.Rhistory

p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action, fill = action))	 + facet_wrap(~ action)
(gg <- ggplotly(p_set1))
for (i in 1:length(set1$action))
{
if(set1$action[i]==0)
set1$action[i]="Touch Down Traces"
else if(set1$action[i]==1)
set1$action[i]="Touch Up Traces"
else
set1$action[i]="Finger Movement Traces"
}
set1 <- ph1[ph1$uid==36,]
for (i in 1:length(set1$action))
{
if(set1$action[i]==0)
set1$action.name[i]="Touch Down Traces"
else if(set1$action[i]==1)
set1$action.name[i]="Touch Up Traces"
else
set1$action.name[i]="Finger Movement Traces"
}
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action, fill = action))	 + facet_wrap(~ action)
(gg <- ggplotly(p_set1))
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action.name, fill = action.name))	 +
facet_wrap(~ action.name)
(gg <- ggplotly(p_set1))
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Touch Traces of User ID=36") +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action.name, fill = action.name))	 +
facet_wrap(~ action.name)
(gg <- ggplotly(p_set1))
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Touch Traces of User ID=36") +
labs(x="X Coodinates", y="Y Coordinates") +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action.name, fill = action.name))	 +
facet_wrap(~ action.name)
(gg <- ggplotly(p_set1))
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Touch Traces of User ID=36 in Nexus 1-Experimenter E") +
labs(x="X Coodinates", y="Y Coordinates") +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action.name, fill = action.name))	 +
facet_wrap(~ action.name)
(gg <- ggplotly(p_set1))
ph2 <- dataset[dataset$p_id == 5,]
set2 <- ph2[ph2$uid==36,]
length(set2$p_id)
ph2 <- dataset[dataset$p_id == 4,]
set2 <- ph2[ph2$uid==36,]
length(set2$p_id)
unique(set1$p_id)
unique(set1$uid)
unique(set1$ph1)
unique(ph1$uid)
a<-ph1[ph1$uid==5]
a<-ph1[ph1$uid==5,]
unique(a$uid)
unique(a$p_id)
a<-ph1[ph1$uid==39,]
unique(a$p_id)
a<-ph1[ph1$uid==16,]
unique(a$p_id)
a<-ph1[ph1$uid==18,]
unique(a$p_id)
a<-ph1[ph1$uid==12,]
unique(a$p_id)
(gg <- ggplotly(p_doc))
for (i in 1:length(set1$docid))
{
if(set1$docid[i]==0)
set1$doc.name[i]="Wikipedia Artice 1"
else if(set1$docid[i]==1)
set1$doc.name[i]="Wikipedia Article 2"
else if(set1$docid[i]==2)
set1$doc.name[i]="Wikipedia Article 3"
else if(set1$docid[i]==3)
set1$doc.name[i]="Image Comparion Game 1"
else if(set1$docid[i]==4)
set1$doc.name[i]="Image Comparion Game 2"
else if(set1$docid[i]==5)
set1$doc.name[i]="Wikipedia Article 4"
else
set1$doc.name[i]="Image Comparion Game 3"
}
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
geom_point(aes(text = paste( "Area :", area)), size = 4) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
smallset<-dataset[sample(nrow(dataset),50000),]
dataS1<-smallset[c(1,9)]
phset<-ddply(dataS1, .(p_id), summarise, prsr_mean=mean(pressure), prsr_sd=sd(pressure))
library("plyr")
phset<-ddply(dataS1, .(p_id), summarise, prsr_mean=mean(pressure), prsr_sd=sd(pressure))
ggplot(dataS1, aes(x=p_id, y=pressure)) + geom_point() + geom_point(data=phset, aes(y=prsr_mean), color='red', size=4)
head(phset)
ggplot(dataS1, aes(x=p_id, y=pressure)) + geom_point() + geom_point(data=phset, aes(y=prsr_sd), color='blue', size=3)
ggplot(dataS1, aes(x=p_id, y=pressure, xlab="Phone_id",ylab="Standard Deviation of Pressure")) +
