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RStudio - Shiny

RStudio - Shiny

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?

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. Outputs change instantly as users modify inputs, without requiring a reload of the browser. 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. ui.R server.R What is Reactivity? ui.r

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.

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

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. 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. Variable sets are a novel and very useful aspect of Displayr. Insert > Control (More).Type the levels, separated by semi-colons, into the Item list.