skin_data <- read.table(file="your file",sep="\t",header=T,row.names=1)
summary_result <- summary(skin_data)
write.csv(summary_result,file="summary_result.csv")
boxplot(skin_data)
install.package("ggplot2")
library(ggplot2)
p<-ggplot(data, aes(x = your column))
p + geom_density(color = "red", fill = "red")
library(psych)
library(corrplot)
library(RColorBrewer)
rm(list=ls())
a <- read.table(file="clipboard",sep="\t",header=T,row.names=1)
ct <- corr.test(a)
corrplot(as.matrix(ct$r),method = "square",tl.cex = 0.8,cl.cex = 0.8,
col = colorRampPalette(c("royalblue","white","firebrick3"))(10),tl.col = "black",cl.pos = "b")
write.table(ct$r,file="Pain_skin_cor.txt",sep="\t",quote=F)
write.table(ct$p,file="Pain_skin_pvalue.txt",sep="\t",quote=F)
data_548 <- read.table(file="covariate.txt",sep="\t",header=T,row.names = 1)
data_548$Sex <-as.factor(data_548$Sex)
result_lm <- c()
for (i in c(4:117)) {
fit <- lm(get(colnames(data_548)[i]) ~ + Age + Sex + BMI,data = data_548)
b <- coef(summary(fit))
rownames(b)[1] <- colnames(data_548)[i]
rownames(b)[1] <-paste(rownames(b)[1],"-Intercept",sep="")
result_lm <- rbind(result_lm,b)
}
write.csv(result_lm,file="result_lm.csv")
