1. Data Visualization with R (I) Dr. Jieh-Shan George YEH jsyeh@pu.edu.tw

2. Outlines Data Visualization with R Visualizing Different Type of Data – Univariate – Univariate Categorical – Bivariate Categorical – Bivariate Continuous vs Categorical – Bivariate Continuous vs Continuous – Bivariate: Continuous vs Time 2

3. Data Visualization with R Both anecdotally, and per Google Trends, R is the language and tool most closely associated with creating data visualizations. – http://www.google.com/trends/explore?hl=en- US#q=R%20language,%20Data%20Visualization,%20D 3.js,%20Processing.js&cmpt=q http://www.google.com/trends/explore?hl=en- US#q=R%20language,%20Data%20Visualization,%20D 3.js,%20Processing.js&cmpt=q 3

4. Google Trend on R & Data Visualization 4

5. 5

6. UNIVARIATE 6

7. plot df<- mtcars str(df) summary(df) summary(df$hp) plot(df$hp) 7

8. Univariate: boxplot # Boxplot for univariate boxplot(df$hp, horizontal=TRUE, notch=TRUE, col="gold") 8

9. Univariate: robustbase::adjbox install.packages("robustba se") library(robustbase) robustbase::adjbox(df$hp, horizontal=TRUE, cex=2, lwd=0.5, main="robustbase::adjbox ()", notch=TRUE, col="skyblue") 9

10. Univariate: vioplot::vioplot install.packages("vioplot") library(vioplot) vioplot::vioplot(df$hp, col="lightgreen", horizontal=TRUE) 10

11. Univariate: Historgam ##the counts component of the result hist(df$hp, xlab="Gross horsepower", ylab="Number of cars", labels=TRUE, col="skyblue") 11

12. Univariate: Historgam # freq=FALSE, probability densities, component density hist(df$hp, xlab="Gross horsepower", ylab="Number of cars", col="skyblue", freq=FALSE) lines(density(df$hp), col="red") 12

13. histogram & adjusted boxplot mat<-matrix(c(1,2)) layout(mat, height=c(0.8, 0.2)) par(mar= c(1, 1, 1, 1), bty="n") # to set or query graphical parameters. hist(df$hp, xlab="Gross horsepower", ylab="Frequency", labels=TRUE ) axis(2) boxplot(df$hp, horizontal=TRUE, notch=TRUE, col="gold") 13

14. Qqplot – for the statistically inclined # qqplot - for the statistically inclined # qqline adds a line to a "theoretical", by default normal, # quantile-quantile plot which passes through the probs quantiles, by default the first and third quartiles. qqnorm(df$hp, col="blue", pch=16) qqline(df$hp, col="red", lwd=2) 14

15. UNIVARIATE CATEGORICAL 15

16. Univariate Categorical #Topics most visited on English Wikipedia on 31 May 2013 Topic <- c("Cult", "Rituparno Ghosh", "Cat anatomy", "Facebook", "Fast & Furious 6", "Liberace", "Game of Thrones", "Jean-Claude Romand", "Game of Thrones (season 3)", "Arrested Development (TV series)") NoHit <- c(291439, 215843, 102960, 93181, 84014, 73162, 70599, 70144, 69752, 69573) wiki <- NoHit names(wiki)<- Topic 16

17. Univariate Categorical: barplot n <- length(wiki) bp<-barplot(wiki, horiz=TRUE, names.arg=c(rep("",10)), col=topo.colors(n), legend.text=TRUE) text( x=wiki, y=bp, labels=wiki, cex=0.8, pos=2) #Add Text to a Plot 17

18. Univariate Categorical: pie # pie pie(wiki, init.angle=90) 18

19. Univariate Categorical: pie3D require(plotrix) pie3D(wiki, labels = names(wiki), explode=0.1) 19

20. Univariate Categorical: dotchart #dotchart dotchart(wiki, pch=19, col=rainbow(n)) 20

21. BIVARIATE CATEGORICAL 21

22. Bivariate Categorical: barpplot #Stacked bar plot mycols <- c("Brown", "Blue", "Yellow", "Green") barplot( HairEyeColor[,,1], col=mycols) legend( x="topright", legend = attr(HairEyeColor, "dimnames")$Eye, pch=18, col=mycols) 22

23. Bivariate Categorical: barpplot barplot( HairEyeColor[,,1], col=mycols, beside=TRUE) legend( x="topright", legend = attr(HairEyeColor, "dimnames")$Eye, pch=18, col=mycols) 23

24. Bivariate Categorical: mosaicplot #mosaic grid mosaicplot(HairEyeColor[,, 1], col=mycols) 24

25. BIVARIATE CONTINUOUS VS CATEGORICAL 25

26. bivariate Continuous vs Categorical: boxplot mtcars attach(mtcars) boxplot(mpg~cyl, data=mtcars, col=c("darkorange","blue","gold")) 26

27. bivariate Continuous vs Categorical: Scatterplot #Scatterplot with(mtcars, plot(x=wt, y=mpg, pch=19, col="darkblue", xlab="Car Weights", ylab="Miles Per Gallon") ) 27

28. bivariate Continuous vs Categorical: Scatterplot – fitted lines #Scatterplot fitted line with(mtcars, abline(lsfit(x=wt, y=mpg), col="red") ) with(mtcars, lines(lowess(x=wt, y=mpg), col="green") ) 28

29. car::scatterplot #car::scatterplot require(car) scatterplot(mpg~wt, data=mtcars) 29

30. Bivariate boxplot - bagplot #Bivariate boxplot - bagplot install.packages(aplpack) require(aplpack) with(mtcars, bagplot(wt, mpg)) 30

31. BIVARIATE CONTINUOUS VS CONTINUOUS 31

32. Bivariate contiuous vs contiuous: hexbin install.packages(hexbin) require(hexbin) x<-sample(c(1:100), 1000, replace=TRUE) y<-sample(c(1:100), 1000, replace=TRUE) bin<-hexbin(x, y, xbins=50) plot(bin, colorcut=seq(0,1,1/16)) 32

33. Bivariate contiuous vs contiuous: hexbin h <- hexbin(rnorm(10000),rnorm(10000)) plot(h, colramp= BTY) 33

34. Bivariate contiuous vs contiuous: hexbin h <- hexbin(rnorm(10000),rnorm(10000)) ## Using plot method for hexbin objects: plot(h, style = "nested.lattice") 34

35. BIVARIATE: CONTINUOUS VS TIME 35

36. Bivariate: continus vs time: require(tseries) data(nino) tt<- window(nino3, from=1990, to=2000) plot(tt) 36

37. # Timeseries - decompostion plot(decompose(nino3)) 37

38. Multivariate data #Multivariate data plot(iris, col=iris$Species) 38

39. Linear model lm1<- lm(mpg~wt, data=mtcars) par(mfrow=c(2,2)) plot(lm1) 39