R优雅的进行统计分析(2)自定义添加统计信息

2022-09-21 14:41:26 浏览数 (1)

❝本节来介绍如何使用R语言来做统计分析，通过「rstatix」包进行统计检验，完全使用tidyverse体系进行数据清洗及可视化，使用add_pvalue,stat_pvalue_manual两个函数来自定义添加p值 ❞

加载R包
代码语言：javascript复制
library(tidyverse)
library(rstatix)
library(ggprism)
library(ggpubr)
library(ggsci)

数据清洗

代码语言：javascript复制
df <- ToothGrowth %>%
  mutate(dose=as.factor(dose)) %>% 
  group_by(dose) %>%
  summarise(value_mean=mean(len),sd=sd(len),se=sd(len)/sqrt(n()))

统计分析

❝此处通过联接原数据来定义位置信息 ❞

代码语言：javascript复制
stat.test <- ToothGrowth %>% t_test(data =., len ~ dose, ref.group  =  "0.5") %>% 
  mutate(p.adj.signif = replace_na(p.adj.signif,""),across("p.adj.signif",str_replace,"ns","")) %>% 
  select(group1,group2,p.adj,p.adj.signif) %>% 
  left_join(.,df,by=c("group2"="dose")) %>% 
  mutate(y.position=value_mean sd 0.3)

定义主题

代码语言：javascript复制
theme_niwot <- function(){
  theme_minimal()  
    theme(axis.title.x = element_blank(),
          axis.line = element_line(color = "#3D4852"),
          axis.ticks = element_line(color = "#3D4852"),
          panel.grid.major.y = element_line(color = "#DAE1E7"),
          panel.grid.major.x = element_blank(),
          plot.margin = unit(rep(0.2,4),"cm"),
          axis.text = element_text(size = 12, color = "#22292F"),
          axis.title = element_text(size = 12, hjust = 1),
          axis.title.y = element_text(margin = margin(r = 12)),
          axis.text.y = element_text(margin = margin(r = 5)),
          axis.text.x = element_text(margin = margin(t = 5)),
          legend.position = "non")
}

数据可视化(1)

❝此次抛弃了上文通过geom_text()添加显著性标记的方法，改用add_pvalue进行 ❞

代码语言：javascript复制
df %>% ggplot(.,aes(dose,value_mean)) 
  geom_errorbar(aes(ymax = value_mean   sd, ymin = value_mean - sd),width = 0.1,color = "grey30") 
  geom_col(width=0.4,aes(fill=dose)) 
  add_pvalue(stat.test,label = "p.adj.signif",label.size=6,
             coord.flip = TRUE, remove.bracket = TRUE) 
  scale_y_continuous(expand=c(0,0),limits = c(0,33))  
  theme_niwot() 
  scale_fill_brewer(palette="Blues")

数据可视化(2)

  • 分开添加线条，使用tip.length并分别自定义线条长度
代码语言：javascript复制
df %>% ggplot(.,aes(dose,value_mean)) 
  geom_errorbar(aes(ymax = value_mean   sd, ymin = value_mean - sd),width = 0.1,color = "grey30") 
  geom_col(width=0.4,aes(fill=dose)) 
  stat_pvalue_manual(stat.test %>% slice(1),label = "p.adj.signif",
                     label.size=6,tip.length = c(0.35,0.003),linetype=2) 
  add_pvalue(stat.test %>% slice(2),label = "p.adj.signif",label.size=6,tip.length = c(0.1,0.003)) 
  scale_y_continuous(expand=c(0,0),limits = c(0,33))  
  theme_niwot() 
  scale_fill_brewer(palette="Blues")

统计分析2

代码语言：javascript复制
stat.test2 <- ToothGrowth %>% mutate(dose=as.factor(dose)) %>% group_by(dose) %>% 
  t_test(len ~ supp) %>%
  adjust_pvalue() %>% add_significance("p.adj") %>% add_xy_position(x="dose") 

stat.test3 <- ToothGrowth %>% 
  t_test(len ~ dose,p.adjust.method = "bonferroni") %>%
  adjust_pvalue() %>% add_significance("p.adj") %>% add_xy_position()

方差分析

代码语言：javascript复制
res.aov <- ToothGrowth %>% mutate(dose=as.factor(dose)) %>% anova_test(len ~ dose)

方差分析事后检验

代码语言：javascript复制
stat.test4 <- ToothGrowth %>% mutate(dose=as.factor(dose)) %>% tukey_hsd(len ~ dose) %>% 
  add_xy_position("dose")
代码语言：javascript复制
ToothGrowth %>% mutate(dose=as.factor(dose)) %>% 
  ggplot(aes(dose,len)) 
  stat_boxplot(geom = "errorbar",width=0.2,aes(fill = supp),position = position_dodge(1))  
  geom_boxplot(aes(fill= supp),position = position_dodge(1)) 
  stat_pvalue_manual(stat.test4,label = "p.adj.signif",label.size=6,hide.ns = T) 
  labs(subtitle = get_test_label(res.aov, detailed = TRUE)) 
  scale_y_continuous(expand=c(0,0),limits = c(0,42)) 
  theme_niwot() 
  scale_fill_jco()

数据可视化(3)

❝按不同分子分别对组内组间进行统计分析，并对整体进行方差分析；想对于add_pvalue而言stat_pvalue_manual的功能更加丰富，hide.ns = T移除不显著的信息 ❞

代码语言：javascript复制
ToothGrowth %>% mutate(dose=as.factor(dose)) %>% 
  ggplot(aes(dose,len)) 
  stat_boxplot(geom = "errorbar",width=0.2,aes(fill = supp),position = position_dodge(1))  
  geom_boxplot(aes(fill= supp),position = position_dodge(1)) 
  stat_pvalue_manual(stat.test2,label = "p.adj.signif",label.size=6,hide.ns = T) 
  stat_pvalue_manual(stat.test3,label = "p.adj.signif",label.size=6,hide.ns = T) 
  labs(subtitle = get_test_label(res.aov, detailed = TRUE)) 
  scale_y_continuous(expand=c(0,0),limits = c(0,42)) 
  theme_niwot() 
  scale_fill_jco()

参考资料

❝https://rpkgs.datanovia.com/rstatix/index.html https://www.datanovia.com/en/lessons/anova-in-r/ ❞

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