#Instruction: First, run line 8 to the end). To create a table, use the function cor.csv(dataframe,"tablename.csv") where "dataframe" is replaced with your dataframe, and "tablename" is replaced with the name of your csv file. After you run it, a new csv file will created at your working directory.

#Example
library(car)
cor.csv(mtcars,"table1.csv")


cor.csv<-function(x,y){
  meanna<-function(a){x<-mean(a,na.rm =T)
  print(x)}
  sdna<-function(a){x<-sd(a,na.rm =T) 
  print(x)}
  
  #the corstars.bai.edited function is a Bai edited version of the code from here that someone else wrote http://www.sthda.com/english/wiki/elegant-correlation-table-using-xtable-r-package
  corstars.bai.edited<-function(x, method=c("pearson", "spearman"), removeTriangle=c("upper", "lower"),
           result=c("none", "html", "latex")){
    #Compute correlation matrix
    require(Hmisc)
    x <- as.matrix(x)
    correlation_matrix<-rcorr(x, type=method[1])
    R <- correlation_matrix$r # Matrix of correlation coeficients
    p <- correlation_matrix$P # Matrix of p-value 
    
    ## Define notions for significance levels; spacing is important.
    mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "*  ", ifelse(p < .10, "+   ", "    "))))
    
    ## trunctuate the correlation matrix to two decimal
    R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
    
    ## build a new matrix that includes the correlations with their apropriate stars
    Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
    diag(Rnew) <- paste(diag(R), " ", sep="")
    rownames(Rnew) <- colnames(x)
    colnames(Rnew) <- paste(colnames(x), "", sep="")
    
    ## remove upper triangle of correlation matrix
    if(removeTriangle[1]=="upper"){
      Rnew <- as.matrix(Rnew)
      Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
      Rnew <- as.data.frame(Rnew)
    }
    
    ## remove lower triangle of correlation matrix
    else if(removeTriangle[1]=="lower"){
      Rnew <- as.matrix(Rnew)
      Rnew[lower.tri(Rnew, diag = TRUE)] <- ""
      Rnew <- as.data.frame(Rnew)
    }
    
    ## remove last column and return the correlation matrix
    Rnew <- cbind(Rnew[1:length(Rnew)-1])
    if (result[1]=="none") return(Rnew)
    else{
      if(result[1]=="html") print(xtable(Rnew), type="html")
      else print(xtable(Rnew), type="latex") 
    }
  }
  
  

  a<-as.data.frame(cbind(data.frame("Mean"=round(apply(x,2,meanna),2),
                                    "SD"=round(apply(x,2,sdna),2)),
                         corstars.bai.edited(x)))
  print(a)
  write.csv(a,y)

}