R可视化04|ggplot2图层-统计变换图层（stat layer）

科技2022-07-12 131

本文介绍ggplot2的一些统计变换方式，续前几篇。

R可视化01|ggplot2-ggplot2简介R可视化02|ggplot2-ggplot2快速绘图R可视化03|ggplot2图层-几何对象图层（geom layer）

1、误差线和误差范围（Revealing uncertainty）

ggplot2依据变量是否是连续（continuous）还是离散（discrete）、是否想展示区间中的中心值和仅仅展示区间，有四类geoms可解决Revealing uncertainty问题。

离散型变量&&区间：geom_errorbar()，geom_linerange()离散型变量&&区间&&中间值：geom_crossbar()，geom_pointrange()连续型变量&&区间：geom_ribbon()连续型变量&&区间&&中间值：geom_smooth(stat = "identity") library('gridExtra') y <- c(18, 11, 16) df <- data.frame(x = 1:3, y = y, se = c(1.2, 0.5, 1.0)) base <- ggplot(df, aes(x, y, ymin = y - se, ymax = y + se))#以下geom使用默认需要设置y值域 p2 <- base + geom_crossbar() p3 <- base + geom_pointrange() p4 <- base + geom_smooth(stat = "identity") p5 <- base + geom_errorbar() p6 <- base + geom_linerange() p7 <- base + geom_ribbon() grid.arrange(p2, p3, p4,p5,p6,p7,nrow = 3)

2、加权数据（Weighted Data）

处理整合数据（aggregated data）的时候，数据的每一行代表了多种特征【一行为多列，每列为一个特征】，这时我们需要通过某种方式把某个特征的影响考虑在内，举几个栗子可能会清晰点什么叫做加加权。

这里使用数据midwest，为一个437x28的数据集，包含28个特征。

> head(midwest) # A tibble: 6 x 28 PID county state area poptotal popdensity popwhite popblack <int> <chr> <chr> <dbl> <int> <dbl> <int> <int> 1 561 ADAMS IL 0.052 66090 1271. 63917 1702 2 562 ALEXA~ IL 0.014 10626 759 7054 3496 3 563 BOND IL 0.022 14991 681. 14477 429 4 564 BOONE IL 0.017 30806 1812. 29344 127 5 565 BROWN IL 0.018 5836 324. 5264 547 6 566 BUREAU IL 0.05 35688 714. 35157 50 # ... with 20 more variables: popamerindian <int>, popasian <int>, # popother <int>, percwhite <dbl>, percblack <dbl>, percamerindan <dbl>, # percasian <dbl>, percother <dbl>, popadults <int>, perchsd <dbl>, # percollege <dbl>, percprof <dbl>, poppovertyknown <int>, # percpovertyknown <dbl>, percbelowpoverty <dbl>, # percchildbelowpovert <dbl>, percadultpoverty <dbl>, # percelderlypoverty <dbl>, inmetro <int>, category <chr> > dim(midwest) [1] 437 28 For simple geoms like lines and points, use the size aesthetic. #for simple geoms like lines and points, use the size aesthetic # 不加权 p1 <- ggplot(midwest, aes(percwhite, percbelowpoverty)) + geom_point() # 通过population加权 p2 <- ggplot(midwest, aes(percwhite, percbelowpoverty)) + geom_point(aes(size = poptotal/1e+06)) + #use the size aesthetic scale_size_area("Population\n(millions)", breaks = c(0.5, 1, 2, 4)) grid.arrange(p1,p2,nrow = 1)

For more complicated grobs which involve some statistical transformation,we specify weights with the weight aesthetic. #For more complicated grobs which involve some statistical transformation, #we specify weights with the weight aesthetic # Unweighted p1 <- ggplot(midwest, aes(percwhite, percbelowpoverty)) + geom_point() + geom_smooth(method = lm, size = 1) #> `geom_smooth()` using formula 'y ~ x' # Weighted by population p2 <- ggplot(midwest, aes(percwhite, percbelowpoverty)) + geom_point(aes(size = poptotal / 1e6)) + geom_smooth(aes(weight = poptotal), method = lm, size = 1) + scale_size_area(guide = "none") #> `geom_smooth()` using formula 'y ~ x' p3 <- ggplot(midwest, aes(percbelowpoverty)) + geom_histogram(binwidth = 1) + ylab("Counties") p4 <- ggplot(midwest, aes(percbelowpoverty)) + geom_histogram(aes(weight = poptotal), binwidth = 1) +#weight aesthetic ylab("Population (1000s)") grid.arrange(p1,p2,p3,p4,nrow = 2)

