Odds_ratio_VIF

# Calculate odds ratio for multivariate version and variance inflation factor (VIF). The function exports csv.
odds<-function(df,digit=2,p_r=4,formula,name="Odds_ratio"){
library(car)
library(dplyr)
  target=as.character(formula[[2]])
  y=df[[target]]
  tmp<-NA
  for (i in 1:length(df)){
    if (length(unique(df[,i]))<3){
      tmp[i]<-0
    } else{
      tmp[i]<-1
    }
  }
  df<-df[,sort(tmp,index.return=TRUE,decreasing=TRUE)$ix]
  df %>% relocate(target, .after = last_col())
  
  multi_odds<-array(NA,dim=c((length(df)),2))
  multi_model<-glm(formula=formula, data=df,family="binomial")

  result<-summary(multi_model)$coefficients[2:length(df),]
  result<-cbind(result,result[,1]-1.96*result[,2])
  result<-cbind(result,result[,1]+1.96*result[,2])
  result<-result[,c(1,5,6,4)]
  result[,1:3]<-round(exp(result[,c(1:3)]),digit)
  result[,4]<-round(result[,4],p_r)

  multi_odds[1:(length(df)-1),1]<-paste0(result[,1]," (",result[,2]," - ",result[,3],")")
  multi_odds[1:(length(df)-1),2]<-paste0(result[,4])
  multi_odds[length(df),1]<-length(df[,1])

  
  single_odds<-array(NA,dim=c((length(df)),2))
  for (i in 1:(length(df)-1)){
  
  single_model<-glm(formula=y ~ df[,i],family="binomial")

  result<-summary(single_model)$coefficients[2,]
  result<-c(result,result[1]-1.96*result[2])
  result<-c(result,result[1]+1.96*result[2])
  result<-result[c(1,5,6,4)]
  result[1:3]<-round(exp(result[c(1:3)]),digit)
  result[4]<-round(result[4],p_r)
  
  single_odds[i,1]<-paste0(result[1]," (",result[2]," - ",result[3],")")
  single_odds[i,2]<-paste0(result[4])

  }
  single_odds[length(df),1]<-length(df[,1])
  name<-paste0(name,".csv")
  model<-lm(y~.,data=df)
  VIF<-c(vif(model))
  VIF[length(df)]<-NA
  odds_data<-data.frame(multi_odds,single_odds,VIF)
  colnames(odds_data)<-c("Multi OR (2.5% - 97.5%)", "p-value","Single OR (2.5% - 97.5%)", "p-value", "vif")
  rownames(odds_data)<-c(colnames(df)[1:(length(df)-1)],"total_num")
  odds_data
  write.csv(odds_data,name)
}