea of what we are going to implement in the next is to quantify the damages, injuries and fatalities and sum these quantities grouping by event type. To do so, first of all we need to translate damages from string to numerical values expressed in a uniform scale.
In other words, we first need to translate string multipliers (data$PROPDMGEXP and data$CROPDMGEXP columns for properties and crop damages respectively) into numeric multipliers and then multiply these multipliers with the corresponding damage values, i.e. data$PROPDMG and CROPDMG columns for properties and crop damages respectively.
Once calculated the Prop and Crop damages of each atmospheric event, we will then sum up these values grouping by event type (column data$EVTYPE). </p>
<p>This is what we are going to do in the next lines. Let's start with a quick look and to the data and let's clean it.</p>
<pre><code>##
## + - 0 1 2 3 4 5 6
## 465934 5 1 216 25 13 4 4 28 4
## 7 8 ? B H K M h m
## 5 1 8 40 6 424665 11330 1 7
</code></pre>
<pre><code>##
## 0 2 ? B K M k m
## 618413 19 1 7 9 281832 1994 21 1
</code></pre>
<p>As you can see, from the tables above, the exponential multipliers CROPDMGEXP and PROPDMGEXP are characters that we first need to clean from errors and then translate into numerical factors.
Values as 1, 2, 3 have to be translated into exponential multiplying factors 10<sup>1,</sup> 10<sup>2,</sup> 10<sup>3</sup><br/>
Chars like B(or b), M (or m), K (or k), H (or h) have to be translated into 10<sup>9,</sup> 10<sup>6,</sup> 10<sup>3,</sup> 10<sup>2</sup> respectively.
Conversely, blank values or characters like “+”, “-”, ? etc are typing errors and will be translated into a multiplier equal to 1.</p>
<p>Let's start by replacing errors.</p>
<pre><code class="r">## Let's first build the conversion table
fact_as_char <- c("B", "b", "M", "m", "K", "k", "h", "H", "9", "8", "7", "6",
"5", "4", "3", "2", "1", "0")
fact_as_num <- c(10^9, 10^9, 10^6, 10^6, 10^3, 10^3, 10^2, 10^2, 10^9, 10^8,
10^7, 10^6, 10^5, 10^4, 10^3, 10^2, 10^1, 1)
factors <- data.frame(fact_as_char, fact_as_num)
## Those EXP string in our dataframe which are not included in the above
## conversion dataframe, are considered errors and will be replaced with
## string '0' which in turn (see the table above) corresponds to the numeric
## value of 1.
data[!(data$PROPDMGEXP %in% fact_as_char), ]$PROPDMGEXP <- "0"
data[!(data$CROPDMGEXP %in% fact_as_char), ]$CROPDMGEXP <- "0"
</code></pre>
<p>Let's now have a look to the corrected results of multipliers. As you can see below, all wrong characters have now been replaced with character “0” and these 2 columns (PROPDMGEXP and CROPDMGEXP) have been cleaned meaning that they now only take permitted values, i.e. values included in the above-defined converting table.</p>
<pre><code class="r">table(data$PROPDMGEXP)
</code></pre>
<pre><code>##
## 0 1 2 3 4 5 6 7 8 B
## 466164 25 13 4 4 28 4 5 1 40
## H K M h m
## 6 424665 11330 1 7
</code></pre>
<pre><code class="r">table(data$CROPDMGEXP)
</code></pre>
<pre><code>##
## 0 2 B K M k m
## 618439 1 9 281832 1994 21 1
</code></pre>
<p>After having cleaned them in their string fomat, let's now convert the columns PROPDMGEXP and CROPDMGEXP into numerical multipliers according to the converting table defined above and let's calculate the properties and crop damages by multiplying the value of the damage (PROPDMG and CROPDMG) of each event with the corresponding numeric multiplier that we have just calculated for PROPDMGEXP and CROPDMGEXP columns.
With this operation we have now calculated the total damage both to crop and to properties of each atmospheric event in the US from 1950 to 2011.</p>
<pre><code class="r">## Translate the string factors into numerical factors according to the
