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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)
    })
    

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#demonstration of data summary tools
#enable necessary functions
source("http://pastebin.com/raw.php?i=JVyTrYRD")

#get demo data
library(RCurl)
url<-getURL("https://docs.google.com/spreadsheet/pub?key=0Ap1AEMfo-fh9dGFHRk81cVJaMTIxUjQzSlo2RS1RZXc&single=true&gid=0&output=csv",ssl.verifypeer = FALSE)
tmp.data<-read.csv(textConnection(url))

#Prepare to generate data summaries and conduct statitcal tests

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#------------------------------------------------------
# demonstration of making edge lists for 
# correlation network analysis 
# by Dmitry Grapov
# 031814
#------------------------------------------------------

# load devium library for all functions
source("http://pastebin.com/raw.php?i=JVyTrYRD") # sources function to download github repo (https://github.com/dgrapov/devium)

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Name	KEGG id
Carnitine	C00318
catechin	C06562
threonine	C00188
threonic acid	C01620
phenylethylamine	C05332
phenylalanine	C00079
N-acetyl-L-ornithine	C00437
mannitol	C00392
glutathione	C00051

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sourceName 	sourceKEGG	sourceCID	targetName	targetKEGG	targetCID	indirect
arachidonic_acid	C00219	444899	TXB2	C05963	5283137	y
arachidonic_acid	C00219	444899	PGE2	C00584	5280360	y
arachidonic_acid	C00219	444899	LTB4	C02165	5280492	y
linoleic_acid	C01595	5280450	9,10,13-TriHOME	C14835	5282965	y
linoleic_acid	C01595	5280450	9,12,13-TriHOME	C14833	9858729	y
linoleic_acid	C01595	5280450	EKODE		5283007	y
arachidonic_acid	C00219	444899	11(12)-EpETrE		53480479	n
arachidonic_acid	C00219	444899	11-HETE	C14780	5283168	n
arachidonic_acid	C00219	444899	12-HETE	C14777	5283155	n

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---
title: "Hands-on with dplyr"
author: "Dmitry Grapov"
output:
  html_document:
    keep_md: yes
---

## Introduction

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library(shiny)
library(ggvis)

shinyApp(
  ui =bootstrapPage(
    actionButton("randomize", "Randomize"),  
    ggvisOutput("plot1"),
    ggvisOutput("plot2"),
    verbatimTextOutput("summary")
  ),

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#R code, testing RECA with the iris data
library(RECA)  

#test data
data(iris)
x<-iris[,-5] 
y<-iris$Species

#similar groups (species) in each chunk (n=3)
chunksvec<-as.numeric(y)

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library(reshape2)

gen.mat.to.edge.list<-function(mat,symmetric=TRUE,diagonal=FALSE,text=FALSE){
	#create edge list from matrix
	# if symmetric duplicates are removed
	mat<-as.matrix(mat)
	id<-is.na(mat) # used to allow missing
	mat[id]<-"nna"
	if(symmetric){mat[lower.tri(mat)]<-"na"} # use to allow missing values
	if(!diagonal){diag(mat)<-"na"}

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#SOM example using wines data set
library(kohonen)
data(wines)
set.seed(7)

#create SOM grid
sommap <- som(scale(wines), grid = somgrid(2, 2, "hexagonal"))

## use hierarchical clustering to cluster the codebook vectors
groups<-3