geom_point() +
geom_point(data=phset, aes(y=prsr_sd), color='blue', size=4) +
geom_point(data=phset, aes(y=prsr_mean), color='red', size=4)
usrdataS1<-ddply(smallset, .(uid), summarise, prsr_mean=mean(pressure), prsr_sd=sd(pressure))
usrdataS1<-ddply(smallset, .(uid), summarise, prsr_mean=mean(pressure), prsr_sd=sd(pressure))
set2<-smallset[c(2,9)]
head(usrdataS1)
usrdataS1
head(usrdataS1)
ggplot(set2, aes(x=uid,y=pressure)) +
geom_point() +
geom_point(data=usrdataS1,aes(y=prsr_mean),color='red',size=4)+
geom_point(data=usrdataS1,aes(y=prsr_sd),color='blue',size=4)
qplot(uid,area,data=smallset,geom = c("point", "smooth"),alpha=I(1/100))
qplot(uid,area,data=smallset,geom = c("point", "smooth"))
qplot(uid,area,data=smallset,gam = c("point", "smooth"))
qplot(uid,area,data=smallset,geom = c("point", "smooth"))
qplot(uid,area,data=smallset,geom = c("point", "smooth"),alpha=I(1/100))
qplot(sqrt(pressure),data=smallset,geom="histogram")
qplot((pressure),data=smallset,geom="histogram")
qplot(pressure,data=smallset,geom="histogram")
qplot(area,data=smallset,geom="histogram")
names(smallset)
qplot(t_ms,data=smallset,geom="histogram")
qplot(action,data=smallset,geom="histogram")
qplot(action,data=smallset,geom="density")
str(dataset)
levels(smallset)
#subset user with id 1
user1 <- dataset[dataset$uid==1,]
#length(user1$p_id)
#check the first few items of the subset
#head(user1)
#Orientation of a particular user
orientation <- function(user,orient)
{
qplot(data = user[(user$ph_ori==orient) ,], x = x_cord,
y= y_cord)
}
#function call to get the various users
#pattern in landscape and portrait
#Orient = 1 : Phone is in vertical position
#Orient = 2 : Phone is in landscape position
orientation(user1,2)
#function to get the pattern for a particular device
#qplot(area,data = user1[(user1$ph_ori)],geom="histogram")
ph1 <- dataset[dataset$p_id == 1,]
#ph1_s <- sample(ph1,11)
#Plot Pressure of users for a random of 100 training sets
ph1_s <- ph1[sample(nrow(ph1),100),]
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size = uid)
#Plot Touch Traces of User ID 36 in Nexus 1-Experimenter E
#Roll the cursor over the graph to check pressure points
set1 <- ph1[ph1$uid==36,]
for (i in 1:length(set1$action))
{
if(set1$action[i]==0)
set1$action.name[i]="Touch Down Traces"
else if(set1$action[i]==1)
set1$action.name[i]="Touch Up Traces"
else
set1$action.name[i]="Finger Movement Traces"
}
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Touch Traces of User ID 36 in Nexus 1-Experimenter E") +
labs(x="X Coodinates", y="Y Coordinates") +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action.name, fill = action.name))	 +
facet_wrap(~ action.name)
(gg <- ggplotly(p_set1))
#Plot Touch Traces of User ID 36 in Droid Incredible-Experimenter E Phone
#Plot traces of accessing wikipedia article & image comparison game
for (i in 1:length(set1$docid))
{
if(set1$docid[i]==0)
set1$doc.name[i]="Wikipedia Artice 1"
else if(set1$docid[i]==1)
set1$doc.name[i]="Wikipedia Article 2"
else if(set1$docid[i]==2)
set1$doc.name[i]="Wikipedia Article 3"
else if(set1$docid[i]==3)
set1$doc.name[i]="Image Comparion Game 1"
else if(set1$docid[i]==4)
set1$doc.name[i]="Image Comparion Game 2"
else if(set1$docid[i]==5)
set1$doc.name[i]="Wikipedia Article 4"
else
set1$doc.name[i]="Image Comparion Game 3"
}
