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Example .  An example of nested downloads using RCurl. 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.
Kyle Matoba is a Finance PhD student at the UCLA Anderson School of Management. He gave a presentation on Algorithmic Trading with R and IBrokers at a recent meeting of the Los Angeles R User Group. The discussion of IBrokers begins near the 12-minute mark. To leave a comment for the author, please follow the link and comment on his blog: FOSS Trading. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more... Algorithmic Trading with IBrokers Algorithmic Trading with IBrokers
Models Collecting Dust? How to Transform Your Results from Interesting to Impactful Models Collecting Dust? How to Transform Your Results from Interesting to Impactful Leading expert James Taylor, author of Decision Management Systems: A Practical Guide to Business Rules and Predictive Analytics, has developed a practical approach you can use to improve adoption and elevate your organization. In this webinar, James will show you proven framework for putting predictive analytics to work: How to begin model-building with the decision in mind to establish consensus with business process owners;Proven ways to tie decisions to organizations, metrics, systems and business processes and;Pitfalls that prevent success and how to avoid them. Join this webinar to increase your team’s value to the organization and come away with an approach that ensures buy in from the beginning of the process through the implementation of recommendations.
Pretty R syntax highlighter
First, it is important to note that the two are very different in that gc does not delete any variables that you are still using- it only frees up the memory for ones that you no longer have access to (whether removed using rm() or, say, created in a function that has since returned). Running gc() will never make you lose variables. The question of whether you should call gc() after calling rm(), though, is a good one. The documentation for gc helpfully notes: A call of gc causes a garbage collection to take place. r - What is the difference between gc() and rm() r - What is the difference between gc() and rm()
Memory Available for Data Storage Description How R manages its workspace. Details R has a variable-sized workspace. Prior to R 2.15.0 there were (rarely-used) command-line options to control its size, but it is now sized automatically. Memory Available for Data Storage
Models Collecting Dust? How to Transform Your Results from Interesting to Impactful
Revolution R Enterprise 5.0 now available for free academic download
This special report from Datanami provides an in-depth view into a series of technical tools and capabilities that are powering the next generation of big data analytics. Used properly, these tools provide increased insight, the possibility for new discoveries, and the ability to make quantitative decisions based on actual operational intelligence. Examine these critical components of the big data analytics stack as individual layers:

Revolution Analytics - Commercial Software & Support for the R Statistics Language

Revolution Analytics - Commercial Software & Support for the R Statistics Language
angeregt vom wachsenden Interesse quantitativen Untersuchungen über die Wirkung von Bloginhalten, wie zuletzt im Beitrag Blogs als Quellen in der bibliothekarischen Fachkommunikation, lässt sich ebenfalls die Verlinkung innerhalb von Blogs näher explorieren. Um schnell an möglichen Daten zu gelangen, erscheint http://blogsearch.google.com/ vielversprechend. Dank R sind die Daten für die weitere statistische Untersuchung der Bloglinks auf den LIBREAS Blog auch ohne Programmierkenntnisse schnell gewonnen: library(XML) google <- "http://blogsearch.google.de/blogsearch_feeds? “Credit to whom credit is due” – Bloganalysen mit Google und R « LIBREAS.Library Ideas “Credit to whom credit is due” – Bloganalysen mit Google und R « LIBREAS.Library Ideas
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Extracting comments from a Blogger.com blog post with R Extracting comments from a Blogger.com blog post with R Note #1: Check out this very useful post by Najko Jahn describing how to extract links to blogs via Google Blog Search . Note #2: I’ll update the code below once I find the time using Najko’s cleaner XPath-based solution. Recently I’ve been working with comments as part of the project on science blogging we’re doing at the Junior Researchers Group “Science and the Internet” . I wrote the script below to quickly extract comments from Atom feeds, such as those generated by Blogger.com . The code isn’t exactly pretty, mostly because I didn’t use an XML parser to properly read the data, instead resorting to brute-force pattern matching, but it gets the job done. Two easier (and cleaner) routes would have been to a) get the data directly from the Google Data API (doesn’t work as far as I can tell, since there seems to be no implementation for R*) or b) parse the data specifically as Atom (doesn’t work as — annoyingly — there is no specific parsing support for Atom in R).
