STAT Week 7. Advanced R Graphics and ggplot2. Advanced R Graphics. ggplot2. February 22, 2018

Transcription

1 and and February 22, 2018

2 and

3 NCAA Basketball data and We will use data from the NCAA basketball tournament from url <- ' hoops <- read.csv(url,stringsasfactors = FALSE) hoops.2011 <- filter(hoops, Season >= 2011) head(hoops.2011) ## Season Daynum Wteam Wscore Lteam Lscore Wloc Numot Wfgm Wfga Wfgm3 Wfga3 ## N ## N ## N ## N ## N ## N ## Wftm Wfta Wor Wdr Wast Wto Wstl Wblk Wpf Lfgm Lfga Lfgm3 Lfga3 Lftm Lfta ## ## ## ## ## ## ## Lor Ldr Last Lto Lstl Lblk Lpf ## ## ## ## ## ##

4 Compute annual averages and points <- hoops.2011 %>% group_by(season) %>% summarise(win.points = mean(wscore), Lose.Points = mean(lscore), Win.3Pt = mean(wfgm3), Lose.3pt = mean(lfgm3)) head(points) ## # A tibble: 6 x 5 ## Season Win.Points Lose.Points Win.3Pt Lose.3pt ## <int> <dbl> <dbl> <dbl> <dbl> ## ## ## ## ## ##

5 Plot types and Average Points Scored by Losing Teams in NCAA Tournament Average Points Scored Season

6 Plot types and plot(points$lose.points~points$season,ylim=c(0,max(points$lose.points)), ylab='average Points Scored', xlab='season', main='average Points Scored by Losing Teams \n in NCAA Tournament', cex.lab=.8, cex.main =

7 Plot types and Average Points Scored by Losing Teams in NCAA Tournament Average Points Scored Season

8 Plot types and plot(points$lose.points~points$season,ylim=c(0,max(points$lose.points)), ylab='average Points Scored', xlab='season', type='h',lwd=3, main='average Points Scored by Losing Teams \n in NCAA Tournament',cex.lab=.8, cex.main =.

9 Lines and Average Points Scored by Losing Teams in NCAA Tournament Average Points Scored Season

10 Lines and plot(points$lose.points~points$season,ylim=c(0,max(points$lose.points)), ylab='average Points Scored', xlab='season', type='n', main='average Points Scored by Losing Teams \n in NCAA Tournament',cex.lab=.8, cex.main =. lines(points$lose.points~points$season,col='red',lwd=1) lines(points$win.points~points$season,col='blue',lwd=1,lty=2)

11 Lines and Legends and Average Points Scored in NCAA Tournament Average Points Scored Winning Team Losing Team Season

12 Lines and Legends and plot(points$lose.points~points$season,ylim=c(0,max(points$win.points)), ylab='average Points Scored', xlab='season', type='n', main='average Points Scored in NCAA Tournament',cex.lab=.8, cex.main =.8) lines(points$lose.points~points$season,col='red',lwd=1) lines(points$win.points~points$season,col='blue',lwd=1,lty=2) legend('bottomright',legend=c('winning Team','Losing Team'),col=c('blue','red'), lwd=c(1,1), lty=c(2,1), cex =.7)

13 Points and Average Points Scored in NCAA Tournament Average Points Scored Average Winning Team Average Losing Team Individual Winning Team Season

14 Points and plot(points$lose.points~points$season,ylim=c(0,max(points$win.points)), ylab='average Points Scored', xlab='season', type='l', lwd=1,col='red', main='average Points Scored in NCAA Tournament',cex.lab=.8, cex.main =.8) lines(points$win.points~points$season,col='blue',lwd=1,lty=2) points(hoops.2011$wscore~hoops.2011$season,pch=16,col=rgb(0,0,.5,.1)) legend('bottomright',legend=c('average Winning Team','Average Losing Team', 'Individual Winning lwd=c(1,1,na), lty=c(2,1,na), pch=c(na,na,16), cex=.7)

15 Annotation and Average Points Scored in NCAA Tournament Average Points Scored Shot Clock Shortened Average Winning Team Average Losing Team Individual Winning Team Season

16 Annotation and plot(points$lose.points~points$season,ylim=c(0,max(points$win.points)), ylab='average Points Scored', xlab='season', type='l', lwd=1,col='red', main='average Points Scored in NCAA Tournament', cex.lab=.8, cex.main =.8) lines(points$win.points~points$season,col='blue',lwd=1,lty=2) points(hoops.2011$wscore~hoops.2011$season,pch=16,col=rgb(0,0,.5,.1)) text(2015,40,'shot Clock Shortened', cex=.6) arrows(x0=2015, y0=42, x1=2016, y1=70, length=0.1, lwd=2) legend('bottomright',legend=c('average Winning Team','Average Losing Team', 'Individual Winning lwd=c(1,1,na), lty=c(2,1,na), pch=c(na,na,16), cex=.6)

17 Axes and Average Points Scored in NCAA Tournament Average Points Scored Average Winning Team Average Losing Team Season

