一文学会，ANOVA双因素方差分析

今天小果通过这一篇文章带大家学会和了解双因素方差分析(ANOVA),话不多说，请看下面代码：

1. 安装需要的R包

install.packages(“ggplot2”)

install.packages(“tidyverse”)

install.packages(“multcompView”)

install.packages(“ggsci”)

1. 载入需要的R包

library(ggplot2)

library(tidyverse)

library(multcompView)

library(ggsci)

1. 代码展示

soil <- read.table(file=”soil.txt”, sep = ‘\t’, header = TRUE)

group <- read.table(file=”group.txt”, sep = ‘\t’, header = TRUE)

#左连接合并两个文件

soil <- left_join(soil, group, by = ‘sample’)

#筛选需要的列

chao1 <- select(soil ,c(‘sample’, ‘treat’, ‘times’, ‘chao1’))

#将这两个因素类型(trate和times)改为因子

chao1$treat <- factor(chao1$treat)

chao1$times <- factor(chao1$times)

#双因素方差分析

anova<- aov(chao1~treat*times, data = chao1)

#计算mean和sd(分组统计mean和times）

dt <- chao1 %>% group_by(treat,times) %>%

summarise(w=mean(chao1), sd = sd(chao1)) %>%

arrange(desc(w)) %>%

ungroup()

#多重配对比较

cld <- TukeyHSD(anova) %>% multcompLetters4(anova,.)

le <- as.data.frame.list(cld$`treat:times`)

#获取字母标记

dt$Tukey <- le$Letters

#开始绘制带有显著性字母标记的柱状图

ggplot(dt, aes(times, w)) +

geom_bar(stat = “identity”,aes(fill=times),show.legend = FALSE,width=0.5) +

geom_errorbar(aes(ymin = w-sd, ymax=w+sd), width = 0.1) +

geom_text(aes(label = Tukey, y = w + sd), vjust = -0.5)+

facet_wrap(~treat)+

scale_fill_npg()+

labs(x=””,y=””)+

theme_test()+

theme(panel.background = element_blank(),

axis.line = element_line(color = “black”),

axis.text = element_text(size = 10,color = “black”),

axis.text.y = element_text(size = 10),

axis.ticks.x = element_blank())

最终通过一个示例数据进行了双因素方差分析，今天小果的分享就到这里奥，下期再见。