L.R.
G GOVERNMENT ARTS COLLEGE FOR
WOMEN (AFFILIATED TO BHARATHIAR
UNIVERSITY)
TIRUPUR-641604.
DEPARTMENT OF COMPUTER SCIENCE
I MSC-COMPUTER SCIENCE
PRACTICAL-III :DATA MINING USING R
NAME :
REG.NO:
CERTIFICATE
L R.G GOVERNMENT ARTS COLLEGE FOR WOMEN
(AFFILIATED TO BHARATHIAR UNIVERSITY)
TIRUPUR-641604
NAME :
REGISTER NO:
CLASS :
This is to certify that it is a bonafide record of practical work done by the above student of
the I-M.SC COMPUTER SCIENCE PRACTICAL-III: DATA MINING USING R during the
academic year 2023-2024.
Staff in Charge. Head of the Department
Submitted for the Bharathiar University Practical Examination held On. . . . . . . . . . . . . . . .
Internal Examiner. External Examiner
CONTENT
INDEX
S.N DATE CONTENTS PA SIGN
O G
E
N
O
1 APRIORI ALGORITHM TO
EXTRACT ASSOCIATION RULES
OF DATA MINING
2 K-MEANS CLUSTERING ALGORITHM
3 HIERARCHICAL CLUSTERING
4 CLASSIFICATION ALGORITHM
5 DECISION TREE
6 LINEAR REGRESSION
7 DATA VISUALZATION
1. APRIORI ALGORITHM TO EXTRACT ASSOCIATION RULES
OF DATA MINING
# Loading Libraries
library(arules)
library(arulesViz)
library(RColorBrewer)
# import dataset
data('Groceries')
# using apriori() function
rules<-apriori(Groceries,parameter=list(supp=0.01,conf=0.2))
# using inspect() function
inspect(rules[1:10])
# using itemFrequencyPlot() function
arules::itemFrequencyPlot(Groceries,topN=20,
col=brewer.pal(8,'Pastel2'),
main='Relative Item Frequency
Plot', type='relative',
ylab='Item Frequency(Relative)')
OUTPUT:
# Loading Libraries
>library(arules)
Loading required package: Matrix
Attaching package: ‘arules’
The following objects are masked from
‘package:base’: abbreviate, write
> library(arulesViz)
> library(RColorBrewer)
> # import dataset
> data('Groceries')
> # using apriori() function
> rules<-apriori(Groceries,parameter=list(supp=0.01,conf=0.2))
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.2 0.1 1 none FALSE TRUE 5 0.01 1
maxlen target ext
10 rules TRUE
Algorithmic
control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 98
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
sorting and recoding items ... [88 item(s)] done [0.00s].
creating transaction tree ... done [0.01s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [232 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
> # using inspect() function
> inspect(rules[1:10])
lhs rhs support confidence coverage
[1] {} => {whole milk} 0.25551601 0.2555160 1.00000000
[2] {hard cheese} => {whole milk} 0.01006609 0.4107884 0.02450432
[3] {butter milk} => {other vegetables} 0.01037112 0.3709091 0.02796136
[4] {butter milk} => {whole milk} 0.01159126 0.4145455 0.02796136
[5] {ham} => {whole milk} 0.01148958 0.4414062 0.02602949