18 Axes and plot(points$lose.points~points$season,ylim=c(0,max(points$win.points)), axes=f, ylab='average Points Scored', xlab='season', type='b',pch=16, lwd=1,col='red', main='average Points Scored in NCAA Tournament', cex.main=.75, cex.lab=.75) lines(points$win.points~points$season,col='blue',lwd=1,lty=2,type='b',pch=17) legend('bottomright',legend=c('average Winning Team','Average Losing Team'),col=c('blue','red'), lwd=1, lty=c(2,1),pch=c(17,16), cex=.7)

19 Axes and Average Points Scored in NCAA Tournament Average Points Scored Average Winning Team Average Losing Team Season

20 Axes and plot(points$lose.points~points$season,ylim=c(0,max(points$win.points)), axes=f, ylab='average Points Scored', xlab='season', type='b',pch=16, lwd=1,col='red', main='average Points Scored in NCAA Tournament', cex.main=.75, cex.lab=.75) lines(points$win.points~points$season,col='blue',lwd=3,lty=2,type='b',pch=17) legend('bottomleft',legend=c('average Winning Team','Average Losing Team'),col=c('blue','red'), lwd=1, lty=c(2,1),pch=c(17,16), cex=.7) axis(4) axis(1, at = 2011:2016,labels=c('10-11','11-12','12-13','13-14','14-15','15-16'))

21 Axes and Average Points Scored in NCAA Tournament Average Points Scored Average Winning Team Average Losing Team Season

22 Axes and plot(points$lose.points~points$season,ylim=c(0,max(points$win.points)), axes=f, ylab='average Points Scored', xlab='season', type='b',pch=16, lwd=1,col='red', main='average Points Scored in NCAA Tournament', cex.main=.75, cex.lab=.75) lines(points$win.points~points$season,col='blue',lwd=3,lty=2,type='b',pch=17) legend('bottomleft',legend=c('average Winning Team','Average Losing Team'),col=c('blue','red'), lwd=1, lty=c(2,1),pch=c(17,16), cex=.7) axis(4) axis(1, at = 2011:2016,labels=c('10-11','11-12','12-13','13-14','14-15','15-16')) box()

23 Superimposed Plots and plot(density(hoops.2011$wscore),xlab='points',ylab='', main='histogram and superimposed density hist(hoops.2011$wscore,add=t,probability = T) histogram and superimposed density curve for points scored by winning team points

24 Expression and plot(density(hoops.2011$wscore),ylab=expression(beta[2]),xlab='', main='examples with Expression',axes=F, type='n') box() text(70,.023, expression(sum(theta[i]^2, i=1, n)),cex=2) Examples with Expression 1 β 2 n θi 2

25 R-Markdown Captions and Captions in R Markdown caption in R header Figure 1: Write caption here

26 Exercise: Advanced Plotting and Use the Seattle Housing Data Set datasets/seattlehousing.csv to create an interesting graphic, include informative titles, labels, and add an annotation.

27 Solution: Advanced Plotting and Houses Sold in Seattle Most homes sell for less than one million Sales Price (million $)

28 Solution: Advanced Plotting and Seattle.in <- read.csv( ' stringsasfactors = F) hist(seattle.in$price,prob=t,breaks="fd", ylab='', col='forestgreen', xlab='sales Price (million $)', main='houses Sold in Seattle', axes=f) axis(1, at = c(0,500000, , , , , ), labels =c('0','.5','1','2.5','4','5.5','7')) arrows(x0= , y0=1.5e-6, x1= , y1=.5e-6, length=0.1, lwd=2) text( ,1.8e-6,'most homes sell for \n less than one million',cex=.8)

29 and

30 Overview and Why? Advantages of consistent underlying grammar of graphics (Wilkinson, 2005) plot specification at a high level of abstraction very flexible theme system for polishing plot appearance

31 Grammar of and The basic idea: independently specify plot building blocks and combine them to create just about any kind of graphical display you want. Building blocks of a graph include: data aesthetic mapping geometric object statistical transformations scales coordinate system position adjustments faceting

32 VS Base and Compared to base graphics, is more verbose for simple / canned graphics is less verbose for complex / custom graphics does not have methods (data should always be in a data.frame) uses a different system for adding plot elements

33 Aesthetic Mapping and Aesthetics are things that you can see. Examples include: position (i.e., on the x and y axes) color ( outside color) fill ( inside color) shape (of points) linetype size Aesthetic mappings are set with the aes() function.