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
geom_point(aes(text = paste( "Area :", area)), size = 4) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size = uid)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size = area)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
length(user1$p_id)
storeUser1 <- write.csv(user1, file="userid_1.csv")
dir()
path()
(gg <- ggplotly(p_set1))
(gg <- ggplotly(p_doc))
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
geom_point(aes(text = paste( "Area :", area)), size = 5) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
labs(x = "X Coordinate", y = "Y Coordinate")
geom_point(aes(text = paste( "Area :", area)), size = 5) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
labs(x = "X Coordinate", y = "Y Coordinate") +
geom_point(aes(text = paste( "Area :", area)), size = 5) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
labs(x = "X Coordinate", y = "Y Coordinate") +
geom_point(aes(text = paste( "Area :", area)), size = 3) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle("Traces of User ID 36 in various applications") +
labs(x = "X Coordinate", y = "Y Coordinate") +
geom_point(aes(text = paste( "Area :", area)), size = 4) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
(gg <- ggplotly(p_doc))
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
ph1_s <- ph1[sample(nrow(ph1),100),]
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=4)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=1)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=0.001)
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
help(plot_ly)
plot_ly(z = ph_ori, type = "surface")
plot_ly(ph1_s, z = ph_ori, type = "surface")
plot_ly(ph1_s, x = x_cord , y = y_cord,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
plot_ly(ph1_s, x = x_cord , y = y_cord, title = "NAme",
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
plot_ly(ph1_s, x = x_cord , y = y_cord, title = NAme,
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
plot_ly(ph1_s, x = x_cord , y = y_cord, title("Name")
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
plot_ly(ph1_s, x = x_cord , y = y_cord, title("Name"),
text=paste("Pressure: ",pressure, "UserID: ", uid),
mode="markers", color = uid, size=uid)
layout = go.Layout (
title='Plot Title')
import plotly.graph_objs as go
import plotly.plotly as py
import plotly.plotly as py
import plotly.graph_objs as go
remove(list=ls())
setwd("D:/Dropbox/UNCC/Courses Fall 15/SPL_5102_Dewan Ahmed/Project/phase3")
#Load required packages
library("ggplot2")
library("plotly")
library("plyr")
library("gdata")
#Removing unused variables from global environment
remove(list=ls())
#loading the dataset
dataset <- read.csv(file="data.csv",header = T, sep=",")
#check the data set
head(dataset,3)
#checking the levels
str(dataset)
#Orientation plots of a user
#Function call to get the various users pattern in landscape and portrait
#The function needs 2 input values:-
#   1. User id - values range from 0 to 40
#   2. Phone orientation - values can be 1 or 2
orientation <- function(userid,orient)
{
#subset user with their respective ID
user <- dataset[dataset$uid == userid,]
qplot(data = user[(user$ph_ori == orient) ,], x = x_cord,
main = toupper(orientname(orient)),
y= y_cord)
}
orientname <- function(orient){
val1 <- NULL
if(orient==1)
{
val1 = "portrait"