[6] {sliced cheese} => {whole milk} 0.01077783 0.4398340 0.02450432
[7] {oil} => {whole milk} 0.01128622 0.4021739 0.02806304
[8] {onions} => {other vegetables} 0.01423488 0.4590164 0.03101169
[9] {onions} => {whole milk} 0.01209964 0.3901639 0.03101169
[10] {berries} => {yogurt} 0.01057448 0.3180428 0.03324860
lift count
[ 1.0000 25
1 00 13
]
[ 1.6076 9
2 82 9
]
[ 1.9169 10
3 16 2
]
[ 1.6223 11
4 85 4
]
[ 1.7275 11
5 09 3
]
[ 1.7213 10
6 56 6
]
[ 1.5739 11
7 68 1
]
[8] 2.372268 140
[9] 1.526965 119
[10] 2.279848 104
> # using itemFrequencyPlot() function
> arules::itemFrequencyPlot(Groceries,topN=20,
+ col=brewer.pal(8,'Pastel2'),
+ main='Relative Item Frequency Plot',
+ type='relative',
+ ylab='Item Frequency(Relative)')
Relative Item Frequency Plot
2.K-Means Clustering
library(cluster)
df=USArrests
#Number of Rows and Columns of the Actul Data
set dim(df)
head(df)
#remove rows with missing
values df=na.omit(df)
dim(df)
#scale each variable to have mean 0 and sd 1
df=scale(df)
head(df)
set.seed(1)
#Cluster the dataset with 5 groups
km=kmeans(df,centers=5,nstart=25)
print(km)
plot(df)
points(km$centers,col=1:5,pch=8,cex=2)
cnt=table(km$cluster)
print(cnt)
final_data=cbind(df,cluster=km$cluster)
head(final_data)
plot(final_data,cex=0.6,main="Final Data")
ag=aggregate(final_data,by=list(cluster=km$cluster),mean)
head(ag)
plot(ag,cex=0.6,main="Aggregate")
OUTPUT:
#K-means Clustering
> #Number of Rows and Columns of the Actaul Data set
> dim(df)
[1] 50 4
> head(df)
Murder Assault UrbanPop Rape
Alabam 13 23 58
a .2 6 21.2
Alaska 10. 263 48
0 44.5
Arizona 8.1 294 80
31.0
Arkansa 8. 190 50
s 8 19.5
Californ 9. 276 91
ia 0 40.6
Colorad 7. 204 78
o 9 38.7
> #remove rows with missing
values
> dim(df)
[1] 50 4
> #scale each variable to have mean 0 and sd 1
> head(df)
Murder Assault UrbanPop Rape
Alabama 1.24256408 0.7828393 -0.5209066 -0.003416473
Alaska 0.50786248 1.1068225 -1.2117642 2.484202941
Arizona 0.07163341 1.4788032 0.9989801 1.042878388
Arkansas 0.23234938 0.2308680 -1.0735927 -0.184916602
California 0.27826823 1.2628144 1.7589234 2.067820292
Colorado 0.02571456 0.3988593 0.8608085 1.864967207
> #Cluster the dataset with 5 groups
> print(km)
K-means clustering with 5 clusters of sizes 7, 10, 10, 11,
12 Cluster means:
Murder Assault UrbanPop Rape
1 1.5803956 0.9662584 -0.7775109 0.04844071
2 -1.1727674 -1.2078573 -1.0045069 -1.10202608
3 -0.6286291 -0.4086988 0.9506200 -0.38883734
4 -0.1642225 -0.3658283 -0.2822467 -0.11697538
5 0.7298036 1.1188219 0.7571799 1.32135653
Clustering vector:
Alabama Alaska Arkan Californi
a
Arizona 15 5 4 sa
Colorado Connecticut s
Delaware Georg
5 ia
Flori
da
5 3 3 5 1
Haw Idah Illino India Iowa
aii o is na
3 2 5 4 2