3、展示数据分布（Displaying Distributions）

这里使用diamonds数据集，为一个53940x10的数据集，每一行为一种钻石，记录钻石属性数据，如4个C（克拉、切割、颜色、纯度，为钻石最重要质量保证）及其它6个属性。

> head(diamonds) # A tibble: 6 x 10 carat cut color clarity depth table price x y z <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 > dim(diamonds) [1] 53940 10

histogram及density展示一维连续数据分布

#直方图 p1 <- ggplot(diamonds, aes(depth)) + geom_histogram() p2 <- ggplot(diamonds, aes(depth)) + geom_histogram(binwidth = 0.1) + xlim(55, 70)#设置x轴显示范围，集聚显示某部分数据 #频率多边形图 p3 <- ggplot(diamonds, aes(depth)) + geom_freqpoly(aes(colour = cut), binwidth = 0.1, na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none") # 隐藏图例 #条件密度图 p4 <- ggplot(diamonds, aes(depth)) + geom_histogram(aes(fill = cut), binwidth = 0.1, position = "fill", na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none") #核密度图 p5 <- ggplot(diamonds, aes(depth)) + geom_density(na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none") #分组和密度图 p6 <- ggplot(diamonds, aes(depth, fill = cut, colour = cut)) + geom_density(alpha = 0.2, na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none") grid.arrange(p1,p2,p3,p4,p5,p6,nrow = 3)

boxplot和violin展示多个连续&&离散变量的分布

p1 <- ggplot(diamonds, aes(clarity, depth)) + geom_boxplot() p2 <- ggplot(diamonds, aes(carat, depth)) + geom_boxplot(aes(group = cut_width(carat, 0.1))) + #cut_width设置离散区间 xlim(NA, 2.05) p3 <- ggplot(diamonds, aes(clarity, depth)) + geom_violin() p4 <- ggplot(diamonds, aes(carat, depth)) + geom_violin(aes(group = cut_width(carat, 0.1))) + xlim(NA, 2.05) #> Warning: Removed 997 rows containing non-finite values (stat_ydensity). grid.arrange(p1,p2,p3,p4,nrow = 2)

三维数据分布

options(repr.plot.width = 8, repr.plot.height = 3, repr.plot.res = 250) # Bubble plots work better with fewer observations small <- faithfuld[seq(1, nrow(faithfuld), by = 10), ] p1 <- ggplot(small, aes(eruptions, waiting)) + geom_point(aes(size = density), alpha = 1/3) + scale_size_area() p2 <- ggplot(faithfuld, aes(eruptions, waiting)) + geom_contour(aes(z = density, colour = ..level..))#.. notation refers to a variable computed internally p3 <- ggplot(faithfuld, aes(eruptions, waiting)) + geom_raster(aes(fill = density)) grid.arrange(p1,p2,p3,nrow = 1)

4、解决散点图遮盖问题（overplotting）

options(repr.plot.width = 7, repr.plot.height = 8, repr.plot.res = 250) df <- data.frame(x = rnorm(2000), y = rnorm(2000)) norm <- ggplot(df, aes(x, y)) + xlab(NULL) + ylab(NULL) #使用小点和空点 p1 <- norm + geom_point() p2 <- norm + geom_point(shape = 1) # Hollow circles p3 <- norm + geom_point(shape = ".") # Pixel sized #使用透明度 p4 <- norm + geom_point(alpha = 1 / 3) p5 <- norm + geom_point(alpha = 1 / 5) p6 <- norm + geom_point(alpha = 1 / 10) #添加随机扰动值（离散变量） p7 <- norm + geom_bin2d() p8 <- norm + geom_bin2d(bins = 10) p9 <- norm + geom_hex() p10 <- norm + geom_hex(bins = 10) grid.arrange(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,nrow = 4)

参考资料：

https://ggplot2-book.org/statistical-summaries.html

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