Converting HTML to plain text usually involves stripping out the HTML tags whilst preserving the most basic of formatting. I wrote a function to do this which works as follows (code can be found on github): The above uses an XPath approach to achieve it’s goal. htmlToText(): Extracting Text from HTML via XPath htmlToText(): Extracting Text from HTML via XPath
Kay Cichini recently wrote a word-cloud R function called GScholarScraper on his blog which when given a search string will scrape the associated search results returned by Google Scholar, across pages, and then produce a word-cloud visualisation. This was of interest to me because around the same time I posted an independent Google Scholar scraper function get_google_scholar_df() which does a similar job of the scraping part of Kay’s function using XPath (whereas he had used Regular Expressions). My function worked as follows: when given a Google Scholar URL it will extract as much information as it can from each search result on the URL webpage into different columns of a dataframe structure. In the comments of his blog post I figured it’d be fun to hack his function to provide an XPath alternative, GScholarXScraper. GScholarXScraper: Hacking the GScholarScraper function with XPath GScholarXScraper: Hacking the GScholarScraper function with XPath
JGR « Fells Stats
A GUI for R - Downloading And Installing Deducer
A Spatial Data Analysis GUI for R « Fells Stats
Eclipse IDE for R Background: Eclipse is an open source Integrated Development Environment (IDE). As with Microsoft's Visual Studio product, Eclipse is programming language-agnostic and supports any language having a suitable plugin for the IDE platform. For Eclipse, the R language plugin is StatET. Figure 1 (above): Eclipse, StatET with R, and the R debugger (bottom window) at work. The R debugger is an R package library and has its own graphical output window separate from Eclipse. The following three (3) part procedure installs Eclipse onto a Windows platform (XP or Windows 7) and adds StatET (R) language support.
RForge.net - development environment for R package developers RForge strives to provide a colaborative environment for R package developers. The ultimate goal is to offer SourceForge-like services (such as SVN repository, place for documentation, downloads, mailing lists, bugzilla, wiki etc.) without the annoying look and feel but with additional features specific to R package development, such as make check on-commit, nighlty builds of packages, testing on various plarforms and full CRAN-like reposity access. The focus is on R-specific features that are not offered by SourceForge or GForge.
Tinn-R | Free Development software downloads at SourceForge Tinn-R Editor - GUI for R Language and Environment Read More The Tinn-R is an open source (GNU General Public License) and free project. It is an editor/word processor ASCII/UNICODE generic for the Windows operating system, very well integrated into the R, with characteristics of Graphical User Interface (GUI) and Integrated Development Environment (IDE). The project is coordinate by José Cláudio Faria/UESC/DCET.
The two posts below are great examples of different approaches of extracting data from websites and parsing it into R. Scraping html tables into R data frames using the XML package How can I use R (Rcurl/XML packages ?!) to scrape this webpage web scraping - Extract Links from Webpage using R
current community your communities Sign up or log in to customize your list. more stack exchange communities Stack Exchange r - extracting node information
Pretty R syntax highlighter
Pretty R syntax highlighter
Questions containing '[r] xml xpath'
r - How do I scrape multiple pages with XML and ReadHTMLTable
xml - Web scraping with R over real estate ads
R preferred by Kaggle competitors
Blog-Reference-Functions/R at master · tonybreyal/Blog-Reference-Functions
Blog-Reference-Functions/R/googleScholarXScraper/googleScholarXScraper.R at master · tonybreyal/Blog-Reference-Functions
Facebook Graph API Explorer with R (on Windows) « Consistently Infrequent
Good GUI for R suitable for a beginner wanting to learn programming in R? - Statistical Analysis - Stack Exchange
A Spatial Data Analysis GUI for R
R] Downloading data from from internet
Web scraping
r - How to transform XML data into a data.frame
Web Scraping Google Scholar (Partial Success) « Consistently Infrequent
Web Scraping Google Scholar: Part 2 (Complete Success) « Consistently Infrequent
Comment faire pour transformer les données XML dans un data.frame? | TecHerald.com
[BioC] PostForm() with KEGG
Blog-Reference-Functions/R/googlePlusXScraper/googlePlusXScraper.R at master · tonybreyal/Blog-Reference-Functions
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Re: [R] Need help extracting info from XML file using XML package
XML package help
library(XML) url <- "http://eu.battle.net/sc2/en/profile/2007578/1/joachifm"
Solomon Messing | On research, visualization and productivity
Web Scraping Google Scholar (Partial Success)
Web Scraping Google Scholar: Part 2 (Complete Success)
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A Short Introduction to the XML package for R
Memory Management in the the XML Package
The XML package. It's crantastic!
Grabbing Tables in Webpages Using the XML Package
The Omega Project for Statistical Computing
Romain Francois, Professional R Enthusiast
R: Web Scraping R-bloggers Facebook Page « Consistently Infrequent
R: A Quick Scrape of Top Grossing Films from boxofficemojo.com « Consistently Infrequent
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[R] Need help extracting info from XML file using XML package from Don MacQueen on 2009-03-02 (R help archive)
Package XML
CRAN - Package somplot