Kansas Kentucky Louisiana Maine
Maryland 4 4 1 2
Massachusetts Michigan Minnesota Mississippi Missouri
3 5 2 1 4
Montana Nebraska Nevada New Hampshire New
Jersey 4 4 5 2 3
New Mexico New York North Carolina North Dakota
Ohio 5 5 1 2
3
Oklahoma Oregon Pennsylvania Rhode Island South Carolina
4 4 3 3 1
South Dakota Tennessee Texas Utah
Vermont 2 1 5 3
Virginia Washington West Virginia Wisconsin
Wyoming 4 3 22 4
Within cluster sum of squares by cluster:
[1] 6.128432 7.443899 9.326266 7.788275 18.257332
(between_SS / total_SS = 75.0 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault"
> plot(df)
> points(km$centers,col=1:5,pch=8,cex=2)
> print(cnt)
12345
7 10 10 11 12
> head(final_data)
Murder Assault UrbanPop Rape cluster
Alaba 1.24256408 0.7828393 -0.5209066 - 1
ma 0.003416473
Alaska 0.50786248 1.1068225 -1.2117642 2.484202941 5
Arizon 0.07163341 1.4788032 0.9989801 1.042878388 5
a
Arkansas 0.23234938 0.2308680 -1.0735927 -0.184916602 4
California 0.27826823 1.2628144 1.7589234 2.067820292 5
Colorado 0.02571456 0.3988593 0.8608085 1.864967207 5
> plot(final_data,cex=0.6,main="Final Data")
> head(ag)
cluster Murder Assault UrbanPop Rape cluster
1 1 1.5803956 0.9662584 -0.7775109 0.04844071 1
2 2 -1.1727674 -1.2078573 -1.0045069 -1.10202608 2
3 3 -0.6286291 -0.4086988 0.9506200 -0.38883734 3
4 4 -0.1642225 -0.3658283 -0.2822467 -0.11697538 4
5 5 0.7298036 1.1188219 0.7571799 1.32135653 5
> plot(ag,cex=0.6,main="Aggregate")
3.HIERARCHICAL CLUSTERING
#Hierarchical Clustering
library(cluster)
df=USArrests
#remove rows with missing
values df=na.omit(df)
#scale each variable to have a mean 0 and sd of
1 df=scale(df)
head(df)
d=dist(df,method="euclidean")
#complete dendogram
hc1=hclust(d,method="complete")
plot(hc1,cex=0.6,main="complete dendogram",hang=-1)
#average dendogram
hc2=hclust(d,method="average")
plot(hc2,cex=0.6,main="Average Dendogram",hang=-1)
abline(h=3.0,col="green")
groups=cutree(hc2,k=4)
print(groups)
table(groups)
rect.hclust(hc2,k=4,border="red")
final_data=cbind(df,cluster=groups)
head(final_data)
plot(final_data,cex=0.6,main="Final Data")
OUTPUT:
#Hierarchical Clustering
> #remove rows with missing values
> #scale each variable to have a mean 0 and sd of 1
> head(df)
Murder Assault UrbanPop Rape
Alabama 1.24256408 0.7828393 -0.5209066 -0.003416473
Alaska 0.50786248 1.1068225 -1.2117642 2.484202941
Arizona 0.07163341 1.4788032 0.9989801 1.042878388
Arkansas 0.23234938 0.2308680 -1.0735927 -0.184916602
California 0.27826823 1.2628144 1.7589234 2.067820292
Colorado 0.02571456 0.3988593 0.8608085 1.864967207
> #complete dendogram
> plot(hc1,cex=0.6,main="complete dendogram",hang=-1)
> #average dendogram
> print(groups)
Alabama Alaska Arkan Californi
a
