8.5 Special-purpose plots

Transcription

1 K /1/9 17:35 page 130 # CHAPTER 8. A GRAPHICAL COMPENDIUM Contour plots A contour plot shows the Cartesan plan wth smlar valued ponts lnked by lnes. The most famlar versons of such plots may be contour maps showng lnes of constant elevatons that are very useful for hkng. contour(x, y, z) or flled.contour(x, y, z) Note: The contour() functon dsplays a standard contour plot. The flled.contour() functon creates a contour plot wth colored areas between the contours D plots Perspectve or surface plots and needle plots can be used to vsualze data n three dmensons. These are partcularly useful when a response s observed over a grd of twodmensonal values. persp(x, y, z) mage(x, y, z) lbrary(scatterplot3d) scatterplot3d(x, y, z) Note: The values provded for x and y must be n ascendng order. 8.5 Specal-purpose plots Choropleth maps lbrary(ggmap) mymap = map_data( state ) # need to add varable to plot p0 = ggplot(map_data, aes(x=x, y=y, group=z)) + geom_polygon(aes(fll = cut_number(z, 5))) + geom_path(colour = gray, lnestyle = 2) + scale_fll_brewer(palette = PuRd ) + coord_map(); plot(p0) Example: Note: More examples of maps can be found n the ggmap package documentaton Interacton plots Example: Interacton plots are used to dsplay means by two varables (as n a two-way analyss of varance, 6.1.9). nteracton.plot(x1, x2, y) Note: The default statstc to compute s the mean; other optons can be specfed usng the fun opton.

2 K /1/9 17:35 page 193 # PLOTTING MAPS Bke rde plot The Poneer Valley of Massachusetts, where we both lve, s a wonderful place to take a bke rde. In combnaton wth technology to track GPS coordnates, tme, and alttude, nformaton regardng outngs can be dsplayed. The data used here can be downloaded, as demonstrated below, from A map can be downloaded for a partcular area from Google Maps, then plotted n conjuncton wth lattude/longtude coordnates usng functons n the ggmap package. These routnes are bult on top of the ggplot2 grammar of graphcs package. We found good locatons for Amherst usng tral and error and plotted the bke rde GPS sgnals wth the map. > lbrary(ggmap) > optons(dgts=4) > amherst = c(lon=-72.52, lat=42.36) > mymap = get_map(locaton=amherst, zoom=13, color="bw") > myrde = read.csv(" > head(myrde, 2) Tme Rde.Tme Rde.Tme..secs. Stopped.Tme :26:54 0:00: :00: :27:52 0:00: :00:00 Stopped.Tme..secs. Lattude Longtude Elevaton..feet. Dstance..mles Speed..mles.h. Pace Pace..secs. Average.Speed..mles.h. Average.Pace 1 NA NA :00: :21: :22:17 Average.Pace..secs. Clmb..feet. Calores The results are shown n Fgure Relatvely poor cell phone servce leads to sparsty n the ponts n the mddle of the fgure. For more complex mult-dmensonal graphcs made wth the same data, see and com/sasrblog-bkerde-redux Choropleth maps Choropleth maps (see and ) are helpful for vsualzng geographc data. In ths example, we use data from the bult-n R dataset, USArrests, whch ncludes Unted States arrests n 1973 per 100, 000 nhabtants n varous categores by state. We ll use the ggmap package to generate the plot. It bulds on the ggplot2 package, whch mplements deas related to the grammar of graphcs [188]. The package uses a syntax where specfc elements of the plot are added to the fnal product usng specal functons connected by the + symbol. Some addtonal work s needed to merge the dataset wth the state nformaton (2.3.11) and to sort the resultng dataframe (2.3.10) so that the shape data for the states s plotted n order.

