coleoguy

slideFunct <- function(data, window, step){
  total <- length(data)
  spots <- seq(from = 1, to = (total - window + 1), by = step)
  result <- vector(length = length(spots))
  for(i in 1:length(spots)){
    result[i] <- mean(data[spots[i]:(spots[i] + window - 1)])
  }
  return(result)
}

JoFrhwld

\documentclass{beamer}
\usetheme{Singapore}
\usepackage{tipa}

%% Make the r-code small
\ifdefined\knitrout
  \renewenvironment{knitrout}{\begin{footnotesize}}{\end{footnotesize}}
\else
\fi

dsparks

# Replicating / improving http://shaheeilyas.com/flags/

# Drawing a scatter plot of raster images
doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("png", "reshape", "ggplot2", "MASS")
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Go to https://www.gosquared.com/resources/2400-flags, download the ZIP,
# and put the 64 x 64 files into a directory of your choosing.

johndharrison

library(RJSONIO)
shinyServer(function(input, output, session) {
  
  output$test <- renderUI({
    noPoints <- as.integer(input$obs)
    series <- toJSON(cbind(seq(88, 266, by = (266-88)%/%(noPoints-1)), 113))
    
    tags$script(HTML(
      paste0("$(document).ready(function () {
            

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")

fractaledmind

(* EXPORT ALL SKIM NOTES TO EVERNOTE WITH HYPERLINKS

-- Stephen Margheim
-- 9/7/13
-- open source

REQUIRED PROGRAMS:
- Skim (pdf viewer and annotator)
- Evernote (cloud based note app)

fletch

#!/opt/local/bin/perl
##
## Slightly more idiomatic and optimized Perl of 2num.pl from
## http://github.com/brendano/awkspeed/tree/master
##
use strict;
use warnings;

open( my $vocab_fh, '>', "vocab" ) or die "vocab: $!\n";

MarkEdmondson1234

  ## in server.r
  output$null_plot <- renderDygraph({
    ## don't output anything unless you have the data ready
    validate(
      need(casualImpactData(), "Model Working")
    )    
    
    ## the data for the plot is in here
    ci <- casualImpactData()$series
    

benmarwick

library("RWeka")
library("tm")

data("crude")

BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
tdm <- TermDocumentMatrix(crude, control = list(tokenize = BigramTokenizer))

inspect(tdm[340:345,1:10])

rasmusab

### An example of building a TensorFlow model from R using rPython ###
# For this script you need to 
# 1. Have python 2.7 installed.
# 2. Install the rPython package in R.
# 3. Install Google's TensorFlow library as per these instructions:
#     http://www.tensorflow.org/get_started/os_setup.md#binary_installation

### Here is how to setup and run a trivial TensorFlow model ###

# Load TensorFlow (I couldn't get this to work without setting sys.argv... )