## conversion table. Caluclate total damages and save the results into 2
## news columns: data$PROPdamage and data$CROPdamage Notice that errors were
## replaced with character '0' which in turn are now converted in a numerical
## factor equal to 1.
data$PROPdamage <- data$PROPDMG * factors[match(data$PROPDMGEXP, factors$fact_as_char),
]$fact_as_num
data$CROPdamage <- data$CROPDMG * factors[match(data$CROPDMGEXP, factors$fact_as_char),
]$fact_as_num
</code></pre>
<h3>CLASSIFICATION OF ATMOSPHERIC EVENTS. PHASE 1: AUTOMATED PROCEDURE</h3>
<h1></h1>
<p>Let's now have a look to EVTYPE (event types) since what we want to do is summing all quantities of interest grouping by event type.</p>
<pre><code class="r">## Let's calculate the number of distinct EVTYPE Let's use only upper case to
## make this counting not case sensitive
data$EVTYPE <- toupper(data$EVTYPE)
events <- unique(data$EVTYPE)
unique_not_corrected_event_names <- unique(data$EVTYPE)
number_of_unique_not_corrected_event_names <- length(unique_not_corrected_event_names)
</code></pre>
<p>In the intial data set 902297 atmospheric events are classified into 898 unique categories which is such a large number that would lead to results not easy to handle and to interpret. We need to define fewer but larger categories to group events and to improve the readability and interpretation of the results. </p>
<p>Additionally, a quick look to these names reveals that a categorization is needed also to clean initial categories from numerous errors and non-uniformities. For instance, in the initial dataset WIND, WND, WINT are 3 different, separated EVTYPEs. A non-uniform classification and typing errors make these 3 categories appear as they were different each other while they actually all represent “Wind” atmospheric events. </p>
<p>To solve the above-mentioned problems, a 2 steps categorization of EVTYPES will be now performed. The first automatic step will programmatically categorize EVTYPE depending on if it contains specific words. A second optional categorization will be later performed as a refinement of this first step. In other words, on optional basis, the second step will allow categorizing all those events, which were not categorized through the first step. You can skip this second step but if you do so, you will reach results similar to those of this study but not exactly the same since by skipping the refinement step, the resulting categorization would be more generic.</p>
<p>Let's start with the first categorization step:<br/>
1. EVTYPES containing one of these words (WIND, WND, WINT) will be now classified as a Wind event type<br/>
2. EVTYPES containing the word HAIL as Hail<br/>
3. EVTYPES containing FLOOD as Flood<br/>
4. EVTYPES containing TORNADO as Tornado<br/>
5. EVTYPES containing LIGHTNING as Lightning<br/>
6. EVTYPES containing (SNOW or ICE or COLD or BLIZZARD or ICY or FREEZ or FROST or ICE) as Low_Temperature<br/>
7. EVTYPES containing (DRY or DROUGHT or WARMTH or HEAT or DRYNESS) as Hot_Dry<br/>
8. EVTYPES containing (RAIN or PRECIPITATION or WET) as Rain </p>
<pre><code class="r">
Word <- c("WIND", "WND", "WINT", "HAIL", "FLOOD", "TORNADO", "LIGHTNING", "SNOW",
"ICE", "COLD", "BLIZZARD", "ICY", "FREEZ", "FROST", "DRY", "DROUGHT", "WARMTH",
"HEAT", "DRYNESS", "RAIN", "PRECIPITATION")
Category <- c("Wind", "Wind", "Wind", "Hail", "Flood", "Tornado", "Lightning",
"Low_temperature", "Low_Temperature", "Low_Temperature", "Low_Temperature",
"Low_Temperature", "Low_Temperature", "Low_Temperature", "Hot_Dry", "Hot_Dry",
"Hot_Dry", "Hot_Dry", "Hot_Dry", "Rain", "Rain")
## Creates a conversion table for the first step categorization
first_classification <- data.frame(Word, Category)
first_classification$Word <- as.character(first_classification$Word)
first_classification$Category <- as.character(first_classification$Category)
</code></pre>
<p>Let's display the classification table and let's use it to implement this first classification step.</p>
<pre><code class="r">
first_classification
</code></pre>
<pre><code>## Word Category
## 1 WIND Wind
## 2 WND Wind
## 3 WINT Wind
## 4 HAIL Hail
## 5 FLOOD Flood
## 6 TORNADO Tornado
## 7 LIGHTNING Lightning
## 8 SNOW Low_temperature
## 9 ICE Low_Temperature
## 10 COLD Low_Temperature
## 11 BLIZZARD Low_Temperature
## 12 ICY Low_Temperature
## 13 FREEZ Low_Temperature
## 14 FROST Low_Temperature
## 15 DRY Hot_Dry
## 16 DROUGHT Hot_Dry
## 17 WARMTH Hot_Dry
## 18 HEAT Hot_Dry
## 19 DRYNESS Hot_Dry
## 20 RAIN Rain
## 21 PRECIPITATION Rain
</code></pre>
<pre><code class="r">
## Let's create and initialize a new column in the daata frame, called
## EV_CATEGORY where we'll store the new classification.
data$EV_CATEGORY <- "***"
## Perform the first classification step: if EVTYPE contains a word of the
## first step conversion table, the corresponding category in the conversion
## table will be stored in the column EV_CATEGORY
for (i in 1:nrow(first_classification)) {
data[grepl(first_classification[i, ]$Word, data$EVTYPE, fixed = TRUE), ]$EV_CATEGORY <- first_classification[i,
]$Category
}
aggr <- aggregate(data[c("FATALITIES", "INJURIES", "PROPdamage", "CROPdamage")],
by = data[c("EV_CATEGORY")], FUN = sum)
</code></pre>
<pre><code class="r">## Notice that those rows which still contains the initializing character ***