3 K /1/9 17:35 page 194 # CHAPTER 12. CASE STUDIES > ggmap(mymap) + geom_pont(aes(x=longtude, y=lattude), data=myrde) lat lon Fgure 12.3: Bke rde plot > lbrary(ggmap); lbrary(dplyr) > USArrests.st = mutate(usarrests, regon=tolower(rownames(usarrests)), murder = cut_number(murder, 5)) > us_state_map = map_data( state ) > map_data = merge(usarrests.st, us_state_map, by="regon") > map_data = arrange(map_data, order) > head(select(map_data, regon, Murder, murder, long, lat, group, order)) regon Murder murder long lat group order 1 alabama 13.2 (12.1,17.4] alabama 13.2 (12.1,17.4] alabama 13.2 (12.1,17.4] alabama 13.2 (12.1,17.4] alabama 13.2 (12.1,17.4] alabama 13.2 (12.1,17.4] The scale fll grey() functon changes the colors from the default unordered multple colors to an ordered and prnt-frendly black and whte (see also scale fle brewer). The ggmap package uses the Mercator projecton (see coord map() n the ggplot2 package and mapproject n the mapproject package). Another mplementaton of choropleth maps can be found n the choroplethr package. The results are dsplayed n Fgure As always, the choce of groupngs can have an mpact on the message conveyed by the graphcal dsplay.

4 K /1/9 17:35 page 195 # DATA SCRAPING 195 > p0 = ggplot(map_data, aes(x=long, y=lat, group=group)) + geom_polygon(aes(fll = murder)) + geom_path(colour= black ) + theme(legend.poston = "bottom", panel.background=element_rect(fll="transparent", color=na)) + scale_fll_grey(start=1, end =.1) + coord_map(); > plot(p0) lat long murder [0.8,3.38] (3.38,6] (6,8.62] (8.62,12.1] (12.1,17.4] 12.4 Data scrapng Fgure 12.4: Choropleth map In ths secton, we demonstrate varous methods for extractng data from web pages, drectly from URLs, va APIs, or usng table formats Scrapng data from HTML fles Here, we automate data harvestng from the web, by scrapng a URL, then readng a datafle wth two lnes per observaton, and plottng the results as tme seres data. The data beng harvested and dsplayed are the sales ranks from Amazon for the Cartoon Gude to Statstcs [53]. We can fnd the Amazon sales rank for a book by downloadng the HTML code for a desred web page and searchng for the approprate lne. The code to do ths reles heavly on (readng more complex data fles) as well as (replacng strngs). An example can be found at html. Many thousands of lnes nto the fle, we fnd the lne we re lookng for. #8,048 n Books (<a href=" Amazon/zgbs/books/ref=pd_dp_ts_b_1">See Top 100 n Books</a>)

5 K /1/9 17:35 page 205 # INTERACTIVE VISUALIZATION Shny n Markdown RStudo supports the Shny system, whch s desgned to smplfy the creaton of nteractve web applcatons. It provdes automatc reactve lnkage between nputs and outputs: when the user clcks on one of the rado buttons, slders, or selectons, the output s rerendered. Avalable control wdgets nclude the functons actonbutton(), checkboxgroupinput (), checkboxinput(), dateinput(), daterangeinput(), fleinput(), helptext(), numercinput(), radobuttons(), selectinput(), slderinput(), submtbutton(), and textinput(). We demonstrate use of ths system by creatng an nteractve choropleth map of the murder rate n US states (as prevously descrbed n ). A template can be created by selectng a new Markdown fle wth the Shny opton pcked. Fgure 12.8 dsplays a Markdown fle that creates a choropleth map that allows control over the number of bns as well as whether to nclude names of the states. --- ttle: "Sample Shny n Markdown" output: html_document runtme: shny --- Shny nputs and outputs can be embedded n a Markdown document. Outputs are automatcally updated whenever nputs change. Ths demonstrates how a standard R plot can be made nteractve by wrappng t n the Shny renderplot functon. The selectinput functon creates the nput wdgets used to control the plot dsplay. {r, echo=false} nputpanel( selectinput("n_breaks", label = "Number of breaks:", choces = c(2, 3, 4, 5, 9), selected = 5), ) selectinput("labels", label = "Dsplay labels?:", choces = c("true", "FALSE"), selected = "TRUE") renderplot({ lbrary(choroplethr); lbrary(dplyr) USArrests.st = mutate(usarrests, regon=tolower(rownames(usarrests)), value = Murder) choroplethr(usarrests.st, "state", ttle="murder Rates by State", showlabels=nput$labels, num_buckets=as.numerc(nput$n_breaks)) }) Fgure 12.8: Shny wthn R Markdown The nputpanel() functon s used n conjuncton wth the selectinput() functon to create two wdgets: one to control the number of groups and the other to control whether to dsplay the labels for the states.

