有观众老爷询问如何对箱线图进行几何形状填充，那么今天就来具体介绍一番；在原有的基础上做了一些小的改动也许恰好您正好有此特殊需求，需要着重体会八个字变实为虚，变虚为实，希望对各位观众老爷有所帮助；下面来看具体案例；

需要获取数据的欢迎关注我的号留言

加载R包

library(tidyverse) library(ggsci) library(ggprism) library(rstatix) library(ggpubr) library(ggpmisc) library(ggpattern)

加载数据

gapminder <- read_tsv("gapminder.xls")

数据清洗

df <- gapminder %>% filter(year %in% c(1957,2002,2007),continent !="Oceania") %>% select(country,year,lifeExp,continent)%>% mutate(paired = rep(1:(n()/3),each=3),year=factor(year))

统计分析

df_p_val1 <- df %>% group_by(continent) %>% wilcox_test(lifeExp ~ year) %>% adjust_pvalue(p.col = "p", method = "bonferroni") %>% add_significance(p.col = "p.adj") %>% add_xy_position(x = "year", dodge = 0.8)

构建填充类型

下面函数来自ggpattern包官方文档
https://coolbutuseless.github.io/package/ggpattern/articles/developing-patterns.html

tiling3_pattern <- function(params, boundary_df, aspect_ratio, legend = FALSE) { args <- as.list(params) args <- args[grep("^pattern_", names(args))] # hexagonal tiling using "regular_polygon" pattern args$pattern <- "polygon_tiling" # three-color tiling using `fill`, `pattern_fill` and their "average" avg_col <- gridpattern::mean_col(params$fill, params$pattern_fill) args$pattern_fill <- c(params$fill, avg_col, args$pattern_fill) args$x <- boundary_df$x args$y <- boundary_df$y args$id <- boundary_df$id args$prefix <- "" do.call(gridpattern::patternGrob, args) } options(ggpattern_geometry_funcs = list(tiling3 = tiling3_pattern))

数据可视化

df %>% ggplot(aes(year,lifeExp)) + stat_boxplot(aes(ymin = ..lower.., ymax = ..upper..),outlier.shape = NA,width=0.5) + stat_boxplot(geom = "errorbar", aes(ymin = ..ymax..),width=0.2,size=0.35) + stat_boxplot(geom = "errorbar", aes(ymax = ..ymin..),width=0.2,size=0.35) + geom_boxplot_pattern(aes(pattern = year,pattern_angle = year,fill=year), pattern = 'tiling3', pattern_angle = 45,color="black", pattern_spacing = 0.045,outlier.shape = NA,linetype = "dashed",width=0.5,size=0.35)+ stat_summary(geom = "crossbar", fun = "median",width = 0.5,color="black",size=0.38)+ stat_pvalue_manual(df_p_val1,label = "p.adj.signif",label.size=5,hide.ns = F)+ scale_size_continuous(range=c(1,3))+ # geom_smooth(method = "lm", formula = NULL,size=1,se=T,color="black",linetype="dashed",aes(group=1))+ stat_cor(label.y = 25,aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~"),group=1),color="black", label.x.npc = "left")+ stat_regline_equation(label.y = 19,aes(group=1),color="black")+ facet_wrap(.~continent,nrow=1)+ scale_fill_npg()+ scale_x_discrete(guide = "prism_bracket")+ scale_y_continuous(limits = c(0,95),minor_breaks = seq(0,95,5),guide = "prism_offset_minor")+ labs(x=NULL,y=NULL)+ theme_prism(base_line_size =0.4)+ theme(plot.margin=unit(c(0.5,0.5,0.5,0.5),units=,"cm"), strip.text = element_text(size=12), axis.line = element_line(color = "black",size = 0.4), panel.grid.minor = element_blank(), panel.grid.major = element_line(size = 0.2,color = "#e5e5e5"), axis.text.y = element_text(color="black",size=10), axis.text.x = element_text(margin = margin(t = -5),color="black",size=10), legend.position = "none", panel.spacing = unit(0,"lines"))+ coord_cartesian()

数据获取

还是熟悉的配方，除了构建三色形状填充外，需要体会的依然是变实为虚，变虚为实绘图思维尽在其中，那么本节介绍到此结束；需要数据的留言交流