Marcus W Beck fawda123

# 'root' character string of directory to search
# 'file_typs' character vector of file types to search
# 'omit_blank' logical indicating of blank lines are counted
# 'recursive' logical indicating if all directories within 'root' are searched
# 'lns' logical indicating if lines are counted, use F for counting characters
# 'trace' logical for monitoring progress
file.lens <- function(root, file_typs, omit_blank = F, recursive = T, 
	lns = T, trace = T){
	
	require(reshape2)

plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,bias=T,wts.only=F,rel.rsc=5,
                    circle.cex=5,node.labs=T,var.labs=T,x.lab=NULL,y.lab=NULL,
                    line.stag=NULL,struct=NULL,cex.val=1,alpha.val=1,
                    circle.col='lightblue',pos.col='black',neg.col='grey',
                    bord.col='lightblue', max.sp = F,...){
 
  require(scales)
  
  #sanity checks
  if('mlp' %in% class(mod.in)) warning('Bias layer not applicable for rsnns object')

lek.fun<-function(mod.in,var.sens=NULL,resp.name=NULL,exp.in=NULL,steps=100,split.vals=seq(0,1,by=0.2),val.out=F){
  
  require(ggplot2)
  require(reshape)
  
  ##
  #sort out exp and resp names based on object type of call to mod.in
  #get matrix for exp vars
  
  #for nnet

#credit http://stackoverflow.com/questions/13649473/add-a-common-legend-for-combined-ggplots
 
require(ggplot2)
require(reshape)
require(gridExtra)
 
p.dat<-data.frame(step=row.names(grp.dat),grp.dat,stringsAsFactors=F)
p.dat<-melt(p.dat,id='step')
p.dat$step<-as.numeric(p.dat$step)

plot.area<-function(x,col=NULL,horiz=F,prop=T,stp.ln=T,grp.ln=T,axs.cex=1,axs.lab=T,lab.cex=1,
                    names=c('Group','Step','Value'),...){
  
  #sort out color fector  
  if(!is.null(col)){
    if(sum(col %in% colors()) != length(col)) stop('col vector must be in "colors()"')
    col<-colorRampPalette(col)(ncol(x))
  }
  else col<-colorRampPalette(c('lightblue','green'))(ncol(x))
  

gar.fun<-function(out.var,mod.in,bar.plot=T,struct=NULL,x.lab=NULL,
                  y.lab=NULL, wts.only = F){
  
  require(ggplot2)
  require(plyr)
  
  # function works with neural networks from neuralnet, nnet, and RSNNS package
  # manual input vector of weights also okay
  
  #sanity checks

shinyServer(function(input, output) {
 
  myData <- reactive({
   read.table(
    input$file$datapath,
    sep='\t',
 	  header=T,
 	  stringsAsFactors=F
    ) 
   })

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)))