R_柱状图（加误差线，显著性差异）

先放最终效果图

结果图

# bar_plot 2021/01/19
# 导入所需的包
library(ggplot2)
library(ggthemr)
library(ggsignif)
library(tidyverse)
library(dplyr)
library(ggpubr)
library(devEMF)

# 导入并处理数据,需要两张表，一个是原始汇总表格，另外一些是每一个测量数据的均值和标准差
data1 <- read.csv(file = "../all_enzymatic.txt", sep = "\t", header = T)
data2 <- dplyr::select(data1,1,3,4,5,6)

# SOD
data_SOD <- dplyr::select(data2,1,5)
SOD_mean <- data_SOD %>% 
  dplyr::group_by(group) %>% 
  dplyr::summarize(
    count=n(),
    mean = mean(SOD),
    sd = sd(SOD)
  )

data_SOD

SOD_mean

###################################################################
# 分析数据,数据量比较少就可以不做了
#正态分布检验：用shapiro.test()
shapiro.test(data_SOD$SOD)    

#方差齐性检验：用bartlett.test()或者leveneTest()
bartlett.test(SOD~group,data = data_SOD)   #巴雷特检验

library(car)  #leveneTest()属于car包
leveneTest(data_SOD$SOD~data_SOD$group)  #列文检验

compare_means(SOD~group,data = data_SOD,
               method = "anova") #显著性计算
####################################################################

#####################################################################
plot_data1 <- SOD_mean
plot_data2 <- data_SOD
p4 <- ggplot()+ 
  geom_bar(data=plot_data1,mapping=aes(x=group,y=mean,fill=group), # fill填充
           position="dodge", # 柱状图格式
           stat="identity", # 数据格式
           width = 0.7)+  # 柱状图尺寸
  scale_fill_manual(values = c("#4E4E56", "#DA635D","#B1938B"))+ # 柱状图颜色
  geom_signif(data=plot_data2,mapping=aes(x=group,y=SOD), # 不同组别的显著性
              comparisons = list(c("C", "HT"), # 哪些组进行比较
                                 c("HI", "HT")),
              annotation=c("**"), # 显著性差异做标记
              map_signif_level=T, # T为显著性，F为p value
              tip_length=c(0.04,0.04,0.05,0.05), # 修改显著性那个线的长短
              y_position = c(4100,3000), # 设置显著性线的位置高度
              size=1, # 修改线的粗细
              textsize = 10, # 修改*标记的大小
              test = "t.test")+ # 检验的类型
  geom_errorbar(data=plot_data1,mapping=aes(x = group,ymin = mean-sd, ymax = mean+sd), # 误差线添加
                width = 0.1, #误差线的宽度
                color = 'black', #颜色
                size=0.8)+ #粗细
  scale_y_continuous(limits =c(0, 4500) ,expand = c(0,0))+ # y轴的范围
  theme_classic(  # 主题设置，这个是无线条主题
    base_line_size = 1 # 坐标轴的粗细
  )+
  labs(title="",x="",y="")+ # 添加标题，x轴，y轴内容
  theme(plot.title = element_text(size = 20,
                                  colour = "red",
                                  hjust = 0.5),
        axis.title.y = element_text(size = 15, 
                                    # family = "myFont", 
                                    color = "black",
                                    face = "bold", 
                                    vjust = 1.9, 
                                    hjust = 0.5, 
                                    angle = 90),
        legend.title = element_text(color="black", # 修改图例的标题
                                    size=15, 
                                    face="bold"),
        legend.text = element_text(color="black", # 设置图例标签文字
                                   size = 10, 
                                   face = "bold"),
        axis.text.x = element_text(size = 13,  # 修改X轴上字体大小，
                                   # family = "myFont", # 类型
                                   color = "black", # 颜色
                                   face = "bold", #  face取值：plain普通，bold加粗，italic斜体，bold.italic斜体加粗
                                   vjust = 0.5, # 位置
                                   hjust = 0.5, 
                                   angle = 0), #角度
        axis.text.y = element_text(size = 13,  # 修改y轴上字体大小，
                                   # family = "myFont", # 类型
                                   color = "black", # 颜色
                                   face = "bold", #  face取值：plain普通，bold加粗，italic斜体，bold.italic斜体加粗
                                   vjust = 0.5, # 位置
                                   hjust = 0.5, 
                                   angle = 0) #角度
        ) 
emf(file = "SOD.emf") # 打开一个矢量图画布，这种格式的图片放在word里不会失真
print(p4) # 打印图片
dev.off() #关闭画布