ggplot2都有哪些使用不多但是却异常强大的图层函数

要说ggplot2中那些使用不多但是却功能强大的图层函数，我首先想到的就是geom_rect、geom_linerange、geom_segment、geom_ploygon。

这四个函数分别定义了ggplot2中的矩形图、垂直线图（线范围图）、线段图、几何多边形图。

这四种图表类型独立使用的机会很少，一般都是作为图表的辅助信息，但是如果能恰到好处的使用，你会挖掘出很多新意。

geom_rect

geom_rect一般用于制作自定义矩形，仅需在美学映射中指定每一个矩形在坐标系统中的左边界、右边界、下边界、上边界即可。

分辨对应xmax,xmax,ymin,ymax等四个美学映射参数，当然单个矩形仍然可以执行分组颜色映射操作。

具体示意如下：

library("showtext")

library("ggplot2")

library("magrittr")

library("reshape2")

library("ggthemes")

library('dplyr')

mydata <- data.frame(

Lebal = c("Point1","Point2","Point3","Point4","Point5"),

xstart = c(5.5,15.7,19.5,37.2,36.9),

xend = c(9.7,28.1,24.6,44.6,47.1),

ystart = c(9.6,23.1,2.3,33.2,9.2),

yend = c(16.1,36.2,11.7,38.5,15.3),

size = c(12,48,30,11.5,28),

class = c("A","A","A","C","C")

)ggplot(mydata)+

geom_rect(aes(xmin = xstart,xmax = xend , ymin = ystart , ymax = yend , fill = class)) +

scale_fill_wsj

按照x轴进行圆周化：ggplot(mydata)+

geom_rect(aes(xmin = xstart,xmax = xend , ymin = ystart , ymax = yend , fill = class)) +

scale_fill_wsj +

ylim(-10,40) +

scale_x_continuous(expand = c(0,0)) +

coord_polar(theta = 'x')#按照y轴进行圆周化

ggplot(mydata)+

geom_rect(aes(xmin = xstart,xmax = xend , ymin = ystart , ymax = yend , fill = class)) +

scale_fill_wsj +

scale_y_continuous(expand = c(0,0)) +

coord_polar(theta = 'y')分面操作：

ggplot(mydata)+

geom_rect(aes(xmin = xstart,xmax = xend , ymin = ystart , ymax = yend , fill = class)) +

scale_fill_wsj +

facet_grid(.~class) +

scale_y_continuous(expand = c(0,0))

想想这几个图如何使用上述图层实现?

geom_segment

geom_segment通常用于制作直线段图，路径图、放射线图等，思路也很简单，只需要指定每一条线段的起点坐标、终点坐标即可。

即分别制定x,y,xend,yend。

具体示意如下：

mydata <- data.frame(

Lebal = c("Segment1","Segment2","Segment3","Segment4","Segment5"),

xstart = c(3.5,4.4,8.3,13.2,20),

ystart = c(5,2.7,4.6,2.2,4.7),

xend = c(7.5,8.7,21,25,23),

yend = c(7.9,4.2,7.2,3.8,4.4),

class = c("A","A","A","C","C")

)ggplot(mydata) +

geom_segment(

aes(

x = xstart,

y = ystart,

xend = xend,

yend = yend,

colour = class

arrow = arrow(length = unit(0.5,"cm")),

size = 1.5

) +

scale_colour_wsj#按照X轴圆周化

ggplot(mydata) +

geom_segment(

aes(

x = xstart ,

y = ystart ,

xend = xend ,

yend = yend ,

colour = class

arrow = arrow(length = unit(0.5,"cm")),

size = 1.5

) +

scale_colour_wsj +

scale_y_continuous(expand = c(0,0)) +

coord_polar(theta = 'x')#按照y轴圆周化

ggplot(mydata) +

geom_segment(

aes(

x = xstart ,

y = ystart ,

xend = xend ,

yend = yend ,

colour = class

arrow = arrow(length = unit(0.5,"cm")),

size = 1.5

) +

scale_colour_wsj +

scale_y_continuous(expand = c(0,0)) +

coord_polar(theta = 'y')#分面操作：

ggplot(mydata) +

geom_segment(

aes(

x = xstart ,

y = ystart ,

xend = xend ,

yend = yend ,

colour = class

arrow = arrow(length = unit(0.5,"cm")),

size = 1.5

) +

facet_grid(.~class) +

scale_colour_wsj +

scale_y_continuous(expand = c(0,0))

感觉没啥卵用？想想这几个图怎么实现？

geom_linerange

这个图层函数乍看起来真的没啥用，貌似很多辅助图形都可以替代，比如那些误差线（geom_errorbar）、垂直线（geom_vline）、水平线(geom_hline)等。

但是其实不然，这个grom_linerange函数可以实现批量美学映射，包含线条颜色自定义、粗细自定义、线型自定义以及线条上下边界的自定义，通过coord_flip函数还可以将垂直线的所有应用转化为水平线应用，非常方便。

只需要指定x值，y轴的上下边界即可。

即x,ymin,ymax。

mydata <- data.frame(

Lebal = c("linerange1","linerange2","linerange3","linerange4","linerange5"),

xstart = c(3.5,7,12,16,20),

ymin = c(2.5,6.5,3,4.5,3.8),

ymax = c(7.5,9.5,9,13.5,4.2),

class = c("A","A","A","C","C")

)ggplot(mydata) +