6 K /1/9 17:35 page 206 # CHAPTER 12. CASE STUDIES The renderplot() functon can then access these values through the nput object. The choroplethr() functon n the choropleth package s used to generate the desred fgure. When the document s run (by clckng Run Document wthn RStudo), the results are dsplayed n a vewer wndow (see Fgure 12.9). Fgure 12.9: Dsplay of Shny document wthn Markdown More nformaton about Shny can be found at shny.rstudo.com Creatng a standalone Shny app It s possble to create standalone Shny applcatons that can be made accessble from the Internet. Ths has a major advantage over other web applcaton frameworks that requre knowledge of HTML, CSS, or JavaScrpt. A Shny applcaton conssts of a drectory wth a fle called app.r whch contans the user-nterface defnton, server scrpt, and any addtonal requred data, scrpts, or other resources. In ths example, we wll re-create our choropleth plot n a drectory n ShnyApps called choropleth. > lbrary(shny) > u = shnyui(bootstrappage( selectinput("n_breaks", label="number of breaks:", choces=c(2, 3, 4, 5, 9), selected=5), selectinput("labels", label="dsplay labels?:", choces = c("true", "FALSE"), selected="true"), plotoutput(outputid="man_plot", heght="300px", wdth="500px") ))

7 K /1/9 17:35 page 207 # MANIPULATING BIGGER DATASETS 207 > server = functon(nput, output) { output$man_plot = renderplot({ lbrary(choroplethr); lbrary(dplyr) USArrests.st = mutate(usarrests, regon=tolower(rownames(usarrests)), value = Murder) choroplethr(usarrests.st, "state", ttle="murder Rates by State", showlabels=nput$labels, num_buckets=as.numerc(nput$n_breaks)) }) } > shnyapp(u=u, server=server) The user nterface s defned and saved n an object called u by callng the functon bootstrappage() and passng the result to the shnyui() functon. Ths defnes two selector wdgets (through calls to selectinput()) and a call to plotoutput() (to dsplay the results). Next we defne the server (whch s saved n an object called server). Ths utlzes smlar code to that ntroduced n Ths process nvolves creatng a functon that calls renderplot() after creatng the choropleth map. Fnally, the shnyapp() functon s called to run the app. These commands are all saved n the app.r fle. The app can be run from wthn RStudo usng the runapp() command. > lbrary(shny) > runapp("~/shnyapps/choropleth") The applcaton wll then appear n a browser. For those wth a Shny server, the app can be vewed externally (n ths case as More nformaton about Shny and Shny servers can be found at shny.rstudo.com Manpulatng bgger datasets In ths example, we consder analyss of the Data Expo 2009 commercal arlne flght dataset [189], whch ncludes detals of n = 123, 534, 969 flghts from 1987 to We consder the number of flghts orgnatng from Bradley Internatonal Arport (code BDL, servng Hartford, CT and Sprngfeld, MA). Because of the sze of the data, we wll demonstrate use of a database system accessed usng a structured query language (SQL) [165]. Full detals are avalable on the Data Expo webste ( dataexpo/2009/sqlte.html) regardng how to download the Expo data as comma-separated fles (1.6 ggabytes of compressed, 12 ggabytes uncompressed through 2008), set up and ndex a database (19 ggabytes), then access t from wthn R. A smple way to access databases from R s through SQLte, a self-contaned, serverless, transactonal SQL database engne. To use ths, the analyst nstalls the sqlte software lbrary ( Next the nput fles must be downloaded to the local machne, a database set up (by runnng sqlte3 ontme.sqlte3) at the shell command lne), table created wth the approprate felds, the fles loaded usng a seres of.mport statements, and access speeded up by addng ndexng. Then the RSQLte package can be used to create a connecton to the database.