hadley

# User facing function
#
# to_math(x_1 + 1^{2 + 4} + 5 + sqrt(y) / 5 %/% 10)
# to_math(paste(x^2, y - 1, z_i))
# to_math(hat(tilde(ring(x))))
to_math <- function(x) {
  to_math_q(substitute(x))
}

to_math_q <- function(x) {

rsaporta

# Requirements
#sudo apt-get install libcurl4-gnutls-dev # for RCurl on linux
#install.packages('RCurl')
#install.packages('RJSONIO')

library('RCurl')
library('RJSONIO')

query <- function(querystring) {
  h = basicTextGatherer()

kendravant

doInstall <- TRUE
toInstall <- c("twitteR", "dismo", "maps", "ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

searchTerm <- "#rstats"
searchResults <- searchTwitter(searchTerm, n = 1000)  # Gather Tweets 
tweetFrame <- twListToDF(searchResults)  # Convert to a nice dF

userInfo <- lookupUsers(tweetFrame$screenName)  # Batch lookup of user info

alexbbrown

<script src="http://d3js.org/d3.v3.js"></script>
<script type="text/javascript">
(function(){
  // Probably not idiomatic javascript.

  this.countValue=0;
  
  updateView = function(message) {
    
    var svg = d3.select(".d3io").select("svg")

dsparks

doInstall <- TRUE
toInstall <- c("twitteR", "dismo", "maps", "ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

searchTerm <- "#rstats"
searchResults <- searchTwitter(searchTerm, n = 1000)  # Gather Tweets 
tweetFrame <- twListToDF(searchResults)  # Convert to a nice dF

userInfo <- lookupUsers(tweetFrame$screenName)  # Batch lookup of user info

abelsonlive

# Brian Abelson @brianabelson
# Harmony Institute
# December 5, 2012

# lda is a wrapper for lda.collapsed.gibbs.sampler in the "lda" package
# it fits topic models using latent dirichlet allocation
# it provides arguments for cleaning the input text and tuning the parameters of the model
# it also returns alot of useful information about the topics/documents in a format that you can easily join back to your original data
# this allows you to easily model outcomes based on the distribution of topics within a collection of texts

timelyportfolio

<style>

.node {
  stroke: #fff;
  stroke-width: 1.5px;
}

.link {
  stroke: #999;
  stroke-opacity: .6;

ddd1600

getCIK = function(ticker) {
  stopifnot(is.character(ticker))
  uri = "http://www.sec.gov/cgi-bin/browse-edgar"

  response = getForm(uri,CIK=ticker,action="getcompany")
  html = htmlParse(response)
  CIKNode = getNodeSet(html, "//acronym[@title=\"Central Index Key\"][text() = \"CIK\"]")
  CIKNodeText = sapply(CIKNode, function(x) xmlValue(getSibling(getSibling(x))))
  CIK = sub(" .*","",CIKNodeText)
  CIK = sub("^0*","",CIK)

mbostock

license: gpl-3.0

fototo

require(quantmod)
require(PerformanceAnalytics)
require(xtsExtra)
require(RColorBrewer)

#unfortunately don't feel like fighting IP lawyers so I cannot share this index data
portfolio <- read.csv("C:\\Users\\Kent.TLEAVELL_NT\\Documents\\old\\R\\lbustruu with tbill.csv",stringsAsFactors=FALSE)
portfolio <- portfolio[2:NROW(portfolio),2:NCOL(portfolio)]
portfolio <- portfolio[,c(1,3,5)]