library(ggplot2)
library(tidyr)  # 使用的gather & spread
library(reshape2) 
library(vcd)
library(plyr)
library(RColorBrewer)
library(ggpubr)

rm(list=ls())
###############################################################

### step1：直方图_密度图
Fig1_R1_dat<- read.csv("C:/Users/zhuwengen/Documents/FLIR0984_ABCD.csv")

colnames(Fig1_R1_dat)
# measure.vars只需要制定一个即可;另外一个默认是除指定的变量外的所有变量.
# 使用melt 函数将宽数据Fig1_dat转换为长数据Fig1_dat_long
Fig1_R1_long <- melt(Fig1_R1_dat,
                     measure.vars = c('A','B','C','D'),           #用于聚合的变量,
                     variable.name = 'group',
                     value.name = 'temp')


mu1_R1 <- ddply(Fig1_R1_long, "group", summarise, grp.mean=mean(temp))


p1_R1 <- ggplot(Fig1_R1_long, aes(x = temp, fill = group)) +
  geom_density(alpha = 0.6, size = 0.5)  +
  xlim(12.5, 15.5) +
  geom_vline(data = mu1_R1, aes(xintercept = grp.mean, color = group),
             linetype="dashed") +
  theme_classic() + 
  scale_fill_manualp1_R1 + scale_fill_manual(values = c("#E41A1C","#377EB8","#4DAF4A","#984EA3")) +
  geom_histogram(aes(y=..density..), 
                 alpha = 0.4, 
                 binwidth = 0.005,
                 position="identity") +
  labs(title="Temperature density curve",
       x = "Pixel Temperature(C)", 
       y = "Frequency")(values = c("#E41A1C","#377EB8","#4DAF4A","#984EA3"))




### step2：小提琴图
library("ggpubr")
Fig1_R1_long$group <- factor(Fig1_R1_long$group, 
                             levels=c("A", "B", "C","D"), 
                             ordered=TRUE)

#  compare_means(temp~group, data = Fig1_R1_long, ref.group = "A") + 
p3 <- ggviolin(Fig1_R1_long,  x = "group", y = "temp", fill = "group",
               palette =  c( "#E41A1C","#377EB8","#4DAF4A","#984EA3"),
               add = "boxplot", 
               add.params = list(fill = "white")) + 
  stat_compare_means(method="anova", label.y= 15.5) + 
  stat_compare_means(label="p.signif",  method = "t.test", ref.group = "A")+ 
  ylim(12.5, 17)