Arizona 12 3 4 sa
Colorado Connecticut s
Delaware Geor
3 gia
Flori
da
3 4 4 3 1
Haw Idah Illino India Iowa
aii o is na
4 4 3 4 4
Kansas Kentucky Louisiana Maine
Maryland 4 4 1 4
Massachusetts Michigan Minnesota Mississippi Missouri
4 3 4 1 3
Montana Nebraska Nevada New Hampshire New Jersey
4 4 3 4 4
New Mexico New York North Carolina North Dakota
Ohio 3 3 1 4
Oklahoma Oregon Pennsylvania Rhode Island South Carolina 4
4 4 4 1
South Dakota Tennessee Texas Utah
Vermont 4 1 3 4
Virginia Washington West Virginia Wisconsin
Wyoming 4 4 44 4
groups
1234
7 1 12 30
> rect.hclust(hc2,k=4,border="red")
> final_data=cbind(df,cluster=groups)
> head(final_data)
Murder Assault UrbanPop Rape cluster
Alaba 1.24256408 0.7828393 -0.5209066 - 1
ma 0.003416473
Alaska 0.50786248 1.1068225 -1.2117642 2.484202941 2
Arizon 0.07163341 1.4788032 0.9989801 1.042878388 3
a
Arkansas 0.23234938 0.2308680 -1.0735927 -0.184916602 4
California 0.27826823 1.2628144 1.7589234 2.067820292 3
Colorado 0.02571456 0.3988593 0.8608085 1.864967207 3
> plot(final_data,cex=0.6,main="Final Data")
4.CLASSIFICATION ALGORITHM
#Classification
Algorithm library(class)
df=data(iris)
#Number of Rows and Columns
dim(iris)
head(iris)
rand=sample(1:nrow(iris),0.9*nrow(iris))
head(rand)
#Scale the values using Normalization
method nor<-function(x)
return((x-min(x))/(max(x)-min(x)))
iris_norm=as.data.frame(lapply(iris[,c(1,2,3,4)],nor))
head(iris_norm)
#Train dataset
iris_train=iris_norm[rand,]
iris_train_target=iris[rand,5
] #Test dataset
iris_test=iris_norm[-rand,]
iris_test_target=iris[-rand,5]
dim(iris_train)
dim(iris_test)
#K-nearesr neighbour Classification
model1=knn(train=iris_train,test=iris_test,cl=iris_train_target,k=7)
#Confusion Matric
tab=table(model1,iris_test_target)
print(tab)
accuracy=function(x)
sum(diag(x)/sum(rowSums(x)))*100
cat("Accuracy classifier=",accuracy(tab))
OUTPUT:
#Classification Algorithm
> #Number of Rows and Columns
> dim(iris)
[1] 150 5
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3 1.4 0.2 seto
. sa
5
2 4.9 3 1.4 0.2 seto
. sa
0
3 4.7 3 1.3 0.2 seto
. sa
2
4 4.6 3 1.5 0.2 seto
. sa
1
5 5.0 3 1.4 0.2 seto
. sa
6
6 5.4 3 1.7 0.4 seto
. sa
9
> head(rand)
[1] 114 107 25 128 14 24
> #Scale the values using Normalization method
> nor<-function(x)
+{
+ return((x-min(x))/(max(x)-min(x)))
+}
> head(iris_norm)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 0.62500 0.067796 0.041666
0.22222222 00 61 67
2 0.41666 0.067796 0.041666
0.16666667 67 61 67
3 0.50000 0.050847 0.041666
0.11111111 00 46 67
4 0.45833 0.084745 0.041666
0.08333333 33 76 67
5 0.19444444 0.6666667 0.06779661 0.04166667
6 0.30555556 0.7916667 0.11864407 0.12500000
> #Train dataset
> iris_train=iris_norm[rand,]
