r4stats.com: R info for SAS, SPSS, and Stata Users r - What is the difference between gc() and rm() Product Review – Revolution R 5.0 So I got the email from Revolution R. Version 5.0 is ready for download, and unlike half hearted attempts by many software companies they make it easy for the academics and researchers to get their free copy. Free as in speech and free as in beer. Some thoughts- 1) R ‘s memory problem is now an issue of marketing and branding. The primary advantage 64-bit architectures bring to R is an increase in the amount of memory available to a given R process.The first benefit of that increase is an increase in the size of data objects you can create. 2) The User Interface is best shown as below or at -(but I am still hoping for the GUI ,Revolution Analytics promised us for Christmas) 3) The partnership with Microsoft HPC is quite awesome given Microsoft’s track record in enterprise software penetration but I am also interested in knowing more about the Oracle version of R and what it will do there.
Impatient R Translations français: Translated by Kate Bondareva. Serbo-Croatian: Translated by Jovana Milutinovich from Geeks Education. Preface This is a tutorial (previously known as “Some hints for the R beginner”) for beginning to learn the R programming language. It is a tree of pages — move through the pages in whatever way best suits your style of learning. You are probably impatient to learn R — most people are. This page has several sections, they can be put into the four categories: General, Objects, Actions, Help. General Introduction Blank screen syndrome Misconceptions because of a previous language Helpful computer environments R vocabulary Epilogue Objects Key objects Reading data into R Seeing objects Saving objects Magic functions, magic objects Some file types Packages Actions What happens at R startup Key actions Errors and such Graphics Vectorization Make mistakes on purpose
What is R? During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn. Watch this 90 second video for an introduction to R This video is free to download, remix and share! Every data analysis technique at your fingertips R includes virtually every data manipulation, statistical model, and chart that the modern data scientist could ever need. Create beautiful and unique data visualizations Get better results faster Draw on the talents of data scientists worldwide Learn More: Which R is Right for Me?
PSPP GNU PSPP is a program for statistical analysis of sampled data. It is a Free replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions. The most important of these exceptions are, that there are no “time bombs”; your copy of PSPP will not “expire” or deliberately stop working in the future. Neither are there any artificial limits on the number of cases or variables which you can use. There are no additional packages to purchase in order to get “advanced” functions; all functionality that PSPP currently supports is in the core package. PSPP is a stable and reliable application. A brief list of some of the PSPP's features follows below. Support for over 1 billion cases. PSPP is particularly aimed at statisticians, social scientists and students requiring fast convenient analysis of sampled data. Downloading PSPP There are some additional ways you can download or otherwise obtain PSPP. Documentation Further information Getting involved Test releases
Example . An example of nested downloads using RCurl. This example uses RCurl to download an HTML document and then collect the name of each link within that document. The purpose of the example is to illustrate how we can combine the RCurl package to download a document and use this directly within the XML (or HTML) parser without having the entire content of the document in memory. We start the download and pass a function to the xmlEventParse() function for processing. As that XML parser needs more input, it fetches more data from the HTTP response stream. This is useful for handling very large data that is returned from Web queries. To do this, we need to use the multi interface for libcurl in order to have asynchronous or non-blocking downloading of the document. The remaining part is how we combine these pieces with RCurl and the XML packages to do the parsing in this asynchronous, interleaved manner. The steps in the code are as explained as follows. perform = FALSE . library(RCurl) library(XML)
Step up your R capabilities with new tools for increased productivity I guess a lot of us actually use many tools to accomplish various things in their everyday life. There is the (not that uncommon) case where you have to build something that others will use in their everyday business life to get insights, information and/or take decisions. The basic implementation scenario here would be to build an excel workbook where you will feed the data and have a overview sheet, named Dashboard…If things are on your side you could set-up a connection to a database (an existing one or one you will create for the data in discussion) and pull data from there. You can build powerful and visually elegant things using this approach. A cool resource to generate tears of joy among colleagues is Chandoo.org. OK, we all love R. But what about interactive results? Unfortunately you will soon realize that building a highly interactive dashboard has limited added value for complex questions, like the ones that predictive analytics would bomb at your inbox. R.
The Endeavour | John D. Cook I help people make decisions in the face of uncertainty. Sounds interesting. I’m a data scientist. Not sure what that means, but it sounds cool. I study machine learning. Hmm. I’m into big data. Even though each of these descriptions makes a different impression, they’re all essentially the same thing. There are distinctions. “Decision-making under uncertainty” emphasizes that you never have complete data, and yet you need to make decisions anyway. “Data science” stresses that there is more to the process of making inferences than what falls under the traditional heading of “statistics.” Despite the hype around the term data science, it’s growing on me. Machine learning, like decision theory, emphasizes the ultimate goal of doing something with data rather than creating an accurate model of the process that generates the data. “Big data” is a big can of worms. Bayesian statistics is much older than what is now sometimes called “classical” statistics.
Quick-R: Home Page Learn R Toolkit | Climate Charts & Graphs As a former Excel chart user, I want to help current Excel users make the transition to more advanced charting R with as little difficulty as possible. This post introduces my LearnR Toolkit to help Excel users move up to R in a systematic, step by step fashion. Introduction As an Excel chart user, I wanted to produce panel charts like this: After using VBA to build Excel panel charts (link), I knew I had to use a more advanced charting tool to continue my global warming, citizen climate science studies. LearnR Toolkit I’ve put together a series of instructional PowerPoint, video modules with supporting R scripts and data files to help Excel users learn R. Here’s a list of the modules with links to the Zip and PPT files. When viewing a PPT file, be sure to put PowerPoint in slide show mode to be able to see the embedded videos. Installing Zip Files The full Learn R Toolkit includes an Introduction and 5 modules, with 80 files. You will need to Extract the Zip file to your hard drive.
r - How do I scrape multiple pages with XML and ReadHTMLTable