## in the EV_CATEGORY are the events which have not been classified yet.
## Let's calculate a few values about these events.
classified_fatalities <- sum(aggr[aggr$EV_CATEGORY != "***", ]$FATALITIES)
classified_injuries <- sum(aggr[aggr$EV_CATEGORY != "***", ]$INJURIES)
classified_CROPdamage <- sum(aggr[aggr$EV_CATEGORY != "***", ]$CROPdamage)
classified_PROPdamage <- sum(aggr[aggr$EV_CATEGORY != "***", ]$PROPdamage)
tot_fatalities <- sum(aggr$FATALITIES)
tot_injuries <- sum(aggr$INJURIES)
tot_CROPdamage <- sum(aggr$CROPdamage)
tot_PROPdamage <- sum(aggr$PROPdamage)
classified_number <- length(unique(data[data$EV_CATEGORY != "***", ]$EVTYPE))
uniq <- unique(data[data$EV_CATEGORY == "***", ]$EVTYPE)
unclassified_names_left <- length(uniq)
write.csv(uniq, "Unique unclassified events.csv")
</code></pre>
<h3>CLASSIFICATION OF ATMOSPHERIC EVENTS. PHASE 2: MANUAL OPTIONAL PROCEDURE</h3>
<h1></h1>
<p>According to this first step classification, the initial 898 unique categories have been reduced since 621 of them were grouped into 21 more compelling categories. This categorization allowed to classify:<br/>
1)95.4 % of total injuries<br/>
2)89.7 % of total fatalities<br/>
3)86.1 % of total crop damages<br/>
4)64.7 % of total property damages into these new categories defined above. </p>
<p>To improve the amount of classified injuries, fatalities, properties and crop damages, a second, manual classification can be performed on optional basis on the remaining 277 events which have not been classified in the first step classification. </p>
<p>In the last line of the last code chunk, these unclassifies 277 events have been saved in a file (“Unique unclassified events.csv”) for a subsequent optional manual classification to refine the final results.<br/>
The manual events classification applied to this study are listed in the Appendix 1.
At this point you have 2 options:<br/>
1) If you want to reproduce exactly this study, you need to take the table in Appendix 1 and save those values in a csv file in your working directory. This file has to be saved as “Unique events after manual classification.csv”, should not contain any header and should use “;” as values separator. By doing so, you will reach ecactly the same results as this study.<br/>
2) Alternatively, you can skip this manual operation. The event types which were not classified in the first step will be all included in a generic category called Other. Results would be analogous to those of this study but less accurate.</p>
<p>In the next few lines, this second conversion table will be open and read in R and will be used to classify the 277 events which have not been classified in the first step classification. </p>
<pre><code class="r">## Let's now load the manually edited classification table which will allows
## us to classify the remaining events.
if (file.exists("Unique events after manual classification.csv")) {
## If the file exists, load the conversion table and apply the second step
## manual classification
Storm_classification <- read.table("Unique events after manual classification.csv",
sep = ";")
names(Storm_classification) <- c("Word", "Category")
Storm_classification$Category <- as.character(Storm_classification$Category)
Storm_classification$Word <- as.character(Storm_classification$Word)
for (i in 1:nrow(Storm_classification)) {
d <- data$EV_CATEGORY == "***" & data$EVTYPE == Storm_classification[i,
]$Word
data[d, ]$EV_CATEGORY <- Storm_classification[i, ]$Category
}
} else {
## If file does not exist, the second step manual classification will be
## skipped and the remaining events will be grouped in the Other category.
message("hol")
d <- data$EV_CATEGORY == "***"
data[d, ]$EV_CATEGORY <- "Other"
}
df <- aggregate(data[c("FATALITIES", "INJURIES", "PROPdamage", "CROPdamage")],
by = data[c("EV_CATEGORY")], FUN = sum)
classified_number <- length(unique(data[data$EV_CATEGORY != "***", ]$EVTYPE))
</code></pre>
<p>The classification process of EVTYPE is now ended.
