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Task-3.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
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\textclass article
\use_default_options true
\begin_modules
knitr
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\maintain_unincluded_children false
\language english
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\use_geometry false
\use_package amsmath 1
\use_package amssymb 1
\use_package cancel 1
\use_package esint 1
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\use_package mathtools 1
\use_package mhchem 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
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\index Index
\shortcut idx
\color #008000
\end_index
\secnumdepth 3
\tocdepth 3
\paragraph_separation indent
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\end_header
\begin_body
\begin_layout Title
GRIP Task 3: Exploratory Data Analysis - Retail
\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
#Importing the necessary libraries
\end_layout
\begin_layout Plain Layout
library(RColorBrewer)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\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
#Reading the dataset
\end_layout
\begin_layout Plain Layout
df<-read.csv('D:
\backslash
\backslash
Important Documents
\backslash
\backslash
Internship
\backslash
\backslash
Task-3
\backslash
\backslash
SampleSuperstore.csv',header=T)
\end_layout
\begin_layout Plain Layout
dim(df)#the dimensions of the dataset
\end_layout
\begin_layout Plain Layout
head(df)#a brief preview of the dataset
\end_layout
\begin_layout Plain Layout
names(df)#colimn names of the dataset
\end_layout
\begin_layout Plain Layout
summary(df)#a brief summary of the dataset
\end_layout
\begin_layout Plain Layout
df<-df[!duplicated(df),]#removing duplicate entries
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Correlation between various observations
\end_layout
\begin_layout Standard
We then make a correlation matrix to know if there is any significant correlatio
n between any two columns
\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
cor_mat<-round(cor(df[,sapply(df,class)!='character']),3)
\end_layout
\begin_layout Plain Layout
cor_mat#The correlation matrix
\end_layout
\begin_layout Plain Layout
cor_mat[lower.tri(cor_mat,diag=T)]<-0
\end_layout
\begin_layout Plain Layout
max(abs(cor_mat))
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
We find that the maximum correlation coefficient is
\begin_inset Formula $0.479$
\end_inset
which implies there is no significant correlation between the columns of
the data.
\end_layout
\begin_layout Section*
Exploratory Data Analysis
\end_layout
\begin_layout Standard
We proceed to the analysis of the dataset
\end_layout
\begin_layout Subsection*
Division based on different attributes
\end_layout
\begin_layout Standard
We first plot some graphs for division of the data based on various attributes
\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
cat_div<-table(df$Category)
\end_layout
\begin_layout Plain Layout
pie(cat_div,col=c('#FF6666','#0099CC','#5eb58a'),main='Division by Category')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
seg_div<-table(df$Segment)
\end_layout
\begin_layout Plain Layout
pie(seg_div,col=c('#549dc7','#cc689a','#c9b36d'),main='Division by Segment')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\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
state_div<-table(df$State)
\end_layout
\begin_layout Plain Layout
barplot(state_div,las=2,cex.names=0.5,
\end_layout
\begin_layout Plain Layout
col=hcl.colors(n=length(unique(df$State)),"dynamic"),
\end_layout
\begin_layout Plain Layout
ylab='Counts',main='Division by States')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
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\begin_layout Standard
\begin_inset ERT
status open
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\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
cat_Subcat<-table(df$Category,df$Sub.Category)
\end_layout
\begin_layout Plain Layout
barplot(cat_Subcat,las=2,col=c('#FF6666','#0099CC','#5eb58a'),
\end_layout
\begin_layout Plain Layout
cex.names=0.75,main='Division by Subcategory',ylab='Count')
\end_layout
\begin_layout Plain Layout
legend('topright',rownames(cat_Subcat),fill=c('#FF6666','#0099CC','#5eb58a'),cex
=0.75)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsection*
Histograms
\end_layout
\begin_layout Standard
We draw some histograms to find out about the nature of the distribution
of the data
\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(mfcol=c(3,2))
\end_layout
\begin_layout Plain Layout
for(i in 1:ncol(df))
\end_layout
\begin_layout Plain Layout
{
\end_layout
\begin_layout Plain Layout
if (class(df[,i])!='character')
\end_layout
\begin_layout Plain Layout
{
\end_layout
\begin_layout Plain Layout
hist(df[,i],xlab=names(df)[i],main=paste('Histogram of ',
\end_layout
\begin_layout Plain Layout
names(df)[i]),col='#313b69')
\end_layout
\begin_layout Plain Layout
}
\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 Standard
The above histograms suggest that the data is not normally distributed.
\end_layout
\begin_layout Subsection*
Table of Quantity by Shipping mode
\end_layout
\begin_layout Standard
Next we create a table for the quantity of items sold categorised by mode
of shipping
\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
table(df$Ship.Mode,df$Quantity)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsection*
Pairs plot
\end_layout
\begin_layout Standard
A pairs plot gives a detailed graphical summary regarding the relationships
between the different variables in the data.
We create a pairs plot for this dataset categorised by Sub-Category.
