Ggplot2 1outof3
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This is the slides I made for NYC Open Data meetup.event detail: http://www.meetup.com/NYC-Open-Data/events/137235202/ NYC Open Data meetup group offers free woekshop twice per week in NYC. Everyone is welcome to come and learn data science and open data topics. Sin up at http://www.meetup.com/NYC-Open-Data/
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Ggplot2 1outof3
- 1. Learnggplot2 AsKungfu Skill A K f Skills Givenby KaiXiao(DataSciencetist) VivianZhang(Co-founder&CTO) Vi i Zh (C f d & CTO) Contact:vivian.zhang@supstat.com Contact: vivian zhang@supstat com
- 2. • I： Point • II： Bar • III：Histogram g • IV：Line • V： Tile • VI：Map
- 3. Introduction • ggplot2 is a plotting system for R • based on the《The Grammar of Graphics》 • which tries to take the good parts of base and lattice graphics and none of the bad parts none of the bad parts • It takes care of many of the fiddly details that make plotting a hassle details that make plotting a hassle • It becomes easy to produce complex multi‐layered graphics p y g p
- 4. Whyweloveggplot2? Why we love ggplot2? • controltheplotasabstractlayersandmakecreativitybecomereality； • getusedtostructuralthinking； d l hi ki • getbeautifulgraphicswhileavoidingcomplicateddetails 1973murdercasesinUSA
- 5. 7BasicConcepts 7 Basic Concepts • Mapping • Scale • Geometric Stat st cs • Statistics • Coordinate • L Layer • Facet
- 7. Scale Scalewillpresentmappingoncoordinatescales. Scale will present mapping on coordinate scales ScaleandMappingiscloselyrelatedconcepts.
- 8. Geometric Geom means the graphical elements such as meansthegraphicalelements,suchas points,linesandpolygons.
- 9. Statistics Statenablesustocalculateanddostatistical Stat enables us to calculate and do statistical analysisbased,suchasaddingaregressionline. Stat
- 10. Coordinate Cood will affect how we observe graphical willaffecthowweobservegraphical elements.Transformationofcoordinatesisuseful. Stat Coord
- 11. Layer Component:data,mapping,geom,stat Usinglayerwillallowuserstoestablishplotsstep i l ill ll bli h l bystep.Itbecomemucheasiertomodifyaplot.
- 13. 7BasicConcepts 7 Basic Concepts • Mapping • Scale • Geometric Stat st cs • Statistics • Coordinate • L Layer • Facet
- 15. Sampledata mpg Sample data--mpg • Fuel economy data from 1999 and 2008 for 38 popular y p p models of car • • • • • • • Details D il Displ : engine displacement, in litres Cyl: number of cylinders Cyl: number of cylinders Trans: type of transmission Drv: front‐wheel, rear wheel drive, 4wd , , Cty: city miles per gallon Hwy: highway miles per gallon
- 16. >library(ggplot2) >str(mpg) 'data.frame': 234obs.of14variables: $manufacturer:Factorw/15levels"audi","chevrolet",..: $model:Factorw/38levels"4runner4wd",..: $displ :num 1.81.8222.82.83.11.81.82... $year:int 1999 1999 2008 2008 1999 1999 2008 1999 $ year : int 19991999200820081999199920081999 $cyl :int 4444666444... $trans:Factorw/10levels"auto(av)","auto(l3)",..: $drv $d :Factorw/3levels"4","f","r": 3l l f $cty :int 18212021161818181620... $hwy $fl :int 29293130262627262528... :Factorw/5levels"c","d","e","p",..: $class:Factorw/7levels"2seater","compact",..:
- 17. aesthetics p < ggplot(data mpg, mapping aes(x cty, y hwy)) p <‐ ggplot(data=mpg mapping=aes(x=cty y=hwy)) p + geom_point()
- 18. > summary(p) data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl, class [234x11] fl l [ ] mapping: x = cty, y = hwy faceting: facet_null() g _ () > summary(p+geom_point()) data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, data: manufacturer model displ year cyl trans drv cty hwy fl, class [234x11] mapping: x = cty, y = hwy faceting: facet_null() f i f ll() ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ g geom_point: na.rm = FALSE _p stat_identity: position_identity: (width = NULL, height = NULL)
- 19. p + geom_point(color red4 size=3) p + geom point(color='red4',size 3)
- 20. #addonemorelayer--color p p<- ggplot(mpg,aes(x=cty,y=hwy,colour=factor(year))) ggp ( pg, ( y,y y, (y ))) p+geom_point()
- 21. #addonemorestat(loess:localpartialpolynomialregression) > p + geom_point() + stat_smooth()
- 22. p <‐ ggplot(data=mpg, mapping=aes(x=cty,y=hwy)) p g p + geom_point(aes(colour=factor(year)))+ _p ( ( (y ))) stat_smooth()
- 23. Twoequallywaystodraw q y y p <‐ ggplot(mpg, aes(x=cty,y=hwy)) p <‐ ggplot(mpg aes(x=cty y=hwy)) p + geom_point(aes(colour=factor(year)))+ () stat_smooth() d <‐ ggplot() + () geom_point(data=mpg, aes(x=cty, y=hwy, colour=factor(year)))+ stat_smooth(data=mpg, aes(x=cty, y=hwy)) print(d)
- 24. Besidethe“whitepaper”canvas,wewillfindgeom andstat canvas. > summary(d) data: [0x0] [ ] faceting: facet_null() ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ mapping: x = cty, y = hwy, colour = factor(year) mapping: x = cty y = hwy colour = factor(year) geom_point: na.rm = FALSE stat_identity: position_identity: (width = NULL, height = NULL) pp g y, y y mapping: x = cty, y = hwy geom_smooth: stat_smooth: method = auto, formula = y ~ x, se = TRUE, n = 80, fullrange = FALSE, level = 0.95, na.rm = FALSE n = 80 fullrange = FALSE level = 0 95 na rm = FALSE position_identity: (width = NULL, height = NULL)
- 32. #Addplotnameandspecifyallinformationyouwanttoadd p<- ggplot(mpg,aes(x=cty,y=hwy)) p+geom_point(aes(colour=class,size=displ), alpha=0.5,position="jitter")+stat_smooth()+ scale_size_continuous(range=c(4,10))+ l i ti ( (4 10)) facet_wrap(~year,ncol=1)+opts(title='modelofcarandmpg')+ labs(y='drivingdistancepergallononhighway',x='drivingdistancepergallononcityroad', size='displacement',colour ='model')
- 36. #estimatedatadentisy p+stat_density2d(aes(fill=..level..),geom="polygon")+ coord_cartesian(xlim =c(0,1.5),ylim=c(0,6000)) coord cartesian(xlim = c(0 1 5) ylim=c(0 6000))
- 42. Resources http://learnr.wordpress.com Redrawallthelatticegraph Redraw all the lattice graph byggplot2
- 44. Resources • http://wiki stdout org/rcookbook/Graphs/ http://wiki.stdout.org/rcookbook/Graphs/ • http://r-blogger.com • htt //St k http://Stackoverflow.com fl • http://xccds1977.blogspot.com • http://r-ke.info/ • http://www.youtube.com/watch?v=vnVJJYi1 mbw