All the initial 902297 events have been categorized into the following 18 categories.</p>
<pre><code class="r">df$EV_CATEGORY
</code></pre>
<pre><code>## [1] "Avalanche" "Duststorm" "Fire"
## [4] "Flood" "Hail" "Hot_Dry"
## [7] "Hurricane" "Landslide" "Lightning"
## [10] "Low_Temperature" "Low_temperature" "Other"
## [13] "Rain" "Sea" "Tornado"
## [16] "Typhoon" "Waterspout" "Wind"
</code></pre>
<h3>EVALUATING THE WEIGHT OF CATEGORY “OTHER”</h3>
<h1></h1>
<p>As you can see, among the new categories, there is one called “Other” which contains all those events which could not be classified in other way.</p>
<p>Before proceeding with our analysis, let's evaluate the “weight” of the category “Other”, compared with the rest of categories to make sure that it is small enough not to miss significant results for a too generic classification: </p>
<pre><code class="r">## Calculate the weight of 'Other' with respect of other categories in terms
## of fatalities, injuries and damages.
Other_fatalities <- sum(df[df$EV_CATEGORY == "Other", ]$FATALITIES)
Other_injuries <- sum(df[df$EV_CATEGORY == "Other", ]$INJURIES)
Other_CROPdamage <- sum(df[df$EV_CATEGORY == "Other", ]$CROPdamage)
Other_PROPdamage <- sum(df[df$EV_CATEGORY == "Other", ]$PROPdamage)
tot_fatalities <- sum(df$FATALITIES)
tot_injuries <- sum(df$INJURIES)
tot_CROPdamage <- sum(df$CROPdamage)
tot_PROPdamage <- sum(df$PROPdamage)
Non_other <- length(data[data$EV_CATEGORY != "Other", ]$EVTYPE)
Other <- length(data[data$EV_CATEGORY == "Other", ]$EVTYPE)
</code></pre>
<p>Of the initial 902297 events, as low as 5640 events have been classified in the group Other while 896657 have been classified in more specific categories. </p>
<p>Additionally, the category “Other” represents:<br/>
1) 0.98 % of total injuries<br/>
2) 0.78 % of total fatalities<br/>
3) 0.02 % of total crop damages<br/>
4) 0.02 % of total proprerty damages </p>
<p>This numbers make me conclude that the category “Other”“ is small enough and we do not need further classification refinements.</p>
<p>The number provided above are true under the assumption that you the manual classification of event type have not been skipped.
If you decided to skip the manual classification step of event types, all the events which have not been classified through the automated first classification step were included in the "Other” category. If you decided to skip the manual classification the numbers presented above would be therefore sensibily higher.<br/>
More specifically, in case of skipping the manual optional classification, the category “Other”“ would represent:<br/>
1) 4.62 % of total injuries<br/>
2) 10.29 % of total fatalities<br/>
3) 13.85 % of total crop damages<br/>
4) 35.34 % of total proprerty damages<br/>
From now on, results will be shown only in the hypotesis that the second manual classification has been performed.</p>
<p>Let's now sum the variables of interest, grouping them by the new categories we have introduced. Finally let's plot the results. We will point out the 5 event types causing the highest number of fatalities, the top 5 categories causing the highest damages to crop and so on. </p>
<h3>BUILDING PLOT 1 (DISPLAYED IN THE RESULTS SECTION): AGGREGATED DATA FROM 1950 to 2011</h3>
<h1></h1>
<p>In this and following section, 3 combined plots will be creatd. Plase refer to the result section, to visualize them and read the comments on them.</p>
<pre><code class="r">
## Let's first load a few library
library("ggplot2")
library("scales")
library("gridExtra")
</code></pre>
<pre><code>## Loading required package: grid
</code></pre>
<pre><code class="r">
## Aggregate the variables of interest according to the calssification we
## made above.
df <- aggregate(data[c("FATALITIES", "INJURIES", "PROPdamage", "CROPdamage")],
by = data[c("EV_CATEGORY")], FUN = sum)
df$EV_CATEGORY <- as.character(df$EV_CATEGORY)
## Calculate the weight of each event with respect of all other events. An
## example to better explain: the tornado which hit the states in 1961, what
## percentage of injuries, fatalities etc have coused with respect of total
## injuries, fatalities etc since 1950?
df$Fat_percentage <- round(df$FATALITIES/sum(df$FATALITIES), 3)
df$Inj_percentage <- round(df$INJURIES/sum(df$INJURIES), 3)
df$CROP_percentage <- round(df$CROPdamage/sum(df$CROPdamage), 3)
df$PROP_percentage <- round(df$PROPdamage/sum(df$PROPdamage), 3)
## Inizializing the 4 new columns which will contain the percentage of each
## event with respect to the rest.
df$Fat_Cat <- "Others"
df$Inj_Cat <- "Others"
df$CROP_Cat <- "Others"
df$PROP_Cat <- "Others"
## Mark the top 5 event type per variable of interest
df <- df[order(-df$Fat_percentage), ]
df[1:5, ]$Fat_Cat <- df[1:5, ]$EV_CATEGORY
df <- df[order(-df$Inj_percentage), ]
df[1:5, ]$Inj_Cat <- df[1:5, ]$EV_CATEGORY
df <- df[order(-df$CROP_percentage), ]
df[1:5, ]$CROP_Cat <- df[1:5, ]$EV_CATEGORY
df <- df[order(-df$PROP_percentage), ]
