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Task-4.lyx
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Task-4.lyx
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#LyX 2.3 created this file. For more info see http://www.lyx.org/
\lyxformat 544
\begin_document
\begin_header
\save_transient_properties true
\origin unavailable
\textclass article
\use_default_options true
\begin_modules
knitr
\end_modules
\maintain_unincluded_children false
\language english
\language_package default
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\use_package amsmath 1
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\cite_engine basic
\cite_engine_type default
\biblio_style plain
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\paperorientation portrait
\suppress_date false
\justification true
\use_refstyle 1
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\index Index
\shortcut idx
\color #008000
\end_index
\secnumdepth 3
\tocdepth 3
\paragraph_separation indent
\paragraph_indentation default
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\end_header
\begin_body
\begin_layout Title
GRIP Task 4: Exploratory Data Analysis - Terrorism
\end_layout
\begin_layout Author
Name: Debartha Paul
\end_layout
\begin_layout Section*
Importing libraries and visualising the data
\end_layout
\begin_layout Standard
We first load the libraries required for our work and then read the dataset.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Fortunately, we do not need to load any other library for this work
\end_layout
\begin_layout Plain Layout
df<-read.csv('D:
\backslash
\backslash
Important Documents
\backslash
\backslash
Internship
\backslash
\backslash
Task-4
\backslash
\backslash
globalterrorismdb_0718dist.csv',
\end_layout
\begin_layout Plain Layout
header=T)#reading the dataset
\end_layout
\begin_layout Plain Layout
dim(df)#dimensions of the dataset
\end_layout
\begin_layout Plain Layout
length(is.na(df))#number of 'na' values
\end_layout
\begin_layout Plain Layout
names(df)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Exploratory Data Analysis
\end_layout
\begin_layout Standard
We now move forward to our Exploratory Data Analysis part.
\end_layout
\begin_layout Subsection*
Attacks by Year:
\end_layout
\begin_layout Standard
The below chart describes the number of attacks by year
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
attack_year<-table(df$iyear)
\end_layout
\begin_layout Plain Layout
plot(attack_year,type='b',main='Terrorism by Year',
\end_layout
\begin_layout Plain Layout
xlab='Year',ylab='No.
of Incidents',pch=19)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
From the above chart, we observe that no.
of attacks increased over the years since 1970 till the early 90s.
However, there was a decline in the no.
of attacks till the late 90s.
Moving further, we obseve a steep incline in the no.
of attacks since the late 90s till 2014(which also has the highest number
of attacks in a calender year) and we note a decline in the no.
of attacks since then.
\end_layout
\begin_layout Subsection*
Type of Attack by Year:
\end_layout
\begin_layout Standard
We next look at the frequency of attacks over the years by their type
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
type_year<-cbind(unique(df$iyear),table(df$iyear,df$attacktype1_txt))
\end_layout
\begin_layout Plain Layout
type_year<-type_year[order(type_year[,1]),]
\end_layout
\begin_layout Plain Layout
par(mfrow=c(3,3))
\end_layout
\begin_layout Plain Layout
for(i in 2:ncol(type_year))
\end_layout
\begin_layout Plain Layout
{
\end_layout
\begin_layout Plain Layout
plot(x=type_year[,1],y=type_year[,i],main=colnames(type_year)[i],
\end_layout
\begin_layout Plain Layout
xlab='Year',ylab='Incidents',type='l',cex.main=0.95,
\end_layout
\begin_layout Plain Layout
col=hcl.colors(9,'dark 2')[i-1])
\end_layout
\begin_layout Plain Layout
}
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We notice some interesting patterns in the type of attacks over the years.
\series bold
Armed Assault
\series default
saw an overall growth till the mid 90s with the no.
of cases dipping before rising steeply from the mid 2000s till 2014 and
then declining again.
\series bold
Assassination
\series default
was very popular since the beginning with the no.
of cases rising in the till the early 90s before this type of attack faced
a drop.
However, since the late 90s, the no.
of cases corresponding to this type of attack increased (almost) exponentially
till the mid 2010s.
