require (XLConnect) wb = loadWorkbook ( "example.xlsx" ) data = readWorksheet (wb, sheet = "data" ) print ( head (data))
WebScraping with R
R in the Cloud
R- geo examples
SVGs with R
I’m happy to present this jam-packed episode of the R-Podcast dedicated to using the ggplot2 package for visualization.
I just returned from the useR! 2012 conference for developers and users of R.
R is an incredibly comprehensive statistics package.
In my last blog, Big Data, R and SAP HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps , I analyzed historical airlines performance data set using R and SAP HANA and put the aggregated analysis on Google Maps. Undoubtedly, Map is a pretty exciting canvas to view and analyze big data sets.
I received the following email today:
Mirai Solutions GmbH ( http://www.mirai-solutions.com ) is very pleased to announce the release of XLConnect 0.2-0 , which can be found at CRAN . As one of the updates, XLConnect has moved to the newest release of Apache POI: 3.8. Also, the lazy evaluation issues with S4 generics are now fixed: generic methods now fully expand the argument list in order to have the arguments immediately evaluated.
I have become quite a big fan of graphics that combine the features of traditional figures (e.g. bar charts, histograms, etc.) with tables.
Small multiples are one of the great ideas of graphics visionary Edward Tufte (e.g., in Envisioning Information ). Briefly, the idea is that if many variations on a theme are presented, differences quickly become apparent.
A paper published this week in Science outlines a new statistic called the maximal information coefficient (MIC), which is able to equally describe the correlation between paired variables regardless of linear or nonlinear relationship. In other words, as Pearson's r gives a measure of the noise surrounding a linear regression, MIC should give similar scores to equally noisy relationships regardless of type. Read more »
Here's a cool application of calendar heat maps : runner Andy used R to catalogue his daily running mileage over the last 2+ years:
In yesterday's webinar , Revolution Analytics CTO David Champagne demonstrated how to integrate statistical graphics and analytic computations created using R software with a variety of third-party applications. In each case Revolution R Enterprise Server is running as a compute server to the client application, with R scripts launched on each user interaction via the RevoDeployR Web Services API .
require ( RColorBrewer ) require ( quantmod ) require ( PerformanceAnalytics )
A couple of days ago, I had posted a short Python script to convert numpy files into a simple binary format which R can read quickly.