df[1:5, ]$PROP_Cat <- df[1:5, ]$EV_CATEGORY
## Let's create the plots to display.
## Let's first disable de scientific notation for numbers
options(scipen = 999)
## Plot 1: fatalities
df1 <- aggregate(df[, c("Fat_percentage", "FATALITIES")], by = df[c("Fat_Cat")],
FUN = sum)
df1 <- df1[order(-df1$Fat_percentage), ]
n <- format(sum(df1$FATALITIES), big.mark = ",")
titl <- paste("Top 5 causes of FATALITIES\nTotal fatalities in the period = ",
n, sep = " ")
p1 <- ggplot(data = df1[df1$Fat_Cat != "Others", ], aes(x = reorder(Fat_Cat,
Fat_percentage), y = Fat_percentage)) + geom_bar(stat = "identity") + scale_size_area() +
xlab("") + ylab("As % of the total in the period") + ggtitle(titl) + scale_y_continuous(labels = percent) +
geom_text(aes(label = percent(Fat_percentage)), vjust = 1.5, colour = "white")
## Plot 2: injuries
df2 <- aggregate(df[, c("Inj_percentage", "INJURIES")], by = df[c("Inj_Cat")],
FUN = sum)
df2 <- df2[order(-df2$Inj_percentage), ]
n <- format(sum(df2$INJURIES), big.mark = ",")
titl <- paste("Top 5 causes of INJURIES\nTotal injuries in the period = ", n,
sep = " ")
p2 <- ggplot(data = df2[df2$Inj_Cat != "Others", ], aes(x = reorder(Inj_Cat,
Inj_percentage), y = Inj_percentage)) + geom_bar(stat = "identity") + scale_size_area() +
xlab("") + ylab("As % of the total in the period") + ggtitle(titl) + scale_y_continuous(labels = percent) +
geom_text(aes(label = percent(Inj_percentage)), vjust = 1.5, colour = "white")
## Plot 3: Property damages
df3 <- aggregate(df[, c("PROP_percentage", "PROPdamage")], by = df[c("PROP_Cat")],
FUN = sum)
df3 <- df3[order(-df3$PROP_percentage), ]
n <- format(round(sum(df3$PROPdamage), 0), big.mark = ",")
titl <- paste("Top 5 causes of PROPERTY DAMAGES\nTotal property damages in the period = ",
n, "$", sep = " ")
p3 <- ggplot(data = df3[df3$PROP_Cat != "Others", ], aes(x = reorder(PROP_Cat,
PROP_percentage), y = PROP_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(PROP_percentage)),
vjust = 1.5, colour = "white")
## Plot 4: Crop damages
df4 <- aggregate(df$CROP_percentage, df$CROPdamage, by = df[c("CROP_Cat")],
FUN = sum)
df4 <- aggregate(df[, c("CROP_percentage", "CROPdamage")], by = df[c("CROP_Cat")],
FUN = sum)
df4 <- df4[order(-df4$CROP_percentage), ]
n <- format(round(sum(df4$CROPdamage), 0), big.mark = ",")
titl <- paste("Top 5 causes of CROP DAMAGES\nTotal crop damages in the period = ",
n, "$", sep = " ")
p4 <- ggplot(data = df4[df4$CROP_Cat != "Others", ], aes(x = reorder(CROP_Cat,
CROP_percentage), y = CROP_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(CROP_percentage)),
vjust = 1.5, colour = "white")
## Create a title
mn = textGrob("MOST HARMFUL AND MOST DAMAGING WEATHER EVENTS IN THE US, 1950-2011\n",
gp = gpar(fontsize = 20))
## The plot has been built but it will be displayed in the results section
</code></pre>
<h3>BUILDING PLOT 2 (DISPLAYED IN THE RESULTS SECTION): DATA PER DECADES</h3>
<h1></h1>
<pre><code class="r">## Let's now create the same graphs but split by decades.
