YanTao

绘制带有误差棒的条形图

library(ggplot2)
# 创建数据集
df <- data.frame(treatment = factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3)), 
                 response = c(2, 5, 4, 6, 9, 7, 3, 5, 8), 
                 group = factor(c(1, 2, 3, 1, 2, 3, 1, 2, 3)), 
                 se = c(0.4, 0.2, 0.4, 0.5, 0.3, 0.2, 0.4, 0.6, 0.7))
head(df) #查看数据集

##   treatment response group  se
## 1    1         2       1    0.4
## 2    1         5       2    0.2
## 3    1         4       3    0.4
## 4    2         6       1    0.5
## 5    2         9       2    0.3
## 6    2         7       3    0.2

# 使用geom_errorbar()绘制带有误差棒的条形图
# 这里一定要注意position要与`geom_bar()`保持一致，由于系统默认dodge是0.9，
# 因此geom_errorbar()里面position需要设置0.9，width设置误差棒的大小
ggplot(data = df, aes(x = treatment, y = response, fill = group)) + 
geom_bar(stat = "identity", position = "dodge") + 
geom_errorbar(aes(ymax = response + se, ymin = response -  se), 
position = position_dodge(0.9), width = 0.15) + 
scale_fill_brewer(palette = "Set1")

绘制带有显著性标记的条形图

label <- c("", "*", "**", "", "**", "*", "", "", "*") #这里随便设置的显著性，还有abcdef等显著性标记符号，原理一样，这里不再重复。
# 添加显著性标记跟上次讲的添加数据标签是一样的，这里我们假设1是对照
ggplot(data = df, aes(x = treatment, y = response, fill = group)) + 
geom_bar(stat = "identity", position = "dodge") + 
geom_errorbar(aes(ymax = response + se, ymin = response -  se), 
position = position_dodge(0.9), width = 0.15) + 
geom_text(aes(y = response +  1.5 * se, label = label, group = group), 
position = position_dodge(0.9), size = 5, fontface = "bold") + 
scale_fill_brewer(palette = "Set1") #这里的label就是刚才设置的，group是数据集中的，fontface设置字体。

绘制两条形图中间带有星号的统计图

#创建一个简单的数据集
Control <- c(2.0,2.5,2.2,2.4,2.1)
Treatment <- c(3.0,3.3,3.1,3.2,3.2)
mean <- c(mean(Control), mean(Treatment))
sd <- c(sd(Control), sd(Treatment))
df1 <- data.frame(V=c("Control", "Treatment"), mean=mean, sd=sd)
df1$V <- factor(df1$V, levels=c("Control", "Treatment"))
#利用geom_segment()绘制图形
ggplot(data=df1, aes(x=V, y=mean, fill=V))+ 
geom_bar(stat = "identity",position = position_dodge(0.9),color="black")+ 
geom_errorbar(aes(ymax=mean+sd, ymin=mean-sd), width=0.05)+ 
geom_segment(aes(x=1, y=2.5, xend=1, yend=3.8))+#绘制control端的竖线 
geom_segment(aes(x=2, y=3.3, xend=2, yend=3.8))+#绘制treatment端竖线 
geom_segment(aes(x=1, y=3.8, xend=1.45, yend=3.8))+ 
geom_segment(aes(x=1.55, y=3.8, xend=2, yend=3.8))+#绘制两段横线 
annotate("text", x=1.5, y=3.8, label="〇", size=5)#annotate函数也可以添加标签

为图形添加标题

图形标题有图标题、坐标轴标题、图例标题等

p <- ggplot(data = df, aes(x = treatment, y = response, fill = group)) + 
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymax = response + se, ymin = response -  se),
position = position_dodge(0.9), width = 0.15) + 
scale_fill_brewer(palette = "Set1")# 利用ggtitle()添加图标题,还有labs（）也可以添加标题，最后会提一下。（有一个问题就是ggtitle()添加的标题总是左对齐）
p + ggtitle("利用ggtitle()添加图标题")

# 利用xlab()\ylab()添加/修改坐标轴标题
p + ggtitle("利用ggtitle()添加图标题") + 
xlab("不同处理") + 
ylab("response") #标题的参数修改在theme里，theme是一个很大的函数，几乎可以定义一切，下次有时间会讲解

# 将四幅图放置于一个页面中
p <- ggplot(data = df, aes(x = treatment, y = response, fill = group)) + 
geom_bar(stat = "identity", position = "dodge") + 
geom_errorbar(aes(ymax = response + se, ymin = response -  se), 
position = position_dodge(0.9), width = 0.15) + 
scale_fill_brewer(palette = "Set1")
p1 <- p + ggtitle("利用ggtitle()添加图标题")
p2 <- p + ggtitle("利用ggtitle()添加图标题") + xlab("不同处理") + ylab("response")
p3 <- ggplot(data = df, aes(x = treatment, y = response, fill = group)) + 
geom_bar(stat = "identity", position = "dodge") + 
geom_errorbar(aes(ymax = response + se, ymin = response -  se), 
position = position_dodge(0.9), width = 0.15) + 
geom_text(aes(y = response +  1.5 * se, label = label, group = group), 
position = position_dodge(0.9), size = 5, fontface = "bold") + 
scale_fill_brewer(palette = "Set1")
library(gridExtra) #没有安装此包先用install.packages('gridExtra')安装
grid.arrange(p, p1, p2, p3)

上次有人问坐标轴旋转的实现，坐标轴旋转有时是很有用的，下面是我看过的一个例子，用来介绍一下。

#先加载他的数据
url.world_ports <- url("http://sharpsightlabs.com/wp-content/datasets/world_ports.RData")
load(url.world_ports)
knitr::kable(df.world_ports[1:5,])#该数据是关于世界上各个港口的数据汇总

library(dplyr) #用于数据操作，与ggplot2一样是R语言必学包#现在绘制条形图(%>%上次说过是管道操作，用于连接各个代码，十分有用)
df.world_ports%>%filter(year==2014)%>% #筛选2014年的数据 
ggplot(aes(x=reorder(port_label, desc(volume)), y=volume))+ 
geom_bar(stat = "identity", fill="darkred")+ 
labs(title="Busiest container ports in the world")+ 
labs(subtitle = '2014, in order of shipping volume')+ #添加副标题 
labs(x = "Port", y = "Shipping\nVolume")+ 
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .4))#调整x轴标签，angle=90表示标签旋转90度，从图中可以看出

#现在旋转坐标轴，并筛选排名小于25的港口，并且添加数据标签
df.world_ports %>% filter(year==2014, rank<=25) %>% #筛选2014年并且rank小于等于25的数据 
ggplot(aes(x=reorder(port, volume), y=volume))+ 
geom_bar(stat = "identity", fill="darkred")+ 
labs(title="Busiest container ports in the world")+ 
labs(subtitle = '2014, in order of shipping volume')+  
labs(x = "Port", y = "Shipping\nVolume")+ 
geom_text(aes(label=volume), hjust=1.2, color="white")+ 
coord_flip()#旋转坐标轴

两图相比，明显第二幅图好，一是可以添加数据标签，二是不用歪着脖子看。
本来打算讲讲图例的但是发现内容太多了，就不讲了，下次吧！