R 语言绘制簇状条形图及其美化

                                                         参考书籍：R数据可视化手册

1．使用ggplot（）和geom_bar()函数绘制基础簇状条形图

library(gcookbook)

ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

geom_bar(position = "dodge",stat ="identity" )

备注：#其中cultivar，data是分类变量，weight是数值

2. 使用scale_fill_brewer()或scale_fill_manual()函数着色

library(gcookbook)

P1<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",colour="black" )+

  scale_fill_brewer(palette="Greens")

# scale_fill_brewer(palette=1)

# scale_fill_brewer(palette="Set2")

P1

P2<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",colour="black" )+

 # scale_fill_manual(values =c("#669933","#FFCC66"))

#scale_fill_manual(values =c("420","414"))

scale_fill_manual(values =c("lightpink1","lightsalmon"))

P2

备注：#添加colour修改为黑色边框，使用scale_fill_brewer()或scale_fill_manual()函数

#注意颜色的映射设定在aes（）内部，而颜色重新设置在aes（）外部完成；此外，scale_fill_brewer()有三种颜色设置scale_fill_brewer(palette="Greens")、scale_fill_brewer(palette=1)、scale_fill_brewer(palette="Set2")，而scale_fill_manual(values =c("#669933","#FFCC66"))、scale_fill_manual(values =c("420","414"))、scale_fill_manual(values =c("lightpink1","lightsalmon"))，选择任何一种函数时可以任选一种方式即可。

3.使用geom_bar()函数修改条形宽度和条形间距

library(gcookbook)

P3<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=1,colour="black" )+

  scale_fill_brewer(palette="Greens")

P3

P4<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=0.5,colour="black" )+

  scale_fill_brewer(palette="Greens")

P4

备注：簇状条形图默认间距为0，如果想增加条形的间距，需要满足position_dodge值大于width值，而position_dodge(0.9)相当于position="dodge"，colour是设置边框颜色。

4.使用geom_text()函数添加数据标签

library(gcookbook)

P5<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=0.5,colour="black"  )+

  geom_text(aes(label=Weight),vjust=1.5,colour="black",position=position_dodge(0.9),size=3)+

  scale_fill_brewer(palette="Greens")

P5

#在条形图顶端上方

P6<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=0.5,colour="black"  )+

  geom_text(aes(label=Weight),vjust=-0.5,colour="black",position=position_dodge(0.5),size=3)+

scale_fill_brewer(palette="Greens")

P6

#在簇状图顶端下方

P7<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=0.5,colour="black"  )+

  geom_text(aes(label=Weight),vjust=1.5,colour="black",position=position_dodge(0.5),size=3)+

  scale_fill_brewer(palette="Greens")

P7

备注：P5，P6, P7不同时因为geom_text ()函数中vjust参数（正负值）与主要调整标签的上下位置有关，而position参数的大小与标签的中间左右对齐有关。

5.使用geom_text()函数或ylim()可调整Y轴的范围

#如果标签超过绘图区域，因此考虑Y轴的范围调整

library(gcookbook)

P8<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=0.5,colour="black"  )+

  geom_text(aes(y=Weight+0.1,label=Weight),vjust=-0.3,colour="black",position=position_dodge(0.5),size=3)+

  scale_fill_brewer(palette="Greens")

P8

#Y轴上限变大

P9<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+

  geom_bar(position = "dodge",stat ="identity",width=0.5,colour="black"  )+

  geom_text(aes(label=Weight),vjust=-0.3,colour="black",position=position_dodge(0.5),size=3)+

  scale_fill_brewer(palette="Greens")+

  ylim(0,max(cabbage_exp$Weight)*1.05)

P9

备注：调整Y轴的范围可根据实际情况而定。

自我总结：

绘制簇状条形图及其美化，主要有以下几个函数：

ggplot(data,aes(x,y,fill)

geom_bar(position = "dodge",stat ="identity" , width=1, colour="black" )

geom_text(aes(label=Weight),vjust=1.5,colour="black",position=position_dodge(0.9),size=3)

scale_fill_brewer(palette="Greens")或scale_fill_manual(values =c("lightpink1","lightsalmon"))

geom_text(aes(label=Weight+0.1),vjust=1.5,colour="black",position=position_dodge(0.9),size=3)或geom_text(aes(label=Weight),vjust=0.3,colour="black",position=position_dodge(0.5),size=3)+

ylim(0,max(cabbage_exp$Weight)*1.05)

以上是我对簇状条形图的总结，望大家批评和指正，我们一起学习。

转载本文请联系原作者获取授权，同时请注明本文来自陈睿科学网博客。

链接地址：https://wap.sciencenet.cn/blog-3511394-1382456.html?mobile=1

收藏

分享