## Defactorize the variable BGN_DATE and coerce it into a data format
data$BGN_DATE <- as.Date(data$BGN_DATE, format = "%m/%d/%Y")
## Calculate the 'decade' from the year stored into BGN_DATE
data$Decade <- as.character(trunc((as.numeric(format(data$BGN_DATE, "%Y")) -
1900)/10) * 10)
## A few adjustments
data[data$Decade == "100", ]$Decade <- "00"
data[data$Decade == "110", ]$Decade <- "10"
## Create a ordered factor with decades
data$Decade = factor(data$Decade, levels = c("50", "60", "70", "80", "90", "00",
"10"))
## Aggregating data of interest by decades
df <- aggregate(data[c("FATALITIES", "INJURIES", "PROPdamage", "CROPdamage")],
by = data[c("EV_CATEGORY", "Decade")], FUN = sum)
df2 <- aggregate(data[c("FATALITIES", "INJURIES", "PROPdamage", "CROPdamage")],
by = data[c("Decade")], FUN = sum)
## Create the plots
## Plot 1: fatalities
titl <- "FATALITIES in the US over decades"
p1b <- ggplot(data = df2, aes(x = Decade, y = FATALITIES)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("Fatalities") + ggtitle(titl) + scale_y_continuous(labels = comma) +
geom_text(aes(label = format(round((FATALITIES), 0), big.mark = ",")), vjust = 1.5,
colour = "white")
## Plot 2: injuries
titl <- "INJURIES in the US over decades"
p2b <- ggplot(data = df2, aes(x = Decade, y = INJURIES)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("Injuries") + ggtitle(titl) + scale_y_continuous(labels = comma) +
geom_text(aes(label = format(round((INJURIES), 0), big.mark = ",")), vjust = 1.5,
colour = "white")
# Plot 3: Property damages
titl <- "PROP DAMAGES in the US over decades in million US$"
p3b <- ggplot(data = df2, aes(x = Decade, y = PROPdamage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("Damages in million US$") + ggtitle(titl) +
scale_y_continuous(labels = comma) + geom_text(aes(label = format(round((PROPdamage/1000000),
0), big.mark = ",")), vjust = -0.2)
## Plot 4: Crop damages
titl <- "CROP DAMAGES in the US over decades in million US$"
p4b <- ggplot(data = df2, aes(x = Decade, y = CROPdamage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("Damages in US$ in million US$") + ggtitle(titl) +
scale_y_continuous(labels = comma) + geom_text(aes(label = format(round((CROPdamage/1000000),
0), big.mark = ",")), vjust = -0.2)
## Title of the plot
mnb = textGrob("DAMAGES, FATALITIES AND INJURIES EVOLUTION OVER DECADES IN THE US\n FROM THE 50s TO THE 10s (10s includes only the year 2011)",
gp = gpar(fontsize = 20))
</code></pre>
<h3>BUILDING PLOT 3 PART 1 (DISPLAYED IN THE RESULTS SECTION): THE 90s</h3>
<h1></h1>
<pre><code class="r">## In this section, graphs referring to years 90s are generated. After the
## initial subsetting operation, the code is exactly the same as for the
## aggregated case already explained and commented above.
Nineties <- df[df$Decade == "90", ]
Nineties$EV_CATEGORY <- as.character(Nineties$EV_CATEGORY)
Nineties$Fat_percentage <- round(Nineties$FATALITIES/sum(Nineties$FATALITIES),
3)
Nineties$Inj_percentage <- round(Nineties$INJURIES/sum(Nineties$INJURIES), 3)
Nineties$CROP_percentage <- round(Nineties$CROPdamage/sum(Nineties$CROPdamage),
3)
Nineties$PROP_percentage <- round(Nineties$PROPdamage/sum(Nineties$PROPdamage),
3)
Nineties$Fat_Cat <- "Others"
Nineties$Inj_Cat <- "Others"
Nineties$CROP_Cat <- "Others"
Nineties$PROP_Cat <- "Others"
Nineties <- Nineties[order(-Nineties$Fat_percentage), ]
Nineties[1:5, ]$Fat_Cat <- Nineties[1:5, ]$EV_CATEGORY
Nineties <- Nineties[order(-Nineties$Inj_percentage), ]
Nineties[1:5, ]$Inj_Cat <- Nineties[1:5, ]$EV_CATEGORY
Nineties <- Nineties[order(-Nineties$CROP_percentage), ]
Nineties[1:5, ]$CROP_Cat <- Nineties[1:5, ]$EV_CATEGORY
Nineties <- Nineties[order(-Nineties$PROP_percentage), ]
Nineties[1:5, ]$PROP_Cat <- Nineties[1:5, ]$EV_CATEGORY
Nineties1 <- aggregate(Nineties[, c("Fat_percentage", "FATALITIES")], by = Nineties[c("Fat_Cat")],
FUN = sum)
Nineties1 <- Nineties1[order(-Nineties1$Fat_percentage), ]
n <- format(sum(Nineties1$FATALITIES), big.mark = ",")
titl <- paste("Top 5 causes of FATALITIES in the 90s\nTotal fatalities in the period = ",
n, sep = " ")
p_nineties_1 <- ggplot(data = Nineties1[Nineties1$Fat_Cat != "Others", ], aes(x = reorder(Fat_Cat,
Fat_percentage), y = Fat_percentage)) + geom_bar(stat = "identity") + scale_size_area() +