> iris_train_target=iris[rand,5]
> #Test dataset
> iris_test=iris_norm[-rand,]
> iris_test_target=iris[-rand,5]
> dim(iris_train)
[1] 135 4
> dim(iris_test)
[1] 15 4
> #K-nearesr neighbour Classification
> model1=knn(train=iris_train,test=iris_test,cl=iris_train_target,k=7)
> #Confusion Matric
> print(tab)
iris_test_target
model1 setosa versicolor
virginica setosa 6 0
versicolor 0 6 1
virginica 0 0 2
> accuracy=function(x)
+{
+ sum(diag(x)/sum(rowSums(x)))*100
+}
cat("Accuracy classifier=",accuracy(tab)) Accuracy classifier= 100>
5.DECISION TREE
#Decision Tree
library(rpart)
data=iris
str=data
head(data)
#creating the decision tree using regression
dtree=rpart(Sepal.Width~Sepal.Length+Petal.Width+Petal.Length+Species,data=iris,method
="anova")
plot(dtree,uniform=TRUE,main="Sepal Width Decision Tree Using Regression")
print(dtree)
text(dtree,use.n=TRUE,cex=.7)
#predicting the Sepal Width
adata<-data.frame(Species='versicolor',Sepal.Length=5.1,Petal.Length=4.5,Petal.Width=1.4)
cat("Predicted Value:\n")
pt=predict(dtree,adata,method="anova")
print(pt)
plot(pt)
#creating the decision tree using
classification df=as.data.frame(data)
dt=rpart(Sepal.Width~Sepal.Length+Petal.Width+Petal.Length+Species,data=df,method="cl
ass")
plot(dt,uniform=TRUE,main="Sepal Width Decision Tree using Classification")
print(dt)
text(dt,use.n=TRUE,cex=.7)
OUTPUT:
> #Decision Tree
> head(data)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3 1.4 0.2 seto
. sa
5
2 4.9 3 1.4 0.2 seto
. sa
0
3 4.7 3 1.3 0.2 seto
. sa
2
4 4.6 3 1.5 0.2 seto
. sa
1
5 5.0 3 1.4 0.2 seto
. sa
6
6 5.4 3 1.7 0.4 seto
. sa
9
> #creating the decision tree using regression
dtree=rpart(Sepal.Width~Sepal.Length+Petal.Width+Petal.Length+Species,data=iris,metho
d
="anova")
> plot(dtree,uniform=TRUE,main="Sepal Width Decision Tree Using Regression")
> print(dtree)
n= 150
node), split, n, deviance, yval
* denotes terminal node
1) root 150 28.3069300 3.057333
2) Species=versicolor,virginica 100 10.9616000 2.872000
4) Petal.Length< 4.05 16 0.7975000 2.487500 *
5) Petal.Length>=4.05 84 7.3480950 2.945238
10) Petal.Width< 1.95 55 3.4920000 2.860000
20) Sepal.Length< 6.35 36 2.5588890 2.805556 *
21) Sepal.Length>=6.35 19 0.6242105 2.963158 *
11) Petal.Width>=1.95 29 2.6986210 3.106897
22) Petal.Length< 5.25 7 0.3285714 2.914286 *
23) Petal.Length>=5.25 22 2.0277270 3.168182 *
3) Species=setosa 50 7.0408000 3.428000
6) Sepal.Length< 5.05 28 2.0496430 3.203571 *
7) Sepal.Length>=5.05 22 1.7859090 3.713636 *
> text(dtree,use.n=TRUE,cex=.7)
> #predicting the Sepal Width
Predicted Value:
2.805556
> plot(pt)
> #creating the decision tree using classification
> plot(dt,uniform=TRUE,main="Sepal Width Decision Tree using Classification")
