Nan Xiao nanxstats

Let's say you have streaming data, and you would like to update a DT::datatable in Shiny without completely re-rendering the table. Here is some code to illustrate how this might be accomplished. Hope it helps.

library(DT)
library(shiny)

ui <- tagList(
  tags$script(HTML(
"
Shiny.addCustomMessageHandler(

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

	library(shiny)
	library(datasets)
	Logged = FALSE;
	PASSWORD <- data.frame(Brukernavn = "withr", Passord = "25d55ad283aa400af464c76d713c07ad")
	# Define server logic required to summarize and view the selected dataset
	shinyServer(function(input, output) {
	source("www/Login.R", local = TRUE)

	observe({
	if (USER$Logged == TRUE) {

	library(shiny)

	disableActionButton <- function(id,session) {
	session$sendCustomMessage(type="jsCode",
	list(code= paste("$('#",id,"').prop('disabled',true)"
	,sep="")))
	}

	shinyServer(function(input, output,session) {

	cppFunction('
	std::vector<int> c_which(NumericVector x, Function f, List args){
	std::vector<int> ind = as< std::vector<int> >(f(x, args));
	std::vector<int> out(ind);
	std::vector<int>::iterator it;
	int j = 0;
	it = std::find(ind.begin(), ind.end(), 1);
	while(it++ != ind.end()){
	out[j++] = it - ind.begin();
	it = std::find(it, ind.end(), 1);

	## ruiling@ 07/08/2012
	## input:
	## x: sample data matrix, d×n
	## d is the dimension of original sample
	## n is the number of samples
	## y: class label vector, n×1
	## r: reduced dimension
	## metric：type of metric in the embedding space
	## 'weighted' ------ weighted eigenvectors
	## 'orthonormalized' ------ orthonormalized