## 多組boxplot ### 應用場景 eg: 展示多個基因在腫瘤和正常組織中的表達量 ```R # 多組boxplot # 構建數據集 rt_box_m <- data.frame(cbind(c(as.numeric(rt['TP53',]), as.numeric(rt['CD274',]), as.numeric(rt['LAMA3', ])), c(rep('TP53', 114), rep('CD274', 114), rep('LAMA3', 114)), rep(c(rep('tumor', 57), rep('normal', 57)), 3)), stringsAsFactors = FALSE) colnames(rt_box_m) <- c('expression', 'gene_name', 'group') rt_box_m$expression <- as.numeric(rt_box_m$expression) ``` > 數據結構如下  ```R library(ggpubr) PP1 <- ggplot(rt_box_m, aes(x = gene_name, y = expression)) + geom_boxplot(aes(fill = group), position = position_dodge(0.9)) print(PP1) # 利用stat_compare_means添加統計檢驗的pvalue PP2 <- PP1 + stat_compare_means(aes(group = group), label = "p.format") print(PP2) ### 利用theme_classic改變主題 PP3 <- PP2 + theme_classic() print(PP3) ```    ### 分面展示這些結果 ```R PF1 <- ggplot(rt_box_m, aes(group, expression)) + geom_boxplot(aes(color = group))+ facet_grid(. ~ gene_name) + #改變分面展示的方向 scale_color_manual(values = c("blue", "red")) + #修改box的顏色 stat_compare_means(label = "p.format") print(PF1) PF2 <- ggplot(rt_box_m, aes(group, expression)) + geom_boxplot(aes(color = group))+ facet_grid(gene_name ~ .) + scale_color_manual(values = c("blue", "red")) + stat_compare_means(label = "p.format") print(PF2) ```