}
else
{
val1 = "landscape"
}
val1 = (paste(val1,"orientation plot"))
return (val1)
}
#Orient = 1 : Phone is in portrait/vertical position
#Orient = 2 : Phone is in landscape position
orientation(1,2)
orientation(1,1)
#Sample data of 50000 training sets
smallset<-dataset[sample(nrow(dataset),50000),]
#Taking phone id and pressure
dataS1<-smallset[c(2,9)]
#Calculate mean & standard deviation of pressure for every user
phset<-ddply(dataS1, .(uid), summarise, prsr_mean=mean(pressure), prsr_sd=sd(pressure))
#Plot mean & sd points of pressure for every users (ID: 0 to 40)
ggplot(dataS1, aes(x=uid, y=pressure)) +
labs(x = "User ID", y = "Pressure Deviation") +
geom_point() + ggtitle("Pressure & Standard Deviation Points of all users")+
geom_point(data=phset, aes(y=prsr_sd), color='blue', size=4) +
geom_point(data=phset, aes(y=prsr_mean), color='red', size=4)
#Subset dataset with phone id 1
ph1 <- dataset[dataset$p_id == 1,]
#Plot Pressure of users for a random of 100 training sets
ph1_s <- ph1[sample(nrow(ph1),100),]
plot_ly(ph1_s, x = x_cord , y = y_cord,main = "Pressure dots of users for a random of 100 training sets",
text=paste("Pressure: ",pressure, "Area: ", area),
mode="markers", color = uid, size=uid )
#Plot Touch Traces of users in Nexus 1-Experimenter E
#Roll the cursor over the graph to check pressure points
#Valid users for phone_id=1 are 36,5,39,16,18,12
user_finger_trace <- function(userid)
{
set1 <- ph1[ph1$uid==userid,]
for (i in 1:length(set1$action))
{
if(set1$action[i]==0)
set1$action.name[i]="Touch Down Traces"
else if(set1$action[i]==1)
set1$action.name[i]="Touch Up Traces"
else
set1$action.name[i]="Finger Movement Traces"
}
p_set1 <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle((paste("Touch Traces of User ID ",userid,
"in Nexus 1-Experimenter E"))) +
labs(x="X Axis", y="Y Axis") +
geom_point(aes(text = paste("Pressure :", pressure)), size = 4) +
geom_smooth(aes(color = action.name, fill = action.name))	 +
facet_wrap(~ action.name)
ggplotly(p_set1)
}
user_finger_trace(36)
#Plot traces of accessing wikipedia article & image comparison game
#Valid users for phone_id=1 are 36,5,39,16,18,12
user_doc_traces <- function(userid)
{
set1 <- ph1[ph1$uid== userid,]
for (i in 1:length(set1$docid))
{
if(set1$docid[i]==0)
set1$doc.name[i]="Wikipedia Artice 1"
else if(set1$docid[i]==1)
set1$doc.name[i]="Wikipedia Article 2"
else if(set1$docid[i]==2)
set1$doc.name[i]="Wikipedia Article 3"
else if(set1$docid[i]==3)
set1$doc.name[i]="Image Comparion Game 1"
else if(set1$docid[i]==4)
set1$doc.name[i]="Image Comparion Game 2"
else if(set1$docid[i]==5)
set1$doc.name[i]="Wikipedia Article 4"
else
set1$doc.name[i]="Image Comparion Game 3"
}
p_doc <- ggplot(data = set1, aes(x = x_cord, y = y_cord)) +
ggtitle((paste("Traces of User ID", userid,
"in various applications"))) +
labs(x = "X Axis", y = "Y Axis") +
geom_point(aes(text = paste( "Area :", area)), size = 4) +
geom_smooth(aes(color = doc.name, fill = doc.name))	 +
facet_wrap(~ doc.name)
ggplotly(p_doc)
}
user_doc_traces(5)
setwd("D:/Dropbox/UNCC/Courses Fall 15/SSDI_6112_AliSever/Project Predictive Analysis")
setwd("D:/Dropbox/UNCC/Courses Fall 15/SSDI_6112_AliSever/Project Predictive Analysis/Final Analysis/Prediction_App")
remove(list=ls())
#Predicting items with highest sales
#Algorithm: Apriori
#Packages Required:
