PCA using Shiny. http://spark.rstudio.com/dgrapov/PCA/

global.R

#check for and/or install dependencies
need<-c("RCurl","ggplot2","gridExtra","reshape2")
for(i in 1:length(need)){
  if(require(need[i], character.only = TRUE)==FALSE){ install.packages(need[i]);library(need[i], character.only = TRUE)} else { library(need[i],character.only = TRUE)}
}

if(require(pcaMethods)==FALSE){
  need<-c('Rcpp', 'rJava',
  'Matrix', 'cluster', 'foreign', 'lattice', 'mgcv', 'survival')
for(i in 1:length(need)){
  if(require(need[i], character.only = TRUE)==FALSE){
    install.packages(need[i],dependencies=TRUE);library(need[i], character.only = TRUE)
  } else { library(need[i],character.only = TRUE)
  }
}#dependancies
  source("http://bioconductor.org/biocLite.R")
  biocLite("pcaMethods")
}

#Functions used in example
#---------------------------
#fxn to load repo from from github
source.git.hub<-function(url = "https://github.com/dgrapov/devium/tree/master/R")
{
  if(require(RCurl)==FALSE){install.packages("RCurl");library(RCurl)} else { library(RCurl)}
  
  #get the names of all scripts to source
  obj<-getURL("https://github.com/dgrapov/devium/tree/master/R",ssl.verifypeer=FALSE)
  tmp<-strsplit(obj,'href=\"/')
  tmp2<-unlist(strsplit(as.character(unlist(tmp)),'class'))
  scripts<-gsub("/blob","",gsub('\" ',"",tmp2[grep("dgrapov/devium/blob/master/R/",tmp2)])) # fix formatting
  #add http for git hub
  scripts<-paste("https://raw.github.com/",scripts,sep="")
  sapply(1:length(scripts),function(i)
  {
    tryCatch( eval( expr = parse( text = getURL(scripts[i],
                                                ssl.verifypeer=FALSE) ),envir=.GlobalEnv),error=function(e){print(paste("can't load:",scripts[i]))})
  })
}

#convert vector to named list
namel<-function (vec){
  tmp<-as.list(vec)
  names(tmp)<-as.character(unlist(vec))
  tmp
}

# app startup	
source.git.hub() 

server.R

shinyServer(function(input, output, session) {
		
		.theme<- theme(
					axis.line = element_line(colour = 'gray', size = .75), 
					panel.background = element_blank(),  
					plot.background = element_blank()
				 )	
	
	
		#file info	
		output$filetable <- renderTable({
			if(is.null(input$files) ) { return() }  else { 
			tmp<-read.csv(input$files$datapath, header=T, stringsAsFactors =T)
			tmp<-tmp[,seq_along(1:ncol(tmp))<=10] # show max 10 columns and binf head tail calls
			rbind(head(tmp,10),tail(tmp,10))
				# input$files 
				
				} 
			
			})
		
		#confirm load
		output$caption<-renderText({
			if (!is.null(PCA.results())) {
					"Principal Components Analysis"
				} else {
					if(is.null(input$files)) { "Load Data" }  else { "Data Loaded"}
				}
			}) 
		
		
		#number of PCs
		output$PCs<-renderUI({
				if (is.null(input$files)) { return(NULL) }
				maxPCs<-ncol(input$files)
				numericInput("PCs", "Number of Principal Components", 
				2, min = 2, max = maxPCs)
			})
			
		PCA.results<-reactive({
				if (is.null(input$files)) { 
						return(NULL) 
					} else {
						# list(data=read.csv(input$files$datapath, header=T, stringsAsFactors =T),
						# data2=rnorm(10))
						
					# }
				#adapted from another devium
				pca.inputs<-list()
				start.data<<-read.csv(input$files$datapath, header=T, stringsAsFactors =T)
				pca.inputs$pca.data<-"start.data"
				pca.inputs$pca.algorithm<-input$method
				pca.inputs$pca.components<-input$PCs
				pca.inputs$pca.center<-input$center
				pca.inputs$pca.scaling<-input$scaling
				pca.inputs$pca.cv<-input$cv # currently not used
				devium.pca.calculate(pca.inputs,return="list",plot=F)
				}
			})	
			
