接着上次继续说说。
1. geom_hex 、stat_bin_hex——二进制计数器的六边形热图
2. geom_freqpoly 、geom_histogram 、stat_bin——直方图和频率多边形
3. geom_jitter——激动点数
4. geom_crossbar 、geom_errorbar 、geom_linerange 、geom_pointrange——垂直间隔：线，横杠和错误栏
5. geom_map——参考地图中的多边形
###1. geom_hex 、stat_bin_hex——二进制计数器的六边形热图
将平面划分为正六边形，计算每个六边形中的个案数，然后（默认情况下）将案例数映射到六边形填充。 六角形箱子避免了有时由geom_bin2d非常规则的对齐产生的视觉伪影。
函数格式：
geom_hex(mapping = NULL, data = NULL, stat = "binhex", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) stat_bin_hex(mapping = NULL, data = NULL, geom = "hex", position = "identity", ..., bins = 30, binwidth = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) library(ggplot2) library(easyGgplot2) d <- ggplot(diamonds, aes(carat, price)) plot1 <- d + geom_hex(col = "white",fill = "green",alpha = 0.8) + guides(fill=FALSE)#去掉图例 plot2 <- d + geom_hex(bins=10,col = "white",fill = "orange",alpha = 0.7) + guides(fill=FALSE) plot3 <- d + geom_hex(binwidth = c(1, 1000),col = "red",fill = "gray",alpha = 0.8) + guides(fill=FALSE) plot4 <- d + geom_hex(binwidth = c(.1, 500),col = "red",fill = "gray",alpha = 0.8) + guides(fill=FALSE) ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2)###2. geom_freqpoly 、geom_histogram 、stat_bin——直方图和频率多边形
通过将x轴划分成箱体并计算每个仓中的观测数量，可视化单个连续变量的分布。 直方图（geom_histogram）用条显示计数; 频率多边形（geom_freqpoly），用线显示计数。 当您想比较分类变量级别的分布时，频率多边形更合适。 stat_bin仅适用于连续x数据。 如果你的x数据是离散的，你可能想使用stat_count。
函数格式：
geom_freqpoly(mapping = NULL, data = NULL, stat = "bin", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_histogram(mapping = NULL, data = NULL, stat = "bin", position = "stack", ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) stat_bin(mapping = NULL, data = NULL, geom = "bar", position = "stack", ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL, breaks = NULL, closed = c("right", "left"), pad = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) library(ggplot2) library(easyGgplot2) plot1 <- ggplot(diamonds, aes(carat)) + geom_histogram(fill = "pink",alpha = 0.7) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. plot2 <- ggplot(diamonds, aes(carat)) + geom_histogram(fill = "orange",alpha = 0.7,binwidth = 0.01) plot3 <- ggplot(diamonds, aes(carat)) + geom_histogram(fill = "green",alpha = 0.7,bins = 50) # Rather than stacking histograms, it's easier to compare frequency # polygons plot4 <- ggplot(diamonds, aes(price, fill = cut)) + geom_histogram(binwidth = 500) ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2) ggplot(diamonds, aes(price, colour = cut)) + geom_freqpoly(binwidth = 500) ggplot(diamonds, aes(price, ..density.., colour = cut)) + geom_freqpoly(binwidth = 500)###3. geom_jitter
jitter geom是geom_point（position =“jitter”）的方便快捷方式。 它为每个点的位置增加了少量的随机变化，并且是处理由较小数据集中的离散性引起的叠加的有用方式。
函数格式：
geom_jitter(mapping = NULL, data = NULL, stat = "identity", position = "jitter", ..., width = NULL, height = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) p <- ggplot(mpg, aes(cyl, hwy)) plot1 <- p + geom_point() plot2 <- p + geom_jitter() #增加映射 plot3 <- p + geom_jitter(aes(colour = class)) plot4 <- p + geom_jitter(aes(colour = fl)) ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2)[外链图片转存失败(img-z0K0sRTZ-1568127010233)(https://img-blog.csdn.net/20170831142500798?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvdTAxMjExMTQ2NQ==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)]
#运用width/height来更改宽度和高度 plot1 <- ggplot(mpg, aes(cyl, hwy)) + geom_jitter() plot2 <- ggplot(mpg, aes(cyl, hwy)) + geom_jitter(width = 0.25) plot3 <- ggplot(mpg, aes(cty, hwy)) + geom_jitter(width = 0.5, height = 0.5) plot4 <- ggplot(mpg, aes(cty, hwy)) + geom_jitter(width = 0.05, height = 0.05) ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2)###4. geom_crossbar 、geom_errorbar 、geom_linerange 、geom_pointrange——垂直间隔：线，横杠和错误栏
表示由x，ymin和ymax定义的垂直间隔的各种方式。 每个案例绘制一个图形对象。
函数格式：
geom_crossbar(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., fatten = 2.5, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_errorbar(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_linerange(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_pointrange(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., fatten = 4, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) #' # Create a simple example dataset df <- data.frame( trt = factor(c(1, 1, 2, 2)), resp = c(1, 5, 3, 4), group = factor(c(1, 2, 1, 2)), upper = c(1.1, 5.3, 3.3, 4.2), lower = c(0.8, 4.6, 2.4, 3.6) ) p <- ggplot(df, aes(trt, resp, colour = group)) plot1 <- p + geom_linerange(aes(ymin = lower, ymax = upper)) plot2 <- p + geom_pointrange(aes(ymin = lower, ymax = upper)) plot3 <- p + geom_crossbar(aes(ymin = lower, ymax = upper), width = 0.2) plot4 <- p + geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2) # Draw lines connecting group means p + geom_line(aes(group = group)) + geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) # If you want to dodge bars and errorbars, you need to manually specify the dodge width p <- ggplot(df, aes(trt, resp, fill = group)) p + geom_col(position = "dodge") + geom_errorbar(aes(ymin = lower, ymax = upper), position = "dodge", width = 0.25) # Because the bars and errorbars have different widths # we need to specify how wide the objects we are dodging are dodge <- position_dodge(width=0.9) p + geom_col(position = dodge) + geom_errorbar(aes(ymin = lower, ymax = upper), position = dodge, width = 0.25)###5. geom_map——参考地图中的多边形
函数格式：
geom_map(mapping = NULL, data = NULL, stat = "identity", ..., map, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3")) values <- data.frame( id = ids, value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5) ) positions <- data.frame( id = rep(ids, each = 4), x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3, 0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3), y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5, 2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2) ) ggplot(values) + geom_map(aes(map_id = id), map = positions) + expand_limits(positions) ggplot(values, aes(fill = value)) + geom_map(aes(map_id = id), map = positions) + expand_limits(positions) ggplot(values, aes(fill = value)) + geom_map(aes(map_id = id), map = positions) + expand_limits(positions) + ylim(0, 3) # Better example crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests) crimesm <- reshape2::melt(crimes, id = 1) if (require(maps)) { states_map <- map_data("state") ggplot(crimes, aes(map_id = state)) + geom_map(aes(fill = Murder), map = states_map) + expand_limits(x = states_map$long, y = states_map$lat) last_plot() + coord_map() ggplot(crimesm, aes(map_id = state)) + geom_map(aes(fill = value), map = states_map) + expand_limits(x = states_map$long, y = states_map$lat) + facet_wrap( ~ variable) }ok,本次结束，下次继续。
