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# Graphs in R

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### Graphs in R

1. 1. Graphs in R Codes are in Blue. For Feedback Mail me: sharmakarishma91@gmail.com
2. 2. Different types of graphs • Line Chart • Bar Chart • Pie Chart • Histogram • Extras: Graphs for – Regression – Association – Neural Networks – Factor Analysis For Feedback Mail me: sharmakarishma91@gmail.com
3. 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)) For Feedback Mail me: sharmakarishma91@gmail.com
4. 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") For Feedback Mail me: sharmakarishma91@gmail.com
5. 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) For Feedback Mail me: sharmakarishma91@gmail.com
6. 6. Histogram • hist(l,col=rainbow(4),xlab="range",ylab=“frequency",main="hi stogram",col.main="forest green",font.main=100) For Feedback Mail me: sharmakarishma91@gmail.com
7. 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")) For Feedback Mail me: sharmakarishma91@gmail.com
8. 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) For Feedback Mail me: sharmakarishma91@gmail.com
9. 9. Contd.. • a <- coefs["x"] • b <- coefs["y"] • c <- -1 • d <- coefs["(Intercept)"] • planes3d(a, b, c, d, alpha=0.5) For Feedback Mail me: sharmakarishma91@gmail.com
10. 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") For Feedback Mail me: sharmakarishma91@gmail.com
11. 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") For Feedback Mail me: sharmakarishma91@gmail.com
12. 12. Factor Analysis • Install packages – psych – GPArotation • fan=read.csv(“fa.csv”,sep=“,”,h=T) • fanl=fa(fan) • fa.plot(fanl) For Feedback Mail me: sharmakarishma91@gmail.com
13. 13. THANK YOU. Reference: http://cran.r-project.org/ For Feedback Mail me: sharmakarishma91@gmail.com