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SiteName  Year	Restoration	Reference	ObserverNames	Precipitation	Temperature
IGH	2005	3	3	Tyler_Amanda	0	48
Kelly	2005	4	2	Patrick_Chelsea	0	48
Carlton	2005	2	3	David_Megan	0	48
IGH	2006	9	6	Tyler_Amanda	0	52
Kelly	2006	9	1	David_Megan	0	52
Carlton	2006	7	3	Patrick_Chelsea	0	52
IGH	2007	12	7	David_Megan	12	41
Kelly	2007	2	18	Jeremy_Lucy	12	41
Carlton	2007	11	2	Patrick_Chelsea	12	41

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column.int<-function(int.fun='dnorm(x)',num.poly=20,from.x=-1.96,to.x=1.96,plot.cum=F){
	
	if (!require(ggplot2) | !require(scales) | !require(gridExtra)) {
	  stop("This app requires the ggplot2, scales, and gridExtra packages. To install, run 'install.packages(\"name\")'.\n")
	}

	if(from.x>to.x) stop('Starting x must be less than ending x')
	
	if(is.character(int.fun)) int.fun<-eval(parse(text=paste('function(x)',int.fun)))
	

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if (!require(ggplot2) | !require(scales) | !require(gridExtra) | !require(shiny)) {
  stop("This app requires the shiny, ggplot2, scales, and gridExtra packages. To install, run 'install.packages(\"name\")'.\n")
}

# Define server logic required to generate and plot a random distribution
shinyServer(function(input, output) {

  output$intPlot <- renderPlot({
    require(ggplot2)
    require(scales)

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mc.int.ex<-function(x.range=c(-3,3),int=c(-1.96,1.96),n=10000,plot=T,
  plot.pts=T,plot.poly=T,cols=list('black','green','blue'),round.val=2,val=F){
	
	require(scales)
	
	#create data for model fit
	x.fit<-seq(x.range[1],x.range[2],length=n)
	y.fit<-dnorm(x.fit)
	dat.fit<-data.frame(x.fit,y.fit)
	

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mc.int<-function(mod.in,int=NULL,n=10000,int.base=0,plot=T,plot.pts=T,
  plot.poly=T,cols=list('black','green','blue'),round.val=2,val=F){
	
	require(scales)
	
	#variable names from model
	if(is.null(mod.in$call$formula)){
		x.names<-colnames(eval(mod.in$call$x))
		y.names<-colnames(eval(mod.in$call$y))
		x.range<-range(eval(mod.in$call$x))

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plot.qual<-function(x,x.locs=c(0.01,0.99),y.locs=c(0,1),steps=NULL,sp.names=NULL,dt.tx=T,rsc=T,
	ln.st=NULL,rs.ln=c(3,15),ln.cl='RdYlGn',alpha=0.7,leg=T,rnks=FALSE,...){

	require(RColorBrewer)
	require(scales)
	
	if(length(x.locs) != 2 | length(y.locs) != 2) 
		stop('x and y dimensions must be two-element vectors')
	
	if(x.locs[1]<0 | x.locs[2]>1 | y.locs[1]<0 | y.locs[2]>1) 

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obs<-100
x<-runif(obs,0,100)
y<-x^2+rnorm(obs,0,800)

require(scales)

#examples with span
par(mfrow=c(2,2),mar=c(4,4,0.5,0.5),family='serif')
span<-round(seq(0.5,0.1,length=4),2)
for(i in span){

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shinyServer(function(input, output) {

  myData <- reactive({
  read.csv(input$file$datapath)
    })

  output$summary <- renderPrint({
     if (is.null(input$file)) {

      return(cat('File not uploaded'))

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plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,wts.only=F,rel.rsc=5,circle.cex=5,node.labs=T,
  line.stag=NULL,cex.val=1,alpha.val=1,circle.col='lightgrey',pos.col='black',neg.col='grey',...){

  require(scales)
	
  #gets weights for neural network, output is list
  #if rescaled argument is true, weights are returned but rescaled based on abs value
  nnet.vals<-function(mod.in,nid,rel.rsc){

    library(scales)

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catch.fun.v2<-function(dows,trace=T,rich.out=F){

  require(XML)

  strt<-Sys.time()

  #create object for output
  fish.out<-vector('list',length(dows))
  names(fish.out)<-dows