compare skewed to normal distrubution

server.R

shinyServer(function(input,output){
  mydat <- reactive(function() {
     scale(rsnorm(input$obs, location = input$mean, scale =input$variance,
                  shape = input$skew),center=TRUE,scale=TRUE)
  })
  
  ndat <- reactive(function() {
       scale(rnorm(input$obs, mean = input$mean, sd =input$variance),center=TRUE, scale=TRUE)
    })
    
    output$distPlot<-reactivePlot(function(){
      mdata<-mydat()
    mdens<-density(mdata)
	  mmean<-mdens$x[which.max(abs(mean(mdens$y)-mdens$y))]
      mmedian<- mdens$x[which.max(abs(median(mdens$y)-mdens$y))]
	  
	  ndata<-ndat()
	  ndens<-density(ndata)
	  nmean<-ndens$x[which.max(abs(mean(ndens$y)-ndens$y))]
      nmedian<- ndens$x[which.max(abs(median(ndens$y)-ndens$y))]
	 
	  data2<-data.frame(distribution=rep(c("normal","empirical"),each=(input$obs) ),value=c(ndata,mdata))
	
      p <-ggplot(data=data2, aes(x=value, color=distribution, fill=distribution)) +theme_dpi()+theme(legend.position = c(0.85, 0.85)) +xlim(c(-10,10))+labs(x="data",y="density",title="Distribution of Data")
      pd<-p+geom_density(aes(y=..density..),alpha=.25 )  + 
	     geom_vline(aes(xintercept=mmean), linetype=1, color=rainbow(2)[1], alpha=.5) + 
		 geom_vline(aes(xintercept=mmedian),linetype=2,color=rainbow(2)[1], alpha=.5)+
		 geom_vline(aes(xintercept=nmean), linetype=1, color=rainbow(2)[2], alpha=.5) + 
		 geom_vline(aes(xintercept=nmedian),linetype=2,color=rainbow(2)[2], alpha=.5)
	#qq plot
     ggQQ <- function(dist,ndist) # 2 distributions
		{
			
			vals<-qqplot(ndist[,1],dist[,1]) 
			data<-data.frame(x=vals$x, y= vals$y) # get same positions
			#qqline
			y <- quantile(ndist[,1], c(0.25, 0.75))
			x <- quantile(dist[,1], c(0.25, 0.75))
			slope <- diff(y)/diff(x)
			int <- y[1L] - slope * x[1L]
			
			
			
			p <- ggplot(data, aes(x=y,y=x)) +
				geom_point()+
				#stat_qq(alpha = 0.5) +
				geom_abline(slope = slope, intercept = int, color="blue") + theme_dpi() +  labs(x="normal quantile",y="actual quantile",title="Quantile-Quantile Plot")

			return(p)
		}
		qq<-ggQQ(data.frame(value=mdata),data.frame(value=ndata))
		grid.arrange(pd,qq, nrow=2)
    
  })
})

ui.R

# Script to demonstrate distributions
library(VGAM)
library(eeptools)
library(shiny)
library(ggplot2)
shinyUI(pageWithSidebar(
  
  # Title
  headerPanel("Comparing Properties of Distributions"),
  
  sidebarPanel(
    sliderInput("obs","Number of tries:",
                min=200,max=5000,value=500,step=250),
    sliderInput("mean","Mean of the Distribution",
                min=-10,max=10,value=0,step=1),
    sliderInput("mode","Mode of the Distribution",
                min=-10,max=10,value=0,step=1),
    sliderInput("variance","Variance of the Distribution",
                min=1,max=5,value=1,step=1),
    sliderInput("skew","skew of the Distribution",
                min=-5,max=5,value=0,step=1)
    
  ),
  
  # GGPLOT
  
  mainPanel(
    plotOutput("distPlot",width = 600, height = 900)
  )

  
))