bbest

license: mit
height: 300
scrolling: no
border: no

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

withr

shinyServer(function(input, output) {
  output$obs <- renderUI({
    sliderInput("obs", "Number of observations:", 
                min = 10000, max = 90000, 
                value = 50000, step = 10000)
  })
  output$distPlot <- renderPlot({
    if (!is.null(input$obs)) {
      dist <- NULL
      for (i in 1:input$obs) {

bluesmoon

Marsaglia-polar.js

This script generates random numbers along a Normal or Log-normal distribution using the Marsaglia polar method.

There are four functions you can use:

• normalRandom: Generate random numbers that follow a Normal distribution.
• normalRandomInRange: Generate random numbers that follow a Normal distribution but are clipped to fit within a range
• normalRandomScaled: Generate random numbers that follow a Normal distribution with a given mean and standard deviation
• lnRandomScaled: Generate random numbers that follow a Log-normal distribution with a given geometric mean and geometric standard deviation

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

jbryer

#List of most used R packages that we wish to install.
libraries = c('cacheSweave', 'Deducer', 'devtools', 'doBy', 'foreign', 'gdata', 
			  'ggplot2', 'Hmisc', 'JGR', 'lubridate', 'maps', 'mapdata', 'mapproj', 
			  'maptools', 'proto', 'psych', 'R2wd', 'RCurl', 'reshape', 
			  'RODBC', 'roxygen2', 'seqinr', 'sm', 'sp', 'sqldf', 'survey', 
			  'WriteXLS', 'XML', 'xtable')

#We will install packages from the main CRAN site
repos = 'http://cran.r-project.org'
#Site provides some prebuilt binaries for Windows