> print(dt)
n= 150
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 150 124 3 (0.0067 0.02 0.027 0.02 0.053 0.033 0.06 0.093 0.067 0.17 0.073 0.087
0.04 0.08 0.04 0.027 0.02 0.04 0.013 0.0067 0.0067 0.0067 0.0067)
2) Petal.Width>=0.8 100 80 3 (0.01 0.03 0.03 0.03 0.08 0.05 0.09 0.14 0.09 0.2 0.07 0.08
0.04 0.03 0 0.01 0 0.02 0 0 0 0 0)
4) Sepal.Length< 6.45 65 55 2.8 (0.015 0.046 0.046 0.046 0.11 0.062 0.14 0.15 0.11 0.14
0.015 0.046 0.031 0.046 0 0 0 0 0 0 0 0 0)
8) Petal.Width< 1.95 56 47 2.7 (0.018 0.054 0.054 0.054 0.11 0.071 0.16 0.11 0.12 0.16
0.018 0.036 0.018 0.018 0 0 0 0 0 0 0 0 0)
16) Sepal.Length< 5.55 12 9 2.4 (0.083 0 0.17 0.25 0.25 0.083 0.083 0 0 0.083 0 0 0 0
0 0 0 0 0 0 0 0 0) *
17) Sepal.Length>=5.55 44 36 2.7 (0 0.068 0.023 0 0.068 0.068 0.18 0.14 0.16 0.18
0.023 0.045 0.023 0.023 0 0 0 0 0 0 0 0 0)
34) Petal.Width< 1.55 29 23 2.9 (0 0.1 0.034 0 0.069 0.1 0.1 0.17 0.21 0.17 0 0.034 0
0 0 0 0 0 0 0 0 0 0)
68) Sepal.Length>=5.95 15 11 2.9 (0 0.2 0.067 0 0.067 0.067 0 0.2 0.27 0.067 0
0.067 0 0 0 0 0 0 0 0 0 0 0) *
69) Sepal.Length< 5.95 14 10 3 (0 0 0 0 0.071 0.14 0.21 0.14 0.14 0.29 0 0 0 0 0 0 0
0 0 0 0 0 0) *
35) Petal.Width>=1.55 15 10 2.7 (0 0 0 0 0.067 0 0.33 0.067 0.067 0.2 0.067 0.067
0.067 0.067 0 0 0 0 0 0 0 0 0) *
9) Petal.Width>=1.95 9 5 2.8 (0 0 0 0 0.11 0 0 0.44 0 0 0 0.11 0.11 0.22 0 0 0 0 0 0 0 0
0) *
5) Sepal.Length>=6.45 35 24 3 (0 0 0 0 0.029 0.029 0 0.11 0.057 0.31 0.17 0.14 0.057 0 0
0.029 0 0.057 0 0 0 0 0) *
3) Petal.Width< 0.8 50 41 3.4 (0 0 0.02 0 0 0 0 0 0.02 0.12 0.08 0.1 0.04 0.18 0.12 0.06
0.06 0.08 0.04 0.02 0.02 0.02 0.02)
6) Sepal.Length< 4.95 20 15 3 (0 0 0.05 0 0 0 0 0 0.05 0.25 0.2 0.2 0 0.15 0 0.1 0 0 0 0 0
0 0)
12) Petal.Length< 1.45 13 8 3 (0 0 0.077 0 0 0 0 0 0.077 0.38 0 0.23 0 0.077 0 0.15 0 0 0
0 0 0 0) *
13) Petal.Length>=1.45 7 3 3.1 (0 0 0 0 0 0 0 0 0 0 0.57 0.14 0 0.29 0 0 0 0 0 0 0 0 0) *
7) Sepal.Length>=4.95 30 24 3.4 (0 0 0 0 0 0 0 0 0 0.033 0 0.033 0.067 0.2 0.2 0.033 0.1
0.13 0.067 0.033 0.033 0.033 0.033)
14) Petal.Length< 1.45 11 7 3.5 (0 0 0 0 0 0 0 0 0 0 0 0.091 0.091 0.091 0.36 0.091 0 0
0.091 0.091 0 0.091 0) *
15) Petal.Length>=1.45 19 14 3.4 (0 0 0 0 0 0 0 0 0 0.053 0 0 0.053 0.26 0.11 0 0.16
0.21 0.053 0 0.053 0 0.053) *
> text(dt,use.n=TRUE,cex=.7)
6.LINEAR REGRESSION
#Linear Regression
setwd("D:/R")
df=read.csv("h2.csv",header=TRUE)
print(df)
lr=lm(height~weight,data=df)
print(lr)
#Linear Regression
plot(df$height,df$weight,col="blue",main="Height_Weight
Regression",cex=1.3,pch=15,xlab="height",ylab="weight")
print(summary(lr))
print(residuals(lr))
coeff=coefficients(lr)
eq=paste0("y",round(coeff[2],1),"*(",round(coeff[1],1),"*x)")
print(eq)