xlab("") + ylab("As % of the total in the period") + ggtitle(titl) + scale_y_continuous(labels = percent) +
geom_text(aes(label = percent(Fat_percentage)), vjust = 1.5, colour = "white")
Nineties2 <- aggregate(Nineties[, c("Inj_percentage", "INJURIES")], by = Nineties[c("Inj_Cat")],
FUN = sum)
Nineties2 <- Nineties2[order(-Nineties2$Inj_percentage), ]
n <- format(sum(Nineties2$INJURIES), big.mark = ",")
titl <- paste("Top 5 causes of INJURIES in the 90s\nTotal injuries in the period = ",
n, sep = " ")
p_nineties_2 <- ggplot(data = Nineties2[Nineties2$Inj_Cat != "Others", ], aes(x = reorder(Inj_Cat,
Inj_percentage), y = Inj_percentage)) + geom_bar(stat = "identity") + scale_size_area() +
xlab("") + ylab("As % of the total in the period") + ggtitle(titl) + scale_y_continuous(labels = percent) +
geom_text(aes(label = percent(Inj_percentage)), vjust = 1.5, colour = "white")
Nineties3 <- aggregate(Nineties[, c("PROP_percentage", "PROPdamage")], by = Nineties[c("PROP_Cat")],
FUN = sum)
Nineties3 <- Nineties3[order(-Nineties3$PROP_percentage), ]
n <- format(round(sum(Nineties3$PROPdamage), 0), big.mark = ",")
titl <- paste("Top 5 causes of PROPERTY DAMAGES in the 90s\nTotal property damages in the period = ",
n, "$", sep = " ")
p_nineties_3 <- ggplot(data = Nineties3[Nineties3$PROP_Cat != "Others", ], aes(x = reorder(PROP_Cat,
PROP_percentage), y = PROP_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(PROP_percentage)),
vjust = 1.5, colour = "white")
Nineties4 <- aggregate(Nineties$CROP_percentage, Nineties$CROPdamage, by = Nineties[c("CROP_Cat")],
FUN = sum)
Nineties4 <- aggregate(Nineties[, c("CROP_percentage", "CROPdamage")], by = Nineties[c("CROP_Cat")],
FUN = sum)
Nineties4 <- Nineties4[order(-Nineties4$CROP_percentage), ]
n <- format(round(sum(Nineties4$CROPdamage), 0), big.mark = ",")
titl <- paste("Top 5 causes of CROP DAMAGES in the 90s\nTotal crop damages in the period = ",
n, "$", sep = " ")
p_nineties_4 <- ggplot(data = Nineties4[Nineties4$CROP_Cat != "Others", ], aes(x = reorder(CROP_Cat,
CROP_percentage), y = CROP_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(CROP_percentage)),
vjust = 1.5, colour = "white")
</code></pre>
<h3>BUILDING PLOT 3 PART 2 (DISPLAYED IN THE RESULTS SECTION): THE 2000s</h3>
<h1></h1>
<pre><code class="r">
## In this section, graphs for the years 2000s are created. After the
## initial subsetting operation, the code is exactly the same as for the
## aggregated case already explained and commented above.
Twothausands <- df[df$Decade == "00" | df$Decade == "10", ]
Twothausands <- aggregate(Twothausands[, c("FATALITIES", "INJURIES", "PROPdamage",
"CROPdamage")], by = Twothausands[c("EV_CATEGORY")], FUN = sum)
Twothausands$EV_CATEGORY <- as.character(Twothausands$EV_CATEGORY)
Twothausands$Fat_percentage <- round(Twothausands$FATALITIES/sum(Twothausands$FATALITIES),
3)
Twothausands$Inj_percentage <- round(Twothausands$INJURIES/sum(Twothausands$INJURIES),
3)
Twothausands$CROP_percentage <- round(Twothausands$CROPdamage/sum(Twothausands$CROPdamage),
3)
Twothausands$PROP_percentage <- round(Twothausands$PROPdamage/sum(Twothausands$PROPdamage),
3)
Twothausands$Fat_Cat <- "Others"
Twothausands$Inj_Cat <- "Others"
Twothausands$CROP_Cat <- "Others"
Twothausands$PROP_Cat <- "Others"
Twothausands <- Twothausands[order(-Twothausands$Fat_percentage), ]
Twothausands[1:5, ]$Fat_Cat <- Twothausands[1:5, ]$EV_CATEGORY
Twothausands <- Twothausands[order(-Twothausands$Inj_percentage), ]
Twothausands[1:5, ]$Inj_Cat <- Twothausands[1:5, ]$EV_CATEGORY
Twothausands <- Twothausands[order(-Twothausands$CROP_percentage), ]
Twothausands[1:5, ]$CROP_Cat <- Twothausands[1:5, ]$EV_CATEGORY
Twothausands <- Twothausands[order(-Twothausands$PROP_percentage), ]
Twothausands[1:5, ]$PROP_Cat <- Twothausands[1:5, ]$EV_CATEGORY
Twothausands1 <- aggregate(Twothausands[, c("Fat_percentage", "FATALITIES")],
by = Twothausands[c("Fat_Cat")], FUN = sum)
Twothausands1 <- Twothausands1[order(-Twothausands1$Fat_percentage), ]
n <- format(sum(Twothausands1$FATALITIES), big.mark = ",")
titl <- paste("Top 5 causes of FATALITIES in the 00s + 2011\nTotal fatalities in the period = ",
n, sep = " ")
p_Twothausands_1 <- ggplot(data = Twothausands1[Twothausands1$Fat_Cat != "Others",
