箱線圖 | 小提琴圖，如何用合适的圖形來展示數據,下面通過2個小例子來系統介紹一下

1. R包的安裝與加載

package.list=c("tidyverse","ggsci","ggsignif") for (package in package.list) { if (!require(package,character.only=T, quietly=T)) { install.packages(package) library(package, character.only=T) } }

準備數據集，在此我們使用ToothGrowth數據集

ToothGrowth %>% as_tibble()

# A tibble: 60 x 3 len supp dose <dbl> <fct> <dbl> 1 4.2 VC 0.5 2 11.5 VC 0.5 3 7.3 VC 0.5 4 5.8 VC 0.5 5 6.4 VC 0.5 6 10 VC 0.5 7 11.2 VC 0.5 8 11.2 VC 0.5 9 5.2 VC 0.5 10 7 VC 0.5 # ... with 50 more rows

注：使用 ctrl shift M可快速打出%>%

下面我們先來做一個簡單的箱線圖

ToothGrowth %>% mutate(dose=as.factor(dose)) %>% ggplot(aes(dose,len,fill=supp)) geom_boxplot()

通常繪制箱線圖最好添加上誤差線，可通過stat_boxplot添加

ToothGrowth %>% mutate(dose=as.factor(dose)) %>% ggplot(aes(dose,len,fill=supp)) geom_boxplot(position = position_dodge(0.7), width = 0.5,show.legend = T,alpha=0.8) stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8)

也可以根據does | supp類型進行分面展示

ToothGrowth %>% mutate(dose=as.factor(dose)) %>% ggplot(aes(dose,len,fill=supp)) geom_boxplot(position = position_dodge(0.7), width = 0.5,show.legend = T,alpha=0.8) stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8) facet_wrap(.~supp,scales = "free") scale_fill_jco()

後續就是一些對圖例和主題的調整，可以參考之前的文檔 ggplot2修飾圖例的那些事

下面讓我們通過小提琴的形式來重新展示數據

ToothGrowth %>% mutate(dose=as.factor(dose)) %>% ggplot(aes(dose,len,fill=supp)) geom_violin(position = position_dodge(0.7),trim = FALSE,alpha=0.8) geom_boxplot(position = position_dodge(0.7), width = 0.15,show.legend = F,alpha=0.8,color="white") stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8,color="white") facet_wrap(.~supp,scales = "free") scale_fill_jco() theme_bw()

可以看到小提琴圖明顯美觀很多，同時我們也可以通過ggsignif添加上分組統計信息

ToothGrowth %>% mutate(dose=as.factor(dose)) %>% ggplot(aes(dose,len,fill=supp)) geom_violin(position = position_dodge(0.7),trim = FALSE,alpha=0.8) geom_boxplot(position = position_dodge(0.7), width = 0.15,show.legend = F,alpha=0.8,color="white") stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8,color="white") geom_signif(comparisons = list(c("0.5","1"), c("0.5","2"), c("1","2")), map_signif_level=T,vjust=0.5,color="black", textsize=5,test=wilcox.test,step_increase=0.1) facet_wrap(.~supp,scales = "free") scale_fill_jco() theme_bw() theme(panel.spacing.x = unit(0.2,"cm"), panel.spacing.y = unit(0.1, "cm"), axis.title = element_blank(), strip.text.x = element_text(size=9,color="black"), strip.background.x = element_blank(), axis.text = element_text(color="black"), axis.ticks.x=element_blank(), legend.text = element_text(color="black",size=9), legend.title=element_blank(), legend.spacing.x=unit(0.1,'cm'), legend.key=element_blank(), legend.key.width=unit(0.5,'cm'), legend.key.height=unit(0.5,'cm'), legend.position = "non", plot.margin=unit(c(0.3,0.3,0.3,0.3),units=,"cm"))

通過一系列主題調整圖形之後，終于有了一定的美感；但是這還遠遠不夠，針對多組數據我們還有更好的數據可視化形式，下一節帶大家繼續探索。

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