		
			
		#make screeplot
		output$screeplot <- renderPlot({
					if (is.null(PCA.results())) { 
						return(NULL)
					} else {
						x<-PCA.results()
						x<-data.frame(x$pca.eigenvalues)
						
						# make.scree.plot(x)	
						make.scree.plot.bar(x)
					}
				})	
				
		# scores diagnostic plot
		output$scores <- renderPlot({
					if (is.null(PCA.results())) { 
						return(NULL)
					} else {
						tmp<-PCA.results()
						scores<-data.frame(tmp$pca.scores)
						if(nrow(tmp$pca.diagnostics)==nrow(scores))
								{
									if(any(tmp$pca.diagnostics$DmodX=="NaN")){tmp$pca.diagnostics$DmodX<-1}
									scores<-data.frame(leverage=tmp$pca.diagnostics$leverage, dmodx=tmp$pca.diagnostics$DmodX,scores)
								} else {
									scores<-data.frame(leverage=1, dmodx=1,scores)
								}
								
						p<-ggplot(scores,mapping = aes_string(x = names(scores)[3], y = names(scores)[4],color=names(scores)[1],size=names(scores)[2])) + 
						scale_size_continuous("DmodX", range = c(4, 10)) + 
						geom_point(alpha=0.75) +.theme
						print(p)
					}
				})	
		#loadings plot
		output$loadings <- renderPlot({
					if (is.null(PCA.results())) { 
						return(NULL)
					} else {
						tmp<-PCA.results()
						loadings<-data.frame(tmp$pca.loadings,names=rownames(tmp$pca.loadings))
						
						#plot
						p<-ggplot(loadings,mapping = aes_string(x = names(loadings)[1], y = names(loadings)[2], label = "names")) + 
						geom_text(size=4,alpha=0.75) +.theme
						print(p)
					}
				})	
				
})

ui.R

# UI for app
shinyUI(pageWithSidebar(
	# title
	headerPanel("Select Options"),
	# h2("Principal Components Analysis (PCA)")
	
	#input
	sidebarPanel
	(
		#data upload
		fileInput("files", "Choose File", multiple=TRUE), 
		uiOutput("PCs"),
		# tabsetPanel(id="dist",
		# tabPanel("Data", value='norm', textInput("dist1","Xdist1", c("norm"))),
		# tabPanel("Analyze", value='unif', textInput("dist2","Xdist2", c("unif")))),
		checkboxInput("center","Center",TRUE),
		selectInput("scaling","Scale", 
						list(none = "none", "unit variance" = "uv", pareto = "pareto")
						),	
		
		selectInput("method","Method", 
						namel(listPcaMethods())
						),
		selectInput("cv","cross-validation",
						list (none = "none", Q2 =  "q2")
						)
		 #helpText("Hints"),	
	),

		# uiOutput("variable"), 	# depends on dataset ( set by output$variable in  server.R)
		# uiOutput("group"),  		# depends on dataset	( set by output$group in  server.R)
		# selectInput("plot.type","Plot Type:", 
						# list(boxplot = "boxplot", histogram = "histogram", density = "density", bar = "bar")
						# ),
		# checkboxInput("show.points", "show points", TRUE)

	# output				
	mainPanel(
		h3(textOutput('caption')),
		tabsetPanel(
			tabPanel("Data",tableOutput("filetable")),
			tabPanel("Scree Plot",plotOutput("screeplot",height = 280*2, width = 250*2)),
			tabPanel("Scores Plot",plotOutput("scores")),
			tabPanel("Loadings Plot",plotOutput("loadings"))
			)
		
	)
))

I can't get this example to work with the Iris data set. I get "Error: could not find function "devium.pca.calculate". It seems like the pcaMethods package doesn't get fully installed it also says

"Warning message:
package ‘pcaMethods’ is not available (for R version 3.2.2) "

When I try to install pcaMethods package