The R Project SVN – R – Blame – /trunk/tests/Examples/graphics-Ex.Rout.save

Rev	Author	Line No.	Line
56353	ripley	1
63202	ripley	2	`R Under development (unstable) (2013-07-06 r63201) -- "Unsuffered Consequences"`
61587	ripley	3	`Copyright (C) 2013 The R Foundation for Statistical Computing`
61435	ripley	4	`Platform: x86_64-unknown-linux-gnu (64-bit)`
56353	ripley	5
		6	`R is free software and comes with ABSOLUTELY NO WARRANTY.`
		7	`You are welcome to redistribute it under certain conditions.`
		8	`Type 'license()' or 'licence()' for distribution details.`
		9
		10	`Natural language support but running in an English locale`
		11
		12	`R is a collaborative project with many contributors.`
		13	`Type 'contributors()' for more information and`
		14	`'citation()' on how to cite R or R packages in publications.`
		15
		16	`Type 'demo()' for some demos, 'help()' for on-line help, or`
		17	`'help.start()' for an HTML browser interface to help.`
		18	`Type 'q()' to quit R.`
		19
		20	`> pkgname <- "graphics"`
		21	`> source(file.path(R.home("share"), "R", "examples-header.R"))`
		22	`> options(warn = 1)`
		23	`> library('graphics')`
		24	`>`
61787	ripley	25	`> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')`
56353	ripley	26	`> cleanEx()`
		27	`> nameEx("abline")`
		28	`> ### * abline`
		29	`>`
		30	`> flush(stderr()); flush(stdout())`
		31	`>`
		32	`> ### Name: abline`
		33	`> ### Title: Add Straight Lines to a Plot`
		34	`> ### Aliases: abline`
		35	`> ### Keywords: aplot`
		36	`>`
		37	`> ### ** Examples`
		38	`>`
61157	ripley	39	`> ## Setup up coordinate system (with x == y aspect ratio):`
		40	`> plot(c(-2,3), c(-1,5), type = "n", xlab = "x", ylab = "y", asp = 1)`
56353	ripley	41	`> ## the x- and y-axis, and an integer grid`
61157	ripley	42	`> abline(h = 0, v = 0, col = "gray60")`
56353	ripley	43	`> text(1,0, "abline( h = 0 )", col = "gray60", adj = c(0, -.1))`
61157	ripley	44	`> abline(h = -1:5, v = -2:3, col = "lightgray", lty = 3)`
		45	`> abline(a = 1, b = 2, col = 2)`
61169	ripley	46	`> text(1,3, "abline( 1, 2 )", col = 2, adj = c(-.1, -.1))`
56353	ripley	47	`>`
		48	`> ## Simple Regression Lines:`
		49	`> require(stats)`
		50	`> sale5 <- c(6, 4, 9, 7, 6, 12, 8, 10, 9, 13)`
		51	`> plot(sale5)`
61169	ripley	52	`> abline(lsfit(1:10, sale5))`
		53	`> abline(lsfit(1:10, sale5, intercept = FALSE), col = 4) # less fitting`
56353	ripley	54	`>`
		55	`> z <- lm(dist ~ speed, data = cars)`
		56	`> plot(cars)`
		57	`> abline(z) # equivalent to abline(reg = z) or`
		58	`> abline(coef = coef(z))`
		59	`>`
		60	`> ## trivial intercept model`
		61	`> abline(mC <- lm(dist ~ 1, data = cars)) ## the same as`
		62	`> abline(a = coef(mC), b = 0, col = "blue")`
		63	`>`
		64	`>`
		65	`>`
		66	`> cleanEx()`
		67	`> nameEx("arrows")`
		68	`> ### * arrows`
		69	`>`
		70	`> flush(stderr()); flush(stdout())`
		71	`>`
		72	`> ### Name: arrows`
		73	`> ### Title: Add Arrows to a Plot`
		74	`> ### Aliases: arrows`
		75	`> ### Keywords: aplot`
		76	`>`
		77	`> ### ** Examples`
		78	`>`
		79	`> x <- stats::runif(12); y <- stats::rnorm(12)`
61169	ripley	80	`> i <- order(x, y); x <- x[i]; y <- y[i]`
61157	ripley	81	`> plot(x,y, main = "arrows(.) and segments(.)")`
56353	ripley	82	`> ## draw arrows from point to point :`
61169	ripley	83	`> s <- seq(length(x)-1) # one shorter than data`
61157	ripley	84	`> arrows(x[s], y[s], x[s+1], y[s+1], col = 1:3)`
56353	ripley	85	`> s <- s[-length(s)]`
61169	ripley	86	`> segments(x[s], y[s], x[s+2], y[s+2], col = "pink")`
56353	ripley	87	`>`
		88	`>`
		89	`>`
		90	`> cleanEx()`
		91	`> nameEx("assocplot")`
		92	`> ### * assocplot`
		93	`>`
		94	`> flush(stderr()); flush(stdout())`
		95	`>`
		96	`> ### Name: assocplot`
		97	`> ### Title: Association Plots`
		98	`> ### Aliases: assocplot`
		99	`> ### Keywords: hplot`
		100	`>`
		101	`> ### ** Examples`
		102	`>`
		103	`> ## Aggregate over sex:`
		104	`> x <- margin.table(HairEyeColor, c(1, 2))`
		105	`> x`
		106	`Eye`
		107	`Hair Brown Blue Hazel Green`
		108	`Black 68 20 15 5`
		109	`Brown 119 84 54 29`
		110	`Red 26 17 14 14`
		111	`Blond 7 94 10 16`
		112	`> assocplot(x, main = "Relation between hair and eye color")`
		113	`>`
		114	`>`
		115	`>`
		116	`> cleanEx()`
		117	`> nameEx("axTicks")`
		118	`> ### * axTicks`
		119	`>`
		120	`> flush(stderr()); flush(stdout())`
		121	`>`
		122	`> ### Name: axTicks`
		123	`> ### Title: Compute Axis Tickmark Locations`
		124	`> ### Aliases: axTicks`
		125	`> ### Keywords: dplot`
		126	`>`
		127	`> ### ** Examples`
		128	`>`
		129	`> plot(1:7, 10*21:27)`
		130	`> axTicks(1)`
		131	`[1] 1 2 3 4 5 6 7`
		132	`> axTicks(2)`
		133	`[1] 210 220 230 240 250 260 270`
		134	`> stopifnot(identical(axTicks(1), axTicks(3)),`
		135	`+ identical(axTicks(2), axTicks(4)))`
		136	`>`
		137	`> ## Show how axTicks() and axis() correspond :`
61169	ripley	138	`> op <- par(mfrow = c(3, 1))`
		139	`> for(x in 9999 * c(1, 2, 8)) {`
		140	`+ plot(x, 9, log = "x")`
61157	ripley	141	`+ cat(formatC(par("xaxp"), width = 5),";", T <- axTicks(1),"\n")`
		142	`+ rug(T, col = adjustcolor("red", 0.5), lwd = 4)`
56353	ripley	143	`+ }`
		144	`1000 1e+05 3 ; 200 500 1000 2000 5000 10000 20000 50000 1e+05 2e+05 5e+05`
		145	`1000 1e+06 2 ; 500 1000 5000 10000 50000 1e+05 5e+05 1e+06`
		146	`1000 1e+07 1 ; 1000 10000 1e+05 1e+06 1e+07`
		147	`> par(op)`
		148	`>`
57122	maechler	149	`> x <- 9.9*10^(-3:10)`
		150	`> plot(x, 1:14, log = "x")`
		151	`> axTicks(1) # now length 5, in R <= 2.13.x gave the following`
		152	`[1] 1e-02 1e+01 1e+04 1e+07 1e+10`
61157	ripley	153	`> axTicks(1, nintLog = Inf) # rather too many`
57122	maechler	154	`[1] 1e-02 1e-01 1e+00 1e+01 1e+02 1e+03 1e+04 1e+05 1e+06 1e+07 1e+08 1e+09`
		155	`[13] 1e+10 1e+11`
		156	`>`
56353	ripley	157	`> ## An example using axTicks() without reference to an existing plot`
		158	`> ## (copying R's internal procedures for setting axis ranges etc.),`
57360	ripley	159	`> ## You do need to supply _all_ of axp, usr, log, nintLog`
56353	ripley	160	`> ## standard logarithmic y axis labels`
		161	`> ylims <- c(0.2, 88)`
		162	`> get_axp <- function(x) 10^c(ceiling(x[1]), floor(x[2]))`
61157	ripley	163	`> ## mimic par("yaxs") == "i"`
56353	ripley	164	`> usr.i <- log10(ylims)`
61157	ripley	165	`> (aT.i <- axTicks(side = 2, usr = usr.i,`
		166	`+ axp = c(get_axp(usr.i), n = 3), log = TRUE, nintLog = 5))`
57362	ripley	167	`[1] 0.2 0.5 1.0 2.0 5.0 10.0 20.0 50.0`
61157	ripley	168	`> ## mimic (default) par("yaxs") == "r"`
56353	ripley	169	`> usr.r <- extendrange(r = log10(ylims), f = 0.04)`
61157	ripley	170	`> (aT.r <- axTicks(side = 2, usr = usr.r,`
		171	`+ axp = c(get_axp(usr.r), 3), log = TRUE, nintLog = 5))`
56353	ripley	172	`[1] 0.2 0.5 1.0 2.0 5.0 10.0 20.0 50.0 100.0`
		173	`>`
		174	`> ## Prove that we got it right :`
61157	ripley	175	`> plot(0:1, ylims, log = "y", yaxs = "i")`
		176	`> stopifnot(all.equal(aT.i, axTicks(side = 2)))`
56353	ripley	177	`>`
61169	ripley	178	`> plot(0:1, ylims, log = "y", yaxs = "r")`
61157	ripley	179	`> stopifnot(all.equal(aT.r, axTicks(side = 2)))`
56353	ripley	180	`>`
		181	`>`
		182	`>`
		183	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		184	`> cleanEx()`
		185	`> nameEx("axis.POSIXct")`
		186	`> ### * axis.POSIXct`
		187	`>`
		188	`> flush(stderr()); flush(stdout())`
		189	`>`
		190	`> ### Name: axis.POSIXct`
		191	`> ### Title: Date and Date-time Plotting Functions`
		192	`> ### Aliases: axis.POSIXct axis.Date`
		193	`> ### Keywords: utilities chron`
		194	`>`
		195	`> ### ** Examples`
		196	`>`
		197	`> with(beaver1, {`
		198	`+ time <- strptime(paste(1990, day, time %/% 100, time %% 100),`
		199	`+ "%Y %j %H %M")`
61157	ripley	200	`+ plot(time, temp, type = "l") # axis at 4-hour intervals.`
56353	ripley	201	`+ # now label every hour on the time axis`
61157	ripley	202	`+ plot(time, temp, type = "l", xaxt = "n")`
56353	ripley	203	`+ r <- as.POSIXct(round(range(time), "hours"))`
61157	ripley	204	`+ axis.POSIXct(1, at = seq(r[1], r[2], by = "hour"), format = "%H")`
56353	ripley	205	`+ })`
		206	`>`
61157	ripley	207	`> plot(.leap.seconds, seq_along(.leap.seconds), type = "n", yaxt = "n",`
		208	`+ xlab = "leap seconds", ylab = "", bty = "n")`
56353	ripley	209	`> rug(.leap.seconds)`
		210	`> ## or as dates`
		211	`> lps <- as.Date(.leap.seconds)`
		212	`> plot(lps, seq_along(.leap.seconds),`
		213	`+ type = "n", yaxt = "n", xlab = "leap seconds",`
		214	`+ ylab = "", bty = "n")`
		215	`> rug(lps)`
		216	`>`
		217	`> ## 100 random dates in a 10-week period`
		218	`> random.dates <- as.Date("2001/1/1") + 70*sort(stats::runif(100))`
		219	`> plot(random.dates, 1:100)`
		220	`> # or for a better axis labelling`
61157	ripley	221	`> plot(random.dates, 1:100, xaxt = "n")`
		222	`> axis.Date(1, at = seq(as.Date("2001/1/1"), max(random.dates)+6, "weeks"))`
		223	`> axis.Date(1, at = seq(as.Date("2001/1/1"), max(random.dates)+6, "days"),`
56353	ripley	224	`+ labels = FALSE, tcl = -0.2)`
		225	`>`
		226	`>`
		227	`>`
		228	`> cleanEx()`
		229	`> nameEx("axis")`
		230	`> ### * axis`
		231	`>`
		232	`> flush(stderr()); flush(stdout())`
		233	`>`
		234	`> ### Name: axis`
		235	`> ### Title: Add an Axis to a Plot`
		236	`> ### Aliases: axis`
		237	`> ### Keywords: aplot`
		238	`>`
		239	`> ### ** Examples`
		240	`>`
		241	`> require(stats) # for rnorm`
		242	`> plot(1:4, rnorm(4), axes = FALSE)`
		243	`> axis(1, 1:4, LETTERS[1:4])`
		244	`> axis(2)`
		245	`> box() #- to make it look "as usual"`
		246	`>`
		247	`> plot(1:7, rnorm(7), main = "axis() examples",`
		248	`+ type = "s", xaxt = "n", frame = FALSE, col = "red")`
		249	`> axis(1, 1:7, LETTERS[1:7], col.axis = "blue")`
		250	`> # unusual options:`
61157	ripley	251	`> axis(4, col = "violet", col.axis = "dark violet", lwd = 2)`
56353	ripley	252	`> axis(3, col = "gold", lty = 2, lwd = 0.5)`
		253	`>`
		254	`> # one way to have a custom x axis`
		255	`> plot(1:10, xaxt = "n")`
61157	ripley	256	`> axis(1, xaxp = c(2, 9, 7))`
56353	ripley	257	`>`
		258	`>`
		259	`>`
		260	`> cleanEx()`
		261	`> nameEx("barplot")`
		262	`> ### * barplot`
		263	`>`
		264	`> flush(stderr()); flush(stdout())`
		265	`>`
		266	`> ### Name: barplot`
		267	`> ### Title: Bar Plots`
		268	`> ### Aliases: barplot barplot.default`
		269	`> ### Keywords: hplot`
		270	`>`
		271	`> ### ** Examples`
		272	`>`
		273	`> require(grDevices) # for colours`
61157	ripley	274	`> tN <- table(Ni <- stats::rpois(100, lambda = 5))`
		275	`> r <- barplot(tN, col = rainbow(20))`
56353	ripley	276	`> #- type = "h" plotting is 'bar'plot`
61169	ripley	277	`> lines(r, tN, type = "h", col = "red", lwd = 2)`
56353	ripley	278	`>`
61157	ripley	279	`> barplot(tN, space = 1.5, axisnames = FALSE,`
56353	ripley	280	`+ sub = "barplot(..., space= 1.5, axisnames = FALSE)")`
		281	`>`
		282	`> barplot(VADeaths, plot = FALSE)`
		283	`[1] 0.7 1.9 3.1 4.3`
		284	`> barplot(VADeaths, plot = FALSE, beside = TRUE)`
		285	`[,1] [,2] [,3] [,4]`
		286	`[1,] 1.5 7.5 13.5 19.5`
		287	`[2,] 2.5 8.5 14.5 20.5`
		288	`[3,] 3.5 9.5 15.5 21.5`
		289	`[4,] 4.5 10.5 16.5 22.5`
		290	`[5,] 5.5 11.5 17.5 23.5`
		291	`>`
		292	`> mp <- barplot(VADeaths) # default`
		293	`> tot <- colMeans(VADeaths)`
		294	`> text(mp, tot + 3, format(tot), xpd = TRUE, col = "blue")`
		295	`> barplot(VADeaths, beside = TRUE,`
		296	`+ col = c("lightblue", "mistyrose", "lightcyan",`
		297	`+ "lavender", "cornsilk"),`
		298	`+ legend = rownames(VADeaths), ylim = c(0, 100))`
		299	`> title(main = "Death Rates in Virginia", font.main = 4)`
		300	`>`
		301	`> hh <- t(VADeaths)[, 5:1]`
		302	`> mybarcol <- "gray20"`
		303	`> mp <- barplot(hh, beside = TRUE,`
		304	`+ col = c("lightblue", "mistyrose",`
		305	`+ "lightcyan", "lavender"),`
61157	ripley	306	`+ legend = colnames(VADeaths), ylim = c(0,100),`
56353	ripley	307	`+ main = "Death Rates in Virginia", font.main = 4,`
		308	`+ sub = "Faked upper 2*sigma error bars", col.sub = mybarcol,`
		309	`+ cex.names = 1.5)`
		310	`> segments(mp, hh, mp, hh + 2sqrt(1000hh/100), col = mybarcol, lwd = 1.5)`
61169	ripley	311	`> stopifnot(dim(mp) == dim(hh)) # corresponding matrices`
56353	ripley	312	`> mtext(side = 1, at = colMeans(mp), line = -2,`
		313	`+ text = paste("Mean", formatC(colMeans(hh))), col = "red")`
		314	`>`
		315	`> # Bar shading example`
		316	`> barplot(VADeaths, angle = 15+10*1:5, density = 20, col = "black",`
		317	`+ legend = rownames(VADeaths))`
		318	`> title(main = list("Death Rates in Virginia", font = 4))`
		319	`>`
		320	`> # border :`
		321	`> barplot(VADeaths, border = "dark blue")`
		322	`>`
		323	`> # log scales (not much sense here):`
61157	ripley	324	`> barplot(tN, col = heat.colors(12), log = "y")`
		325	`> barplot(tN, col = gray.colors(20), log = "xy")`
56353	ripley	326	`>`
		327	`> # args.legend`
		328	`> barplot(height = cbind(x = c(465, 91) / 465 * 100,`
		329	`+ y = c(840, 200) / 840 * 100,`
		330	`+ z = c(37, 17) / 37 * 100),`
		331	`+ beside = FALSE,`
		332	`+ width = c(465, 840, 37),`
		333	`+ col = c(1, 2),`
		334	`+ legend.text = c("A", "B"),`
		335	`+ args.legend = list(x = "topleft"))`
		336	`>`
		337	`>`
		338	`>`
		339	`> cleanEx()`
		340	`> nameEx("box")`
		341	`> ### * box`
		342	`>`
		343	`> flush(stderr()); flush(stdout())`
		344	`>`
		345	`> ### Name: box`
		346	`> ### Title: Draw a Box around a Plot`
		347	`> ### Aliases: box`
		348	`> ### Keywords: aplot`
		349	`>`
		350	`> ### ** Examples`
		351	`>`
61169	ripley	352	`> plot(1:7, abs(stats::rnorm(7)), type = "h", axes = FALSE)`
56353	ripley	353	`> axis(1, at = 1:7, labels = letters[1:7])`
		354	`> box(lty = '1373', col = 'red')`
		355	`>`
		356	`>`
		357	`>`
		358	`> cleanEx()`
		359	`> nameEx("boxplot")`
		360	`> ### * boxplot`
		361	`>`
		362	`> flush(stderr()); flush(stdout())`
		363	`>`
		364	`> ### Name: boxplot`
		365	`> ### Title: Box Plots`
		366	`> ### Aliases: boxplot boxplot.default boxplot.formula`
		367	`> ### Keywords: hplot`
		368	`>`
		369	`> ### ** Examples`
		370	`>`
		371	`> ## boxplot on a formula:`
		372	`> boxplot(count ~ spray, data = InsectSprays, col = "lightgray")`
		373	`> # add notches (somewhat funny here):`
		374	`> boxplot(count ~ spray, data = InsectSprays,`
		375	`+ notch = TRUE, add = TRUE, col = "blue")`
		376	`Warning in bxp(list(stats = c(7, 11, 14, 18.5, 23, 7, 12, 16.5, 18, 21, :`
		377	`some notches went outside hinges ('box'): maybe set notch=FALSE`
		378	`>`
		379	`> boxplot(decrease ~ treatment, data = OrchardSprays,`
		380	`+ log = "y", col = "bisque")`
		381	`>`
61157	ripley	382	`> rb <- boxplot(decrease ~ treatment, data = OrchardSprays, col = "bisque")`
56353	ripley	383	`> title("Comparing boxplot()s and non-robust mean +/- SD")`
		384	`>`
		385	`> mn.t <- tapply(OrchardSprays$decrease, OrchardSprays$treatment, mean)`
		386	`> sd.t <- tapply(OrchardSprays$decrease, OrchardSprays$treatment, sd)`