34 Geometic Objects (geom) and Geometric objects are the actual marks we put on a plot. Examples include: points (geom_point) lines (geom_line) boxplot (geom_boxplot) A plot must have at least one geom; there is no upper limit. You can add a geom to a plot using the + operator

35 Graphical Primitives/ ggplot and graph.a <- ggplot(data = hoops.2011, aes(lfgm,wfgm)) graph.a Wfgm Lfgm

36 Adding Geoms: geom_point() and graph.a + geom_point() Wfgm Lfgm

37 Adding Geoms: geom_smooth() and graph.a + geom_point() + geom_smooth(method = 'loess') Wfgm Lfgm

38 Adding Geoms: geom_rug() and graph.a + geom_point() + geom_smooth(method = 'loess') + geom_rug() Wfgm Lfgm

39 Adding Geoms: geom_density2d() and graph.a + geom_point() + geom_smooth(method = 'loess') + geom_rug() + geom_density2d() Wfgm Lfgm

40 Adding Geoms: geom_jitter() and graph.a + geom_rug() + geom_density2d() + geom_jitter() Wfgm Lfgm

41 Adding Geoms: labs() and graph.a + geom_rug() + geom_density2d() + geom_jitter() + labs(x='losing Team Field Goals Made', y = 'Winning Team Field Goals Made') Winning Team Field Goals Made Losing Team Field Goals Made

42 Scales: xlim() and ylim() and graph.a + geom_rug() + geom_density2d() + geom_jitter() + labs(x='losing Team Field Goals Made', y = 'Winning Team Field Goals Made') + xlim(c(0,max(hoops.2011$wfgm))) + ylim(c(0,max(hoops.2011$wfgm))) Winning Team Field Goals Made Losing Team Field Goals Made

43 Themes and graph.a + geom_point() + theme_bw() + labs(x='losing Team Field Goals Made', y = 'Winning Team F Winning Team Field Goals Made Losing Team Field Goals Made

44 More about aes and graph.a + geom_jitter(col = 'firebrick4') Wfgm Lfgm

45 More about aes and graph.a + geom_jitter(aes(col = as.factor(season)))

46 More about aes and graph.a + geom_jitter(aes(col = as.factor(season))) Wfgm as.factor(season) Lfgm

47 More about aes and graph.a + geom_jitter(aes(col = as.factor(season)), size=3,alpha=.4) Wfgm 40 as.factor(season) Lfgm

48 More about aes and graph.a + geom_jitter(aes(shape = as.factor(season),col=wscore), size=3,alpha=.4) Wfgm Lfgm Wscore

49 Faceting and graph.a + facet_wrap(~season)

50 Faceting and graph.a + facet_wrap(~season) + geom_jitter(alpha=.5, aes(color=wfgm3)) Wfgm Lfgm Wfgm

51 Maps and library(maps) usa <- map_data("usa") usa.map <- ggplot() + geom_polygon(data = usa, aes(x=long, y = lat, group = group)) + coord_fixed(1.3) usa.map lat long

52 Maps and labs <- data.frame(long = c( , , , , ), stringsasfactors = FALSE, lat = c( , , , , ), names = c("bozeman, MT", "Golden, CO", "Blacksburg, VA",'Decorah, IA', "Davis, CA") ) usa.map + geom_point(data = labs, aes(x = long, y = lat), color = "yellow", size = 4) + annotate(geom='text', y=44,x=-111,label='bozeman,mt',col='white') Bozeman,MT lat long

53 More Maps: ggmap and library(ggmap) ## Google Maps API Terms of Service: ## Please cite ggmap if you use it: see citation("ggmap") for details. mymap <- get_map(location = c(lon = ,lat = ), source = "google", maptype = "terrain", crop = FALSE, zoom = 7) ## Source : # plot map yellowstone.map <- ggmap(mymap)

54 More Maps: ggmap STAT 408 and yellowstone.map 46 lat lon 108

55 Exercise: and Now use to create an interesting graph using the Seattle Housing data set.

56 Solution: and Seattle Housing Sales: Price vs. Square Footage Living Space zipcode Sales Price (million dollars) Housing price depends on zipcode Living Space (square foot)

57 Solution: and library() Seattle.in$zipcode <- as.factor(seattle.in$zipcode) graph.a <- ggplot(data = Seattle.in, aes(sqft_living,price)) graph.a + geom_jitter(aes(col = zipcode)) + theme(plot.title = element_text(size=20))+ geom_smooth(method='loess')+ ggtitle('seattle Housing Sales: Price vs. Square Footage Living Space') + ylab('sales Price (million dollars)') + xlab('living Space (square foot)')+ scale_y_continuous(breaks=c(seq(0, ,by= )), labels=as.character(0:7)) + annotate('text',3500, , label = 'Housing price depends on zipcode') + annotate("rect", xmin = 0, xmax = 7250, ymin = , ymax = , alpha =.6) + geom_segment(aes(x=3500, xend=3500, y= , yend= ), arrow = arrow(length = unit(0.5, "cm")))

58 Solution: map STAT 408 and ## Source : ## Source : Location of Housing Sales in Seattle, WA 48.5 price 48.0 lat 2e e+06 6e lon 121

59 Solution: map and mymap <- get_map(location = 'Seattle', source = "google", maptype = "terrain", crop = FALSE, zoom = 8) Seattle.map <- ggmap(mymap) Seattle.map + geom_point(data=seattle.in, aes(x=long, y=lat, size=price), alpha=.25) + labs(title = 'Location of Housing Sales in Seattle, WA') + theme(plot.title = element_text(si