#Linear Equation
new.weights=data.frame(weight=c(60,50)
) print(new.weights)
df1=predict(lr,newdata=new.weights)
print(df1)
df2=data.frame(df1,new.weights)
names(df2)=c("height","weight")
print(df2)
df3=rbind(df,df2)
print(df3)
write.csv(df3,"h3.csv")
pie(table(df3$height))
OUTPUT:
> #Linear Regression
> setwd("D:/R")
> df=read.csv("h2.csv",header=TRUE)
> print(df)
height weight
1 8
0
174
2 7
0
150
3 7
5
160
4 8
5
180
> lr=lm(height~weight,data=df)
> print(lr)
Call:
lm(formula = height ~ weight, data =
df) Coefficients:
(Intercept) weight
4.80 2.08
> #Linear Regression
> print(summary(lr))
Call:
lm(formula = height ~ weight, data =
df) Residuals:
1 2 3 4
2.8 -0.4 -0.8 -1.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8000 16.4463 0.292 0.7979
weight 2.0800 0.2117 9.827 0.0102 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.366 on 2 degrees of freedom
Multiple R-squared: 0.9797, Adjusted R-squared: 0.9696
F-statistic: 96.57 on 1 and 2 DF, p-value: 0.0102
> print(residuals(lr))
1 2 3 4
2.8 -0.4 -0.8 -1.6
> print(eq)
[1] "y2.1*(4.8*x)"
> #Linear Equation
> print(new.weights)
weight
1 60
2 50
> print(df1)
1 2
129.6 108.8
> print(df2)
height weight
1 129.6 60
2 108.8 50
> df3=rbind(df,df2)
> print(df3)
height
weight
1 174.0 8
0
2 150.0 7
0
3 160.0 7
5
4 180.0 8
5
11 6
129.6 0
21 5
108.8 0
> write.csv(df3,"h3.csv")
> pie(table(df3$height))
7.DATA VISUALIZATION
#Data Visualization
X=iris
dim(X)
summary(X)
head(X)
hist(X$Sepal.Length,main='Histogram',col='green')
barplot(X$Sepal.Length[1:10],main='Barplot',col='red',xlab='Sepal.Length'
) pie(table(X$Sepal.Length),main='pie-chart')
pairs(X)
plot(X$Sepal.Length,main='plot-chart',col='blue')
boxplot(X,main='Boxplot',col='yellow')
OUTPUT:
> #Data Visualization
> dim(X)
[1] 150 5
> summary(X)
Sepal.Length Sepal.Width
Petal.Length Min. :4.300
Min. :2.000 Min. :1.000 1st Qu.:5.100
1st Qu.:2.800 1st Qu.:1.600
Median :5.800 Median :3.000 Median :4.350
Mean :5.843 Mean :3.057 Mean :3.758 3rd
Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 Max.
:7.900 Max. :4.400 Max. :6.900
Petal.Width Species
Min. :0.100 setosa :50
1st Qu.:0.300 versicolor:50
Median :1.300 virginica :50
Mean :1.199
3rd Qu.:1.800
Max. :2.500
> head(X)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3 1.4 0.2 seto
. sa
5
2 4.9 3 1.4 0.2 seto
. sa
0
3 4.7 3 1.3 0.2 seto
. sa
2
4 4.6 3 1.5 0.2 seto
. sa
1
5 5.0 3 1.4 0.2 seto
. sa
6
6 5.4 3 1.7 0.4 seto
. sa
9
> hist(X$Sepal.Length,main='Histogram',col='green')
> barplot(X$Sepal.Length[1:10],main='Barplot',col='red',xlab='Sepal.Length')
> pie(table(X$Sepal.Length),main='pie-chart')
> pairs(X)
> plot(X$Sepal.Length,main='plot-chart',col='blue')
> boxplot(X,main='Boxplot',col='yellow')
