1. Graphs in R
Codes are in Blue.
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2. Different types of graphs
• Line Chart
• Bar Chart
• Pie Chart
• Histogram
• Extras: Graphs for
– Regression
– Association
– Neural Networks
– Factor Analysis
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3. Line Chart
• l<-c(3,5,8,12,15,32,56) #data
• plot(l)
• plot(l,type=“l",col="blue")
• plot(l,type="o",col="blue")
title(main="line",col.main="red",font.main="12")
• plot(l,type="o",col="blue",main="line
chart",col.main="red",font.main=180,sub="line",col.sub="pin
k",font.sub=100,xlab="range",ylab="l",xlim=c(0,15),ylim=c(0,6
0))
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4. Contd..
• title(main="line
chart",col.main="red",font.main=180,sub="line",col.sub="pin
k",font.sub=100,xlab="range",ylab="l",xlim=c(0,15),ylim=c(0,6
0))
• text(l,pos=4,cex=3)
• b<-c(2,4,6,13,18,35,38,60) #data
• plot(l,type="o",pch=10,col="dark green")
• lines(b,type="o",pch=22,col="maroon")
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5. Bar Chart
• barplot(l,main="barplot",col.main="blue",font.main=60,densi
ty=c(10,20,30,40,50,60,70),xlab="range",ylab="b")
• barplot(l,main="barplot",col.main="blue",font.main=60,col=r
ainbow(7),xlab="range",ylab="b")
• z<-c(l,b) #using the previous data l and b
• barplot(as.matrix(z),col=rainbow(7),beside=T,cex.axis=1)
• box()
• legend(0,50,c(3,5,8,12,15,32,56),cex=1,bty="n",fill=rainbow(7
))
• barplot(l,main="barplot",col.main="blue",font.main=60,col=r
ainbow(7),xlab="range",ylab="b",space=5)
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6. Histogram
• hist(l,col=rainbow(4),xlab="range",ylab=“frequency",main="hi
stogram",col.main="forest green",font.main=100)
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7. Pie chart
• pie(b,main="piechart",col.main=rainbow(1),col=rainbow(7),la
bels=c("2","4","6","13","18","35","38","60"))
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8. Regression
• x <- rnorm(100)
• y <- rnorm(100)
• z <- 0.2*x - 0.3*y + rnorm(100, sd=0.3)
• fit <- lm(z ~ x + y)
• plot(fit)
• install.packages(“rgl”) # from cran library
• library(rgl)
• plot3d(x,y,z, type="s", col="red", size=1)
• coefs <- coef(fit)
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9. Contd..
• a <- coefs["x"]
• b <- coefs["y"]
• c <- -1
• d <- coefs["(Intercept)"]
• planes3d(a, b, c, d, alpha=0.5)
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10. Neural Network
• nn <-
neuralnet(case~age+parity+induced+spontaneous,data=infert
, hidden=3)
• plot(nn)
• plot(nn,rep="best",col.entry.synapse = "red",col.entry =
"green",col.hidden = "blue",col.hidden.synapse =
"brown",col.out = "orange",col.out.synapse =
"magenta",col.intercept = " dark green")
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11. Association
• Install packages
– arules
– arulesViz
• data(Groceries) # in-built dataset in R
• rules <- apriori(Groceries, parameter=list(support=0.001,
confidence=0.5))
• plot(rules)
• plot(rules, method="matrix", measure="lift")
• plot(rules, method="matrix3d", measure="lift")
• plot(rules, method="matrix", measure=c("lift", "confidence"))
• plot(rules, method="grouped")
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12. Factor Analysis
• Install packages
– psych
– GPArotation
• fan=read.csv(“fa.csv”,sep=“,”,h=T)
• fanl=fa(fan)
• fa.plot(fanl)
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13. THANK YOU.
Reference: http://cran.r-project.org/
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