\series bold
Bombings/Explosions
\series default
were somewhat common till the mid 2000s before gaining popularity as the
no.
of cases soared up till the mid 2010s.
It has seen a decline since then.
\series bold
Facility/Infrastructure Attacks
\series default
gained popularity just before the 90s and then again since the mid 2000s.
\series bold
Hijacking
\series default
has an uneven history with sometimes gaining popularity and sometimes not.
\series bold
Hostage Taking (Barricade Incident)
\series default
was initially a very common form of attack until the mid 90s when these
incidents were significantly reduced.
However, since 2010, the no.
or these events rose at an increasing rate.
\series bold
Hostage Taking (Kidnapping)
\series default
was initially a not-so-common form of attack.
However, it saw a steady increase and the no.
of cases leaped at a tremendous pace since the early 2000s.
\series bold
Unarmed Assault
\series default
was very common in the mid 90s before subsiding in the 2000s.
However, since 2010, the no.
of cases of this type is on the rise.
\series bold
Unknown
\series default
attacks gained popularity since the 2010s and has been on the rise since
then.
\end_layout
\begin_layout Subsection*
Attack by Region:
\end_layout
\begin_layout Standard
We next look at the frequency of attacks based on regions
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
attack_reg<-table(df$region_txt)
\end_layout
\begin_layout Plain Layout
par(mar=c(10,4,4,1)+.1)
\end_layout
\begin_layout Plain Layout
barplot(attack_reg,las=2,cex.names=0.8,
\end_layout
\begin_layout Plain Layout
col=hcl.colors(nrow(attack_reg),'set 2'),
\end_layout
\begin_layout Plain Layout
ylab='Count',main='Attacks by Region')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We find that the most affected region by terrorism in the world is
\series bold
Middle East & North Africa
\series default
followed closely by
\series bold
South Asia
\series default
.
The third most affected region is
\series bold
South America
\series default
, however, there's a big gap between the first second and the third.
\end_layout
\begin_layout Subsection*
Target type:
\end_layout
\begin_layout Standard
We next look at the most common targets of the terrorists by type
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
par(mar=c(10,4,4,1)+.1)
\end_layout
\begin_layout Plain Layout
targ_type<-table(df$targtype1_txt)
\end_layout
\begin_layout Plain Layout
max_targ_type<-tail(sort(targ_type),5)
\end_layout
\begin_layout Plain Layout
barplot(max_targ_type,col=hcl.colors(5,'warm'),las=2,
\end_layout
\begin_layout Plain Layout
cex.names=0.8,main='Top 5 Targets')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We observe that
\series bold
Private Citizens & Properties
\series default
is the most popular target type for the terrorists.
However, violence against
\series bold
Military
\series default
,
\series bold
Police
\series default
,
\series bold
Government(General)
\series default
and
\series bold
Business
\series default
are also not uncommon.
\end_layout
\begin_layout Subsection*
Attacks by Region over the years:
\end_layout
\begin_layout Standard
We now look at the attacks over the years on various regions
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
reg_year<-table(df$region_txt,df$iyear)
\end_layout
\begin_layout Plain Layout
plot(x=colnames(reg_year),y=reg_year[1,],type='l',lwd=1.5,
\end_layout
\begin_layout Plain Layout
ylim=c(min(reg_year),max(reg_year)),xlim=c(1970,2017),
\end_layout
\begin_layout Plain Layout
col=hcl.colors(nrow(reg_year),'dark 2')[1],
\end_layout
\begin_layout Plain Layout
xlab='Year',ylab='No.
of Incidents',
\end_layout
\begin_layout Plain Layout
main='Incidents by Region')
\end_layout
\begin_layout Plain Layout
for(i in 2:nrow(reg_year))
\end_layout
\begin_layout Plain Layout
{
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(x=colnames(reg_year),y=reg_year[i,],type='l',lwd=1.5,
\end_layout
\begin_layout Plain Layout
ylim=c(min(reg_year),max(reg_year)),xlim=c(1970,2017),
\end_layout
\begin_layout Plain Layout
xaxt='n',yaxt='n',xlab=NA,ylab=NA,
\end_layout
\begin_layout Plain Layout
col=hcl.colors(nrow(reg_year),'dark 2')[i])
\end_layout
\begin_layout Plain Layout
}
\end_layout
\begin_layout Plain Layout
legend(x=1970,y=7000,rownames(reg_year),lwd=1.5,
\end_layout
\begin_layout Plain Layout
col=hcl.colors(nrow(reg_year),'dark 2'),
\end_layout
\begin_layout Plain Layout
cex=0.5,title='Region')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We notice an interesting trend of attacks in various regions over the years.
