# Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Install Package: 'Ctrl + Shift + B'
# Check Package: 'Ctrl + Shift + E'
# Test Package: 'Ctrl + Shift + T'
# # sig_show ="line"
# # data = data_wt
# # i= 3
# # #使用案例
# PlotresultBar = aovMuiBarPlot(data = data_wt, i= 3,sig_show ="abc",result = result[[1]])
# PlotresultBar[[1]]
###----使用方差检验结果和多重比较结果做展示: 柱状图展示
aovMuiBarPlot = function(data = data_wt, i= 3,sig_show ="line",result = result){
library(ggplot2)
result = result
Mytheme <- theme_bw()+
# scale_fill_manual(values = mi, guide = guide_legend(title = NULL))+
theme(
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title = element_text(vjust = -8.5,hjust = 0.1),
axis.title.y =element_text(size = 20,face = "bold",colour = "black"),
axis.title.x =element_text(size = 24,face = "bold",colour = "black"),
axis.text = element_text(size = 20,face = "bold"),
axis.text.x = element_text(colour = "black",size = 14),
axis.text.y = element_text(colour = "black",size = 14),
legend.text = element_text(size = 15,face = "bold"),
legend.position = "none"#是否删除图例
)
data_wt = data
name_i = colnames(data_wt[i])
#求取均值和方差
wen1 = as.data.frame(tapply(as.vector(as.matrix(data_wt[i])),data_wt$group,mean,na.rm=TRUE))
wen2 = as.data.frame(tapply(as.vector(as.matrix(data_wt[i])),data_wt$group,sd,na.rm=TRUE))
went = cbind(wen1,wen2)
colnames(went) = c("mean" ,"SD")
went
aa = result
wentao = merge(aa,went, by="row.names",all=F)
wentao
# wentao$Row.names = NULL
# FileName <- paste(plotname ,name_i,method_Mc,"_aov_bar", ".csv", sep = "_")
# write.csv(wentao,FileName,quote = F)
library(tidyverse)
# colnames(wentao) = c(colnames(wentao[1:4]),"mean" ,"SD")
#使用的tidyverse函数,对数据框添加两列,目的为柱状图添加bar
aa = mutate(wentao, ymin = mean - SD, ymax = mean + SD)
a = max(aa$ymax)*1.2##用于设置y轴最大值
### 出图柱状图
p = ggplot(aa , aes(x = group, y = mean,colour= group)) +
geom_bar(aes(colour= group,fill = group),stat = "identity", width = 0.4,position = "dodge") +
geom_errorbar(aes(ymin=ymin,
ymax=ymax),
colour="black",width=0.1,size = 1)+
# geom_hline(aes(yintercept=mean(as.vector(as.matrix(data_wt[i])))), colour="black", linetype=2) +
# geom_vline(aes(xintercept=0), colour="black", linetype="dashed") +
scale_y_continuous(expand = c(0,0),limits = c(0,a))+#
labs(
# x=paste(name_i,"of all group", sep = "_"),
y=name_i
# title = paste("Normality test",p1,"Homogeneity of variance",p2,sep = ":")
)
p
if (sig_show == "line") {
zuhe = combn(aa$group,2)
xxxx <- tapply(zuhe,rep(1:ncol(zuhe),each=nrow(zuhe)),function(i)i)
xxxx
sig_lis = rep("a",dim(zuhe)[2])
for (i in 1:dim(zuhe)[2]) {
library(tidyverse)
library("ggsignif")
if (filter(aa, group == xxxx[[i]][1])$groups == filter(aa, group == xxxx[[i]][2])$groups) {
sig_lis[i] = "no_sig"
}
if (filter(aa, group == xxxx[[i]][1])$groups != filter(aa, group == xxxx[[i]][2])$groups) {
sig_lis[i] = "*"
}
}
p = p +
geom_signif(comparisons = xxxx, annotations=sig_lis,
y_position = (seq(from=1, to=max(aa$mean)/4,length.out=dim(zuhe)[2]) + max(aa$mean)), tip_length = rep(0.03,dim(zuhe)[2]),color = "black")
p
}
if (sig_show == "abc") {
p = p + geom_text(aes(label = groups,y=ymax, x = group,vjust = -0.3,size = 6))
p
}
#as.vector(as.matrix(data_wt[i]))为进行差异分析的一组数据
p=p+Mytheme
p
if (length(unique(data_wt$group))>3){ p=p+theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
return(list(p,wentao))
}
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