		387	`> xi <- 0.3 + seq(rb$n)`
		388	`> points(xi, mn.t, col = "orange", pch = 18)`
		389	`> arrows(xi, mn.t - sd.t, xi, mn.t + sd.t,`
		390	`+ code = 3, col = "pink", angle = 75, length = .1)`
		391	`>`
		392	`> ## boxplot on a matrix:`
		393	`> mat <- cbind(Uni05 = (1:100)/21, Norm = rnorm(100),`
		394	+ `5T` = rt(100, df = 5), Gam2 = rgamma(100, shape = 2))
		395	`> boxplot(as.data.frame(mat),`
		396	`+ main = "boxplot(as.data.frame(mat), main = ...)")`
61157	ripley	397	`> par(las = 1) # all axis labels horizontal`
56353	ripley	398	`> boxplot(as.data.frame(mat), main = "boxplot(*, horizontal = TRUE)",`
		399	`+ horizontal = TRUE)`
		400	`>`
		401	`> ## Using 'at = ' and adding boxplots -- example idea by Roger Bivand :`
		402	`>`
		403	`> boxplot(len ~ dose, data = ToothGrowth,`
		404	`+ boxwex = 0.25, at = 1:3 - 0.2,`
		405	`+ subset = supp == "VC", col = "yellow",`
		406	`+ main = "Guinea Pigs' Tooth Growth",`
		407	`+ xlab = "Vitamin C dose mg",`
		408	`+ ylab = "tooth length",`
		409	`+ xlim = c(0.5, 3.5), ylim = c(0, 35), yaxs = "i")`
		410	`> boxplot(len ~ dose, data = ToothGrowth, add = TRUE,`
		411	`+ boxwex = 0.25, at = 1:3 + 0.2,`
		412	`+ subset = supp == "OJ", col = "orange")`
		413	`> legend(2, 9, c("Ascorbic acid", "Orange juice"),`
		414	`+ fill = c("yellow", "orange"))`
		415	`>`
		416	`> ## more examples in help(bxp)`
		417	`>`
		418	`>`
		419	`>`
		420	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		421	`> cleanEx()`
		422	`> nameEx("boxplot.matrix")`
		423	`> ### * boxplot.matrix`
		424	`>`
		425	`> flush(stderr()); flush(stdout())`
		426	`>`
		427	`> ### Name: boxplot.matrix`
		428	`> ### Title: Draw a Boxplot for each Column (Row) of a Matrix`
		429	`> ### Aliases: boxplot.matrix`
		430	`> ### Keywords: hplot`
		431	`>`
		432	`> ### ** Examples`
		433	`>`
		434	`> ## Very similar to the example in ?boxplot`
		435	`> mat <- cbind(Uni05 = (1:100)/21, Norm = rnorm(100),`
		436	`+ T5 = rt(100, df = 5), Gam2 = rgamma(100, shape = 2))`
		437	`> boxplot(mat, main = "boxplot.matrix(...., main = ...)",`
		438	`+ notch = TRUE, col = 1:4)`
		439	`>`
		440	`>`
		441	`>`
		442	`> cleanEx()`
		443	`> nameEx("bxp")`
		444	`> ### * bxp`
		445	`>`
		446	`> flush(stderr()); flush(stdout())`
		447	`>`
		448	`> ### Name: bxp`
		449	`> ### Title: Draw Box Plots from Summaries`
		450	`> ### Aliases: bxp`
		451	`> ### Keywords: aplot`
		452	`>`
		453	`> ### ** Examples`
		454	`>`
		455	`> require(stats)`
		456	`> set.seed(753)`
61169	ripley	457	`> (bx.p <- boxplot(split(rt(100, 4), gl(5, 20))))`
56353	ripley	458	`$stats`
		459	`[,1] [,2] [,3] [,4] [,5]`
		460	`[1,] -1.66391873 -2.02625162 -2.12785004 -2.76510496 -1.70034047`
		461	`[2,] -0.55308292 -0.65897584 -0.86705616 -1.63431484 -0.81848966`
		462	`[3,] -0.06763313 0.04887846 0.09674026 -0.06712275 -0.01150075`
		463	`[4,] 0.68813940 0.91705734 1.05562526 0.56746581 0.49017934`
		464	`[5,] 1.14222667 3.16270157 2.07574986 2.09523462 1.87734641`
		465
		466	`$n`
		467	`[1] 20 20 20 20 20`
		468
		469	`$conf`
		470	`[,1] [,2] [,3] [,4] [,5]`
		471	`[1,] -0.5061554 -0.5079321 -0.5825407 -0.8450091 -0.4738519`
		472	`[2,] 0.3708891 0.6056890 0.7760212 0.7107636 0.4508504`
		473
		474	`$out`
		475	`[1] 4.115274 3.224584 3.920438 4.168341 -4.357819 2.498006`
		476
		477	`$group`
		478	`[1] 1 1 1 4 5 5`
		479
		480	`$names`
		481	`[1] "1" "2" "3" "4" "5"`
		482
61169	ripley	483	`> op <- par(mfrow = c(2, 2))`
56353	ripley	484	`> bxp(bx.p, xaxt = "n")`
61157	ripley	485	`> bxp(bx.p, notch = TRUE, axes = FALSE, pch = 4, boxfill = 1:5)`
56353	ripley	486	`Warning in bxp(bx.p, notch = TRUE, axes = FALSE, pch = 4, boxfill = 1:5) :`
		487	`some notches went outside hinges ('box'): maybe set notch=FALSE`
61157	ripley	488	`> bxp(bx.p, notch = TRUE, boxfill = "lightblue", frame = FALSE,`
		489	`+ outl = FALSE, main = "bxp(*, frame= FALSE, outl= FALSE)")`
56353	ripley	490	`Warning in bxp(bx.p, notch = TRUE, boxfill = "lightblue", frame = FALSE, :`
		491	`some notches went outside hinges ('box'): maybe set notch=FALSE`
61157	ripley	492	`> bxp(bx.p, notch = TRUE, boxfill = "lightblue", border = 2:6,`
56353	ripley	493	`+ ylim = c(-4,4), pch = 22, bg = "green", log = "x",`
61169	ripley	494	`+ main = "... log = 'x', ylim = *")`
56353	ripley	495	`Warning in bxp(bx.p, notch = TRUE, boxfill = "lightblue", border = 2:6, :`
		496	`some notches went outside hinges ('box'): maybe set notch=FALSE`
		497	`> par(op)`
61169	ripley	498	`> op <- par(mfrow = c(1, 2))`
56353	ripley	499	`>`
		500	`> ## single group -- no label`
61157	ripley	501	`> boxplot (weight ~ group, data = PlantGrowth, subset = group == "ctrl")`
56353	ripley	502	`> ## with label`
		503	`> bx <- boxplot(weight ~ group, data = PlantGrowth,`
61157	ripley	504	`+ subset = group == "ctrl", plot = FALSE)`
61169	ripley	505	`> bxp(bx, show.names=TRUE)`
56353	ripley	506	`> par(op)`
		507	`>`
61157	ripley	508	`> z <- split(rnorm(1000), rpois(1000, 2.2))`
		509	`> boxplot(z, whisklty = 3, main = "boxplot(z, whisklty = 3)")`
56353	ripley	510	`>`
		511	`> ## Colour support similar to plot.default:`
61157	ripley	512	`> op <- par(mfrow = 1:2, bg = "light gray", fg = "midnight blue")`
		513	`> boxplot(z, col.axis = "skyblue3", main = "boxplot(*, col.axis=..,main=..)")`
		514	`> plot(z[[1]], col.axis = "skyblue3", main = "plot(*, col.axis=..,main=..)")`
56353	ripley	515	`> mtext("par(bg=\"light gray\", fg=\"midnight blue\")",`
		516	`+ outer = TRUE, line = -1.2)`
		517	`> par(op)`
		518	`>`
		519	`> ## Mimic S-Plus:`
61157	ripley	520	`> splus <- list(boxwex = 0.4, staplewex = 1, outwex = 1, boxfill = "grey40",`
		521	`+ medlwd = 3, medcol = "white", whisklty = 3, outlty = 1, outpch = NA)`
		522	`> boxplot(z, pars = splus)`
56353	ripley	523	`> ## Recycled and "sweeping" parameters`
61157	ripley	524	`> op <- par(mfrow = c(1,2))`
		525	`> boxplot(z, border = 1:5, lty = 3, medlty = 1, medlwd = 2.5)`
		526	`> boxplot(z, boxfill = 1:3, pch = 1:5, lwd = 1.5, medcol = "white")`
56353	ripley	527	`> par(op)`
		528	`> ## too many possibilities`
61157	ripley	529	`> boxplot(z, boxfill = "light gray", outpch = 21:25, outlty = 2,`
56353	ripley	530	`+ bg = "pink", lwd = 2,`
		531	`+ medcol = "dark blue", medcex = 2, medpch = 20)`
		532	`>`
		533	`>`
		534	`>`
		535	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		536	`> cleanEx()`
		537	`> nameEx("cdplot")`
		538	`> ### * cdplot`
		539	`>`
		540	`> flush(stderr()); flush(stdout())`
		541	`>`
		542	`> ### Name: cdplot`
		543	`> ### Title: Conditional Density Plots`
		544	`> ### Aliases: cdplot cdplot.default cdplot.formula`
		545	`> ### Keywords: hplot`
		546	`>`
		547	`> ### ** Examples`
		548	`>`
		549	`> ## NASA space shuttle o-ring failures`
		550	`> fail <- factor(c(2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1,`
		551	`+ 1, 2, 1, 1, 1, 1, 1),`
		552	`+ levels = 1:2, labels = c("no", "yes"))`
		553	`> temperature <- c(53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70,`
		554	`+ 70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81)`
		555	`>`
		556	`> ## CD plot`
		557	`> cdplot(fail ~ temperature)`
		558	`> cdplot(fail ~ temperature, bw = 2)`
		559	`> cdplot(fail ~ temperature, bw = "SJ")`
		560	`>`
		561	`> ## compare with spinogram`
		562	`> (spineplot(fail ~ temperature, breaks = 3))`
		563	`fail`
		564	`temperature no yes`
		565	`[50,60] 0 3`
		566	`(60,70] 8 3`
		567	`(70,80] 7 1`
		568	`(80,90] 1 0`
		569	`>`
		570	`> ## highlighting for failures`
		571	`> cdplot(fail ~ temperature, ylevels = 2:1)`
		572	`>`
		573	`> ## scatter plot with conditional density`
		574	`> cdens <- cdplot(fail ~ temperature, plot = FALSE)`
		575	`> plot(I(as.numeric(fail) - 1) ~ jitter(temperature, factor = 2),`
		576	`+ xlab = "Temperature", ylab = "Conditional failure probability")`
		577	`> lines(53:81, 1 - cdens[[1]](53:81), col = 2)`
		578	`>`
		579	`>`
		580	`>`
		581	`> cleanEx()`
		582	`> nameEx("clip")`
		583	`> ### * clip`
		584	`>`
		585	`> flush(stderr()); flush(stdout())`
		586	`>`
		587	`> ### Name: clip`
		588	`> ### Title: Set Clipping Region`
		589	`> ### Aliases: clip`
		590	`> ### Keywords: dplot`
		591	`>`
		592	`> ### ** Examples`
		593	`>`
		594	`> x <- rnorm(1000)`
61157	ripley	595	`> hist(x, xlim = c(-4,4))`
56353	ripley	596	`> usr <- par("usr")`
		597	`> clip(usr[1], -2, usr[3], usr[4])`
		598	`> hist(x, col = 'red', add = TRUE)`
		599	`> clip(2, usr[2], usr[3], usr[4])`
		600	`> hist(x, col = 'blue', add = TRUE)`
		601	`> do.call("clip", as.list(usr)) # reset to plot region`
		602	`>`
		603	`>`
		604	`>`
		605	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		606	`> cleanEx()`
		607	`> nameEx("contour")`
		608	`> ### * contour`
		609	`>`
		610	`> flush(stderr()); flush(stdout())`
		611	`>`
		612	`> ### Name: contour`
		613	`> ### Title: Display Contours`
		614	`> ### Aliases: contour contour.default`
		615	`> ### Keywords: hplot aplot`
		616	`>`
		617	`> ### ** Examples`
		618	`>`
		619	`> require(grDevices) # for colours`
		620	`> x <- -6:16`
		621	`> op <- par(mfrow = c(2, 2))`
		622	`> contour(outer(x, x), method = "edge", vfont = c("sans serif", "plain"))`
		623	`> z <- outer(x, sqrt(abs(x)), FUN = "/")`
		624	`> image(x, x, z)`
		625	`> contour(x, x, z, col = "pink", add = TRUE, method = "edge",`
		626	`+ vfont = c("sans serif", "plain"))`
		627	`> contour(x, x, z, ylim = c(1, 6), method = "simple", labcex = 1)`
		628	`> contour(x, x, z, ylim = c(-6, 6), nlev = 20, lty = 2, method = "simple")`
		629	`> par(op)`
		630	`>`
		631	`> ## Persian Rug Art:`
		632	`> x <- y <- seq(-4pi, 4pi, len = 27)`
		633	`> r <- sqrt(outer(x^2, y^2, "+"))`
		634	`> opar <- par(mfrow = c(2, 2), mar = rep(0, 4))`
		635	`> for(f in pi^(0:3))`
		636	`+ contour(cos(r^2)*exp(-r/f),`
		637	`+ drawlabels = FALSE, axes = FALSE, frame = TRUE)`
		638	`>`
		639	`> rx <- range(x <- 10*1:nrow(volcano))`
		640	`> ry <- range(y <- 10*1:ncol(volcano))`
61169	ripley	641	`> ry <- ry + c(-1, 1) * (diff(rx) - diff(ry))/2`
56353	ripley	642	`> tcol <- terrain.colors(12)`
		643	`> par(opar); opar <- par(pty = "s", bg = "lightcyan")`
61169	ripley	644	`> plot(x = 0, y = 0, type = "n", xlim = rx, ylim = ry, xlab = "", ylab = "")`
56353	ripley	645	`> u <- par("usr")`
		646	`> rect(u[1], u[3], u[2], u[4], col = tcol[8], border = "red")`
		647	`> contour(x, y, volcano, col = tcol[2], lty = "solid", add = TRUE,`
		648	`+ vfont = c("sans serif", "plain"))`
		649	`> title("A Topographic Map of Maunga Whau", font = 4)`
		650	`> abline(h = 2000:4, v = 2000:4, col = "lightgray", lty = 2, lwd = 0.1)`
		651	`>`
		652	`> ## contourLines produces the same contour lines as contour`
		653	`> line.list <- contourLines(x, y, volcano)`
61169	ripley	654	`> plot(x = 0, y = 0, type = "n", xlim = rx, ylim = ry, xlab = "", ylab = "")`
56353	ripley	655	`> u <- par("usr")`
		656	`> rect(u[1], u[3], u[2], u[4], col = tcol[8], border = "red")`
		657	`> contour(x, y, volcano, col = tcol[2], lty = "solid", add = TRUE,`
		658	`+ vfont = c("sans serif", "plain"))`
		659	`> templines <- function(clines) {`
		660	`+ lines(clines[[2]], clines[[3]])`
		661	`+ }`
		662	`> invisible(lapply(line.list, templines))`
		663	`> par(opar)`
		664	`>`
		665	`>`
		666	`>`
		667	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		668	`> cleanEx()`
		669	`> nameEx("convertXY")`
		670	`> ### * convertXY`
		671	`>`
		672	`> flush(stderr()); flush(stdout())`
		673	`>`
		674	`> ### Name: convertXY`
		675	`> ### Title: Convert between Graphics Coordinate Systems`
		676	`> ### Aliases: grconvertX grconvertY`
		677	`> ### Keywords: dplot`
		678	`>`
		679	`> ### ** Examples`
		680	`>`
		681	`> op <- par(omd=c(0.1, 0.9, 0.1, 0.9), mfrow = c(1, 2))`
		682	`> plot(1:4)`
		683	`> for(tp in c("in", "dev", "ndc", "nfc", "npc", "nic"))`
		684	`+ print(grconvertX(c(1.0, 4.0), "user", tp))`
		685	`[1] 1.577778 3.022222`
56942	ripley	686	`[1] 113.6 217.6`
56353	ripley	687	`[1] 0.2253968 0.4317460`
		688	`[1] 0.3134921 0.8293651`
		689	`[1] 0.03703704 0.96296296`
		690	`[1] 0.1567460 0.4146825`
		691	`> par(op)`
		692	`>`
		693	`>`
		694	`>`
		695	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		696	`> cleanEx()`
		697	`> nameEx("coplot")`
		698	`> ### * coplot`
		699	`>`
		700	`> flush(stderr()); flush(stdout())`
		701	`>`
		702	`> ### Name: coplot`
		703	`> ### Title: Conditioning Plots`
		704	`> ### Aliases: coplot co.intervals`
		705	`> ### Keywords: hplot aplot`
		706	`>`
		707	`> ### ** Examples`
		708	`>`
		709	`> ## Tonga Trench Earthquakes`
		710	`> coplot(lat ~ long \| depth, data = quakes)`
61157	ripley	711	`> given.depth <- co.intervals(quakes$depth, number = 4, overlap = .1)`
		712	`> coplot(lat ~ long \| depth, data = quakes, given.v = given.depth, rows = 1)`
56353	ripley	713	`>`
		714	`> ## Conditioning on 2 variables:`
		715	`> ll.dm <- lat ~ long \| depth * mag`
		716	`> coplot(ll.dm, data = quakes)`
61169	ripley	717	`> coplot(ll.dm, data = quakes, number = c(4, 7), show.given = c(TRUE, FALSE))`
		718	`> coplot(ll.dm, data = quakes, number = c(3, 7),`
		719	`+ overlap = c(-.5, .1)) # negative overlap DROPS values`
56353	ripley	720	`>`
		721	`> ## given two factors`
61157	ripley	722	`> Index <- seq(length = nrow(warpbreaks)) # to get nicer default labels`
56353	ripley	723	`> coplot(breaks ~ Index \| wool * tension, data = warpbreaks,`
		724	`+ show.given = 0:1)`
		725	`> coplot(breaks ~ Index \| wool * tension, data = warpbreaks,`
		726	`+ col = "red", bg = "pink", pch = 21,`
		727	`+ bar.bg = c(fac = "light blue"))`
		728	`>`
		729	`> ## Example with empty panels:`
		730	`> with(data.frame(state.x77), {`
		731	`+ coplot(Life.Exp ~ Income \| Illiteracy * state.region, number = 3,`
		732	`+ panel = function(x, y, ...) panel.smooth(x, y, span = .8, ...))`
		733	`+ ## y ~ factor -- not really sensible, but 'show off':`
		734	`+ coplot(Life.Exp ~ state.region \| Income * state.division,`
		735	`+ panel = panel.smooth)`
		736	`+ })`
		737	`>`
		738	`>`
		739	`>`
		740	`> cleanEx()`
		741	`> nameEx("curve")`
		742	`> ### * curve`
		743	`>`
		744	`> flush(stderr()); flush(stdout())`
		745	`>`
		746	`> ### Name: curve`
		747	`> ### Title: Draw Function Plots`
		748	`> ### Aliases: curve plot.function`
		749	`> ### Keywords: hplot`
		750	`>`
		751	`> ### ** Examples`
		752	`>`
		753	`> plot(qnorm) # default range c(0, 1) is appropriate here,`
		754	`> # but end values are -/+Inf and so are omitted.`
		755	`> plot(qlogis, main = "The Inverse Logit : qlogis()")`
		756	`> abline(h = 0, v = 0:2/2, lty = 3, col = "gray")`
		757	`>`
		758	`> curve(sin, -2pi, 2pi, xname = "t")`
		759	`> curve(tan, xname = "t", add = NA,`
		760	`+ main = "curve(tan) --> same x-scale as previous plot")`
		761	`>`
		762	`> op <- par(mfrow = c(2, 2))`
		763	`> curve(x^3 - 3*x, -2, 2)`
		764	`> curve(x^2 - 2, add = TRUE, col = "violet")`
		765	`>`
		766	`> ## simple and advanced versions, quite similar:`
		767	`> plot(cos, -pi, 3*pi)`
		768	`> curve(cos, xlim = c(-pi, 3*pi), n = 1001, col = "blue", add = TRUE)`
		769	`>`
		770	`> chippy <- function(x) sin(cos(x)*exp(-x/2))`
		771	`> curve(chippy, -8, 7, n = 2001)`
		772	`> plot (chippy, -8, -5)`
		773	`>`
		774	`> for(ll in c("", "x", "y", "xy"))`
61157	ripley	775	`+ curve(log(1+x), 1, 100, log = ll, sub = paste0("log = '", ll, "'"))`
56353	ripley	776	`> par(op)`
		777	`>`
		778	`>`
		779	`>`
		780	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		781	`> cleanEx()`
		782	`> nameEx("dotchart")`
		783	`> ### * dotchart`
		784	`>`
		785	`> flush(stderr()); flush(stdout())`
		786	`>`
		787	`> ### Name: dotchart`
		788	`> ### Title: Cleveland's Dot Plots`
		789	`> ### Aliases: dotchart`
		790	`> ### Keywords: hplot`
		791	`>`
		792	`> ### ** Examples`
		793	`>`
		794	`> dotchart(VADeaths, main = "Death Rates in Virginia - 1940")`
61169	ripley	795	`> op <- par(xaxs = "i") # 0 -- 100%`
56353	ripley	796	`> dotchart(t(VADeaths), xlim = c(0,100),`
		797	`+ main = "Death Rates in Virginia - 1940")`
		798	`> par(op)`
		799	`>`
		800	`>`
		801	`>`
		802	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		803	`> cleanEx()`
		804	`> nameEx("filled.contour")`
		805	`> ### * filled.contour`
		806	`>`
		807	`> flush(stderr()); flush(stdout())`
		808	`>`
		809	`> ### Name: filled.contour`
		810	`> ### Title: Level (Contour) Plots`
58477	ripley	811	`> ### Aliases: filled.contour .filled.contour`
56353	ripley	812	`> ### Keywords: hplot aplot`
		813	`>`
		814	`> ### ** Examples`
		815	`>`
		816	`> require(grDevices) # for colours`
61169	ripley	817	`> filled.contour(volcano, color = terrain.colors, asp = 1) # simple`
56353	ripley	818	`>`
		819	`> x <- 10*1:nrow(volcano)`
		820	`> y <- 10*1:ncol(volcano)`
		821	`> filled.contour(x, y, volcano, color = terrain.colors,`
		822	`+ plot.title = title(main = "The Topography of Maunga Whau",`
		823	`+ xlab = "Meters North", ylab = "Meters West"),`
		824	`+ plot.axes = { axis(1, seq(100, 800, by = 100))`
		825	`+ axis(2, seq(100, 600, by = 100)) },`
61157	ripley	826	`+ key.title = title(main = "Height\n(meters)"),`
61169	ripley	827	`+ key.axes = axis(4, seq(90, 190, by = 10))) # maybe also asp = 1`
56353	ripley	828	`> mtext(paste("filled.contour(.) from", R.version.string),`
		829	`+ side = 1, line = 4, adj = 1, cex = .66)`
		830	`>`
		831	`> # Annotating a filled contour plot`
		832	`> a <- expand.grid(1:20, 1:20)`
		833	`> b <- matrix(a[,1] + a[,2], 20)`
		834	`> filled.contour(x = 1:20, y = 1:20, z = b,`
61169	ripley	835	`+ plot.axes = { axis(1); axis(2); points(10, 10) })`
56353	ripley	836	`>`
		837	`> ## Persian Rug Art:`
		838	`> x <- y <- seq(-4pi, 4pi, len = 27)`
		839	`> r <- sqrt(outer(x^2, y^2, "+"))`
		840	`> filled.contour(cos(r^2)exp(-r/(2pi)), axes = FALSE)`
		841	`> ## rather, the key should be labeled:`
		842	`> filled.contour(cos(r^2)exp(-r/(2pi)), frame.plot = FALSE,`
		843	`+ plot.axes = {})`
		844	`>`
		845	`>`
		846	`>`
		847	`> cleanEx()`
		848	`> nameEx("fourfoldplot")`
		849	`> ### * fourfoldplot`
		850	`>`
		851	`> flush(stderr()); flush(stdout())`
		852	`>`
		853	`> ### Name: fourfoldplot`
		854	`> ### Title: Fourfold Plots`
		855	`> ### Aliases: fourfoldplot`
		856	`> ### Keywords: hplot`
		857	`>`
		858	`> ### ** Examples`
		859	`>`
		860	`> ## Use the Berkeley admission data as in Friendly (1995).`
		861	`> x <- aperm(UCBAdmissions, c(2, 1, 3))`
		862	`> dimnames(x)[[2]] <- c("Yes", "No")`
		863	`> names(dimnames(x)) <- c("Sex", "Admit?", "Department")`
		864	`> stats::ftable(x)`
		865	`Department A B C D E F`
		866	`Sex Admit?`
		867	`Male Yes 512 353 120 138 53 22`
		868	`No 313 207 205 279 138 351`
		869	`Female Yes 89 17 202 131 94 24`
		870	`No 19 8 391 244 299 317`
		871	`>`
		872	`> ## Fourfold display of data aggregated over departments, with`
		873	`> ## frequencies standardized to equate the margins for admission`
		874	`> ## and sex.`
		875	`> ## Figure 1 in Friendly (1994).`
		876	`> fourfoldplot(margin.table(x, c(1, 2)))`
		877	`>`
		878	`> ## Fourfold display of x, with frequencies in each table`
		879	`> ## standardized to equate the margins for admission and sex.`
		880	`> ## Figure 2 in Friendly (1994).`
		881	`> fourfoldplot(x)`
		882	`>`
		883	`> ## Fourfold display of x, with frequencies in each table`
		884	`> ## standardized to equate the margins for admission. but not`
		885	`> ## for sex.`
		886	`> ## Figure 3 in Friendly (1994).`
		887	`> fourfoldplot(x, margin = 2)`
		888	`>`
		889	`>`
		890	`>`
		891	`> cleanEx()`
		892	`> nameEx("grid")`
		893	`> ### * grid`
		894	`>`
		895	`> flush(stderr()); flush(stdout())`
		896	`>`
		897	`> ### Name: grid`
		898	`> ### Title: Add Grid to a Plot`
		899	`> ### Aliases: grid`
		900	`> ### Keywords: aplot`
		901	`>`
		902	`> ### ** Examples`
		903	`>`
		904	`> plot(1:3)`
		905	`> grid(NA, 5, lwd = 2) # grid only in y-direction`
		906	`>`
61169	ripley	907	`> ## maybe change the desired number of tick marks: par(lab = c(mx, my, 7))`
56353	ripley	908	`> op <- par(mfcol = 1:2)`
		909	`> with(iris,`
		910	`+ {`
		911	`+ plot(Sepal.Length, Sepal.Width, col = as.integer(Species),`
		912	`+ xlim = c(4, 8), ylim = c(2, 4.5), panel.first = grid(),`
		913	`+ main = "with(iris, plot(...., panel.first = grid(), ..) )")`
		914	`+ plot(Sepal.Length, Sepal.Width, col = as.integer(Species),`
61169	ripley	915	`+ panel.first = grid(3, lty = 1, lwd = 2),`
		916	`+ main = "... panel.first = grid(3, lty = 1, lwd = 2), ..")`
56353	ripley	917	`+ }`
		918	`+ )`
		919	`> par(op)`
		920	`>`
		921	`>`
		922	`>`
		923	`>`
		924	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		925	`> cleanEx()`
		926	`> nameEx("hist.POSIXt")`
		927	`> ### * hist.POSIXt`
		928	`>`
		929	`> flush(stderr()); flush(stdout())`
		930	`>`
		931	`> ### Name: hist.POSIXt`
		932	`> ### Title: Histogram of a Date or Date-Time Object`
		933	`> ### Aliases: hist.POSIXt hist.Date`
		934	`> ### Keywords: chron dplot hplot`
		935	`>`
		936	`> ### ** Examples`
		937	`>`
		938	`> hist(.leap.seconds, "years", freq = TRUE)`
		939	`> hist(.leap.seconds,`
59697	ripley	940	`+ seq(ISOdate(1970, 1, 1), ISOdate(2015, 1, 1), "5 years"))`
56353	ripley	941	`>`
		942	`> ## 100 random dates in a 10-week period`
		943	`> random.dates <- as.Date("2001/1/1") + 70*stats::runif(100)`
		944	`> hist(random.dates, "weeks", format = "%d %b")`
		945	`>`
		946	`>`
		947	`>`
		948	`> cleanEx()`
		949	`> nameEx("hist")`
		950	`> ### * hist`
		951	`>`
		952	`> flush(stderr()); flush(stdout())`
		953	`>`
		954	`> ### Name: hist`
		955	`> ### Title: Histograms`
		956	`> ### Aliases: hist hist.default`
		957	`> ### Keywords: dplot hplot distribution`
		958	`>`
		959	`> ### ** Examples`
		960	`>`
61157	ripley	961	`> op <- par(mfrow = c(2, 2))`
56353	ripley	962	`> hist(islands)`
61157	ripley	963	`> utils::str(hist(islands, col = "gray", labels = TRUE))`
61587	ripley	964	`List of 6`
		965	`$ breaks : num [1:10] 0 2000 4000 6000 8000 10000 12000 14000 16000 18000`
		966	`$ counts : int [1:9] 41 2 1 1 1 1 0 0 1`
		967	`$ density : num [1:9] 4.27e-04 2.08e-05 1.04e-05 1.04e-05 1.04e-05 ...`
		968	`$ mids : num [1:9] 1000 3000 5000 7000 9000 11000 13000 15000 17000`
		969	`$ xname : chr "islands"`
		970	`$ equidist: logi TRUE`
56353	ripley	971	`- attr(*, "class")= chr "histogram"`
		972	`>`
61157	ripley	973	`> hist(sqrt(islands), breaks = 12, col = "lightblue", border = "pink")`
56353	ripley	974	`> ##-- For non-equidistant breaks, counts should NOT be graphed unscaled:`
		975	`> r <- hist(sqrt(islands), breaks = c(40:5, 103:5, 70, 100, 140),`
61169	ripley	976	`+ col = "blue1")`
		977	`> text(r$mids, r$density, r$counts, adj = c(.5, -.5), col = "blue3")`
56353	ripley	978	`> sapply(r[2:3], sum)`
61587	ripley	979	`counts density`
		980	`48.000000 0.215625`
56353	ripley	981	`> sum(r$density * diff(r$breaks)) # == 1`
		982	`[1] 1`
		983	`> lines(r, lty = 3, border = "purple") # -> lines.histogram(*)`
		984	`> par(op)`
		985	`>`
		986	`> require(utils) # for str`
61157	ripley	987	`> str(hist(islands, breaks = 12, plot = FALSE)) #-> 10 (~= 12) breaks`
61587	ripley	988	`List of 6`
		989	`$ breaks : num [1:10] 0 2000 4000 6000 8000 10000 12000 14000 16000 18000`
		990	`$ counts : int [1:9] 41 2 1 1 1 1 0 0 1`
		991	`$ density : num [1:9] 4.27e-04 2.08e-05 1.04e-05 1.04e-05 1.04e-05 ...`
		992	`$ mids : num [1:9] 1000 3000 5000 7000 9000 11000 13000 15000 17000`
		993	`$ xname : chr "islands"`
		994	`$ equidist: logi TRUE`
56353	ripley	995	`- attr(*, "class")= chr "histogram"`
61157	ripley	996	`> str(hist(islands, breaks = c(12,20,36,80,200,1000,17000), plot = FALSE))`
61587	ripley	997	`List of 6`
		998	`$ breaks : num [1:7] 12 20 36 80 200 1000 17000`
		999	`$ counts : int [1:6] 12 11 8 6 4 7`
		1000	`$ density : num [1:6] 0.03125 0.014323 0.003788 0.001042 0.000104 ...`
		1001	`$ mids : num [1:6] 16 28 58 140 600 9000`
		1002	`$ xname : chr "islands"`
		1003	`$ equidist: logi FALSE`
56353	ripley	1004	`- attr(*, "class")= chr "histogram"`
		1005	`>`
61157	ripley	1006	`> hist(islands, breaks = c(12,20,36,80,200,1000,17000), freq = TRUE,`
56353	ripley	1007	`+ main = "WRONG histogram") # and warning`
		1008	`Warning in plot.histogram(r, freq = freq1, col = col, border = border, angle = angle, :`
60841	ripley	1009	`the AREAS in the plot are wrong -- rather use 'freq = FALSE'`
56353	ripley	1010	`>`
		1011	`> require(stats)`
		1012	`> set.seed(14)`
		1013	`> x <- rchisq(100, df = 4)`
		1014	`> ## Don't show:`
		1015	`> op <- par(mfrow = 2:1, mgp = c(1.5, 0.6, 0), mar = .1 + c(3,3:1))`
		1016	`> ## End Don't show`
		1017	`> ## Comparing data with a model distribution should be done with qqplot()!`
61157	ripley	1018	`> qqplot(x, qchisq(ppoints(x), df = 4)); abline(0, 1, col = 2, lty = 2)`
56353	ripley	1019	`>`
		1020	`> ## if you really insist on using hist() ... :`
		1021	`> hist(x, freq = FALSE, ylim = c(0, 0.2))`
		1022	`> curve(dchisq(x, df = 4), col = 2, lty = 2, lwd = 2, add = TRUE)`
		1023	`> ## Don't show:`
		1024	`> par(op)`
		1025	`> ## End Don't show`
		1026	`>`
		1027	`>`
		1028	`>`
		1029	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1030	`> cleanEx()`
		1031	`> nameEx("identify")`
		1032	`> ### * identify`
		1033	`>`
		1034	`> flush(stderr()); flush(stdout())`
		1035	`>`
		1036	`> ### Name: identify`
		1037	`> ### Title: Identify Points in a Scatter Plot`
		1038	`> ### Aliases: identify identify.default`
		1039	`> ### Keywords: iplot`
		1040	`>`
		1041	`> ### ** Examples`
		1042	`>`
		1043	`> ## A function to use identify to select points, and overplot the`
		1044	`> ## points with another symbol as they are selected`
61157	ripley	1045	`> identifyPch <- function(x, y = NULL, n = length(x), pch = 19, ...)`
56353	ripley	1046	`+ {`
		1047	`+ xy <- xy.coords(x, y); x <- xy$x; y <- xy$y`
		1048	`+ sel <- rep(FALSE, length(x)); res <- integer(0)`
		1049	`+ while(sum(sel) < n) {`
61157	ripley	1050	`+ ans <- identify(x[!sel], y[!sel], n = 1, plot = FALSE, ...)`
56353	ripley	1051	`+ if(!length(ans)) break`
		1052	`+ ans <- which(!sel)[ans]`
		1053	`+ points(x[ans], y[ans], pch = pch)`
		1054	`+ sel[ans] <- TRUE`
		1055	`+ res <- c(res, ans)`
		1056	`+ }`
		1057	`+ res`
		1058	`+ }`
		1059	`>`
		1060	`>`
		1061	`>`
		1062	`> cleanEx()`
		1063	`> nameEx("image")`
		1064	`> ### * image`
		1065	`>`
		1066	`> flush(stderr()); flush(stdout())`
		1067	`>`
		1068	`> ### Name: image`
		1069	`> ### Title: Display a Color Image`
		1070	`> ### Aliases: image image.default`
		1071	`> ### Keywords: hplot aplot`
		1072	`>`
		1073	`> ### ** Examples`
		1074	`>`
		1075	`> require(grDevices) # for colours`
61157	ripley	1076	`> x <- y <- seq(-4pi, 4pi, len = 27)`
56353	ripley	1077	`> r <- sqrt(outer(x^2, y^2, "+"))`
61157	ripley	1078	`> image(z = z <- cos(r^2)*exp(-r/6), col = gray((0:32)/32))`
56353	ripley	1079	`> image(z, axes = FALSE, main = "Math can be beautiful ...",`
		1080	`+ xlab = expression(cos(r^2) * e^{-r/6}))`
		1081	`> contour(z, add = TRUE, drawlabels = FALSE)`
		1082	`>`
		1083	`> # Volcano data visualized as matrix. Need to transpose and flip`
		1084	`> # matrix horizontally.`
		1085	`> image(t(volcano)[ncol(volcano):1,])`
		1086	`>`
		1087	`> # A prettier display of the volcano`
		1088	`> x <- 10*(1:nrow(volcano))`
		1089	`> y <- 10*(1:ncol(volcano))`
		1090	`> image(x, y, volcano, col = terrain.colors(100), axes = FALSE)`
		1091	`> contour(x, y, volcano, levels = seq(90, 200, by = 5),`
		1092	`+ add = TRUE, col = "peru")`
		1093	`> axis(1, at = seq(100, 800, by = 100))`
		1094	`> axis(2, at = seq(100, 600, by = 100))`
		1095	`> box()`
		1096	`> title(main = "Maunga Whau Volcano", font.main = 4)`
		1097	`>`
		1098	`>`
		1099	`>`
		1100	`> cleanEx()`
		1101	`> nameEx("layout")`
		1102	`> ### * layout`
		1103	`>`
		1104	`> flush(stderr()); flush(stdout())`
		1105	`>`
		1106	`> ### Name: layout`
		1107	`> ### Title: Specifying Complex Plot Arrangements`
		1108	`> ### Aliases: layout layout.show lcm`
		1109	`> ### Keywords: iplot dplot environment`
		1110	`>`
		1111	`> ### ** Examples`
		1112	`>`
		1113	`> def.par <- par(no.readonly = TRUE) # save default, for resetting...`
		1114	`>`
		1115	`> ## divide the device into two rows and two columns`
		1116	`> ## allocate figure 1 all of row 1`
		1117	`> ## allocate figure 2 the intersection of column 2 and row 2`
		1118	`> layout(matrix(c(1,1,0,2), 2, 2, byrow = TRUE))`
		1119	`> ## show the regions that have been allocated to each plot`
		1120	`> layout.show(2)`
		1121	`>`
		1122	`> ## divide device into two rows and two columns`
		1123	`> ## allocate figure 1 and figure 2 as above`
		1124	`> ## respect relations between widths and heights`
61157	ripley	1125	`> nf <- layout(matrix(c(1,1,0,2), 2, 2, byrow = TRUE), respect = TRUE)`
56353	ripley	1126	`> layout.show(nf)`
		1127	`>`
		1128	`> ## create single figure which is 5cm square`
61157	ripley	1129	`> nf <- layout(matrix(1), widths = lcm(5), heights = lcm(5))`
56353	ripley	1130	`> layout.show(nf)`
		1131	`>`
		1132	`>`
		1133	`> ##-- Create a scatterplot with marginal histograms -----`
		1134	`>`
		1135	`> x <- pmin(3, pmax(-3, stats::rnorm(50)))`
		1136	`> y <- pmin(3, pmax(-3, stats::rnorm(50)))`
61157	ripley	1137	`> xhist <- hist(x, breaks = seq(-3,3,0.5), plot = FALSE)`
		1138	`> yhist <- hist(y, breaks = seq(-3,3,0.5), plot = FALSE)`
56353	ripley	1139	`> top <- max(c(xhist$counts, yhist$counts))`
61169	ripley	1140	`> xrange <- c(-3, 3)`
		1141	`> yrange <- c(-3, 3)`
61157	ripley	1142	`> nf <- layout(matrix(c(2,0,1,3),2,2,byrow = TRUE), c(3,1), c(1,3), TRUE)`
56353	ripley	1143	`> layout.show(nf)`
		1144	`>`
61157	ripley	1145	`> par(mar = c(3,3,1,1))`
		1146	`> plot(x, y, xlim = xrange, ylim = yrange, xlab = "", ylab = "")`
		1147	`> par(mar = c(0,3,1,1))`
		1148	`> barplot(xhist$counts, axes = FALSE, ylim = c(0, top), space = 0)`
		1149	`> par(mar = c(3,0,1,1))`
		1150	`> barplot(yhist$counts, axes = FALSE, xlim = c(0, top), space = 0, horiz = TRUE)`
56353	ripley	1151	`>`
61169	ripley	1152	`> par(def.par) #- reset to default`
56353	ripley	1153	`>`
		1154	`>`
		1155	`>`
		1156	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1157	`> cleanEx()`
		1158	`> nameEx("legend")`
		1159	`> ### * legend`
		1160	`>`
		1161	`> flush(stderr()); flush(stdout())`
		1162	`>`
		1163	`> ### Name: legend`
		1164	`> ### Title: Add Legends to Plots`
		1165	`> ### Aliases: legend`
		1166	`> ### Keywords: aplot`
		1167	`>`
		1168	`> ### ** Examples`
		1169	`>`
		1170	`> ## Run the example in '?matplot' or the following:`
		1171	`> leg.txt <- c("Setosa Petals", "Setosa Sepals",`
		1172	`+ "Versicolor Petals", "Versicolor Sepals")`
		1173	`> y.leg <- c(4.5, 3, 2.1, 1.4, .7)`
		1174	`> cexv <- c(1.2, 1, 4/5, 2/3, 1/2)`
61255	ripley	1175	`> matplot(c(1, 8), c(0, 4.5), type = "n", xlab = "Length", ylab = "Width",`
56353	ripley	1176	`+ main = "Petal and Sepal Dimensions in Iris Blossoms")`
		1177	`> for (i in seq(cexv)) {`
61255	ripley	1178	`+ text (1, y.leg[i] - 0.1, paste("cex=", formatC(cexv[i])), cex = 0.8, adj = 0)`
56353	ripley	1179	`+ legend(3, y.leg[i], leg.txt, pch = "sSvV", col = c(1, 3), cex = cexv[i])`
		1180	`+ }`
		1181	`>`
		1182	`> ## 'merge = TRUE' for merging lines & points:`
		1183	`> x <- seq(-pi, pi, len = 65)`
		1184	`> plot(x, sin(x), type = "l", ylim = c(-1.2, 1.8), col = 3, lty = 2)`
		1185	`> points(x, cos(x), pch = 3, col = 4)`
		1186	`> lines(x, tan(x), type = "b", lty = 1, pch = 4, col = 6)`
61258	ripley	1187	`> title("legend(..., lty = c(2, -1, 1), pch = c(NA, 3, 4), merge = TRUE)",`
56353	ripley	1188	`+ cex.main = 1.1)`
61255	ripley	1189	`> legend(-1, 1.9, c("sin", "cos", "tan"), col = c(3, 4, 6),`
61258	ripley	1190	`+ text.col = "green4", lty = c(2, -1, 1), pch = c(NA, 3, 4),`
61169	ripley	1191	`+ merge = TRUE, bg = "gray90")`
56353	ripley	1192	`>`
		1193	`> ## right-justifying a set of labels: thanks to Uwe Ligges`
		1194	`> x <- 1:5; y1 <- 1/x; y2 <- 2/x`
61157	ripley	1195	`> plot(rep(x, 2), c(y1, y2), type = "n", xlab = "x", ylab = "y")`
		1196	`> lines(x, y1); lines(x, y2, lty = 2)`
56353	ripley	1197	`> temp <- legend("topright", legend = c(" ", " "),`
		1198	`+ text.width = strwidth("1,000,000"),`
		1199	`+ lty = 1:2, xjust = 1, yjust = 1,`
		1200	`+ title = "Line Types")`
		1201	`> text(temp$rect$left + temp$rect$w, temp$text$y,`
61157	ripley	1202	`+ c("1,000", "1,000,000"), pos = 2)`
56353	ripley	1203	`>`
		1204	`>`
		1205	`> ##--- log scaled Examples ------------------------------`
		1206	`> leg.txt <- c("a one", "a two")`
		1207	`>`
61255	ripley	1208	`> par(mfrow = c(2, 2))`
56353	ripley	1209	`> for(ll in c("","x","y","xy")) {`
61157	ripley	1210	`+ plot(2:10, log = ll, main = paste0("log = '", ll, "'"))`
		1211	`+ abline(1, 1)`
61255	ripley	1212	`+ lines(2:3, 3:4, col = 2)`
		1213	`+ points(2, 2, col = 3)`
61157	ripley	1214	`+ rect(2, 3, 3, 2, col = 4)`
		1215	`+ text(c(3,3), 2:3, c("rect(2,3,3,2, col=4)",`
61255	ripley	1216	`+ "text(c(3,3),2:3,\"c(rect(...)\")"), adj = c(0, 0.3))`
61157	ripley	1217	`+ legend(list(x = 2,y = 8), legend = leg.txt, col = 2:3, pch = 1:2,`
61169	ripley	1218	`+ lty = 1, merge = TRUE) #, trace = TRUE)`
56353	ripley	1219	`+ }`
61157	ripley	1220	`> par(mfrow = c(1,1))`
56353	ripley	1221	`>`
		1222	`> ##-- Math expressions: ------------------------------`
		1223	`> x <- seq(-pi, pi, len = 65)`
61157	ripley	1224	`> plot(x, sin(x), type = "l", col = 2, xlab = expression(phi),`
56353	ripley	1225	`+ ylab = expression(f(phi)))`
61157	ripley	1226	`> abline(h = -1:1, v = pi/2*(-6:6), col = "gray90")`
56353	ripley	1227	`> lines(x, cos(x), col = 3, lty = 2)`
61169	ripley	1228	`> ex.cs1 <- expression(plain(sin) * phi, paste("cos", phi)) # 2 ways`
61157	ripley	1229	`> utils::str(legend(-3, .9, ex.cs1, lty = 1:2, plot = FALSE,`
61255	ripley	1230	`+ adj = c(0, 0.6))) # adj y !`
56353	ripley	1231	`List of 2`
		1232	`$ rect:List of 4`
		1233	`..$ w : num 1.2`
		1234	`..$ h : num 0.251`
		1235	`..$ left: num -3`
		1236	`..$ top : num 0.9`
		1237	`$ text:List of 2`
		1238	`..$ x: num [1:2] -2.29 -2.29`
		1239	`..$ y: num [1:2] 0.816 0.733`
61255	ripley	1240	`> legend(-3, 0.9, ex.cs1, lty = 1:2, col = 2:3, adj = c(0, 0.6))`
56353	ripley	1241	`>`
		1242	`> require(stats)`
		1243	`> x <- rexp(100, rate = .5)`
		1244	`> hist(x, main = "Mean and Median of a Skewed Distribution")`
61157	ripley	1245	`> abline(v = mean(x), col = 2, lty = 2, lwd = 2)`
		1246	`> abline(v = median(x), col = 3, lty = 3, lwd = 2)`
		1247	`> ex12 <- expression(bar(x) == sum(over(x[i], n), i == 1, n),`
		1248	`+ hat(x) == median(x[i], i == 1, n))`
61255	ripley	1249	`> utils::str(legend(4.1, 30, ex12, col = 2:3, lty = 2:3, lwd = 2))`
56353	ripley	1250	`List of 2`
		1251	`$ rect:List of 4`
		1252	`..$ w : num 4.27`
		1253	`..$ h : num 6.78`
		1254	`..$ left: num 4.1`
		1255	`..$ top : num 30`
		1256	`$ text:List of 2`
		1257	`..$ x: num [1:2] 5.22 5.22`
		1258	`..$ y: num [1:2] 27.3 24.6`
		1259	`>`
		1260	`> ## 'Filled' boxes -- for more, see example(plot.factor)`
61157	ripley	1261	`> op <- par(bg = "white") # to get an opaque box for the legend`
56353	ripley	1262	`> plot(cut(weight, 3) ~ group, data = PlantGrowth, col = NULL,`
		1263	`+ density = 16*(1:3))`
		1264	`> par(op)`
		1265	`>`
		1266	`> ## Using 'ncol' :`
		1267	`> x <- 0:64/64`
		1268	`> matplot(x, outer(x, 1:7, function(x, k) sin(k * pi * x)),`
		1269	`+ type = "o", col = 1:7, ylim = c(-1, 1.5), pch = "*")`
61157	ripley	1270	`> op <- par(bg = "antiquewhite1")`
		1271	`> legend(0, 1.5, paste("sin(", 1:7, "pi * x)"), col = 1:7, lty = 1:7,`
56353	ripley	1272	`+ pch = "*", ncol = 4, cex = 0.8)`
61157	ripley	1273	`> legend(.8,1.2, paste("sin(", 1:7, "pi * x)"), col = 1:7, lty = 1:7,`
56353	ripley	1274	`+ pch = "*", cex = 0.8)`
61157	ripley	1275	`> legend(0, -.1, paste("sin(", 1:4, "pi * x)"), col = 1:4, lty = 1:4,`
56353	ripley	1276	`+ ncol = 2, cex = 0.8)`
61157	ripley	1277	`> legend(0, -.4, paste("sin(", 5:7, "pi * x)"), col = 4:6, pch = 24,`
56353	ripley	1278	`+ ncol = 2, cex = 1.5, lwd = 2, pt.bg = "pink", pt.cex = 1:3)`
		1279	`> par(op)`
		1280	`>`
		1281	`> ## point covering line :`
		1282	`> y <- sin(3pix)`
61157	ripley	1283	`> plot(x, y, type = "l", col = "blue",`
56353	ripley	1284	`+ main = "points with bg & legend(*, pt.bg)")`
61157	ripley	1285	`> points(x, y, pch = 21, bg = "white")`
		1286	`> legend(.4,1, "sin(c x)", pch = 21, pt.bg = "white", lty = 1, col = "blue")`
56353	ripley	1287	`>`
		1288	`> ## legends with titles at different locations`
61169	ripley	1289	`> plot(x, y, type = "n")`
61157	ripley	1290	`> legend("bottomright", "(x,y)", pch = 1, title = "bottomright")`
		1291	`> legend("bottom", "(x,y)", pch = 1, title = "bottom")`
		1292	`> legend("bottomleft", "(x,y)", pch = 1, title = "bottomleft")`
		1293	`> legend("left", "(x,y)", pch = 1, title = "left")`
		1294	`> legend("topleft", "(x,y)", pch = 1, title = "topleft, inset = .05",`
56353	ripley	1295	`+ inset = .05)`
61157	ripley	1296	`> legend("top", "(x,y)", pch = 1, title = "top")`
		1297	`> legend("topright", "(x,y)", pch = 1, title = "topright, inset = .02",`
56353	ripley	1298	`+ inset = .02)`
61157	ripley	1299	`> legend("right", "(x,y)", pch = 1, title = "right")`
		1300	`> legend("center", "(x,y)", pch = 1, title = "center")`
56353	ripley	1301	`>`
57622	maechler	1302	`> # using text.font (and text.col):`
61255	ripley	1303	`> op <- par(mfrow = c(2, 2), mar = rep(2.1, 4))`
57622	maechler	1304	`> c6 <- terrain.colors(10)[1:6]`
		1305	`> for(i in 1:4) {`
61157	ripley	1306	`+ plot(1, type = "n", axes = FALSE, ann = FALSE); title(paste("text.font =",i))`
57622	maechler	1307	`+ legend("top", legend = LETTERS[1:6], col = c6,`
61157	ripley	1308	`+ ncol = 2, cex = 2, lwd = 3, text.font = i, text.col = c6)`
57622	maechler	1309	`+ }`
		1310	`> par(op)`
56353	ripley	1311	`>`
		1312	`>`
57622	maechler	1313	`>`
56353	ripley	1314	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1315	`> cleanEx()`
		1316	`> nameEx("lines")`
		1317	`> ### * lines`
		1318	`>`
		1319	`> flush(stderr()); flush(stdout())`
		1320	`>`
		1321	`> ### Name: lines`
		1322	`> ### Title: Add Connected Line Segments to a Plot`
		1323	`> ### Aliases: lines lines.default`
		1324	`> ### Keywords: aplot`
		1325	`>`
		1326	`> ### ** Examples`
		1327	`>`
		1328	`> # draw a smooth line through a scatter plot`
61157	ripley	1329	`> plot(cars, main = "Stopping Distance versus Speed")`
56353	ripley	1330	`> lines(stats::lowess(cars))`
		1331	`>`
		1332	`>`
		1333	`>`
		1334	`> cleanEx()`
		1335	`> nameEx("matplot")`
		1336	`> ### * matplot`
		1337	`>`
		1338	`> flush(stderr()); flush(stdout())`
		1339	`>`
		1340	`> ### Name: matplot`
		1341	`> ### Title: Plot Columns of Matrices`
		1342	`> ### Aliases: matplot matpoints matlines`
		1343	`> ### Keywords: hplot aplot array`
		1344	`>`
		1345	`> ### ** Examples`
		1346	`>`
		1347	`> require(grDevices)`
		1348	`> matplot((-4:5)^2, main = "Quadratic") # almost identical to plot(*)`
		1349	`> sines <- outer(1:20, 1:4, function(x, y) sin(x / 20 * pi * y))`
		1350	`> matplot(sines, pch = 1:4, type = "o", col = rainbow(ncol(sines)))`
		1351	`> matplot(sines, type = "b", pch = 21:23, col = 2:5, bg = 2:5,`
		1352	`+ main = "matplot(...., pch = 21:23, bg = 2:5)")`
		1353	`>`
		1354	`> x <- 0:50/50`
		1355	`> matplot(x, outer(x, 1:8, function(x, k) sin(kpi x)),`
		1356	`+ ylim = c(-2,2), type = "plobcsSh",`
		1357	`+ main= "matplot(,type = \"plobcsSh\" )")`
		1358	`> ## pch & type = vector of 1-chars :`
		1359	`> matplot(x, outer(x, 1:4, function(x, k) sin(kpi x)),`
		1360	`+ pch = letters[1:4], type = c("b","p","o"))`
		1361	`>`
		1362	`> lends <- c("round","butt","square")`
		1363	`> matplot(matrix(1:12, 4), type="c", lty=1, lwd=10, lend=lends)`
		1364	`> text(cbind(2.5, 2*c(1,3,5)-.4), lends, col= 1:3, cex = 1.5)`
		1365	`>`
		1366	`> table(iris$Species) # is data.frame with 'Species' factor`
		1367
		1368	`setosa versicolor virginica`
		1369	`50 50 50`
		1370	`> iS <- iris$Species == "setosa"`
		1371	`> iV <- iris$Species == "versicolor"`
		1372	`> op <- par(bg = "bisque")`
61157	ripley	1373	`> matplot(c(1, 8), c(0, 4.5), type = "n", xlab = "Length", ylab = "Width",`
56353	ripley	1374	`+ main = "Petal and Sepal Dimensions in Iris Blossoms")`
		1375	`> matpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = "sS", col = c(2,4))`
		1376	`> matpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = "vV", col = c(2,4))`
		1377	`> legend(1, 4, c(" Setosa Petals", " Setosa Sepals",`
		1378	`+ "Versicolor Petals", "Versicolor Sepals"),`
		1379	`+ pch = "sSvV", col = rep(c(2,4), 2))`
		1380	`>`
		1381	`> nam.var <- colnames(iris)[-5]`
		1382	`> nam.spec <- as.character(iris[1+50*0:2, "Species"])`
		1383	`> iris.S <- array(NA, dim = c(50,4,3),`
		1384	`+ dimnames = list(NULL, nam.var, nam.spec))`
		1385	`> for(i in 1:3) iris.S[,,i] <- data.matrix(iris[1:50+50*(i-1), -5])`
		1386	`>`
61169	ripley	1387	`> matplot(iris.S[, "Petal.Length",], iris.S[, "Petal.Width",], pch = "SCV",`
61157	ripley	1388	`+ col = rainbow(3, start = 0.8, end = 0.1),`
56353	ripley	1389	`+ sub = paste(c("S", "C", "V"), dimnames(iris.S)[[3]],`
		1390	`+ sep = "=", collapse= ", "),`
		1391	`+ main = "Fisher's Iris Data")`
		1392	`> par(op)`
		1393	`>`
		1394	`>`
		1395	`>`
		1396	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1397	`> cleanEx()`
		1398	`> nameEx("mosaicplot")`
		1399	`> ### * mosaicplot`
		1400	`>`
		1401	`> flush(stderr()); flush(stdout())`
		1402	`>`
		1403	`> ### Name: mosaicplot`
		1404	`> ### Title: Mosaic Plots`
		1405	`> ### Aliases: mosaicplot mosaicplot.default mosaicplot.formula`
		1406	`> ### Keywords: hplot`
		1407	`>`
		1408	`> ### ** Examples`
		1409	`>`
		1410	`> require(stats)`
		1411	`> mosaicplot(Titanic, main = "Survival on the Titanic", color = TRUE)`
		1412	`> ## Formula interface for tabulated data:`
		1413	`> mosaicplot(~ Sex + Age + Survived, data = Titanic, color = TRUE)`
		1414	`>`
		1415	`> mosaicplot(HairEyeColor, shade = TRUE)`
		1416	`> ## Independence model of hair and eye color and sex. Indicates that`
61435	ripley	1417	`> ## there are more blue eyed blonde females than expected in the case`
56353	ripley	1418	`> ## of independence and too few brown eyed blonde females.`
		1419	`> ## The corresponding model is:`
		1420	`> fm <- loglin(HairEyeColor, list(1, 2, 3))`
		1421	`2 iterations: deviation 5.684342e-14`
		1422	`> pchisq(fm$pearson, fm$df, lower.tail = FALSE)`
		1423	`[1] 5.320872e-23`
		1424	`>`
		1425	`> mosaicplot(HairEyeColor, shade = TRUE, margin = list(1:2, 3))`
		1426	`> ## Model of joint independence of sex from hair and eye color. Males`
		1427	`> ## are underrepresented among people with brown hair and eyes, and are`
		1428	`> ## overrepresented among people with brown hair and blue eyes.`
		1429	`> ## The corresponding model is:`
		1430	`> fm <- loglin(HairEyeColor, list(1:2, 3))`
		1431	`2 iterations: deviation 5.684342e-14`
		1432	`> pchisq(fm$pearson, fm$df, lower.tail = FALSE)`
		1433	`[1] 0.1891745`
		1434	`>`
		1435	`> ## Formula interface for raw data: visualize cross-tabulation of numbers`
		1436	`> ## of gears and carburettors in Motor Trend car data.`
		1437	`> mosaicplot(~ gear + carb, data = mtcars, color = TRUE, las = 1)`
		1438	`> # color recycling`
		1439	`> mosaicplot(~ gear + carb, data = mtcars, color = 2:3, las = 1)`
		1440	`>`
		1441	`>`
		1442	`>`
		1443	`> cleanEx()`
		1444	`> nameEx("mtext")`
		1445	`> ### * mtext`
		1446	`>`
		1447	`> flush(stderr()); flush(stdout())`
		1448	`>`
		1449	`> ### Name: mtext`
		1450	`> ### Title: Write Text into the Margins of a Plot`
		1451	`> ### Aliases: mtext`
		1452	`> ### Keywords: aplot`
		1453	`>`
		1454	`> ### ** Examples`
		1455	`>`
61157	ripley	1456	`> plot(1:10, (-4:5)^2, main = "Parabola Points", xlab = "xlab")`
56353	ripley	1457	`> mtext("10 of them")`
		1458	`> for(s in 1:4)`
61169	ripley	1459	`+ mtext(paste("mtext(..., line= -1, {side, col, font} = ", s,`
56353	ripley	1460	`+ ", cex = ", (1+s)/2, ")"), line = -1,`
61157	ripley	1461	`+ side = s, col = s, font = s, cex = (1+s)/2)`
56353	ripley	1462	`> mtext("mtext(..., line= -2)", line = -2)`
61157	ripley	1463	`> mtext("mtext(..., line= -2, adj = 0)", line = -2, adj = 0)`
56353	ripley	1464	`> ##--- log axis :`
61169	ripley	1465	`> plot(1:10, exp(1:10), log = "y", main = "log =\"y\"", xlab = "xlab")`
61157	ripley	1466	`> for(s in 1:4) mtext(paste("mtext(...,side=", s ,")"), side = s)`
56353	ripley	1467	`>`
		1468	`>`
		1469	`>`
		1470	`> cleanEx()`
		1471	`> nameEx("pairs")`
		1472	`> ### * pairs`
		1473	`>`
		1474	`> flush(stderr()); flush(stdout())`
		1475	`>`
		1476	`> ### Name: pairs`
		1477	`> ### Title: Scatterplot Matrices`
		1478	`> ### Aliases: pairs pairs.default pairs.formula`
		1479	`> ### Keywords: hplot`
		1480	`>`
		1481	`> ### ** Examples`
		1482	`>`
		1483	`> pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species",`
		1484	`+ pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])`
		1485	`>`
		1486	`> ## formula method`
		1487	`> pairs(~ Fertility + Education + Catholic, data = swiss,`
		1488	`+ subset = Education < 20, main = "Swiss data, Education < 20")`
		1489	`>`
		1490	`> pairs(USJudgeRatings)`
61011	maechler	1491	`> ## show only lower triangle (and suppress labeling for whatever reason):`
61157	ripley	1492	`> pairs(USJudgeRatings, text.panel = NULL, upper.panel = NULL)`
56353	ripley	1493	`>`
		1494	`> ## put histograms on the diagonal`
		1495	`> panel.hist <- function(x, ...)`
		1496	`+ {`
		1497	`+ usr <- par("usr"); on.exit(par(usr))`
		1498	`+ par(usr = c(usr[1:2], 0, 1.5) )`
		1499	`+ h <- hist(x, plot = FALSE)`
		1500	`+ breaks <- h$breaks; nB <- length(breaks)`
		1501	`+ y <- h$counts; y <- y/max(y)`
61157	ripley	1502	`+ rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)`
56353	ripley	1503	`+ }`
61157	ripley	1504	`> pairs(USJudgeRatings[1:5], panel = panel.smooth,`
		1505	`+ cex = 1.5, pch = 24, bg = "light blue",`
		1506	`+ diag.panel = panel.hist, cex.labels = 2, font.labels = 2)`
56353	ripley	1507	`>`
		1508	`> ## put (absolute) correlations on the upper panels,`
		1509	`> ## with size proportional to the correlations.`
61157	ripley	1510	`> panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)`
56353	ripley	1511	`+ {`
		1512	`+ usr <- par("usr"); on.exit(par(usr))`
		1513	`+ par(usr = c(0, 1, 0, 1))`
		1514	`+ r <- abs(cor(x, y))`
61157	ripley	1515	`+ txt <- format(c(r, 0.123456789), digits = digits)[1]`
		1516	`+ txt <- paste0(prefix, txt)`
56353	ripley	1517	`+ if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)`
		1518	`+ text(0.5, 0.5, txt, cex = cex.cor * r)`
		1519	`+ }`
61157	ripley	1520	`> pairs(USJudgeRatings, lower.panel = panel.smooth, upper.panel = panel.cor)`
56353	ripley	1521	`>`
59643	ripley	1522	`> pairs(iris[-5], log = "xy") # plot all variables on log scale`
62705	maechler	1523	`> pairs(iris, log = 1:4, # log the first four`
		1524	`+ main = "Lengths and Widths in [log]", line.main=1.5, oma=c(2,2,3,2))`
56353	ripley	1525	`>`
		1526	`>`
59643	ripley	1527	`>`
56353	ripley	1528	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1529	`> cleanEx()`
		1530	`> nameEx("panel.smooth")`
		1531	`> ### * panel.smooth`
		1532	`>`
		1533	`> flush(stderr()); flush(stdout())`
		1534	`>`
		1535	`> ### Name: panel.smooth`
		1536	`> ### Title: Simple Panel Plot`
		1537	`> ### Aliases: panel.smooth`
		1538	`> ### Keywords: hplot dplot`
		1539	`>`
		1540	`> ### ** Examples`
		1541	`>`
61169	ripley	1542	`> pairs(swiss, panel = panel.smooth, pch = ".") # emphasize the smooths`
		1543	`> pairs(swiss, panel = panel.smooth, lwd = 2, cex = 1.5, col = "blue") # hmm...`
56353	ripley	1544	`>`
		1545	`>`
		1546	`>`
		1547	`> cleanEx()`
		1548	`> nameEx("par")`
		1549	`> ### * par`
		1550	`>`
		1551	`> flush(stderr()); flush(stdout())`
		1552	`>`
		1553	`> ### Name: par`
		1554	`> ### Title: Set or Query Graphical Parameters`
		1555	`> ### Aliases: par .Pars 'graphical parameter' 'graphical parameters'`
		1556	`> ### Keywords: iplot dplot environment`
		1557	`>`
		1558	`> ### ** Examples`
		1559	`>`
		1560	`> op <- par(mfrow = c(2, 2), # 2 x 2 pictures on one plot`
		1561	`+ pty = "s") # square plotting region,`
		1562	`> # independent of device size`
		1563	`>`
		1564	`> ## At end of plotting, reset to previous settings:`
		1565	`> par(op)`
		1566	`>`
		1567	`> ## Alternatively,`
		1568	`> op <- par(no.readonly = TRUE) # the whole list of settable par's.`
		1569	`> ## do lots of plotting and par(.) calls, then reset:`
		1570	`> par(op)`
		1571	`> ## Note this is not in general good practice`
		1572	`>`
		1573	`> par("ylog") # FALSE`
		1574	`[1] FALSE`
		1575	`> plot(1 : 12, log = "y")`
		1576	`> par("ylog") # TRUE`
		1577	`[1] TRUE`
		1578	`>`
		1579	`> plot(1:2, xaxs = "i") # 'inner axis' w/o extra space`
		1580	`> par(c("usr", "xaxp"))`
		1581	`$usr`
		1582	`[1] 1.00 2.00 0.96 2.04`
		1583
		1584	`$xaxp`
		1585	`[1] 1 2 5`
		1586
		1587	`>`
		1588	`> ( nr.prof <-`
61169	ripley	1589	`+ c(prof.pilots = 16, lawyers = 11, farmers = 10, salesmen = 9, physicians = 9,`
		1590	`+ mechanics = 6, policemen = 6, managers = 6, engineers = 5, teachers = 4,`
		1591	`+ housewives = 3, students = 3, armed.forces = 1))`
56353	ripley	1592	`prof.pilots lawyers farmers salesmen physicians mechanics`
		1593	`16 11 10 9 9 6`
		1594	`policemen managers engineers teachers housewives students`
		1595	`6 6 5 4 3 3`
		1596	`armed.forces`
		1597	`1`
		1598	`> par(las = 3)`
		1599	`> barplot(rbind(nr.prof)) # R 0.63.2: shows alignment problem`
61169	ripley	1600	`> par(las = 0) # reset to default`
56353	ripley	1601	`>`
		1602	`> require(grDevices) # for gray`
		1603	`> ## 'fg' use:`
61157	ripley	1604	`> plot(1:12, type = "b", main = "'fg' : axes, ticks and box in gray",`
		1605	`+ fg = gray(0.7), bty = "7" , sub = R.version.string)`
56353	ripley	1606	`>`
		1607	`> ex <- function() {`
		1608	`+ old.par <- par(no.readonly = TRUE) # all par settings which`
		1609	`+ # could be changed.`
		1610	`+ on.exit(par(old.par))`
		1611	`+ ## ...`
		1612	`+ ## ... do lots of par() settings and plots`
		1613	`+ ## ...`
		1614	`+ invisible() #-- now, par(old.par) will be executed`
		1615	`+ }`
		1616	`> ex()`
		1617	`>`
59180	maechler	1618	`> ## Line types`
		1619	`> showLty <- function(ltys, xoff = 0, ...) {`
		1620	`+ stopifnot((n <- length(ltys)) >= 1)`
		1621	`+ op <- par(mar = rep(.5,4)); on.exit(par(op))`
61169	ripley	1622	`+ plot(0:1, 0:1, type = "n", axes = FALSE, ann = FALSE)`
59180	maechler	1623	`+ y <- (n:1)/(n+1)`
		1624	`+ clty <- as.character(ltys)`
62642	hornik	1625	`+ mytext <- function(x, y, txt)`
		1626	`+ text(x, y, txt, adj = c(0, -.3), cex = 0.8, ...)`
59180	maechler	1627	`+ abline(h = y, lty = ltys, ...); mytext(xoff, y, clty)`
		1628	`+ y <- y - 1/(3*(n+1))`
62642	hornik	1629	`+ abline(h = y, lty = ltys, lwd = 2, ...)`
		1630	`+ mytext(1/8+xoff, y, paste(clty," lwd = 2"))`
59180	maechler	1631	`+ }`
		1632	`> showLty(c("solid", "dashed", "dotted", "dotdash", "longdash", "twodash"))`
61169	ripley	1633	`> par(new = TRUE) # the same:`
61157	ripley	1634	`> showLty(c("solid", "44", "13", "1343", "73", "2262"), xoff = .2, col = 2)`
59180	maechler	1635	`> showLty(c("11", "22", "33", "44", "12", "13", "14", "21", "31"))`
56353	ripley	1636	`>`
		1637	`>`
59180	maechler	1638	`>`
56353	ripley	1639	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1640	`> cleanEx()`
		1641	`> nameEx("persp")`
		1642	`> ### * persp`
		1643	`>`
		1644	`> flush(stderr()); flush(stdout())`
		1645	`>`
		1646	`> ### Name: persp`
		1647	`> ### Title: Perspective Plots`
		1648	`> ### Aliases: persp persp.default`
		1649	`> ### Keywords: hplot aplot`
		1650	`>`
		1651	`> ### ** Examples`
		1652	`>`
		1653	`> require(grDevices) # for trans3d`
		1654	`> ## More examples in demo(persp) !!`
		1655	`> ## -----------`
		1656	`>`
		1657	`> # (1) The Obligatory Mathematical surface.`
		1658	`> # Rotated sinc function.`
		1659	`>`
		1660	`> x <- seq(-10, 10, length= 30)`
		1661	`> y <- x`
61169	ripley	1662	`> f <- function(x, y) { r <- sqrt(x^2+y^2); 10 * sin(r)/r }`
56353	ripley	1663	`> z <- outer(x, y, f)`
		1664	`> z[is.na(z)] <- 1`
		1665	`> op <- par(bg = "white")`
		1666	`> persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue")`
		1667	`> persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue",`
		1668	`+ ltheta = 120, shade = 0.75, ticktype = "detailed",`
		1669	`+ xlab = "X", ylab = "Y", zlab = "Sinc( r )"`
		1670	`+ ) -> res`
		1671	`> round(res, 3)`
		1672	`[,1] [,2] [,3] [,4]`
		1673	`[1,] 0.087 -0.025 0.043 -0.043`
		1674	`[2,] 0.050 0.043 -0.075 0.075`
		1675	`[3,] 0.000 0.074 0.042 -0.042`
		1676	`[4,] 0.000 -0.273 -2.890 3.890`
		1677	`>`
		1678	`> # (2) Add to existing persp plot - using trans3d() :`
		1679	`>`
		1680	`> xE <- c(-10,10); xy <- expand.grid(xE, xE)`
61157	ripley	1681	`> points(trans3d(xy[,1], xy[,2], 6, pmat = res), col = 2, pch = 16)`
		1682	`> lines (trans3d(x, y = 10, z = 6 + sin(x), pmat = res), col = 3)`
56353	ripley	1683	`>`
		1684	`> phi <- seq(0, 2*pi, len = 201)`
		1685	`> r1 <- 7.725 # radius of 2nd maximum`
		1686	`> xr <- r1 * cos(phi)`
		1687	`> yr <- r1 * sin(phi)`
		1688	`> lines(trans3d(xr,yr, f(xr,yr), res), col = "pink", lwd = 2)`
		1689	`> ## (no hidden lines)`
		1690	`>`
		1691	`> # (3) Visualizing a simple DEM model`
		1692	`>`
		1693	`> z <- 2 * volcano # Exaggerate the relief`
		1694	`> x <- 10 * (1:nrow(z)) # 10 meter spacing (S to N)`
		1695	`> y <- 10 * (1:ncol(z)) # 10 meter spacing (E to W)`
		1696	`> ## Don't draw the grid lines : border = NA`
		1697	`> par(bg = "slategray")`
		1698	`> persp(x, y, z, theta = 135, phi = 30, col = "green3", scale = FALSE,`
		1699	`+ ltheta = -120, shade = 0.75, border = NA, box = FALSE)`
		1700	`>`
		1701	`> # (4) Surface colours corresponding to z-values`
		1702	`>`
		1703	`> par(bg = "white")`
		1704	`> x <- seq(-1.95, 1.95, length = 30)`
		1705	`> y <- seq(-1.95, 1.95, length = 35)`
61169	ripley	1706	`> z <- outer(x, y, function(a, b) a*b^2)`
56353	ripley	1707	`> nrz <- nrow(z)`
		1708	`> ncz <- ncol(z)`
		1709	`> # Create a function interpolating colors in the range of specified colors`
61435	ripley	1710	`> jet.colors <- colorRampPalette( c("blue", "green") )`
56353	ripley	1711	`> # Generate the desired number of colors from this palette`
		1712	`> nbcol <- 100`
		1713	`> color <- jet.colors(nbcol)`
		1714	`> # Compute the z-value at the facet centres`
		1715	`> zfacet <- z[-1, -1] + z[-1, -ncz] + z[-nrz, -1] + z[-nrz, -ncz]`
		1716	`> # Recode facet z-values into color indices`
		1717	`> facetcol <- cut(zfacet, nbcol)`
61157	ripley	1718	`> persp(x, y, z, col = color[facetcol], phi = 30, theta = -30)`
56353	ripley	1719	`>`
		1720	`> par(op)`
		1721	`>`
		1722	`>`
		1723	`>`
		1724	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1725	`> cleanEx()`
		1726	`> nameEx("pie")`
		1727	`> ### * pie`
		1728	`>`
		1729	`> flush(stderr()); flush(stdout())`
		1730	`>`
		1731	`> ### Name: pie`
		1732	`> ### Title: Pie Charts`
		1733	`> ### Aliases: pie`
		1734	`> ### Keywords: hplot`
		1735	`>`
		1736	`> ### ** Examples`
		1737	`>`
		1738	`> require(grDevices)`
		1739	`> pie(rep(1, 24), col = rainbow(24), radius = 0.9)`
		1740	`>`
		1741	`> pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)`
		1742	`> names(pie.sales) <- c("Blueberry", "Cherry",`
		1743	`+ "Apple", "Boston Cream", "Other", "Vanilla Cream")`
		1744	`> pie(pie.sales) # default colours`
		1745	`> pie(pie.sales, col = c("purple", "violetred1", "green3",`
		1746	`+ "cornsilk", "cyan", "white"))`
61157	ripley	1747	`> pie(pie.sales, col = gray(seq(0.4, 1.0, length = 6)))`
56353	ripley	1748	`> pie(pie.sales, density = 10, angle = 15 + 10 * 1:6)`
61157	ripley	1749	`> pie(pie.sales, clockwise = TRUE, main = "pie(*, clockwise = TRUE)")`
61169	ripley	1750	`> segments(0, 0, 0, 1, col = "red", lwd = 2)`
		1751	`> text(0, 1, "init.angle = 90", col = "red")`
56353	ripley	1752	`>`
		1753	`> n <- 200`
61169	ripley	1754	`> pie(rep(1, n), labels = "", col = rainbow(n), border = NA,`
56353	ripley	1755	`+ main = "pie(*, labels=\"\", col=rainbow(n), border=NA,..")`
		1756	`>`
		1757	`>`
		1758	`>`
		1759	`> cleanEx()`
		1760	`> nameEx("plot")`
		1761	`> ### * plot`
		1762	`>`
		1763	`> flush(stderr()); flush(stdout())`
		1764	`>`
		1765	`> ### Name: plot`
		1766	`> ### Title: Generic X-Y Plotting`
		1767	`> ### Aliases: plot`
		1768	`> ### Keywords: hplot`
		1769	`>`
		1770	`> ### ** Examples`
		1771	`>`
		1772	`> require(stats)`
		1773	`> plot(cars)`
		1774	`> lines(lowess(cars))`
		1775	`>`
56579	ripley	1776	`> plot(sin, -pi, 2*pi) # see ?plot.function`
56353	ripley	1777	`>`
		1778	`> ## Discrete Distribution Plot:`
61169	ripley	1779	`> plot(table(rpois(100, 5)), type = "h", col = "red", lwd = 10,`
		1780	`+ main = "rpois(100, lambda = 5)")`
56353	ripley	1781	`>`
		1782	`> ## Simple quantiles/ECDF, see ecdf() {library(stats)} for a better one:`
		1783	`> plot(x <- sort(rnorm(47)), type = "s", main = "plot(x, type = \"s\")")`
		1784	`> points(x, cex = .5, col = "dark red")`
		1785	`>`
		1786	`>`
		1787	`>`
		1788	`> cleanEx()`
		1789	`> nameEx("plot.dataframe")`
		1790	`> ### * plot.dataframe`
		1791	`>`
		1792	`> flush(stderr()); flush(stdout())`
		1793	`>`
		1794	`> ### Name: plot.data.frame`
		1795	`> ### Title: Plot Method for Data Frames`
		1796	`> ### Aliases: plot.data.frame`
		1797	`> ### Keywords: hplot methods`
		1798	`>`
		1799	`> ### ** Examples`
		1800	`>`
61157	ripley	1801	`> plot(OrchardSprays[1], method = "jitter")`
56353	ripley	1802	`> plot(OrchardSprays[c(4,1)])`
		1803	`> plot(OrchardSprays)`
		1804	`>`
		1805	`> plot(iris)`
		1806	`> plot(iris[5:4])`
		1807	`> plot(women)`
		1808	`>`
		1809	`>`
		1810	`>`
		1811	`> cleanEx()`
		1812	`> nameEx("plot.default")`
		1813	`> ### * plot.default`
		1814	`>`
		1815	`> flush(stderr()); flush(stdout())`
		1816	`>`
		1817	`> ### Name: plot.default`
		1818	`> ### Title: The Default Scatterplot Function`
		1819	`> ### Aliases: plot.default`
		1820	`> ### Keywords: hplot`
		1821	`>`
		1822	`> ### ** Examples`
		1823	`>`
		1824	`> Speed <- cars$speed`
		1825	`> Distance <- cars$dist`
61169	ripley	1826	`> plot(Speed, Distance, panel.first = grid(8, 8),`
56353	ripley	1827	`+ pch = 0, cex = 1.2, col = "blue")`
		1828	`> plot(Speed, Distance,`
		1829	`+ panel.first = lines(stats::lowess(Speed, Distance), lty = "dashed"),`
		1830	`+ pch = 0, cex = 1.2, col = "blue")`
		1831	`>`
		1832	`> ## Show the different plot types`
		1833	`> x <- 0:12`
		1834	`> y <- sin(pi/5 * x)`
		1835	`> op <- par(mfrow = c(3,3), mar = .1+ c(2,2,3,1))`
		1836	`> for (tp in c("p","l","b", "c","o","h", "s","S","n")) {`
61157	ripley	1837	`+ plot(y ~ x, type = tp, main = paste0("plot(*, type = \"", tp, "\")"))`
56353	ripley	1838	`+ if(tp == "S") {`
61169	ripley	1839	`+ lines(x, y, type = "s", col = "red", lty = 2)`
61157	ripley	1840	`+ mtext("lines(*, type = \"s\", ...)", col = "red", cex = 0.8)`
56353	ripley	1841	`+ }`
		1842	`+ }`
		1843	`> par(op)`
		1844	`>`
		1845	`> ##--- Log-Log Plot with custom axes`
61169	ripley	1846	`> lx <- seq(1, 5, length = 41)`
56353	ripley	1847	`> yl <- expression(e^{-frac(1,2) * {log[10](x)}^2})`
		1848	`> y <- exp(-.5*lx^2)`
61157	ripley	1849	`> op <- par(mfrow = c(2,1), mar = par("mar")+c(0,1,0,0))`
		1850	`> plot(10^lx, y, log = "xy", type = "l", col = "purple",`
		1851	`+ main = "Log-Log plot", ylab = yl, xlab = "x")`
61169	ripley	1852	`> plot(10^lx, y, log = "xy", type = "o", pch = ".", col = "forestgreen",`
61157	ripley	1853	`+ main = "Log-Log plot with custom axes", ylab = yl, xlab = "x",`
56353	ripley	1854	`+ axes = FALSE, frame.plot = TRUE)`
		1855	`> my.at <- 10^(1:5)`
61157	ripley	1856	`> axis(1, at = my.at, labels = formatC(my.at, format = "fg"))`
56353	ripley	1857	`> at.y <- 10^(-5:-1)`
61157	ripley	1858	`> axis(2, at = at.y, labels = formatC(at.y, format = "fg"), col.axis = "red")`
56353	ripley	1859	`> par(op)`
		1860	`>`
		1861	`>`
		1862	`>`
		1863	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1864	`> cleanEx()`
		1865	`> nameEx("plot.design")`
		1866	`> ### * plot.design`
		1867	`>`
		1868	`> flush(stderr()); flush(stdout())`
		1869	`>`
		1870	`> ### Name: plot.design`
		1871	`> ### Title: Plot Univariate Effects of a Design or Model`
		1872	`> ### Aliases: plot.design`
		1873	`> ### Keywords: hplot`
		1874	`>`
		1875	`> ### ** Examples`
		1876	`>`
		1877	`> require(stats)`
61169	ripley	1878	`> plot.design(warpbreaks) # automatic for data frame with one numeric var.`
56353	ripley	1879	`>`
		1880	`> Form <- breaks ~ wool + tension`
		1881	`> summary(fm1 <- aov(Form, data = warpbreaks))`
57044	ripley	1882	`Df Sum Sq Mean Sq F value Pr(>F)`
		1883	`wool 1 451 450.7 3.339 0.07361 .`
		1884	`tension 2 2034 1017.1 7.537 0.00138 **`
		1885	`Residuals 50 6748 135.0`
56353	ripley	1886	`---`
61435	ripley	1887	`Signif. codes: 0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1`
61169	ripley	1888	`> plot.design( Form, data = warpbreaks, col = 2) # same as above`
56353	ripley	1889	`>`
		1890	`> ## More than one y :`
		1891	`> utils::str(esoph)`
		1892	`'data.frame': 88 obs. of 5 variables:`
		1893	`$ agegp : Ord.factor w/ 6 levels "25-34"<"35-44"<..: 1 1 1 1 1 1 1 1 1 1 ...`
		1894	`$ alcgp : Ord.factor w/ 4 levels "0-39g/day"<"40-79"<..: 1 1 1 1 2 2 2 2 3 3 ...`
		1895	`$ tobgp : Ord.factor w/ 4 levels "0-9g/day"<"10-19"<..: 1 2 3 4 1 2 3 4 1 2 ...`
		1896	`$ ncases : num 0 0 0 0 0 0 0 0 0 0 ...`
		1897	`$ ncontrols: num 40 10 6 5 27 7 4 7 2 1 ...`
		1898	`> plot.design(esoph) ## two plots; if interactive you are "ask"ed`
		1899	`>`
		1900	`> ## or rather, compare mean and median:`
		1901	`> op <- par(mfcol = 1:2)`
		1902	`> plot.design(ncases/ncontrols ~ ., data = esoph, ylim = c(0, 0.8))`
		1903	`> plot.design(ncases/ncontrols ~ ., data = esoph, ylim = c(0, 0.8),`
		1904	`+ fun = median)`
		1905	`> par(op)`
		1906	`>`
		1907	`>`
		1908	`>`
		1909	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1910	`> cleanEx()`
		1911	`> nameEx("plot.factor")`
		1912	`> ### * plot.factor`
		1913	`>`
		1914	`> flush(stderr()); flush(stdout())`
		1915	`>`
		1916	`> ### Name: plot.factor`
		1917	`> ### Title: Plotting Factor Variables`
		1918	`> ### Aliases: plot.factor`
		1919	`> ### Keywords: hplot`
		1920	`>`
		1921	`> ### ** Examples`
		1922	`>`
		1923	`> require(grDevices)`
61169	ripley	1924	`> plot(weight ~ group, data = PlantGrowth) # numeric vector ~ factor`
		1925	`> plot(cut(weight, 2) ~ group, data = PlantGrowth) # factor ~ factor`
56353	ripley	1926	`> ## passing "..." to spineplot() eventually:`
		1927	`> plot(cut(weight, 3) ~ group, data = PlantGrowth,`
		1928	`+ col = hcl(c(0, 120, 240), 50, 70))`
		1929	`>`
61169	ripley	1930	`> plot(PlantGrowth$group, axes = FALSE, main = "no axes") # extremely silly`
56353	ripley	1931	`>`
		1932	`>`
		1933	`>`
		1934	`> cleanEx()`
		1935	`> nameEx("plot.formula")`
		1936	`> ### * plot.formula`
		1937	`>`
		1938	`> flush(stderr()); flush(stdout())`
		1939	`>`
		1940	`> ### Name: plot.formula`
		1941	`> ### Title: Formula Notation for Scatterplots`
		1942	`> ### Aliases: plot.formula lines.formula points.formula text.formula`
		1943	`> ### Keywords: hplot aplot`
		1944	`>`
		1945	`> ### ** Examples`
		1946	`>`
		1947	`> op <- par(mfrow = c(2,1))`
		1948	`> plot(Ozone ~ Wind, data = airquality, pch = as.character(Month))`
		1949	`> plot(Ozone ~ Wind, data = airquality, pch = as.character(Month),`
		1950	`+ subset = Month != 7)`
		1951	`> par(op)`
		1952	`>`
		1953	`> ## text.formula() can be very natural:`
		1954	`> wb <- within(warpbreaks, {`
		1955	`+ time <- seq_along(breaks); W.T <- wool:tension })`
		1956	`> plot(breaks ~ time, data = wb, type = "b")`
		1957	`> text(breaks ~ time, data = wb, label = W.T, col = 1+as.integer(wool))`
		1958	`>`
		1959	`>`
		1960	`>`
		1961	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		1962	`> cleanEx()`
		1963	`> nameEx("plot.table")`
		1964	`> ### * plot.table`
		1965	`>`
		1966	`> flush(stderr()); flush(stdout())`
		1967	`>`
		1968	`> ### Name: plot.table`
		1969	`> ### Title: Plot Methods for 'table' Objects`
		1970	`> ### Aliases: plot.table lines.table points.table`
		1971	`> ### Keywords: hplot category`
		1972	`>`
		1973	`> ### ** Examples`
		1974	`>`
		1975	`> ## 1-d tables`
		1976	`> (Poiss.tab <- table(N = stats::rpois(200, lambda = 5)))`
		1977	`N`
		1978	`1 2 3 4 5 6 7 8 9 10 11`
		1979	`4 14 25 38 40 33 21 16 4 2 3`
		1980	`> plot(Poiss.tab, main = "plot(table(rpois(200, lambda = 5)))")`
		1981	`>`
		1982	`> plot(table(state.division))`
		1983	`>`
		1984	`> ## 4-D :`
		1985	`> plot(Titanic, main ="plot(Titanic, main= *)")`
		1986	`>`
		1987	`>`
		1988	`>`
		1989	`>`
		1990	`> cleanEx()`
		1991	`> nameEx("plot.window")`
		1992	`> ### * plot.window`
		1993	`>`
		1994	`> flush(stderr()); flush(stdout())`
		1995	`>`
		1996	`> ### Name: plot.window`
		1997	`> ### Title: Set up World Coordinates for Graphics Window`
		1998	`> ### Aliases: plot.window xlim ylim asp`
		1999	`> ### Keywords: aplot`
		2000	`>`
		2001	`> ### ** Examples`
		2002	`>`
		2003	`> ##--- An example for the use of 'asp' :`
		2004	`> require(stats) # normally loaded`
		2005	`> loc <- cmdscale(eurodist)`
		2006	`> rx <- range(x <- loc[,1])`
		2007	`> ry <- range(y <- -loc[,2])`
61157	ripley	2008	`> plot(x, y, type = "n", asp = 1, xlab = "", ylab = "")`
56353	ripley	2009	`> abline(h = pretty(rx, 10), v = pretty(ry, 10), col = "lightgray")`
61157	ripley	2010	`> text(x, y, labels(eurodist), cex = 0.8)`
56353	ripley	2011	`>`
		2012	`>`
		2013	`>`
		2014	`> cleanEx()`
		2015	`> nameEx("plot.xy")`
		2016	`> ### * plot.xy`
		2017	`>`
		2018	`> flush(stderr()); flush(stdout())`
		2019	`>`
		2020	`> ### Name: plot.xy`
		2021	`> ### Title: Basic Internal Plot Function`
		2022	`> ### Aliases: plot.xy`
		2023	`> ### Keywords: aplot`
		2024	`>`
		2025	`> ### ** Examples`
		2026	`>`
		2027	`> points.default # to see how it calls "plot.xy(xy.coords(x, y), ...)"`
		2028	`function (x, y = NULL, type = "p", ...)`
		2029	`plot.xy(xy.coords(x, y), type = type, ...)`
63202	ripley	2030	`<bytecode: 0x2332048>`
56353	ripley	2031	`<environment: namespace:graphics>`
		2032	`>`
		2033	`>`
		2034	`>`
		2035	`> cleanEx()`
		2036	`> nameEx("plothistogram")`
		2037	`> ### * plothistogram`
		2038	`>`
		2039	`> flush(stderr()); flush(stdout())`
		2040	`>`
		2041	`> ### Name: plot.histogram`
		2042	`> ### Title: Plot Histograms`
		2043	`> ### Aliases: plot.histogram lines.histogram`
		2044	`> ### Keywords: hplot iplot`
		2045	`>`
		2046	`> ### ** Examples`
		2047	`>`
		2048	`> (wwt <- hist(women$weight, nclass = 7, plot = FALSE))`
		2049	`$breaks`
		2050	`[1] 115 120 125 130 135 140 145 150 155 160 165`
		2051
		2052	`$counts`
		2053	`[1] 3 1 2 2 1 1 2 1 1 1`
		2054
		2055	`$density`
		2056	`[1] 0.04000000 0.01333333 0.02666667 0.02666667 0.01333333 0.01333333`
		2057	`[7] 0.02666667 0.01333333 0.01333333 0.01333333`
		2058
		2059	`$mids`
		2060	`[1] 117.5 122.5 127.5 132.5 137.5 142.5 147.5 152.5 157.5 162.5`
		2061
		2062	`$xname`
		2063	`[1] "women$weight"`
		2064
		2065	`$equidist`
		2066	`[1] TRUE`
		2067
		2068	`attr(,"class")`
		2069	`[1] "histogram"`
		2070	`> plot(wwt, labels = TRUE) # default main & xlab using wwt$xname`
		2071	`> plot(wwt, border = "dark blue", col = "light blue",`
		2072	`+ main = "Histogram of 15 women's weights", xlab = "weight [pounds]")`
		2073	`>`
		2074	`> ## Fake "lines" example, using non-default labels:`
		2075	`> w2 <- wwt; w2$counts <- w2$counts - 1`
		2076	`> lines(w2, col = "Midnight Blue", labels = ifelse(w2$counts, "> 1", "1"))`
		2077	`>`
		2078	`>`
		2079	`>`
		2080	`> cleanEx()`
		2081	`> nameEx("points")`
		2082	`> ### * points`
		2083	`>`
		2084	`> flush(stderr()); flush(stdout())`
		2085	`>`
		2086	`> ### Name: points`
		2087	`> ### Title: Add Points to a Plot`
		2088	`> ### Aliases: points points.default pch`
		2089	`> ### Keywords: aplot`
		2090	`>`
		2091	`> ### ** Examples`
		2092	`>`
		2093	`> require(stats) # for rnorm`
61169	ripley	2094	`> plot(-4:4, -4:4, type = "n") # setting up coord. system`
56353	ripley	2095	`> points(rnorm(200), rnorm(200), col = "red")`
		2096	`> points(rnorm(100)/2, rnorm(100)/2, col = "blue", cex = 1.5)`
		2097	`>`
		2098	`> op <- par(bg = "light blue")`
61169	ripley	2099	`> x <- seq(0, 2*pi, len = 51)`
56353	ripley	2100	`> ## something "between type='b' and type='o'":`
61157	ripley	2101	`> plot(x, sin(x), type = "o", pch = 21, bg = par("bg"), col = "blue", cex = .6,`
		2102	`+ main = 'plot(..., type="o", pch=21, bg=par("bg"))')`
56353	ripley	2103	`> par(op)`
		2104	`>`
		2105	`> ## Not run:`
		2106	`> ##D ## The figure was produced by calls like`
61157	ripley	2107	`> ##D png("pch.png", height = 0.7, width = 7, res = 100, units = "in")`
56353	ripley	2108	`> ##D par(mar = rep(0,4))`
61157	ripley	2109	`> ##D plot(c(-1, 26), 0:1, type = "n", axes = FALSE)`
56353	ripley	2110	`> ##D text(0:25, 0.6, 0:25, cex = 0.5)`
		2111	`> ##D points(0:25, rep(0.3, 26), pch = 0:25, bg = "grey")`
		2112	`> ## End(Not run)`
		2113	`>`
		2114	`> ##-------- Showing all the extra & some char graphics symbols ---------`
		2115	`> pchShow <-`
		2116	`+ function(extras = c("*",".", "o","O","0","+","-","\|","%","#"),`
		2117	`+ cex = 3, ## good for both .Device=="postscript" and "x11"`
		2118	`+ col = "red3", bg = "gold", coltext = "brown", cextext = 1.2,`
		2119	`+ main = paste("plot symbols : points (... pch = *, cex =",`
		2120	`+ cex,")"))`
		2121	`+ {`
		2122	`+ nex <- length(extras)`
		2123	`+ np <- 26 + nex`
		2124	`+ ipch <- 0:(np-1)`
		2125	`+ k <- floor(sqrt(np))`
		2126	`+ dd <- c(-1,1)/2`
		2127	`+ rx <- dd + range(ix <- ipch %/% k)`
		2128	`+ ry <- dd + range(iy <- 3 + (k-1)- ipch %% k)`
		2129	`+ pch <- as.list(ipch) # list with integers & strings`
		2130	`+ if(nex > 0) pch[26+ 1:nex] <- as.list(extras)`
61157	ripley	2131	`+ plot(rx, ry, type = "n", axes = FALSE, xlab = "", ylab = "", main = main)`
56353	ripley	2132	`+ abline(v = ix, h = iy, col = "lightgray", lty = "dotted")`
		2133	`+ for(i in 1:np) {`
		2134	`+ pc <- pch[[i]]`
		2135	`+ ## 'col' symbols with a 'bg'-colored interior (where available) :`
		2136	`+ points(ix[i], iy[i], pch = pc, col = col, bg = bg, cex = cex)`
		2137	`+ if(cextext > 0)`
		2138	`+ text(ix[i] - 0.3, iy[i], pc, col = coltext, cex = cextext)`
		2139	`+ }`
		2140	`+ }`
		2141	`>`
		2142	`> pchShow()`
		2143	`> pchShow(c("o","O","0"), cex = 2.5)`
		2144	`> pchShow(NULL, cex = 4, cextext = 0, main = NULL)`
		2145	`>`
		2146	`>`
		2147	`>`
		2148	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2149	`> cleanEx()`
		2150	`> nameEx("polygon")`
		2151	`> ### * polygon`
		2152	`>`
		2153	`> flush(stderr()); flush(stdout())`
		2154	`>`
		2155	`> ### Name: polygon`
		2156	`> ### Title: Polygon Drawing`
		2157	`> ### Aliases: polygon`
		2158	`> ### Keywords: aplot`
		2159	`>`
		2160	`> ### ** Examples`
		2161	`>`
61169	ripley	2162	`> x <- c(1:9, 8:1)`
		2163	`> y <- c(1, 2*(5:3), 2, -1, 17, 9, 8, 2:9)`
		2164	`> op <- par(mfcol = c(3, 1))`
		2165	`> for(xpd in c(FALSE, TRUE, NA)) {`
56353	ripley	2166	`+ plot(1:10, main = paste("xpd =", xpd))`
61157	ripley	2167	`+ box("figure", col = "pink", lwd = 3)`
61169	ripley	2168	`+ polygon(x, y, xpd = xpd, col = "orange", lty = 2, lwd = 2, border = "red")`
56353	ripley	2169	`+ }`
		2170	`> par(op)`
		2171	`>`
		2172	`> n <- 100`
		2173	`> xx <- c(0:n, n:0)`
61169	ripley	2174	`> yy <- c(c(0, cumsum(stats::rnorm(n))), rev(c(0, cumsum(stats::rnorm(n)))))`
61157	ripley	2175	`> plot (xx, yy, type = "n", xlab = "Time", ylab = "Distance")`
		2176	`> polygon(xx, yy, col = "gray", border = "red")`
56353	ripley	2177	`> title("Distance Between Brownian Motions")`
		2178	`>`
		2179	`> # Multiple polygons from NA values`
		2180	`> # and recycling of col, border, and lty`
61169	ripley	2181	`> op <- par(mfrow = c(2, 1))`
		2182	`> plot(c(1, 9), 1:2, type = "n")`
56353	ripley	2183	`> polygon(1:9, c(2,1,2,1,1,2,1,2,1),`
61157	ripley	2184	`+ col = c("red", "blue"),`
		2185	`+ border = c("green", "yellow"),`
		2186	`+ lwd = 3, lty = c("dashed", "solid"))`
61169	ripley	2187	`> plot(c(1, 9), 1:2, type = "n")`
56353	ripley	2188	`> polygon(1:9, c(2,1,2,1,NA,2,1,2,1),`
61157	ripley	2189	`+ col = c("red", "blue"),`
		2190	`+ border = c("green", "yellow"),`
		2191	`+ lwd = 3, lty = c("dashed", "solid"))`
56353	ripley	2192	`> par(op)`
		2193	`>`
		2194	`> # Line-shaded polygons`
61169	ripley	2195	`> plot(c(1, 9), 1:2, type = "n")`
56353	ripley	2196	`> polygon(1:9, c(2,1,2,1,NA,2,1,2,1),`
61157	ripley	2197	`+ density = c(10, 20), angle = c(-45, 45))`
56353	ripley	2198	`>`
		2199	`>`
		2200	`>`
		2201	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2202	`> cleanEx()`
		2203	`> nameEx("polypath")`
		2204	`> ### * polypath`
		2205	`>`
		2206	`> flush(stderr()); flush(stdout())`
		2207	`>`
		2208	`> ### Name: polypath`
		2209	`> ### Title: Path Drawing`
		2210	`> ### Aliases: polypath`
		2211	`> ### Keywords: aplot`
		2212	`>`
		2213	`> ### ** Examples`
		2214	`>`
		2215	`> plotPath <- function(x, y, col = "grey", rule = "winding") {`
		2216	`+ plot.new()`
		2217	`+ plot.window(range(x, na.rm = TRUE), range(y, na.rm = TRUE))`
		2218	`+ polypath(x, y, col = col, rule = rule)`
		2219	`+ if (!is.na(col))`
		2220	`+ mtext(paste("Rule:", rule), side = 1, line = 0)`
		2221	`+ }`
		2222	`>`
		2223	`> plotRules <- function(x, y, title) {`
		2224	`+ plotPath(x, y)`
		2225	`+ plotPath(x, y, rule = "evenodd")`
		2226	`+ mtext(title, side = 3, line = 0)`
		2227	`+ plotPath(x, y, col = NA)`
		2228	`+ }`
		2229	`>`
		2230	`> op <- par(mfrow = c(5, 3), mar = c(2, 1, 1, 1))`
		2231	`>`
		2232	`> plotRules(c(.1, .1, .9, .9, NA, .2, .2, .8, .8),`
		2233	`+ c(.1, .9, .9, .1, NA, .2, .8, .8, .2),`
		2234	`+ "Nested rectangles, both clockwise")`
		2235	`> plotRules(c(.1, .1, .9, .9, NA, .2, .8, .8, .2),`
		2236	`+ c(.1, .9, .9, .1, NA, .2, .2, .8, .8),`
		2237	`+ "Nested rectangles, outer clockwise, inner anti-clockwise")`
		2238	`> plotRules(c(.1, .1, .4, .4, NA, .6, .9, .9, .6),`
		2239	`+ c(.1, .4, .4, .1, NA, .6, .6, .9, .9),`
		2240	`+ "Disjoint rectangles")`
		2241	`> plotRules(c(.1, .1, .6, .6, NA, .4, .4, .9, .9),`
		2242	`+ c(.1, .6, .6, .1, NA, .4, .9, .9, .4),`
		2243	`+ "Overlapping rectangles, both clockwise")`
		2244	`> plotRules(c(.1, .1, .6, .6, NA, .4, .9, .9, .4),`
		2245	`+ c(.1, .6, .6, .1, NA, .4, .4, .9, .9),`
		2246	`+ "Overlapping rectangles, one clockwise, other anti-clockwise")`
		2247	`>`
		2248	`> par(op)`
		2249	`>`
		2250	`>`
		2251	`>`
		2252	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2253	`> cleanEx()`
56927	ripley	2254	`> nameEx("rasterImage")`
		2255	`> ### * rasterImage`
56353	ripley	2256	`>`
		2257	`> flush(stderr()); flush(stdout())`
		2258	`>`
		2259	`> ### Name: rasterImage`
		2260	`> ### Title: Draw One or More Raster Images`
		2261	`> ### Aliases: rasterImage`
		2262	`> ### Keywords: aplot`
		2263	`>`
		2264	`> ### ** Examples`
		2265	`>`
		2266	`> require(grDevices)`
		2267	`> ## set up the plot region:`
		2268	`> op <- par(bg = "thistle")`
61157	ripley	2269	`> plot(c(100, 250), c(300, 450), type = "n", xlab = "", ylab = "")`
		2270	`> image <- as.raster(matrix(0:1, ncol = 5, nrow = 3))`
56353	ripley	2271	`Warning in matrix(0:1, ncol = 5, nrow = 3) :`
		2272	`data length [2] is not a sub-multiple or multiple of the number of rows [3]`
61157	ripley	2273	`> rasterImage(image, 100, 300, 150, 350, interpolate = FALSE)`
56353	ripley	2274	`> rasterImage(image, 100, 400, 150, 450)`
		2275	`> rasterImage(image, 200, 300, 200 + xinch(.5), 300 + yinch(.3),`
61157	ripley	2276	`+ interpolate = FALSE)`
		2277	`> rasterImage(image, 200, 400, 250, 450, angle = 15, interpolate = FALSE)`
56353	ripley	2278	`> par(op)`
		2279	`>`
		2280	`>`
		2281	`>`
		2282	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2283	`> cleanEx()`
		2284	`> nameEx("rect")`
		2285	`> ### * rect`
		2286	`>`
		2287	`> flush(stderr()); flush(stdout())`
		2288	`>`
		2289	`> ### Name: rect`
		2290	`> ### Title: Draw One or More Rectangles`
		2291	`> ### Aliases: rect`
		2292	`> ### Keywords: aplot`
		2293	`>`
		2294	`> ### ** Examples`
		2295	`>`
		2296	`> require(grDevices)`
		2297	`> ## set up the plot region:`
		2298	`> op <- par(bg = "thistle")`
61157	ripley	2299	`> plot(c(100, 250), c(300, 450), type = "n", xlab = "", ylab = "",`
56353	ripley	2300	`+ main = "2 x 11 rectangles; 'rect(100+i,300+i, 150+i,380+i)'")`
		2301	`> i <- 4*(0:10)`
		2302	`> ## draw rectangles with bottom left (100, 300)+i`
		2303	`> ## and top right (150, 380)+i`
61169	ripley	2304	`> rect(100+i, 300+i, 150+i, 380+i, col = rainbow(11, start = 0.7, end = 0.1))`
61157	ripley	2305	`> rect(240-i, 320+i, 250-i, 410+i, col = heat.colors(11), lwd = i/5)`
56353	ripley	2306	`> ## Background alternating ( transparent / "bg" ) :`
		2307	`> j <- 10*(0:5)`
		2308	`> rect(125+j, 360+j, 141+j, 405+j/2, col = c(NA,0),`
		2309	`+ border = "gold", lwd = 2)`
		2310	`> rect(125+j, 296+j/2, 141+j, 331+j/5, col = c(NA,"midnightblue"))`
		2311	`> mtext("+ 2 x 6 rect(*, col = c(NA,0)) and col = c(NA,\"m..blue\"))")`
		2312	`>`
		2313	`> ## an example showing colouring and shading`
61157	ripley	2314	`> plot(c(100, 200), c(300, 450), type= "n", xlab = "", ylab = "")`
56353	ripley	2315	`> rect(100, 300, 125, 350) # transparent`
61157	ripley	2316	`> rect(100, 400, 125, 450, col = "green", border = "blue") # coloured`
		2317	`> rect(115, 375, 150, 425, col = par("bg"), border = "transparent")`
		2318	`> rect(150, 300, 175, 350, density = 10, border = "red")`
		2319	`> rect(150, 400, 175, 450, density = 30, col = "blue",`
		2320	`+ angle = -30, border = "transparent")`
56353	ripley	2321	`>`
61157	ripley	2322	`> legend(180, 450, legend = 1:4, fill = c(NA, "green", par("fg"), "blue"),`
		2323	`+ density = c(NA, NA, 10, 30), angle = c(NA, NA, 30, -30))`
56353	ripley	2324	`>`
		2325	`> par(op)`
		2326	`>`
		2327	`>`
		2328	`>`
		2329	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2330	`> cleanEx()`
		2331	`> nameEx("rug")`
		2332	`> ### * rug`
		2333	`>`
		2334	`> flush(stderr()); flush(stdout())`
		2335	`>`
		2336	`> ### Name: rug`
		2337	`> ### Title: Add a Rug to a Plot`
		2338	`> ### Aliases: rug`
		2339	`> ### Keywords: aplot`
		2340	`>`
		2341	`> ### ** Examples`
		2342	`>`
61169	ripley	2343	`> require(stats) # both 'density' and its default method`
56353	ripley	2344	`> with(faithful, {`
		2345	`+ plot(density(eruptions, bw = 0.15))`
		2346	`+ rug(eruptions)`
		2347	`+ rug(jitter(eruptions, amount = 0.01), side = 3, col = "light blue")`
		2348	`+ })`
		2349	`>`
		2350	`>`
		2351	`>`
		2352	`> cleanEx()`
		2353	`> nameEx("screen")`
		2354	`> ### * screen`
		2355	`>`
		2356	`> flush(stderr()); flush(stdout())`
		2357	`>`
		2358	`> ### Name: screen`
		2359	`> ### Title: Creating and Controlling Multiple Screens on a Single Device`
		2360	`> ### Aliases: screen split.screen erase.screen close.screen`
		2361	`> ### Keywords: aplot dplot device`
		2362	`>`
		2363	`> ### ** Examples`
		2364	`>`
		2365	`> if (interactive()) {`
		2366	`+ par(bg = "white") # default is likely to be transparent`
61169	ripley	2367	`+ split.screen(c(2, 1)) # split display into two screens`
		2368	`+ split.screen(c(1, 3), screen = 2) # now split the bottom half into 3`
56353	ripley	2369	`+ screen(1) # prepare screen 1 for output`
		2370	`+ plot(10:1)`
		2371	`+ screen(4) # prepare screen 4 for output`
		2372	`+ plot(10:1)`
		2373	`+ close.screen(all = TRUE) # exit split-screen mode`
		2374	`+`
61169	ripley	2375	`+ split.screen(c(2, 1)) # split display into two screens`
		2376	`+ split.screen(c(1, 2), 2) # split bottom half in two`
56353	ripley	2377	`+ plot(1:10) # screen 3 is active, draw plot`
		2378	`+ erase.screen() # forgot label, erase and redraw`
61157	ripley	2379	`+ plot(1:10, ylab = "ylab 3")`
56353	ripley	2380	`+ screen(1) # prepare screen 1 for output`
		2381	`+ plot(1:10)`
		2382	`+ screen(4) # prepare screen 4 for output`
61157	ripley	2383	`+ plot(1:10, ylab = "ylab 4")`
56353	ripley	2384	`+ screen(1, FALSE) # return to screen 1, but do not clear`
61157	ripley	2385	`+ plot(10:1, axes = FALSE, lty = 2, ylab = "") # overlay second plot`
56353	ripley	2386	`+ axis(4) # add tic marks to right-hand axis`
		2387	`+ title("Plot 1")`
		2388	`+ close.screen(all = TRUE) # exit split-screen mode`
		2389	`+ }`
		2390	`>`
		2391	`>`
		2392	`>`
		2393	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2394	`> cleanEx()`
		2395	`> nameEx("segments")`
		2396	`> ### * segments`
		2397	`>`
		2398	`> flush(stderr()); flush(stdout())`
		2399	`>`
		2400	`> ### Name: segments`
		2401	`> ### Title: Add Line Segments to a Plot`
		2402	`> ### Aliases: segments`
		2403	`> ### Keywords: aplot`
		2404	`>`
		2405	`> ### ** Examples`
		2406	`>`
		2407	`> x <- stats::runif(12); y <- stats::rnorm(12)`
61169	ripley	2408	`> i <- order(x, y); x <- x[i]; y <- y[i]`
61157	ripley	2409	`> plot(x, y, main = "arrows(.) and segments(.)")`
56353	ripley	2410	`> ## draw arrows from point to point :`
61169	ripley	2411	`> s <- seq(length(x)-1) # one shorter than data`
56353	ripley	2412	`> arrows(x[s], y[s], x[s+1], y[s+1], col= 1:3)`
		2413	`> s <- s[-length(s)]`
		2414	`> segments(x[s], y[s], x[s+2], y[s+2], col= 'pink')`
		2415	`>`
		2416	`>`
		2417	`>`
		2418	`> cleanEx()`
		2419	`> nameEx("smoothScatter")`
		2420	`> ### * smoothScatter`
		2421	`>`
		2422	`> flush(stderr()); flush(stdout())`
		2423	`>`
		2424	`> ### Name: smoothScatter`
		2425	`> ### Title: Scatterplots with Smoothed Densities Color Representation`
		2426	`> ### Aliases: smoothScatter`
		2427	`> ### Keywords: hplot`
		2428	`>`
		2429	`> ### ** Examples`
		2430	`>`
61169	ripley	2431	`> ## A largish data set`
		2432	`> n <- 10000`
		2433	`> x1 <- matrix(rnorm(n), ncol = 2)`
		2434	`> x2 <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)`
		2435	`> x <- rbind(x1, x2)`
56353	ripley	2436	`>`
61169	ripley	2437	`> oldpar <- par(mfrow = c(2, 2))`
		2438	`> smoothScatter(x, nrpoints = 0)`
56353	ripley	2439	`KernSmooth 2.23 loaded`
		2440	`Copyright M. P. Wand 1997-2009`
61169	ripley	2441	`> smoothScatter(x)`
56353	ripley	2442	`>`
61169	ripley	2443	`> ## a different color scheme:`
		2444	`> Lab.palette <- colorRampPalette(c("blue", "orange", "red"), space = "Lab")`
		2445	`> smoothScatter(x, colramp = Lab.palette)`
56353	ripley	2446	`>`
61169	ripley	2447	`> ## somewhat similar, using identical smoothing computations,`
		2448	`> ## but considerably less efficient for really large data:`
		2449	`> plot(x, col = densCols(x), pch = 20)`
56353	ripley	2450	`>`
61169	ripley	2451	`> ## use with pairs:`
		2452	`> par(mfrow = c(1, 1))`
		2453	`> y <- matrix(rnorm(40000), ncol = 4) + 3*rnorm(10000)`
		2454	`> y[, c(2,4)] <- -y[, c(2,4)]`
		2455	`> pairs(y, panel = function(...) smoothScatter(..., nrpoints = 0, add = TRUE))`
56353	ripley	2456	`>`
61169	ripley	2457	`> par(oldpar)`
56353	ripley	2458	`>`
		2459	`>`
		2460	`>`
		2461	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2462	`> cleanEx()`
		2463	`> nameEx("spineplot")`
		2464	`> ### * spineplot`
		2465	`>`
		2466	`> flush(stderr()); flush(stdout())`
		2467	`>`
		2468	`> ### Name: spineplot`
		2469	`> ### Title: Spine Plots and Spinograms`
		2470	`> ### Aliases: spineplot spineplot.default spineplot.formula`
		2471	`> ### Keywords: hplot`
		2472	`>`
		2473	`> ### ** Examples`
		2474	`>`
		2475	`> ## treatment and improvement of patients with rheumatoid arthritis`
		2476	`> treatment <- factor(rep(c(1, 2), c(43, 41)), levels = c(1, 2),`
		2477	`+ labels = c("placebo", "treated"))`
		2478	`> improved <- factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),`
		2479	`+ levels = c(1, 2, 3),`
		2480	`+ labels = c("none", "some", "marked"))`
		2481	`>`
		2482	`> ## (dependence on a categorical variable)`
		2483	`> (spineplot(improved ~ treatment))`
		2484	`improved`
		2485	`treatment none some marked`
		2486	`placebo 29 7 7`
		2487	`treated 13 7 21`
		2488	`>`
		2489	`> ## applications and admissions by department at UC Berkeley`
		2490	`> ## (two-way tables)`
		2491	`> (spineplot(margin.table(UCBAdmissions, c(3, 2)),`
		2492	`+ main = "Applications at UCB"))`
		2493	`Gender`
		2494	`Dept Male Female`
		2495	`A 825 108`
		2496	`B 560 25`
		2497	`C 325 593`
		2498	`D 417 375`
		2499	`E 191 393`
		2500	`F 373 341`
		2501	`> (spineplot(margin.table(UCBAdmissions, c(3, 1)),`
		2502	`+ main = "Admissions at UCB"))`
		2503	`Admit`
		2504	`Dept Admitted Rejected`
		2505	`A 601 332`
		2506	`B 370 215`
		2507	`C 322 596`
		2508	`D 269 523`
		2509	`E 147 437`
		2510	`F 46 668`
		2511	`>`
		2512	`> ## NASA space shuttle o-ring failures`
		2513	`> fail <- factor(c(2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1,`
		2514	`+ 1, 1, 1, 2, 1, 1, 1, 1, 1),`
		2515	`+ levels = c(1, 2), labels = c("no", "yes"))`
		2516	`> temperature <- c(53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70,`
		2517	`+ 70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81)`
		2518	`>`
		2519	`> ## (dependence on a numerical variable)`
		2520	`> (spineplot(fail ~ temperature))`
		2521	`fail`
		2522	`temperature no yes`
		2523	`[50,55] 0 1`
		2524	`(55,60] 0 2`
		2525	`(60,65] 0 1`
		2526	`(65,70] 8 2`
		2527	`(70,75] 3 1`
		2528	`(75,80] 4 0`
		2529	`(80,85] 1 0`
		2530	`> (spineplot(fail ~ temperature, breaks = 3))`
		2531	`fail`
		2532	`temperature no yes`
		2533	`[50,60] 0 3`
		2534	`(60,70] 8 3`
		2535	`(70,80] 7 1`
		2536	`(80,90] 1 0`
		2537	`> (spineplot(fail ~ temperature, breaks = quantile(temperature)))`
		2538	`fail`
		2539	`temperature no yes`
		2540	`[53,67] 4 4`
		2541	`(67,70] 4 2`
		2542	`(70,75] 3 1`
		2543	`(75,81] 5 0`
		2544	`>`
		2545	`> ## highlighting for failures`
		2546	`> spineplot(fail ~ temperature, ylevels = 2:1)`
		2547	`>`
		2548	`>`
		2549	`>`
		2550	`> cleanEx()`
		2551	`> nameEx("stars")`
		2552	`> ### * stars`
		2553	`>`
		2554	`> flush(stderr()); flush(stdout())`
		2555	`>`
		2556	`> ### Name: stars`
		2557	`> ### Title: Star (Spider/Radar) Plots and Segment Diagrams`
		2558	`> ### Aliases: stars`
		2559	`> ### Keywords: hplot multivariate`
		2560	`>`
		2561	`> ### ** Examples`
		2562	`>`
		2563	`> require(grDevices)`
		2564	`> stars(mtcars[, 1:7], key.loc = c(14, 2),`
		2565	`+ main = "Motor Trend Cars : stars(*, full = F)", full = FALSE)`
		2566	`> stars(mtcars[, 1:7], key.loc = c(14, 1.5),`
61157	ripley	2567	`+ main = "Motor Trend Cars : full stars()", flip.labels = FALSE)`
56353	ripley	2568	`>`
		2569	`> ## 'Spider' or 'Radar' plot:`
61169	ripley	2570	`> stars(mtcars[, 1:7], locations = c(0, 0), radius = FALSE,`
		2571	`+ key.loc = c(0, 0), main = "Motor Trend Cars", lty = 2)`
56353	ripley	2572	`>`
		2573	`> ## Segment Diagrams:`
		2574	`> palette(rainbow(12, s = 0.6, v = 0.75))`
		2575	`> stars(mtcars[, 1:7], len = 0.8, key.loc = c(12, 1.5),`
		2576	`+ main = "Motor Trend Cars", draw.segments = TRUE)`
		2577	`> stars(mtcars[, 1:7], len = 0.6, key.loc = c(1.5, 0),`
		2578	`+ main = "Motor Trend Cars", draw.segments = TRUE,`
61157	ripley	2579	`+ frame.plot = TRUE, nrow = 4, cex = .7)`
56353	ripley	2580	`>`
		2581	`> ## scale linearly (not affinely) to [0, 1]`
		2582	`> USJudge <- apply(USJudgeRatings, 2, function(x) x/max(x))`
		2583	`> Jnam <- row.names(USJudgeRatings)`
61169	ripley	2584	`> Snam <- abbreviate(substring(Jnam, 1, regexpr("[,.]",Jnam) - 1), 7)`
56353	ripley	2585	`> stars(USJudge, labels = Jnam, scale = FALSE,`
		2586	`+ key.loc = c(13, 1.5), main = "Judge not ...", len = 0.8)`
		2587	`> stars(USJudge, labels = Snam, scale = FALSE,`
		2588	`+ key.loc = c(13, 1.5), radius = FALSE)`
		2589	`>`
		2590	`> loc <- stars(USJudge, labels = NULL, scale = FALSE,`
		2591	`+ radius = FALSE, frame.plot = TRUE,`
		2592	`+ key.loc = c(13, 1.5), main = "Judge not ...", len = 1.2)`
		2593	`> text(loc, Snam, col = "blue", cex = 0.8, xpd = TRUE)`
		2594	`>`
		2595	`> ## 'Segments':`
		2596	`> stars(USJudge, draw.segments = TRUE, scale = FALSE, key.loc = c(13,1.5))`
		2597	`>`
		2598	`> ## 'Spider':`
61169	ripley	2599	`> stars(USJudgeRatings, locations = c(0, 0), scale = FALSE, radius = FALSE,`
		2600	`+ col.stars = 1:10, key.loc = c(0, 0), main = "US Judges rated")`
56747	maechler	2601	`> ## Same as above, but with colored lines instead of filled polygons.`
61169	ripley	2602	`> stars(USJudgeRatings, locations = c(0, 0), scale = FALSE, radius = FALSE,`
		2603	`+ col.lines = 1:10, key.loc = c(0, 0), main = "US Judges rated")`
56353	ripley	2604	`> ## 'Radar-Segments'`
61157	ripley	2605	`> stars(USJudgeRatings[1:10,], locations = 0:1, scale = FALSE,`
		2606	`+ draw.segments = TRUE, col.segments = 0, col.stars = 1:10, key.loc = 0:1,`
		2607	`+ main = "US Judges 1-10 ")`
56353	ripley	2608	`> palette("default")`
61169	ripley	2609	`> stars(cbind(1:16, 10*(16:1)), draw.segments = TRUE,`
56353	ripley	2610	`+ main = "A Joke -- do not use symbols on 2D data!")`