#					1. gdata  - To read CSV files
#					2. arules - For manipulating and analyzing transaction data and patterns
#							  - Also provides interfaces to C implementations of the association mining algorithms Apriori and Eclat
#					3. e1071  - Library for modelling svm.
#About Dataset:
#				Total Training Sets = 983
#				Total Products		= 98
#Load the libraries
library("arules")
library("e1071")
library("gdata")
#Load the dataset
dataset<-read.csv(file="data.csv",header=T,sep=",")
subset<-dataset[c(2,5)]
subset<-subset[!duplicated(subset[c(1,2)]),]
aggSubSet<-split(subset$Product.Name,subset$Customer.Name)
trns<-as(aggSubSet,"transactions")
Rules<-apriori(trns,parameter=list(supp=0.05,conf=0.6,target="rules",
minlen=2))
ItemSet<-inspect(Rules[1:100])
#write.csv(ItemSet, file= "ItenSet.csv")
#create an item frequency bar plot for inspecting the item frequency distribution of products sold
#Top 10 items being purchased
itemFrequencyPlot(trns, topN = 30, type="absolute",
xlab="Most Frequent Item (MFI)",
ylab="Frequency",main="Top 30 Frequent Items")
#Compare Item Frequencies
#Transactions of the item with large frequencr (frankfurter)
#with the average in dataset
trns.high<-trns[trns %in% "frankfurter"]
# plot with the averages of the population plotted as a line
#(for first 20 items)
#itemFrequencyPlot(trns.high[, 1:20],type="relative",population = trns[, 1:20])
# plot lift ratio (frequency in x / frequency in population)
# for items with a support of 20% in the population
itemFrequencyPlot(trns.high, population = trns, support = 0.2,lift = TRUE, horiz = TRUE)
barplot(sort(itemFrequency(trns), decreasing=TRUE))
########################################################################################################
########################################################################################################
################ Analyzing purchase of Most frequent Item using SVM ################################
#######################Item Analyzed is Frankfurter ####################################################
svmSubset<-dataset[c(3,5,6,7)]
svmSubset <- svmSubset[svmSubset$Product.Name =="frankfurter",]
svmSubset<-svmSubset[c(1,3,4)]
##svmSubset$Quantity <- svmSubset$Unit.Price * svmSubset$Quantity
svmSubset$Total.Cost <- svmSubset$Unit.Price * svmSubset$Quantity
####names(svmSubset)[3] <- "Total.Cost"
svmSubset$Order.Year <- as.numeric(format(as.Date(svmSubset$Order.Date,"%m/%d/%Y"),"%Y"))
svmSubset$Order.Date <- as.numeric(format(as.Date(svmSubset$Order.Date,"%m/%d/%Y"),"%m"))
names(svmSubset)[1] <- "Order.Month"
for(i in 1:length(svmSubset$Total.Cost))
{
med <- median(svmSubset$Total.Cost)
if((svmSubset$Total.Cost[i])>med)
svmSubset$Order.Val[i]="High"
else
svmSubset$Order.Val[i]="Low"
}
svmSubset2 <- svmSubset[c(6,1,3)]
processData <- write.csv(svmSubset2, file= "newdata.csv")
#processData<-read.csv(file="newdata.csv",header=T,sep="," )
cleanData <- read.csv(file="newdata.csv",header=T,sep=",")
cleanData <- cleanData[c(2,3,4)]
model<-svm(Order.Val ~ .,data=cleanData)
#model<-svm(Order.Val ~ .,data=cleanData,kernel="polynomial",degree=3,coef0=0.030,cost=1.3,tolerance=0.008)
#model<-svm(Order.Val ~ .,data=cleanData,kernel="radial",gamma=0.01,cost=10)
#plot(model,cleanData)
#Now, next step is to do the prediction.
#So we classify 75% of dataset as training dataset and 25% as testing dataset
#Create an index
index<-1:nrow(cleanData)
#Create testindex to sample out the 30% of the dataset
testindex<-sample(index,trunc(length(index)*25/100))
#Segregate the testdataset and trainingdataset using the testindex
testset<-cleanData[testindex,]
trainingset<-cleanData[-testindex,]
#Plot model on trainingset
plot(model,trainingset)
#Prediction using the model which has been trained using the trainingset
prediction<-predict(model,testset[,-1])
#Generate the confusion matrix to find the accuracy
tab <-table(pred=prediction,true=testset[,1])
accuracy <- ((tab[1]+tab[4])/sum(tab))*100
accuracy
tab
remove(list=ls())