While initially,
\series bold
South America
\series default
saw more unrests in the period between 1980 to 2000,
\series bold
South Asia
\series default
and
\series bold
Middle East & North Africa
\series default
saw a huge rise in terror activities.
We also notice that in the year 2014, there has been a global unrest, which
cause every single region to have a spike in the annual uprisings.
\end_layout
\begin_layout Subsection*
Affected Country:
\end_layout
\begin_layout Standard
The below graph shows the most attacked countries
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
country<-table(df$country_txt)
\end_layout
\begin_layout Plain Layout
max_country<-tail(sort(country),5)
\end_layout
\begin_layout Plain Layout
barplot(max_country,col=hcl.colors(5,'warm'),
\end_layout
\begin_layout Plain Layout
las=2,cex.names=0.8,main='Top 5 Countries by Attacks')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We find that
\series bold
Iraq
\series default
is the most affected country in the world.
It has a huge gap with
\series bold
Pakistan
\series default
which is placed second in the list.
\end_layout
\begin_layout Subsection*
Weapons Used:
\end_layout
\begin_layout Standard
In the below given graph, we aim to look at the weapons used by the terrorists
to carry out the attacks
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
weapon<-table(df$weaptype1_txt)
\end_layout
\begin_layout Plain Layout
barplot(weapon,xaxt='n',col=hcl.colors(length(unique(df$weaptype1_txt)),'dynamic'
),
\end_layout
\begin_layout Plain Layout
main='Weapon Type')
\end_layout
\begin_layout Plain Layout
legend('topright',rownames(weapon),
\end_layout
\begin_layout Plain Layout
fill=hcl.colors(length(unique(df$weaptype1_txt)),'dynamic'),
\end_layout
\begin_layout Plain Layout
cex=0.5,title='Weapon Type')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We note from the graph that the most common weapon used was
\series bold
Explosives
\series default
, followed by
\series bold
Firearms
\series default
.
These two itself consisted of the max percentage of weapons used for the
attacks.
\end_layout
\begin_layout Subsection*
Casualties by Year:
\end_layout
\begin_layout Standard
Finally, we look at the casualties by year globally
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
df[is.na(df$nkill),]$nkill<-0
\end_layout
\begin_layout Plain Layout
year_kill<-tapply(df$nkill,df$iyear,sum)
\end_layout
\begin_layout Plain Layout
year_kill<-as.table(year_kill)
\end_layout
\begin_layout Plain Layout
barplot(year_kill,las=2,cex.names=0.7,main='No.
of kills',
\end_layout
\begin_layout Plain Layout
xlab='Year',ylab='Kills',col='#a1383a')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection*
Conclusions
\end_layout
\begin_layout Standard
We find that in the year 2014, maximum number of deaths occurred across
the world.
This is in keeping with the fact that there was an increase in global terrorism
in that particular year.
\end_layout
\begin_layout Standard
Further, looking at this graph, we can relate it to the graph of the Attacks
by Year.
We proceed to further find a correlation between the number of deaths and
the number of attacks in a calendar year.
\end_layout
\begin_layout Subsection*
Correlation:
\end_layout
\begin_layout Standard
We find the correlation coefficient between Casualties and No.
of Attacks
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
attack_kill<-cbind(year_kill,attack_year)
\end_layout
\begin_layout Plain Layout
atk_cor<-cor(attack_kill)
\end_layout
\begin_layout Plain Layout
atk_cor
\end_layout
\begin_layout Plain Layout
atk_cor[lower.tri(atk_cor,diag=T)]<-0
\end_layout
\begin_layout Plain Layout
max(atk_cor)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout