ggplot2代碼文檔.docVIP

附錄代碼library(ggplot2)library(gtable)library(grid) # 需要 editGrob() 函數(shù)p - qplot(wt, mpg, data = mtcars, colour = cyl, main = Title text)p# 修改圖形元件：圖形題目字體改為斜體紅色g - ggplotGrob(p)idx - which(g$layout$name = title)g$grobsidx - editGrob(g$grobsidx, gp = gpar(fontface = italic, col = red)# 重新繪制grid.draw(g)第一章代碼：第二章代碼：library(ggplot2)# 圖2.1 無代碼# 章節(jié)2.2set.seed(1410) # 讓樣本可重復dsmall - diamondssample(nrow(diamonds), 100), # 章節(jié)2.3qplot(carat, price, data = diamonds)# 章節(jié)2.3qplot(log(carat), log(price), data = diamonds)# 章節(jié)2.3qplot(carat, x * y * z, data = diamonds)# 圖2.2 將 color 變量映射到點的顏色 (左)，cut 變量映射到點的形狀 (右)qplot(carat, price, data = dsmall, colour = color)qplot(carat, price, data = dsmall, shape = cut)# 圖2.3 將 alpha 值從 1/10(左) 變動到 1/100(中) 再到# 1/200(右)，來看大部分的點在哪里 進行重疊。qplot(carat, price, data = diamonds, alpha = I(1/10)qplot(carat, price, data = diamonds, alpha = I(1/100)qplot(carat, price, data = diamonds, alpha = I(1/200)# 圖2.4# 重量與價格的散點圖中加入了平滑曲線。左圖為dsmall數(shù)據(jù)集，右圖為完整數(shù)據(jù)集。qplot(carat, price, data = dsmall, geom = c(point, smooth)qplot(carat, price, data = diamonds, geom = c(point, smooth)# 圖2.5 span 參數(shù)的作用。左圖是 span=0.2，右圖是 span=1。qplot(carat, price, data = dsmall, geom = c(point, smooth), span = 0.2)qplot(carat, price, data = dsmall, geom = c(point, smooth), span = 1)# 圖2.6 在運用廣義可加模型作為平滑器時 formula 參數(shù)的作用。左圖是# formula=ys( x)，右圖是 formula=ys(x,bs=cs)。library(mgcv)qplot(carat, price, data = dsmall, geom = c(point, smooth), method = gam, formula = y s(x)qplot(carat, price, data = dsmall, geom = c(point, smooth), method = gam, formula = y s(x, bs = cs)# 圖2.7 在運用線性模型作為平滑器時 formula 參數(shù)的作用。左圖是 formula =# y x 的默認值， 右圖是 formula = y ns(x, 5)。library(splines)qplot(carat, price, data = dsmall, geom = c(point, smooth), method = lm)qplot(carat, price, data = dsmall, geom = c(point, smooth), method = lm, formula = y ns(x, 5)# 圖2.8 (書中無代碼)# 利用擾動點圖(左)和箱線圖(右)來考察以顏色為條件的每克拉價格的分布。# 隨著顏色的改變(從左到右)，每克拉價格的跨度逐漸減小，但分布的中位數(shù)沒有明顯的變化。qplot(color, price/carat, data = diamonds, geom = jitter)qplot(color, price/carat, data = diamonds, geom = boxplot)# 圖2.9 改變 alpha 的取值，從左到右分別為 1/5，1/50 和# 1/200。隨著不透明度的降低，# 我們可以看出數(shù)據(jù)集中的地方。然而，箱線圖依然是一個更好的選擇。qplot(color, price/carat, data = diamonds, geom = jitter, alpha = I(1/5)qplot(color, price/carat, data = diamonds, geom = jitter, alpha = I(1/50)qplot(color, price/carat, data = diamonds, geom = jitter, alpha = I(1/200)# 圖2.10 展示鉆石重量的分布。左圖使用的是 geom=histogram 右圖使用的是# geom= density。qplot(carat, data = diamonds, geom = histogram)qplot(carat, data = diamonds, geom = density)# 圖2.11 變動直方圖的組距可以顯示出有意思的模式。從左到右，組距分別為# 1，0.1 和 0.01。只有重量在 0 到 3 克拉之間的鉆石顯示在圖中。qplot(carat, data = diamonds, geom = histogram, binwidth = 1, xlim = c(0, 3)qplot(carat, data = diamonds, geom = histogram, binwidth = 0.1, xlim = c(0, 3)qplot(carat, data = diamonds, geom = histogram, binwidth = 0.01, xlim = c(0, 3)# 圖2.12# 當一個分類變量被映射到某個圖形屬性上，幾何對象會自動按這個變量進行拆分。# 左圖是重疊的密度曲線圖，右圖是堆疊起來的直方圖。qplot(carat, data = diamonds, geom = density, colour = color)qplot(carat, data = diamonds, geom = histogram, fill = color)# 圖2.13 鉆石顏色的條形圖。左圖顯示的是分組的計數(shù)，右圖是按 weight=carat# 進行加 權(quán)，展示了每種顏色的鉆石的總重量。qplot(color, data = diamonds, geom = bar)qplot(color, data = diamonds, geom = bar, weight = carat) + scale_y_continuous(carat)# 圖2.14# 衡量失業(yè)程度的兩張時序圖。左圖是失業(yè)人口的比例，右圖是失業(yè)星期數(shù)的中位# 數(shù)。圖形是用 geom=line 進行繪制的。qplot(date, unemploy/pop, data = economics, geom = line)qplot(date, uempmed, data = economics, geom = line)# 圖2.15# 展示失業(yè)率和失業(yè)時間長度之間關系的路徑圖。左圖是重疊在一起的的散點圖和路# 徑圖，右圖只有路徑圖，其中年份用顏色進行了展示。year - function(x) as.POSIXlt(x)$year + 1900qplot(unemploy/pop, uempmed, data = economics, geom = c(point, path)qplot(unemploy/pop, uempmed, data = economics, geom = path, colour = year(date)# 圖2.16# 展示以顏色為條件的重量的直方圖。左圖展示的是頻數(shù)，右圖展示的是頻率。頻率# 圖可以使得比較不同組的分布時不會受該組樣本量大小的影響。高質(zhì)量的鉆石# (顏色 D) 在小# 尺寸上的分布是偏斜的，而隨著質(zhì)量的下降，重量的分布會變得越來越平坦。qplot(carat, data = diamonds, facets = color ., geom = histogram, binwidth = 0.1, xlim = c(0, 3)qplot(carat, .density., data = diamonds, facets = color ., geom = histogram, binwidth = 0.1, xlim = c(0, 3)# 章節(jié)2.7qplot(carat, price, data = dsmall, xlab = Price ($), ylab = Weight (carats), main = Price-weight relationship)# 章節(jié)2.7qplot(carat, price/carat, data = dsmall, ylab = expression(frac(price, carat), xlab = Weight (carats), main = Small diamonds, xlim = c(0.2, 1)# 章節(jié)2.7qplot(carat, price, data = dsmall, log = xy)第三章代碼：library(ggplot2)# 圖3.1 發(fā)動機排量(以升為單位 displ)對高速公路耗油量(英里每加侖# hwy)散點圖。點# 根據(jù)汽缸數(shù)目著色。該圖可以發(fā)現(xiàn)影響燃油經(jīng)濟性最重要的因素：發(fā)動機排量大# 小。qplot(displ, hwy, data = mpg, colour = factor(cyl)# 圖3.2 ：無代碼# 圖3.3 更復雜的圖形一般沒有特定的名稱。這幅圖在圖3.1的基礎上對每個# 組添加了回歸線。這個圖應該叫什么名字呢？qplot(displ, hwy, data = mpg, colour = factor(cyl) + geom_smooth(data = subset(mpg, cyl != 5), method = lm)# 圖3.6 一個含有分面和多個圖層的復雜圖形qplot(displ, hwy, data = mpg, facets = . year) + geom_smooth()# 圖3.8# 四種不同標度的圖例。從左到右依次是:連續(xù)型變量映射到大小和顏色，離散型變# 量映射到形狀和顏色。x - 1:10y - factor(letters1:5)qplot(x, x, size = x)qplot(x, x, 1:10, colour = x)qplot(y, y, 1:10, shape = y)qplot(y, y, 1:10, colour = y)# 圖3.9# 三種不同坐標系的坐標軸和網(wǎng)格線:笛卡爾(Cartesian)、半對數(shù)(semi-log)和極# 坐標系(polar)。極坐標系展示了非笛卡爾坐標系的缺點:很難畫好坐標軸。x1 - c(1, 10)y1 - c(1, 5)p - qplot(x1, y1, geom = blank, xlab = NULL, ylab = NULL) + theme_bw()pp + coord_trans(y = log10)p + coord_polar()p - qplot(displ, hwy, data = mpg, colour = factor(cyl)summary(p)# 保存圖形對象save(p, file = plot.rdata)# 讀入圖形對象load(plot.rdata)# 將圖片保存成png格式ggsave(plot.png, width = 5, height = 5) 第四章代碼library(ggplot2)# 通過ggplot創(chuàng)建圖形對象p - ggplot(diamonds, aes(carat, price, colour = cut)# 添加“點”幾何對象p - p + layer(geom = point)# 例：手動創(chuàng)建圖形對象并添加圖層p - ggplot(diamonds, aes(x = carat)p - p + layer(geom = bar, geom_params = list(fill = steelblue), stat = bin, stat_params = list(binwidth = 2)p# 應用“快捷函數(shù)”，得到與上例相同的圖形p + geom_histogram(binwidth = 2, fill = steelblue)# 在用ggplot創(chuàng)建的圖形對象上添加圖層ggplot(msleep, aes(sleep_rem/sleep_total, awake) + geom_point()# 等價于qplot(sleep_rem/sleep_total, awake, data = msleep)# 也可以給qplot添加圖層qplot(sleep_rem/sleep_total, awake, data = msleep) + geom_smooth()# 等價于qplot(sleep_rem/sleep_total, awake, data = msleep, geom = c(point, smooth)# 或ggplot(msleep, aes(sleep_rem/sleep_total, awake) + geom_point() + geom_smooth()# 例：summary給出圖形對象的默認設置和每個圖層的信息p - ggplot(msleep, aes(sleep_rem/sleep_total, awake)summary(p)p - p + geom_point()summary(p)# 例：用不同的數(shù)據(jù)初始化后添加相同的圖層library(scales)bestfit - geom_smooth(method = lm, se = F, colour = alpha(steelblue, 0.5), size = 2)qplot(sleep_rem, sleep_total, data = msleep) + bestfitqplot(awake, brainwt, data = msleep, log = y) + bestfitqplot(bodywt, brainwt, data = msleep, log = xy) + bestfit# 用%*%添加新的數(shù)據(jù)集來代替原來的數(shù)據(jù)集p - ggplot(mtcars, aes(mpg, wt, colour = cyl) + geom_point()pmtcars - transform(mtcars, mpg = mpg2)p %+% mtcars# aes函數(shù)的參數(shù)aes(x = weight, y = height, colour = age)# 也可以使用變量的函數(shù)值作為參數(shù)aes(weight, height, colour = sqrt(age)p - ggplot(mtcars)summary(p)p - p + aes(wt, hp)summary(p)# 使用默認的參數(shù)映射來添加圖層p - ggplot(mtcars, aes(x = mpg, y = wt)p + geom_point()# 圖4.1 修改圖形屬性。用factor(cyl)修改顏色(左)，用disp修改y坐標(右)。p + geom_point(aes(colour = factor(cyl)p + geom_point(aes(y = disp)p - ggplot(mtcars, aes(mpg, wt)p + geom_point(colour = darkblue)# 注意這里將顏色映射到darkblue與上面將顏色設定給darkblue的區(qū)別p + geom_point(aes(colour = darkblue)# The difference between (left) setting colour to codedarkblue and# (right) mapping colour to codedarkblue. When codedarkblue# is mapped to colour, it is treated as a regular value and scaled with# the default colour scale. This results in pinkish points and a legend.# 圖4.2# 將顏色設定為darkblue(左)與將顏色映射到darkblue(右)的區(qū)別。當顏色映# 射到darkblue時，darkblue將被看作一個普通的字符串，使用默認的顏色標# 度進行標度轉(zhuǎn)換，結(jié)果得到了粉紅色的點和圖例。qplot(mpg, wt, data = mtcars, colour = I(darkblue)qplot(mpg, wt, data = mtcars, colour = darkblue)# 圖4.3 正確分組時(分組變量group =# Subject)每個個體的折線圖(左)。錯誤的分組時連# 接所有觀測點的折線圖(右)。此處省略了分組圖形屬性，效果等同于group =# 1。data(Oxboys, package = nlme)# 左圖的代碼p - ggplot(Oxboys, aes(age, height, group = Subject) + geom_line()# 或qplot(age, height, data = Oxboys, group = Subject, geom = line)# 右圖的代碼qplot(age, height, data = Oxboys, geom = line)# 圖4.4# 給Oxboys數(shù)據(jù)添加光滑曲線。左圖用了和折線圖同樣的分組變量，得到了每個男# 孩的擬合直線。右圖在平滑層里用了aes(group = 1)，得到了所有男孩的擬合直# 線。 左圖p + geom_smooth(aes(group = Subject), method = lm, se = F)# 或qplot(age, height, data = Oxboys, group = Subject, geom = line) + geom_smooth(method = lm, se = F)# 右圖p + geom_smooth(aes(group = 1), method = lm, size = 2, se = F)# 或qplot(age, height, data = Oxboys, group = Subject, geom = line) + geom_smooth(aes(group = 1), method = lm, size = 2, se = F)# 圖4.5# 如果想用箱線圖來查看每個時期的身高分布，默認的分組是正確的(左圖)。如果# 想用textttgeom_line()添加每個男孩的軌跡，就需要在新圖層里設定# aes(group = Subject)(右圖)。 左圖qplot(Occasion, height, data = Oxboys, geom = boxplot)# 右圖qplot(Occasion, height, data = Oxboys, geom = boxplot) + geom_line(aes(group = Subject), colour = #3366FF)# 或boysbox - ggplot(Oxboys, aes(Occasion, height) + geom_boxplot()boysbox + geom_line(aes(group = Subject), colour = #3366FF)# 圖4.6# 對于線條和路徑，線段的圖形屬性是由起始點的圖形屬性決定的。如果顏色是離# 散的(左圖)，在相鄰的顏色間插入其他顏色是沒有任何意義的。如果顏色是連續(xù)# 的(右圖)，可以在相鄰的顏色間進行插補，但默認條件下R不會這樣做。df - data.frame(x = 1:3, y = 1:3, colour = c(1, 3, 5)qplot(x, y, data = df, colour = factor(colour), size = I(5) + geom_line(aes(group = 1), size = 2)qplot(x, y, data = df, colour = colour, size = I(5) + geom_line(size = 2)# 用線性插值法做顏色漸變線條xgrid - with(df, seq(min(x), max(x), length = 50)interp - data.frame(x = xgrid, y = approx(df$x, df$y, xout = xgrid)$y, colour = approx(df$x, df$colour, xout = xgrid)$y)qplot(x, y, data = df, colour = colour, size = I(5) + geom_line(data = interp, size = 2)# 圖4.7 一個條形圖(左)按組分解后得到的疊加條形圖(右)，兩者輪廓相同。qplot(color, data = diamonds)qplot(color, data = diamonds, fill = cut)# 例：生成變量ggplot(diamonds, aes(carat) + geom_histogram(aes(y = .density.), binwidth = 0.1)# 或qplot(carat, .density., data = diamonds, geom = histogram, binwidth = 0.1)# 圖4.8 應用于條形圖的三種位置調(diào)整。從左到右依次是:堆疊(stacking)，填充# (filling)和并列(dodging)dplot - ggplot(diamonds, aes(clarity, fill = cut)dplot + geom_bar(position = stack)dplot + geom_bar(position = fill)dplot + geom_bar(position = dodge)# 圖4.9 同一調(diào)整(identity# adjustment)不適用于條形圖(左)，因為后畫的條形會擋住先# 畫的條形。但它適用于線型圖(右)，因為線條不存在相互遮掩的問題。dplot + geom_bar(position = identity)qplot(clarity, data = diamonds, geom = line, colour = cut, stat = bin, group = cut)# 圖4.10 直方圖的三種變體。頻率多邊形(frequency# polygon)(左)；散點圖，點的大小和# 高度都映射給了頻率(中)；熱圖(heatmap)用顏色來表示頻率。d - ggplot(diamonds, aes(carat) + xlim(0, 3)d + stat_bin(aes(ymax = .count.), binwidth = 0.1, geom = area)d + stat_bin(aes(size = .density.), binwidth = 0.1, geom = point, position = identity)d + stat_bin(aes(y = 1, fill = .count.), binwidth = 0.1, geom = tile, position = identity)# 例：nlme包的Oxboys數(shù)據(jù)集require(nlme, quiet = TRUE, warn.conflicts = FALSE)model - lme(height age, data = Oxboys, random = 1 + age | Subject)oplot - ggplot(Oxboys, aes(age, height, group = Subject) + geom_line()age_grid - seq(-1, 1, length = 10)subjects - unique(Oxboys$Subject)preds - expand.grid(age = age_grid, Subject = subjects)preds$height - predict(model, preds)oplot + geom_line(data = preds, colour = #3366FF, size = 0.4)Oxboys$fitted - predict(model)Oxboys$resid - with(Oxboys, fitted - height)oplot %+% Oxboys + aes(y = resid) + geom_smooth(aes(group = 1)model2 - update(model, height age + I(age2)Oxboys$fitted2 - predict(model2)Oxboys$resid2 - with(Oxboys, fitted2 - height)oplot %+% Oxboys + aes(y = resid2) + geom_smooth(aes(group = 1) 第五章代碼library(effects)library(ggplot2)# 圖5.1# 使用不同的基本幾何對象繪制相同數(shù)據(jù)的效果。從左上到右下的圖形名稱分別為：散# 點圖、條形圖、線圖、面積圖、路徑圖、含標簽的散點圖、色深圖/水平圖和多邊形圖。注意# 觀察條形圖、面積圖和瓦片圖的坐標軸范圍：這三種幾何對象占據(jù)了數(shù)據(jù)本身范圍以外的空# 間，于是坐標軸被自動拉伸了。df - data.frame(x = c(3, 1, 5),y = c(2, 4, 6),label = c(a,b,c)p - ggplot(df, aes(x, y) + xlab(NULL) + ylab(NULL)p + geom_point() + labs(title = geom_point)p + geom_bar(stat=identity) +labs(title = geom_bar(stat=identity)p + geom_line() + labs(title = geom_line)p + geom_area() + labs(title = geom_area)p + geom_path() + labs(title = geom_path)p + geom_text(aes(label = label) + labs(title = geom_text)p + geom_tile() + labs(title = geom_tile)p + geom_polygon() + labs(title = geom_polygon)# 圖5.2# 永遠不要指望依靠默認的參數(shù)就能對某個具體的分布獲得一個表現(xiàn)力強的圖形# (左圖)。(右圖) 對 x 軸進行了放大，xlim = c(55,# 70)，并選取了一個更小的組距寬度， binwidth =# 0.1，較左圖揭示出了更多細節(jié)。我們可以發(fā)現(xiàn)這個分布是輕度右偏的。同時別# 忘了在標題中寫上重要參數(shù) (如組距寬度) 的信息。qplot(depth, data = diamonds, geom = histogram)qplot(depth, data = diamonds, geom = histogram, xlim = c(55, 70), binwidth = 0.1)# 圖5.3# 鉆石數(shù)據(jù)切割和深度分布的三種視圖。從上至下分別是分面直方圖、條件密度圖和頻# 率多邊形圖。它們都顯示了出一個有趣的模式：隨著鉆石質(zhì)量的提高，分布逐漸向左偏移且# 愈發(fā)對稱。depth_dist - ggplot(diamonds, aes(depth) + xlim(58, 68)depth_dist + geom_histogram(aes(y = .density.), binwidth = 0.1) + facet_grid(cut .)depth_dist + geom_histogram(aes(fill = cut), binwidth = 0.1, position = fill)depth_dist + geom_freqpoly(aes(y = .density., colour = cut), binwidth = 0.1)# 圖5.4 箱線圖可以用于觀察針對一個類別型變量 (如 cut) 取條件時# (左圖)，或連續(xù)型變量 (如 carat) 取條件時# (右圖)，連續(xù)型變量的分布情況。對于連續(xù)型變量，必須設置 group 圖# 形屬性以得到多個箱線圖。此處使用了group = round_any(carat, 0.1, floor)# 來獲得針 對變量carat 以 0.1 個單位為大小分箱后的箱線圖。library(plyr)qplot(cut, depth, data = diamonds, geom = boxplot)qplot(carat, depth, data = diamonds, geom = boxplot, group = round_any(carat, 0.1, floor), xlim = c(0, 3)# 圖5.5 幾何對象 jitter# 可在二維分布中有一個離散型變量時繪制出一個較為粗略的圖# 形?？傮w來說，這種數(shù)據(jù)打散的處理對小數(shù)據(jù)集更有效。上圖展示了 mpg# 數(shù)據(jù)集中離散型變 量 class 和連續(xù)型變量 city，下圖則將連續(xù)型變量 city# 替換為離散型變量 drv。qplot(class, cty, data = mpg, geom = jitter)qplot(class, drv, data = mpg, geom = jitter)# 圖5.6# 密度圖實際上就是直方圖的平滑化版本。它的理論性質(zhì)比較理想，但難以由圖回溯到# 數(shù)據(jù)本身。左圖為變量 depth 的密度圖。右圖為按照變量 cut# 的不同取值上色的版本。qplot(depth, data = diamonds, geom = density, xlim = c(54, 70)qplot(depth, data = diamonds, geom = density, xlim = c(54, 70), fill = cut, alpha = I(0.2)# 圖5.7# 修改使用的符號可以幫助我們處理輕微到中等程度的過度繪制問題。從左至右分別# 為：默認的 shape、shape = 1(中空的點)，以及 shape= .(像素大小的點)。df - data.frame(x = rnorm(2000), y = rnorm(2000)norm - ggplot(df, aes(x, y)norm + geom_point()norm + geom_point(shape = 1)norm + geom_point(shape = .) # 點的大小為像素級# 圖5.8 以從一個二元正態(tài)數(shù)據(jù)中抽樣所得的數(shù)據(jù)為例，使用 alpha# 混合來減輕過度繪制問題。alpha 值從左至右分別為：1/3, 1/5, 1/10。norm + geom_point(colour = black, alpha = 1/3)norm + geom_point(colour = black, alpha = 1/5)norm + geom_point(colour = black, alpha = 1/10)# 圖5.9 diamond 數(shù)據(jù)中的變量table 和變量 depth# 組成的圖形，展示了如何使用數(shù)據(jù)打 散和 alpha# 混合來減輕離散型數(shù)據(jù)中的過度繪制問題。從左至右為：不加任何處理的點，使用# 默認擾動參數(shù)打散后的點，橫向擾動參數(shù)為 0:5 (橫軸單位距離的一半)# 時打散后的點，alpha 值 1/10，alpha 值 1/50，alpha 值 1/200。td - ggplot(diamonds, aes(table, depth) + xlim(50, 70) + ylim(50, 70)td + geom_point()td + geom_jitter()jit - position_jitter(width = 0.5)td + geom_jitter(position = jit)td + geom_jitter(position = jit, colour = black, alpha = 1/10)td + geom_jitter(position = jit, colour = black, alpha = 1/50)td + geom_jitter(position = jit, colour = black, alpha = 1/200)# 圖5.10# 第一行使用正方形顯示分箱，第二行使用六邊形顯示。左欄使用默認分箱參數(shù)，中# 欄使用參數(shù)bins = 10，右欄使用參數(shù)binwidth = c(0.02,# 200)。為了節(jié)約空間，均略去 了圖例。d - ggplot(diamonds, aes(carat, price) + xlim(1, 3) + theme(legend.position = none)d + stat_bin2d()d + stat_bin2d(bins = 10)d + stat_bin2d(binwidth = c(0.02, 200)d + stat_binhex()d + stat_binhex(bins = 10)d + stat_binhex(binwidth = c(0.02, 200)# 圖5.11# 使用密度估計對點的密度建模并進行可視化。上圖為基于點和等值線的密度展示，# 下圖為基于色深的密度展示。d - ggplot(diamonds, aes(carat, price) + xlim(1, 3) + theme(legend.position = none)d + geom_point() + geom_density2d()d + stat_density2d(geom = point, aes(size = .density.), contour = F) + scale_size_area()d + stat_density2d(geom = tile, aes(fill = .density.), contour = F)last_plot() + scale_fill_gradient(limits = c(1e-05, 8e-04)# 圖5.12 函數(shù) borders() 的使用實例。左圖展示了美國 (2006 年 1 月)# 五十萬人口以上的城 市，右圖為德克薩斯州的城市區(qū)劃。library(maps)data(us.cities)big_cities 5e+05)qplot(long, lat, data = big_cities) + borders(state, size = 0.5)tx_cities - subset(us.cities, country.etc = TX)ggplot(tx_cities, aes(long, lat) + borders(county, texas, colour = grey70) + geom_point(colour = black, alpha = 0.5)# 圖5.13# 左側(cè)的等值域圖展示了各州人身傷害案件的數(shù)量，右側(cè)的等值域圖展示了人身傷害# 和謀殺類案件的比率。library(maps)states - map_data(state)arrests - USArrestsnames(arrests) - tolower(names(arrests)arrests$region - tolower(rownames(USArrests)choro - merge(states, arrests, by = region)# 由于繪制多邊形時涉及順序問題 且merge破壞了原始排序 故將行重新排序choro - choroorder(choro$order), qplot(long, lat, data = choro, group = group, fill = assault, geom = polygon)qplot(long, lat, data = choro, group = group, fill = assault/murder, geom = polygon)# 章節(jié)5.7library(plyr) # ddply()在新版本中已被剝離并整合到plyr包中，這里先載入該包ia - map_data(