生信分析中，经常要建立分险模型，对每个患者进行分险评分，根据这些评分对患者进行分组，不同分组的预后差异很大。

### 1. 构造数据
risk_df<- data.frame(samples=paste0("S",1:100),score=runif(100,1,10),surv_time =rnorm(100,10,2),surv_status = sample(c("alive","dead"),100,replace = TRUE)
)
library(dplyr)
risk_df <- mutate(risk_df,group= ifelse(score>5,"high_risk","low_risk"))
head(risk_df)### 2.风险评分绘图
library(ggplot2)
## 样品按score值排序
risk_df <- risk_df[order(risk_df$score),]
p1 <- ggplot(data = risk_df) +geom_point(aes(x=seq(1:100), y=score, color=group)) + scale_color_manual(values = c("red","grey")) + # 自定义颜色映射ggtitle("Risk Score") + theme(plot.title = element_text(hjust = 0.5)) + # title居中xlab("") + ylab("Risk Score") +geom_vline(aes(xintercept=45),colour = "#BB0000",linetype = "dashed")### 3.生存状态散点图
p2 <- ggplot(data = risk_df) +geom_point(aes(x=seq(1:100), y=surv_time, color=surv_status)) + ggtitle("Survival status") + theme(plot.title = element_text(hjust = 0.5)) + # title居中xlab("patients") + ylab("Survival time (years)")### 4.组成图
library(cowplot)
plot_grid(p2,p1,ncol = 1, align = "h",labels = c("A","B"))