], aes(x = reorder(Fat_Cat, Fat_percentage), y = Fat_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(Fat_percentage)),
vjust = 1.5, colour = "white")
Twothausands2 <- aggregate(Twothausands[, c("Inj_percentage", "INJURIES")],
by = Twothausands[c("Inj_Cat")], FUN = sum)
Twothausands2 <- Twothausands2[order(-Twothausands2$Inj_percentage), ]
n <- format(sum(Twothausands2$INJURIES), big.mark = ",")
titl <- paste("Top 5 causes of INJURIES in the 00s + 2011\nTotal injuries in the period = ",
n, sep = " ")
p_Twothausands_2 <- ggplot(data = Twothausands2[Twothausands2$Inj_Cat != "Others",
], aes(x = reorder(Inj_Cat, Inj_percentage), y = Inj_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(Inj_percentage)),
vjust = 1.5, colour = "white")
Twothausands3 <- aggregate(Twothausands[, c("PROP_percentage", "PROPdamage")],
by = Twothausands[c("PROP_Cat")], FUN = sum)
Twothausands3 <- Twothausands3[order(-Twothausands3$PROP_percentage), ]
n <- format(round(sum(Twothausands3$PROPdamage), 0), big.mark = ",")
titl <- paste("Top 5 causes of PROPERTY DAMAGES in the 00s + 2011\nTotal property damages in the period = ",
n, "$", sep = " ")
p_Twothausands_3 <- ggplot(data = Twothausands3[Twothausands3$PROP_Cat != "Others",
], aes(x = reorder(PROP_Cat, PROP_percentage), y = PROP_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(PROP_percentage)),
vjust = 1.5, colour = "white")
Twothausands4 <- aggregate(Twothausands$CROP_percentage, Twothausands$CROPdamage,
by = Twothausands[c("CROP_Cat")], FUN = sum)
Twothausands4 <- aggregate(Twothausands[, c("CROP_percentage", "CROPdamage")],
by = Twothausands[c("CROP_Cat")], FUN = sum)
Twothausands4 <- Twothausands4[order(-Twothausands4$CROP_percentage), ]
n <- format(round(sum(Twothausands4$CROPdamage), 0), big.mark = ",")
titl <- paste("Top 5 causes of CROP DAMAGES in the 00s + 2011\nTotal crop damages in the period = ",
n, "$", sep = " ")
p_Twothausands_4 <- ggplot(data = Twothausands4[Twothausands4$CROP_Cat != "Others",
], aes(x = reorder(CROP_Cat, CROP_percentage), y = CROP_percentage)) + geom_bar(stat = "identity") +
scale_size_area() + xlab("") + ylab("As % of the total in the period") +
ggtitle(titl) + scale_y_continuous(labels = percent) + geom_text(aes(label = percent(CROP_percentage)),
vjust = 1.5, colour = "white")
mnc = textGrob("MOST HARMFUL AND MOST DAMAGING WEATHER EVENTS IN THE US\nComparison between the 90s vs 00s+2011",
gp = gpar(fontsize = 20))
</code></pre>
<h2>RESULTS</h2>
<h1></h1>
<p>Let's see which were the worst 5 types of waether events for each variable of interest. We will presents the result by commenting the 3 following combined graphs:<br/>
1) 1 combinined graph showing the top 5 causes of damage to property, damage to crop, injuries and fatalities in the 1950-2011 period<br/>
2) 1 combinined graph showing the evolution across decades of damage to crop and to property, injuries and fatalities<br/>
3) 1 combinined graph comparing the 5 causes of damage to property, damage to crop, injuries and fatalities in the 90s versus the corresponding causes in the 2000s </p>
<pre><code class="r">## Display 1950-2011 plot
grid.arrange(p1, p2, p3, p4, nrow = 2, main = mn)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-17"/> </p>
<p>If we consider the whole period from the 1950 to 2011, Tornado has been the most harmful weather event in the US causing on its own nearly 40% of total fatalities and up to 65% of total injuries in the period for weather conditions.<br/>
However the graph also shows that Tornado is not the most destructive atmospheric event: it is "only” the third major cause of property damage and it is not even in the top 5 weather causes of highest damage to crop.</p>
<p>Hot_dry was the type of weather event causing of highest damage to crop accounting for nearly a third of all dmage to crops since 1950.<br/>
Flood is even worse since not only it is the event causing the highest damage to properties (nearly 40%) but it is also the event causing the second highest damage to crops accounting for a 25% of all damages..</p>
<pre><code class="r">
## Display the by decades plot
grid.arrange(p1b, p2b, p3b, p4b, nrow = 2, main = mnb)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-18"/> </p>