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rCharts — rCharts 0.1.0 documentation.


NVD3. rMaps. Adventures in Analytics and Visualization: Scraping Pro-Football Data and Interactive Charts using rCharts, ggplot2, and shiny. Graphs/Charts. In this post, I use a simulated dataset (7 variables -3 factor and 4 numeric - and a sample size of 50) to create graphs/charts using base R, and replicate them using ggplot2, and rCharts.


This is not an attempt to create an exhaustive database of graphs/charts of all possible combinations, but it is an exercise to generate some of the common ones (in my view). These include dot plots, histograms, box plots, bar charts, scatter plots, density curves, and line graphs and a few more. I am sure the code can be further optimized and it could use some finishing touches with many things like legends, axes labels, and color, but at the core, I think it does its job.

Pages of the 3 versions of graphs/charts were generated using slidify. Thanks to Ramnath Vaidyanathan for having answers to all questions and to the kind rCharts, ggplot2, and R community for the free knowledge base available on the Internet.

..and Shiny

Dygraphs. rNotebook. If you’re like me, you love ipython notebook but often write R.


RStudio’s integrated RMarkdown is nice, but for some contexts like quick demos or basic training, a browser-based interface is unbeatable. What if we could get the best of both worlds – an ipython notebook for R? The answer is rNotebook, and if you haven’t seen it yet, take a moment to watch the video below from Ramnath Vaidyanathan, its creator: Now, before I get your hopes up too high, it’s not under very active development and it doesn’t come with the maturity that you get from ipython notebook (e.g., keyboard shortcuts, password-protection, multiple notebooks). That said, it’s definitely worth a try! To get started quickly, I’ve put together a basic gist that will get an Ubuntu 13.04 up and running with a live rNotebook instance.



D3. rCharts NYT Interactive Home Price. Disclaimer and Attribution I claim absolutely no credit for this visualization, which I consider one of the most best I have ever seen.

rCharts NYT Interactive Home Price

All credit belongs to the original source. If anybody believes this to be not fair use, I will take it down immediately. I am implicitly assuming approval for this fork due to the data.stories interview. Another Favorite from NYT I think we all know the data visualization team at NYT is simply amazing. Over at @nytgraphics, @KevinQ and @shancarter really know how to wiggle a baseline. I immediately knew the Case Shiller Home Price Index visualization would be perfect for reuse with any cumulative growth time series data. Generalize the d3 code a little more to adapt to the dataBuild in R with rCharts to make it reusable. Reusable Version in rCharts As I mentioned above, this visualization works well with any cumulative growth time series, so let's apply it to the managers dataset supplied by the PerformanceAnalytics package.

Get Data and Transform. Getting started · Polychart/polychart2 Wiki. Wiki ▸ Getting Started Getting Started Polychart.js is a flexible library for plotting interactive charts.

Getting started · Polychart/polychart2 Wiki

This tutorial will go through the basics of how to create a chart, and how to tweak it to your liking. We will do this by examples: each example contains a plot, the code, and an explanation of what the code does. Notice that all charts here are actually PNG images, so to view interactive charts, go to the demo page. 1. Let's start with creating a simple bar chart. The first function call is to the function, which creates a wrapper on a dataset.

The second function call actually creates the chart. A layer contains information about what is to be plotted. rCharts. Recently, I had blogged about two R packages, rCharts and rNVD3 that provided R users a lattice like interface to create interactive visualizations using popular javascript libraries.


There was a lot of repeated code between the two packages, which lead me to think that it might be possible to integrates multiple JS libraries into a single package with a common lattice like interface. After heavy refactoring, I finally managed to implement three popular JS libraries in rCharts: Polycharts, NVD3 and MorrisJS. rCharts uses reference classes, which I believe is one of the best things to happen to R. It allowed me to keep the code base pretty concise, while implementing a fair degree of functionality. 2 Knitr/R Markdown/Rstudio issues: Highcharts and Morris.js. Ramnathv/rCharts. rCharts. rCharts/inst/apps/notebook/www/example.Rmd at master · ramnathv/rCharts.