		2611	`>`
		2612	`>`
		2613	`>`
		2614	`> cleanEx()`
		2615	`> nameEx("stem")`
		2616	`> ### * stem`
		2617	`>`
		2618	`> flush(stderr()); flush(stdout())`
		2619	`>`
		2620	`> ### Name: stem`
		2621	`> ### Title: Stem-and-Leaf Plots`
		2622	`> ### Aliases: stem`
		2623	`> ### Keywords: univar distribution`
		2624	`>`
		2625	`> ### ** Examples`
		2626	`>`
		2627	`> stem(islands)`
		2628
		2629	`The decimal point is 3 digit(s) to the right of the \|`
		2630
		2631
		2632	`2 \| 07`
		2633	`4 \| 5`
		2634	`6 \| 8`
		2635	`8 \| 4`
		2636	`10 \| 5`
		2637	`12 \|`
		2638	`14 \|`
		2639	`16 \| 0`
		2640
		2641	`> stem(log10(islands))`
		2642
		2643	`The decimal point is at the \|`
		2644
		2645	`1 \| 1111112222233444`
		2646	`1 \| 5555556666667899999`
		2647	`2 \| 3344`
		2648	`2 \| 59`
		2649	`3 \|`
		2650	`3 \| 5678`
		2651	`4 \| 012`
		2652
		2653	`>`
		2654	`>`
		2655	`>`
		2656	`> cleanEx()`
		2657	`> nameEx("stripchart")`
		2658	`> ### * stripchart`
		2659	`>`
		2660	`> flush(stderr()); flush(stdout())`
		2661	`>`
		2662	`> ### Name: stripchart`
		2663	`> ### Title: 1-D Scatter Plots`
		2664	`> ### Aliases: stripchart stripchart.default stripchart.formula`
		2665	`> ### Keywords: hplot`
		2666	`>`
		2667	`> ### ** Examples`
		2668	`>`
		2669	`> x <- stats::rnorm(50)`
		2670	`> xr <- round(x, 1)`
		2671	`> stripchart(x) ; m <- mean(par("usr")[1:2])`
		2672	`> text(m, 1.04, "stripchart(x, \"overplot\")")`
		2673	`> stripchart(xr, method = "stack", add = TRUE, at = 1.2)`
		2674	`> text(m, 1.35, "stripchart(round(x,1), \"stack\")")`
		2675	`> stripchart(xr, method = "jitter", add = TRUE, at = 0.7)`
		2676	`> text(m, 0.85, "stripchart(round(x,1), \"jitter\")")`
		2677	`>`
		2678	`> stripchart(decrease ~ treatment,`
61431	ripley	2679	`+ main = "stripchart(OrchardSprays)",`
56353	ripley	2680	`+ vertical = TRUE, log = "y", data = OrchardSprays)`
		2681	`>`
		2682	`> stripchart(decrease ~ treatment, at = c(1:8)^2,`
61431	ripley	2683	`+ main = "stripchart(OrchardSprays)",`
56353	ripley	2684	`+ vertical = TRUE, log = "y", data = OrchardSprays)`
		2685	`>`
		2686	`>`
		2687	`>`
		2688	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2689	`> cleanEx()`
		2690	`> nameEx("strwidth")`
		2691	`> ### * strwidth`
		2692	`>`
		2693	`> flush(stderr()); flush(stdout())`
		2694	`>`
		2695	`> ### Name: strwidth`
		2696	`> ### Title: Plotting Dimensions of Character Strings and Math Expressions`
		2697	`> ### Aliases: strwidth strheight`
		2698	`> ### Keywords: dplot character`
		2699	`>`
		2700	`> ### ** Examples`
		2701	`>`
		2702	`> str.ex <- c("W","w","I",".","WwI.")`
61169	ripley	2703	`> op <- par(pty = "s"); plot(1:100, 1:100, type = "n")`
56353	ripley	2704	`> sw <- strwidth(str.ex); sw`
		2705	`[1] 3.2600930 2.4934186 0.9600698 0.9600698 7.6736512`
		2706	`> all.equal(sum(sw[1:4]), sw[5])`
		2707	`[1] TRUE`
		2708	`> #- since the last string contains the others`
		2709	`>`
61169	ripley	2710	`> sw.i <- strwidth(str.ex, "inches"); 25.4 * sw.i # width in [mm]`
56353	ripley	2711	`[1] 3.996267 3.056467 1.176867 1.176867 9.406467`
		2712	`> unique(sw / sw.i)`
		2713	`[1] 20.72093 20.72093`
		2714	`> # constant factor: 1 value`
61169	ripley	2715	`> mean(sw.i / strwidth(str.ex, "fig")) / par('fin')[1] # = 1: are the same`
56353	ripley	2716	`[1] 1`
		2717	`>`
		2718	`> ## See how letters fall in classes`
		2719	`> ## -- depending on graphics device and font!`
		2720	`> all.lett <- c(letters, LETTERS)`
61169	ripley	2721	`> shL <- strheight(all.lett, units = "inches") * 72 # 'big points'`
		2722	`> table(shL) # all have same heights ...`
56353	ripley	2723	`shL`
		2724	`8.616`
		2725	`52`
		2726	`> mean(shL)/par("cin")[2] # around 0.6`
		2727	`[1] 43.08`
		2728	`>`
61169	ripley	2729	`> (swL <- strwidth(all.lett, units = "inches") * 72) # 'big points'`
56353	ripley	2730	`[1] 6.672 6.672 6.000 6.672 6.672 3.336 6.672 6.672 2.664 2.664`
		2731	`[11] 6.000 2.664 9.996 6.672 6.672 6.672 6.672 3.996 6.000 3.336`
		2732	`[21] 6.672 6.000 8.664 6.000 6.000 6.000 8.004 8.004 8.664 8.664`
		2733	`[31] 8.004 7.332 9.336 8.664 3.336 6.000 8.004 6.672 9.996 8.664`
		2734	`[41] 9.336 8.004 9.336 8.664 8.004 7.332 8.664 8.004 11.328 8.004`
		2735	`[51] 8.004 7.332`
		2736	`> split(all.lett, factor(round(swL, 2)))`
		2737	$`2.66`
		2738	`[1] "i" "j" "l"`
		2739
		2740	$`3.34`
		2741	`[1] "f" "t" "I"`
		2742
		2743	$`4`
		2744	`[1] "r"`
		2745
		2746	$`6`
		2747	`[1] "c" "k" "s" "v" "x" "y" "z" "J"`
		2748
		2749	$`6.67`
		2750	`[1] "a" "b" "d" "e" "g" "h" "n" "o" "p" "q" "u" "L"`
		2751
		2752	$`7.33`
		2753	`[1] "F" "T" "Z"`
		2754
		2755	$`8`
		2756	`[1] "A" "B" "E" "K" "P" "S" "V" "X" "Y"`
		2757
		2758	$`8.66`
		2759	`[1] "w" "C" "D" "H" "N" "R" "U"`
		2760
		2761	$`9.34`
		2762	`[1] "G" "O" "Q"`
		2763
		2764	$`10`
		2765	`[1] "m" "M"`
		2766
		2767	$`11.33`
		2768	`[1] "W"`
		2769
		2770	`>`
		2771	`> sumex <- expression(sum(x[i], i=1,n), e^{i * pi} == -1)`
		2772	`> strwidth(sumex)`
		2773	`[1] 5.795241 11.484959`
		2774	`> strheight(sumex)`
		2775	`[1] 8.057449 3.420738`
		2776	`>`
61169	ripley	2777	`> par(op) #- reset to previous setting`
56353	ripley	2778	`>`
		2779	`>`
		2780	`>`
		2781	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		2782	`> cleanEx()`
		2783	`> nameEx("sunflowerplot")`
		2784	`> ### * sunflowerplot`
		2785	`>`
		2786	`> flush(stderr()); flush(stdout())`
		2787	`>`
		2788	`> ### Name: sunflowerplot`
		2789	`> ### Title: Produce a Sunflower Scatter Plot`
		2790	`> ### Aliases: sunflowerplot sunflowerplot.default sunflowerplot.formula`
		2791	`> ### Keywords: hplot smooth nonparametric`
		2792	`>`
		2793	`> ### ** Examples`
		2794	`>`
		2795	`> require(stats)`
		2796	`> require(grDevices)`
		2797	`>`
		2798	`> ## 'number' is computed automatically:`
		2799	`> sunflowerplot(iris[, 3:4])`
		2800	`> ## Imitating Chambers et al., p.109, closely:`
61157	ripley	2801	`> sunflowerplot(iris[, 3:4], cex = .2, cex.fact = 1, size = .035, seg.lwd = .8)`
56353	ripley	2802	`> ## or`
61157	ripley	2803	`> sunflowerplot(Petal.Width ~ Petal.Length, data = iris,`
		2804	`+ cex = .2, cex.fact = 1, size = .035, seg.lwd = .8)`
56353	ripley	2805	`>`
		2806	`>`
61169	ripley	2807	`> sunflowerplot(x = sort(2*round(rnorm(100))), y = round(rnorm(100), 0),`
56353	ripley	2808	`+ main = "Sunflower Plot of Rounded N(0,1)")`
		2809	`> ## Similarly using a "xyTable" argument:`
61169	ripley	2810	`> xyT <- xyTable(x = sort(2*round(rnorm(100))), y = round(rnorm(100), 0),`
61157	ripley	2811	`+ digits = 3)`
		2812	`> utils::str(xyT, vec.len = 20)`
56353	ripley	2813	`List of 3`
		2814	`$ x : num [1:25] -6 -4 -4 -4 -4 -2 -2 -2 -2 -2 0 0 0 0 0 0 2 2 2 2 2 4 4 4 4`
		2815	`$ y : num [1:25] 0 -2 0 1 2 -2 -1 0 1 2 -3 -2 -1 0 1 2 -2 -1 0 1 3 -1 0 1 2`
		2816	`$ number: int [1:25] 1 1 1 1 3 1 4 8 4 1 1 3 5 17 12 2 1 8 12 6 1 1 3 2 1`
		2817	`> sunflowerplot(xyT, main = "2nd Sunflower Plot of Rounded N(0,1)")`
		2818	`>`
		2819	`> ## A 'marked point process' {explicit 'number' argument}:`
61169	ripley	2820	`> sunflowerplot(rnorm(100), rnorm(100), number = rpois(n = 100, lambda = 2),`
61157	ripley	2821	`+ main = "Sunflower plot (marked point process)",`
		2822	`+ rotate = TRUE, col = "blue4")`
56353	ripley	2823	`>`
		2824	`>`
		2825	`>`
		2826	`> cleanEx()`
		2827	`> nameEx("symbols")`
		2828	`> ### * symbols`
		2829	`>`
		2830	`> flush(stderr()); flush(stdout())`
		2831	`>`
		2832	`> ### Name: symbols`
		2833	`> ### Title: Draw Symbols (Circles, Squares, Stars, Thermometers, Boxplots)`
		2834	`> ### Aliases: symbols`
		2835	`> ### Keywords: aplot hplot multivariate`
		2836	`>`
		2837	`> ### ** Examples`
		2838	`>`
		2839	`> require(stats); require(grDevices)`
		2840	`> x <- 1:10`
		2841	`> y <- sort(10*runif(10))`
		2842	`> z <- runif(10)`
		2843	`> z3 <- cbind(z, 2*runif(10), runif(10))`
		2844	`> symbols(x, y, thermometers = cbind(.5, 1, z), inches = .5, fg = 1:10)`
		2845	`> symbols(x, y, thermometers = z3, inches = FALSE)`
61169	ripley	2846	`> text(x, y, apply(format(round(z3, digits = 2)), 1, paste, collapse = ","),`
56353	ripley	2847	`+ adj = c(-.2,0), cex = .75, col = "purple", xpd = NA)`
		2848	`>`
		2849	`> ## Note that example(trees) shows more sensible plots!`
		2850	`> N <- nrow(trees)`
		2851	`> with(trees, {`
		2852	`+ ## Girth is diameter in inches`
		2853	`+ symbols(Height, Volume, circles = Girth/24, inches = FALSE,`
		2854	`+ main = "Trees' Girth") # xlab and ylab automatically`
		2855	`+ ## Colours too:`
		2856	`+ op <- palette(rainbow(N, end = 0.9))`
		2857	`+ symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N,`
		2858	`+ fg = "gray30", main = "symbols(*, circles = Girth/16, bg = 1:N)")`
		2859	`+ palette(op)`
		2860	`+ })`
		2861	`>`
		2862	`>`
		2863	`>`
		2864	`> cleanEx()`
		2865	`> nameEx("title")`
		2866	`> ### * title`
		2867	`>`
		2868	`> flush(stderr()); flush(stdout())`
		2869	`>`
		2870	`> ### Name: title`
		2871	`> ### Title: Plot Annotation`
		2872	`> ### Aliases: title`
		2873	`> ### Keywords: aplot`
		2874	`>`
		2875	`> ### ** Examples`
		2876	`>`
		2877	`> plot(cars, main = "") # here, could use main directly`
		2878	`> title(main = "Stopping Distance versus Speed")`
		2879	`>`
		2880	`> plot(cars, main = "")`
61157	ripley	2881	`> title(main = list("Stopping Distance versus Speed", cex = 1.5,`
		2882	`+ col = "red", font = 3))`
56353	ripley	2883	`>`
		2884	`> ## Specifying "..." :`
		2885	`> plot(1, col.axis = "sky blue", col.lab = "thistle")`
		2886	`> title("Main Title", sub = "sub title",`
		2887	`+ cex.main = 2, font.main= 4, col.main= "blue",`
		2888	`+ cex.sub = 0.75, font.sub = 3, col.sub = "red")`
		2889	`>`
		2890	`>`
		2891	`> x <- seq(-4, 4, len = 101)`
		2892	`> y <- cbind(sin(x), cos(x))`
		2893	`> matplot(x, y, type = "l", xaxt = "n",`
		2894	`+ main = expression(paste(plain(sin) * phi, " and ",`
		2895	`+ plain(cos) * phi)),`
		2896	`+ ylab = expression("sin" * phi, "cos" * phi), # only 1st is taken`
		2897	`+ xlab = expression(paste("Phase Angle ", phi)),`
		2898	`+ col.main = "blue")`
		2899	`> axis(1, at = c(-pi, -pi/2, 0, pi/2, pi),`
		2900	`+ labels = expression(-pi, -pi/2, 0, pi/2, pi))`
		2901	`> abline(h = 0, v = pi/2 * c(-1,1), lty = 2, lwd = .1, col = "gray70")`
		2902	`>`
		2903	`>`
		2904	`>`
		2905	`> cleanEx()`
		2906	`> nameEx("units")`
		2907	`> ### * units`
		2908	`>`
		2909	`> flush(stderr()); flush(stdout())`
		2910	`>`
		2911	`> ### Name: units`
		2912	`> ### Title: Graphical Units`
		2913	`> ### Aliases: xinch yinch xyinch`
		2914	`> ### Keywords: dplot`
		2915	`>`
		2916	`> ### ** Examples`
		2917	`>`
61169	ripley	2918	`> all(c(xinch(), yinch()) == xyinch()) # TRUE`
56353	ripley	2919	`[1] TRUE`
		2920	`> xyinch()`
		2921	`[1] 1.5000000 0.4185559`
		2922	`> xyinch #- to see that is really delta{"usr"} / "pin"`
		2923	`function (xy = 1, warn.log = TRUE)`
		2924	`{`
		2925	`if (warn.log && (par("xlog") \|\| par("ylog")))`
60841	ripley	2926	`warning("log scale: xyinch() is nonsense")`
56353	ripley	2927	`u <- par("usr")`
		2928	`xy * c(u[2L] - u[1L], u[4L] - u[3L])/par("pin")`
		2929	`}`
63202	ripley	2930	`<bytecode: 0x2cda550>`
56353	ripley	2931	`<environment: namespace:graphics>`
		2932	`>`
		2933	`> ## plot labels offset 0.12 inches to the right`
		2934	`> ## of plotted symbols in a plot`
		2935	`> with(mtcars, {`
61157	ripley	2936	`+ plot(mpg, disp, pch = 19, main = "Motor Trend Cars")`
56353	ripley	2937	`+ text(mpg + xinch(0.12), disp, row.names(mtcars),`
61169	ripley	2938	`+ adj = 0, cex = .7, col = "blue")`
56353	ripley	2939	`+ })`
		2940	`>`
		2941	`>`
		2942	`>`
		2943	`> cleanEx()`
		2944	`> nameEx("xspline")`
		2945	`> ### * xspline`
		2946	`>`
		2947	`> flush(stderr()); flush(stdout())`
		2948	`>`
		2949	`> ### Name: xspline`
		2950	`> ### Title: Draw an X-spline`
		2951	`> ### Aliases: xspline`
		2952	`> ### Keywords: aplot`
		2953	`>`
		2954	`> ### ** Examples`
		2955	`>`
		2956	`> ## based on examples in ?grid.xspline`
		2957	`>`
		2958	`> xsplineTest <- function(s, open = TRUE,`
		2959	`+ x = c(1,1,3,3)/4,`
		2960	`+ y = c(1,3,3,1)/4, ...) {`
61157	ripley	2961	`+ plot(c(0,1), c(0,1), type = "n", axes = FALSE, xlab = "", ylab = "")`
		2962	`+ points(x, y, pch = 19)`
56353	ripley	2963	`+ xspline(x, y, s, open, ...)`
		2964	`+ text(x+0.05c(-1,-1,1,1), y+0.05c(-1,1,1,-1), s)`
		2965	`+ }`
61157	ripley	2966	`> op <- par(mfrow = c(3,3), mar = rep(0,4), oma = c(0,0,2,0))`
56353	ripley	2967	`> xsplineTest(c(0, -1, -1, 0))`
		2968	`> xsplineTest(c(0, -1, 0, 0))`
		2969	`> xsplineTest(c(0, -1, 1, 0))`
		2970	`> xsplineTest(c(0, 0, -1, 0))`
		2971	`> xsplineTest(c(0, 0, 0, 0))`
		2972	`> xsplineTest(c(0, 0, 1, 0))`
		2973	`> xsplineTest(c(0, 1, -1, 0))`
		2974	`> xsplineTest(c(0, 1, 0, 0))`
		2975	`> xsplineTest(c(0, 1, 1, 0))`
61157	ripley	2976	`> title("Open X-splines", outer = TRUE)`
56353	ripley	2977	`>`
61157	ripley	2978	`> par(mfrow = c(3,3), mar = rep(0,4), oma = c(0,0,2,0))`
		2979	`> xsplineTest(c(0, -1, -1, 0), FALSE, col = "grey80")`
		2980	`> xsplineTest(c(0, -1, 0, 0), FALSE, col = "grey80")`
		2981	`> xsplineTest(c(0, -1, 1, 0), FALSE, col = "grey80")`
		2982	`> xsplineTest(c(0, 0, -1, 0), FALSE, col = "grey80")`
		2983	`> xsplineTest(c(0, 0, 0, 0), FALSE, col = "grey80")`
		2984	`> xsplineTest(c(0, 0, 1, 0), FALSE, col = "grey80")`
		2985	`> xsplineTest(c(0, 1, -1, 0), FALSE, col = "grey80")`
		2986	`> xsplineTest(c(0, 1, 0, 0), FALSE, col = "grey80")`
		2987	`> xsplineTest(c(0, 1, 1, 0), FALSE, col = "grey80")`
		2988	`> title("Closed X-splines", outer = TRUE)`
56353	ripley	2989	`>`
		2990	`> par(op)`
		2991	`>`
		2992	`> x <- sort(stats::rnorm(5))`
		2993	`> y <- sort(stats::rnorm(5))`
61157	ripley	2994	`> plot(x, y, pch = 19)`
		2995	`> res <- xspline(x, y, 1, draw = FALSE)`
56353	ripley	2996	`> lines(res)`
		2997	`> ## the end points may be very close together,`
		2998	`> ## so use last few for direction`
		2999	`> nr <- length(res$x)`
61157	ripley	3000	`> arrows(res$x[1], res$y[1], res$x[4], res$y[4], code = 1, length = 0.1)`
		3001	`> arrows(res$x[nr-3], res$y[nr-3], res$x[nr], res$y[nr], code = 2, length = 0.1)`
56353	ripley	3002	`>`
		3003	`>`
		3004	`>`
		3005	`> graphics::par(get("par.postscript", pos = 'CheckExEnv'))`
		3006	`> ### * <FOOTER>`
		3007	`> ###`
62439	ripley	3008	`> options(digits = 7L)`
61787	ripley	3009	`> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")`
63202	ripley	3010	`Time elapsed: 3.278 0.059 3.347 0 0`
56353	ripley	3011	`> grDevices::dev.off()`
		3012	`null device`
		3013	`1`
		3014	`> ###`
		3015	`> ### Local variables: ***`
		3016	`> ### mode: outline-minor ***`
		3017	`> ### outline-regexp: "\$> \$?### []+" **`
		3018	`> ### End: ***`
		3019	`> quit('no')`

The R Project SVN R

(root)/trunk/tests/Examples/graphics-Ex.Rout.save – Rev 63243