Euclid - Breakthrough analytics for offline retail Let's Rapplicate! | rapporter It's been a while since you last heard from Rapporter, and we came up with a (hopefully) good excuse for our absence from the blogosphere: Rapplications. To demystify: we developed an API that allows you to create dynamic reports by using the R templates and datasets available on Rapporter. All you need is an account on Rapporter (you can get it here) and of course an access to some templates and datasets in there. Once you're logged in click on Settings > Rapplications > New, or simply visit this link. You should see a form similar to this one: Title and Description fields are pretty self-explanatory. In Output format field you can choose between various document types - currently we support: PDF, DOCX, ODT, HTML, and partial HTML (which returns just rendered report, and not the full HTML page). In case you want some instant gratification, you can grab the <iframe> code from the bottom of the page and include it in your website. Anyway, remember that there's an API behind this?
Probability and Statistics Cookbook | Matthias Vallentin The cookbook contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive reference reduced to the mathematical essence, rather than aiming for elaborate explanations. Download Last updated: January 24, 2014 Language: english The LaTeX source code is available on github and comes with a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To reproduce in a different context, please contact me. The cookbook aims to be language agnostic and factors out its textual elements into a separate dictionary. The current translation setup is heavily geared to Roman languages, as this was the easiest way to begin with. Here are the 3 most recent entries of the changelog file (for all versions of the cookbook): 2014-01-24 Matthias Vallentin <vallentin@icir.org> * Fix wrong denominator in alternative CLT representations.
Tutorial: Building 'Shiny' Applications with R Shiny is a new package from RStudio that makes it incredibly easy to build interactive web applications with R. For an introduction and live examples, visit the Shiny homepage. Features Build useful web applications with only a few lines of code—no JavaScript required. Shiny applications are automatically “live” in the same way that spreadsheets are live. Installation Shiny is available on CRAN, so you can install it in the usual way from your R console: install.packages("shiny") Let’s Go! This tutorial covers the basics of Shiny and provides detailed examples of using much of its capabilities. The Hello Shiny example is a simple application that generates a random distribution with a configurable number of observations and then plots it. > library(shiny)> runExample("01_hello") Shiny applications have two components: a user-interface definition and a server script. The user interface is defined in a source file named ui.R: ui.R library(shiny) shinyUI(pageWithSidebar( headerPanel("Hello Shiny!")
Project Daytona: Iterative MapReduce on Windows Azure Microsoft has developed an iterative MapReduce runtime for Windows Azure, code-named Daytona. Project Daytona is designed to support a wide class of data analytics and machine-learning algorithms. It can scale to hundreds of server cores for analysis of distributed data. Project Daytona was developed as part of the eXtreme Computing Group’s Cloud Research Engagement Initiative. News On July 26, 2011, we released an updated Daytona community technical preview (CTP) that contains fixes that are related to scalability. Overview Project Daytona on Window Azure is now available, along with a deployment guide, developer and user documentation, and code samples for both data analysis algorithms and client application. Included in the CTP Refresh (July 26, 2011) This refresh to the Daytona CTP contains the following enhancments: Included in the CTP Release (July 18, 2011) About Project Daytona
P-Value of a Test of Significance P-Value of a Test of Significance This applet illustrates the P-value of a test of significance. The setting is the same as Section 6.2 of PBS: testing hypotheses about the mean of a normal distribution whose standard deviation you know. To set up the test, fill in the boxes: What null hypothesis H0 about the mean μ do you want to test? The normal curve shows the sampling distribution of the sample mean when your null hypothesis is true. count as evidence against H0 in favor of your alternative Ha. from data, the graph will show you the P-value for this : it is the probability -- calculated taking H0 to be true -- of getting a value at least that far away from H0 in the direction of the arrow. You can enter a sample from data you already have. and its P-value. from this one sample.
Data Science Toolkit Using JavaScript visualization libraries with R This is a short tutorial on knitr/markdown and JS visualization packages googleVis and rCharts. With these packages you can create web pages with interactive visualizations just using R. This will require minimal or no knowledge of HTML or JavaScript. You need to have the following R packages and their dependencies installed: knitrgoogleVisrCharts (not on CRAN) The tutorial is organized in four parts. You can download the .Rmd files (or clone the repository from github) and run knit2html() on them in your R console, or if you are using RStudio you can click "knit HTML" button on the upper left corner. The best way to go through the tutorial is to examine the code chunks and explanations in .Rmd files, and then check the HTML output from knit2html(). 1. markdown_knitr.Rmd shows basics of markdown and knitr integration. 2. 3. 4. inShare0
Real time Site Personalization and behavioral targeting solution How to Create an Online Choice Simulator | Displayr A choice simulator is an online app or an Excel workbook that allows users to specify different scenarios and get predictions. Here is an example of a choice simulator. Choice simulators have many names: decision support systems, market simulators, preference simulators, desktop simulators, conjoint simulators, and choice model simulators. In this post, I show how to create an online choice simulator, with the calculations done using R, and the simulator is hosted in Displayr. First of all, choice simulators are based on models. So, the first step in building a choice simulator is to obtain the model results that are to be used in the simulator. If practical, it is usually a good idea to have model results at the case level (e.g., respondent level), as the resulting simulator can then be easily automatically weighted and/or filtered. The table below shows estimated parameters of respondents from a discrete choice experiment of the market for eggs. R Code to paste: Author: Tim Bock
Social Media and Text Mining Analytics | Social CRM Tools | Collective Intellect Oracle Social Cloud is a cloud service that helps you manage and scale your relationship with customers on social media channels. Oracle has integrated the best-in-class social relationship management (SRM) components - social listening, social engagement, social publishing, social content & apps, and social analytics - into one unified cloud service to give you the most complete SRM solution on the market. Why Oracle? Only Oracle can connect every interaction your customer has with your brand.
Sentiment and Text Analytics: Lexalytics, Inc.