第二章 条形图

二、条形图

1，简单的条形图

library(gcookbook)
ggplot(pg_mean,aes(group,weight,fill=group))+geom_bar(stat = "identity")
str(pg_mean)
View(pg_mean)

2，绘制簇状条形图

View(cabbage_exp)
ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(position = "dodge",stat = "identity")

ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(stat = "identity",position = "dodge",colour="black")+
  scale_fill_brewer(palette = "Paste11")

3，绘制频数分布条形图

ggplot(diamonds,aes(cut))+geom_bar()
x <- table(diamonds$cut)
barplot(x)
View(diamonds)
ggplot(diamonds,aes(cut,price))+
  geom_bar(stat = "identity")

4,条形图着色

upc <- subset(uspopchange,rank(Change)>40)
ggplot(upc,aes(Abb,Change,fill=Region))+
  geom_bar(stat = "identity")

ggplot(upc,aes(x=reorder(Abb,Change),Change,fill=Region))+
  geom_bar(stat = "identity",colour="black")+
  scale_fill_manual(values = c("#669933","#FFCC66"))+
  xlab("State")

5,对正负条形图分别着色

csub <- subset(climate,Source=="Berkeley"&Year>=1900)
csub$pos <- csub$Anomaly10y>=0
csub

ggplot(csub,aes(Year,Anomaly10y,fill=pos))+
  geom_bar(stat = "identity",position = "identity")

ggplot(csub,aes(Year,Anomaly10y,fill=pos))+
  geom_bar(stat = "identity",position = "identity",colour="black",size=0.25)+
  scale_fill_manual(values = c("#CCEEFF","#FFDDDD"),guide=F)

6. 调整条形宽度和条形间距

ggplot(pg_mean,aes(group,weight))+
  geom_bar(stat = "identity")
ggplot(pg_mean,aes(group,weight))+
  geom_bar(stat = "identity",width=0.5)
ggplot(pg_mean,aes(group,weight))+
  geom_bar(stat = "identity",width=1)

ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(stat = "identity",width = 0.5,position ="dodge")
ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(stat = "identity",width = 0.5,position = position_dodge(0.7))

7.绘制堆积条形图

ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(stat = "identity",width = 0.5)
#调整图例顺序
ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(stat = "identity",width = 0.5)+
  guides(fill=guide_legend(reverse = T))
#调整堆叠顺序
library(dplyr)
ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar, order=desc(Cultivar)))+
  geom_bar(stat = "identity",width = 0.5)

ggplot(cabbage_exp,aes(Date,Weight, fill= Cultivar))+
  geom_bar(stat = "identity",colour="black")+
  guides(fill=guide_legend(reverse = T))+
  scale_fill_brewer(palette = "Paste11")

8.绘制百分比堆积条形图

library(gcookbook)
library(plyr)
ce <- ddply(cabbage_exp,"Date",transform,percent_weight=Weight/sum(Weight)*100)
ggplot(ce,aes(Date,percent_weight,fill=Cultivar))+
  geom_bar(stat = "identity")

9.添加数据标签

ggplot(cabbage_exp,aes(interaction(Date,Cultivar),Weight))+
  geom_bar(stat = "identity",width = 0.5)+
  geom_text(aes(label=Weight),vjust=1.5,colour="white")

ggplot(cabbage_exp,aes(interaction(Date,Cultivar),Weight))+
  geom_bar(stat = "identity")+
  geom_text(aes(label=Weight),vjust=-0.2,colour="black")
ggplot(cabbage_exp,aes(interaction(Date,Cultivar),Weight))+
  geom_bar(stat = "identity")+
  geom_text(aes(label=Weight),vjust=-0.2,colour="black")+
  ylim(0,max(cabbage_exp$Weight*1.05))

ggplot(cabbage_exp,aes(interaction(Date,Cultivar),Weight))+
  geom_bar(stat = "identity")+
  geom_text(aes(label=Weight, y=Weight+0.1),colour="black")
 
ggplot(cabbage_exp,aes(Date,Weight,fill=Cultivar))+
  geom_bar(stat = "identity",position = "dodge")+
  geom_text(aes(label=Weight),vjust=1.5,colour="white",
            size=3,position = position_dodge(.9))

10.绘制Cleveland图

library(gcookbook)
tophit <- tophitters2001[1:25,]
ggplot(tophit,aes(avg,name))+
  geom_point()
tophit[,c("name","lg","avg")]

ggplot(tophit,aes(avg,y=reorder(name,avg)))+
  geom_point(size=3)+
  theme_bw()+
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_line(colour="grey60",linetype = "dashed"))

ggplot(tophit,aes(x=reorder(name,avg),avg))+
  geom_point(size=3)+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 60,hjust = 1),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_line(colour="grey60",linetype = "dashed"))

#根据因子lg进行分组，并排序，reorder不能同时对两组排序
nameorder <- tophit$name[order(tophit$lg,tophit$avg)]
tophit$name <- factor(tophit$name,levels = nameorder)

ggplot(tophit,aes(avg,name))+
  geom_segment(aes(yend=name),xend=0,colour="grey50")+
  geom_point(size=3,aes(colour=lg))+
  scale_color_brewer(palette = "Set1",limits=c("NL","AL"))+
  theme_bw()+
  theme(panel.grid.major.y = element_blank(),
        legend.position = c(1,0.55),
        legend.justification = c(1,0.5))


ggplot(tophit,aes(avg,name))+
  geom_segment(aes(yend=name),xend=0,colour="grey50")+
  geom_point(size=3,aes(colour=lg))+
  scale_color_brewer(palette = "Set1",limits=c("NL","AL"),guide=F)+
  theme_bw()+
  theme(panel.grid.major.y = element_blank())+
  facet_grid(lg~.,scales = "free_y",space = "free_y")