Data normality test

For testing the data is from normal or not, the Shapiro test is more powerful then other tests. But Shapiro has restrict in R, the sample size must less than 5000.
So facing data points great than 5000, we use Kolmogorov-Smirnov tests.
The code as following:

normalityTest <- function(x, group=NULL, alpha=0.05){

   if(is.null(group)){
      x <- na.omit(x)
      if(all(x==x[1]) | length(x)<5 ){
         return(FALSE)  
      } else if (length(x) > 5000){ 
         return(ks.test(x, "pnorm", mean(x), sd(x))$p.value>alpha)
      } else {
         return(shapiro.test(x)$p.value>alpha)
      }  
   } else {
      ng <- as.character(unique(group))
      for(ng.ind in 1:length(ng)){
         tmp <- x[group==ng[ng.ind]]
         tmp <- na.omit(tmp) 
         if(all(tmp==tmp[1]) | length(tmp)<5){
            stop(return(FALSE))
         } else {
             if(length(tmp) > 5000){
               p <- ks.test(x, "pnorm", mean(x), sd(x))$p.value
             } else {   
               p <- shapiro.test(tmp)$p.value
             }   
             if(p < alpha) stop(return(FALSE))
         }   
      }
      return(TRUE)
   }
}