R进行三因素方差分析

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本节来介绍如何使用R做三因素方差分析，绘制并排堆砌条形图并添加显著性字母标记，下面来看具体例子

清理变量

rm(list=ls())

安装并加载R包

package.list=c("tidyverse","ggthemes","multcompView","egg")

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

加载数据

df <- read_tsv("co2.xls")

数据结构

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8
4   Qn1 Quebec nonchilled  350   37.2
5   Qn1 Quebec nonchilled  500   35.3
6   Qn1 Quebec nonchilled  675   39.2

三因素方差分析

anova <- aov(uptake ~ factor(conc)*Type*Treatment,data=df)
summary(anova)

多重均值比较

Tukey <- TukeyHSD(anova)
cld <- multcompLetters4(anova, Tukey)

整理数据

dt <- group_by(CO2, conc, Type, Treatment) %>%
  summarise(uptake_mean=mean(uptake), sd=sd(uptake)) %>%
  arrange(desc(uptake_mean))
cld <- as.data.frame.list(cld$`factor(conc):Type:Treatment`)
dt$Tukey <- cld$Letters

> dt
# A tibble: 28 × 6
# Groups:   conc, Type [14]
    conc Type   Treatment  uptake_mean    sd Tukey
                    
 1  1000 Quebec nonchilled        43.2  3.06 a    
 2   675 Quebec nonchilled        41.5  2.35 a    
 3  1000 Quebec chilled           40.8  1.91 ab   
 4   350 Quebec nonchilled        40.4  2.75 ab   
 5   500 Quebec nonchilled        39.6  3.90 abc  
 6   675 Quebec chilled           37.5  2.10 abcd 
 7   250 Quebec nonchilled        37.4  2.76 abcd 
 8   500 Quebec chilled           36.7  3.61 abcde
 9   350 Quebec chilled           35.8  2.62 abcde
10   250 Quebec chilled           34.5  3.93 abcde

dt列出了三个因素的列，即uptake的平均值、标准差和表示平均值之间显着差异的字母

绘制基础图形

ggplot(dt,aes(x = factor(conc), y = uptake_mean, fill = Type:Treatment)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymax = uptake_mean + sd, ymin = uptake_mean - sd),
                position = position_dodge(0.9), width = 0.25, color = "Gray25") +
  xlab(expression(CO[2]~Concentration~'('~mL~L^-1~')')) +
  ylab(expression(CO[2]~Uptake~'('~µmol~m^2~s^-1~')')) +
  scale_fill_brewer(palette = "Greens") +
  theme_few()

分面设置

ggplot(dt, aes(x = factor(conc), y = uptake_mean, fill = Treatment)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymax = uptake_mean + sd, ymin = uptake_mean - sd),
                position = position_dodge(0.9), width = 0.25, color = "Gray25") +
  xlab(expression(CO[2]~Concentration~'('~mL~L^-1~')')) +
  ylab(expression(CO[2]~Uptake~'('~µmol~m^2~s^-1~')')) +
  scale_fill_brewer(palette = "Greens") +
  theme_few() +
  facet_grid(.~Type, labeller = label_both)

调整细节

p <- ggplot(dt, aes(x = factor(conc), y = uptake_mean, fill = Treatment)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymax = uptake_mean + sd, ymin = uptake_mean - sd),
                position = position_dodge(0.9), width = 0.25, color = "Gray25") +
  xlab(expression(CO[2]~Concentration~'('~mL~L^-1~')')) +
  ylab(expression(CO[2]~Uptake~'('~µmol~m^2~s^-1~')')) +
  theme_few() +
  theme(legend.position = c(0.95,0.98),legend.justification = c(1, 1),
        legend.title = element_blank(),
        axis.text=element_text(color="black"),
        axis.title = element_text(color="black")) +
  scale_fill_manual(values = c("#C1D5A5", "#84A17C")) +
  scale_y_continuous(expand = expansion(0),limits = c(0,50),breaks = seq(0,50,5))+
  facet_grid(.~Type, labeller = label_both) +
  geom_text(aes(label=Tukey, y = uptake_mean + sd + 2), size = 3, color = "Gray25",
            show.legend = FALSE,position = position_dodge(0.9))

tag_facet(p, fontface = 1, tag_pool = c("(a) Quebec",
                                        "(b) Mississipi"),
          open = NULL, close = NULL, hjust = -0.05)