\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
pairs(~df$Postal.Code+df$Sales+df$Quantity+df$Discount+df$Profit,
\end_layout
\begin_layout Plain Layout
labels=c('Postal Code','Sales','Quantity','Discount','Profit'),
\end_layout
\begin_layout Plain Layout
col=hcl.colors(length(unique(df$Sub.Category)),'dark 3'),
\end_layout
\begin_layout Plain Layout
pch=19,oma=c(3,5,3,12))
\end_layout
\begin_layout Plain Layout
par(xpd=T)
\end_layout
\begin_layout Plain Layout
legend("bottomright",fill=hcl.colors(length(unique(df$Sub.Category)),'dark
3'),
\end_layout
\begin_layout Plain Layout
legend=c(unique(df$Sub.Category)),cex=0.5,title='Sub-Category')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsection*
Heat maps
\end_layout
\begin_layout Standard
We then draw some heat maps for representing sales and profits by different
attributes over all the states and cities
\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
cat_city_sales<-ftable(with(df,tapply(Sales,list(Category,City),sum)))
\end_layout
\begin_layout Plain Layout
cat_city_sales[which(is.na(cat_city_sales)==T)]<-0
\end_layout
\begin_layout Plain Layout
data<-as.matrix(cat_city_sales)
\end_layout
\begin_layout Plain Layout
heatmap(t(data),Colv=NA,Rowv=NA,cexCol=0.8,
\end_layout
\begin_layout Plain Layout
col=colorRampPalette(brewer.pal(9,"Blues"))(25),
\end_layout
\begin_layout Plain Layout
main='Heatmap for Sales in City by Category')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
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\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
cat_state_sales<-ftable(with(df,tapply(Sales,list(Category,State),sum)))
\end_layout
\begin_layout Plain Layout
cat_state_sales[which(is.na(cat_state_sales)==T)]<-0
\end_layout
\begin_layout Plain Layout
data<-as.matrix(cat_state_sales)
\end_layout
\begin_layout Plain Layout
heatmap(t(data),Colv=NA,Rowv=NA,cexCol=0.8,
\end_layout
\begin_layout Plain Layout
col=colorRampPalette(brewer.pal(9,"Blues"))(25),
\end_layout
\begin_layout Plain Layout
main='Heatmap for Sales in States by Category')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\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
cat_city_pro<-ftable(with(df,tapply(Profit,list(Category,City),sum)))
\end_layout
\begin_layout Plain Layout
cat_city_pro[which(is.na(cat_city_pro)==T)]<-0
\end_layout
\begin_layout Plain Layout
data<-as.matrix(cat_city_pro)
\end_layout
\begin_layout Plain Layout
heatmap(t(data),Colv=NA,Rowv=NA,cexCol=0.8,
\end_layout
\begin_layout Plain Layout
col=colorRampPalette(brewer.pal(9,"Blues"))(25),
\end_layout
\begin_layout Plain Layout
main='Heatmap for Profit in City by Category')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\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
cat_state_pro<-ftable(with(df,tapply(Profit,list(Category,State),sum)))
\end_layout
\begin_layout Plain Layout
cat_state_pro[which(is.na(cat_state_pro)==T)]<-0
\end_layout
\begin_layout Plain Layout
data<-as.matrix(cat_state_pro)
\end_layout
\begin_layout Plain Layout
heatmap(t(data),Colv=NA,Rowv=NA,cexCol=0.8,
\end_layout
\begin_layout Plain Layout
col=colorRampPalette(brewer.pal(9,"Blues"))(25),
\end_layout
\begin_layout Plain Layout
main='Heatmap for Profit in State by Category')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\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
seg_city_pro<-ftable(with(df,tapply(Profit,list(Segment,City),sum)))
\end_layout
\begin_layout Plain Layout
seg_city_pro[which(is.na(seg_city_pro)==T)]<-0
\end_layout
\begin_layout Plain Layout
data<-as.matrix(seg_city_pro)
\end_layout
\begin_layout Plain Layout
heatmap(t(data),Colv=NA,Rowv=NA,cexCol=0.8,
\end_layout
\begin_layout Plain Layout
col=colorRampPalette(brewer.pal(9,"Blues"))(25),
\end_layout
\begin_layout Plain Layout
main='Heatmap for Profit in City by Segment')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\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
seg_state_pro<-ftable(with(df,tapply(Profit,list(Segment,State),sum)))
\end_layout
\begin_layout Plain Layout
seg_state_pro[which(is.na(seg_state_pro)==T)]<-0
\end_layout
\begin_layout Plain Layout
data<-as.matrix(seg_state_pro)
\end_layout
\begin_layout Plain Layout
heatmap(t(data),Colv=NA,Rowv=NA,cexCol=0.8,
\end_layout
\begin_layout Plain Layout
col=colorRampPalette(brewer.pal(9,"Blues"))(25),
\end_layout
\begin_layout Plain Layout
main='Heatmap for Profit in State by Segment')
\end_layout
\begin_layout Plain Layout