R Command Line

April 16, 2010
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R Command Line

I am an R user! And I see a whole army of R users, here in defiance of tyranny. You’ve come to use R as free men… and free men you are. What will you do with that freedom? Will you use R? Use R and you may use the command line. Use SAS,

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Because it’s Friday: When infographics go bad

April 16, 2010
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Because it’s Friday: When infographics go bad

Phil Gyford laments crappy infographics swamping the good: Phil Gyford on Flickr: Infographic (used under Creative Commons license)

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R – not the epic fail we thought

April 16, 2010
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I usually like AnnMaria's witty insight. I can relate to a lot of what she is saying. After all SAS and family life are large parts of my life, too. But you can imagine the reaction she provoked in saying the following:I know that R is free and I am ac...

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Rcpp 0.7.12

April 16, 2010
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A new bug fix versions 0.7.12 of Rcpp is awaiting inclusion into CRAN and Debian. It is also available from here. This is another bug-fix version related solely to a build failure on Windows. Trying to protect paths with spaces has the side-effect of ...

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Rcpp 0.7.12

April 16, 2010
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A new bug fix versions 0.7.12 of Rcpp is awaiting inclusion into CRAN and Debian. It is also available from here. This is another bug-fix version related solely to a build failure on Windows. Trying to protect paths with spaces has the side-effect...

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An article attacking R gets responses from the R blogosphere – some reflections

April 16, 2010
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An article attacking R gets responses from the R blogosphere – some reflections

In this post I reflect on the current state of the R blogosphere, and share my hopes for the future

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The Next Big Thing: SAS and SPSS!…wait, what?

April 15, 2010
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The Next Big Thing: SAS and SPSS!…wait, what?

Thanks to the R Bloggers aggregator I came across Yihui Xie’s post on a piece currently making the rounds about statistical analysis platforms. In The Next Big Thing, AnnMaria De Mars makes the argument that R—as a statistical computing platform—is not well suited for what she views as the next big things in data

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Solving optimization problems numerically in R with optim()

April 15, 2010
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Often in game theory (and presumably other applied math settings) we are interested in the behavior of equations with no explicit solution. In this talk, Andrew Little demonstrates how I use the optim() and other functions in R for such situations in m...

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Saving the world with R

April 15, 2010
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Tuesday's meeting of the Bay Area R UseR Group at the LinkedIn offices was a great event. The headline speaker was Joe Adler, author of the excellent R reference manual, R in a Nutshell. Joe's presentation was an in-depth look at the relative speed of various options in R for looking up values from a key in a key-value...

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R is an Epic Fail?

April 15, 2010
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I came across this blog post just now: The Next Big Thing, and of course these words caught my attention: However, for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On

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I’d be more than happy with the unlinked data web

April 14, 2010
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I’d be more than happy with the unlinked data web

Visit this URL and you’ll find a perfectly-formatted CSV file containing information about recent earthquakes. A nice feature of R is the ability to slurp such a URL straight into a data frame: quakes <- read.csv("http://neic.usgs.gov/neis/gis/qed.asc", header = T) colnames(quakes) # "Date" "TimeUTC" "Latitude" "Longitude" "Magnitude" "Depth" # number of recent quakes nrow(quakes) #

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Zelig and Matching in R with an Application to Conflict and Leader Tenure

April 14, 2010
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Andrew Little discusses two econometric packages developed by Gary King of Harvard, and how he has used them in his research at the August, 2009 NYC R Statistical Programming Meetup. Zelig - a single, easy-to-use package that can estimate, help inter...

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Lots of new Videos in Rchive

April 14, 2010
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I have just uploaded a bunch of new videos the Rchive (yea, that’s what I am calling it now). Most of the videos are from the April NYC meetup, which include the following talks: Pankaj Chopra—using R and Bioconductor (http://www.bioconductor.org/) for biomarker detection in cancer Andrew Ilardi—an R project that analyzes a list of stocks while reaching out

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Portfolio Correlation Analysis Tool

April 14, 2010
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Andrew Ilardi presents R project that analyzes a list of stocks to the NYC R Statistical Programming Meetup on April 8, 2010. Andrew's tool reaches out to the web to pull historical stock prices, then plots Quarter over Quarter correlations and a yea...

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Biomarker detection in cancer (gene expression analysis)

April 14, 2010
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Pankaj Chopra discusses using R and Bioconductor (http://www.bioconductor.org/) for biomarker detection in cancer to the NYC R Statistical Programming Meetup on April 8, 2010.

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New York Pizza – How to Find the Best

April 14, 2010
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Jared Lander discusses the science---and statistics---of finding the best pizza in NYC to the NYC R Statistical Programming Meetup on April 8, 2010.

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Object Oriented Programming with R: My notebook

April 14, 2010
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In the following post, I describe how I've used the OOP features of R to create and use the following class hierarchy:Your browser does not support the <CANVAS> element !/* generated with svg2canvas by Pierre Lindenbaum http://plindenbaum.blogspot.com [email protected] */function paint1271278100588(){var canvas=document.getElementById('ctx1271278100588');if (!canvas.getContext) return;var c=

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Object Oriented Programming with R: My notebook

April 14, 2010
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In the following post, I describe how I've used the OOP features of R to create and use the following class hierarchy:Your browser does not support the <CANVAS> element !/* generated with svg2canvas by Pierre Lindenbaum http://plindenbaum.blogspot.com [email protected] */function paint1271278100588(){var canvas=document.getElementById('ctx1271278100588');if (!canvas.getContext) return;var c=

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Slides from High-Performance Analytics webinar now available

April 14, 2010
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Thanks to everyone who attended the webinar I presented this morning, High-Performance Analytics with REvolution R and Windows HPC Server. My slides are now available for download at the link below; even if you're not using Windows, I hope the slides are a useful introduction to the foreach parallel programming construct in general. If you do use R on...

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Get at least 12 observations before making a confidence interval?

April 14, 2010
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Get at least 12 observations before making a confidence interval?

How many observations should you have before constructing a confidence interval?

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“The next big thing”, R, and Statistics in the cloud

April 14, 2010
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A friend just e-mailed me about a blog post by Dr. AnnMaria De Mars titled “The Next Big Thing”. In it Dr. De Mars wrote (I allowed myself to emphasize some parts of the text): Contrary to what some people seem to think, R is definitely not the next big thing, either. I am always surprised when people ask me why I think...

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R: parallel processing using multicore package

April 14, 2010
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I have been meaning to look at adding some parallel processing to R as I have some scripts that are painfully slow and embarrassingly parallel. There seem to be a lot of packages around for doing parallel computing, listed here.I decided to look at mul...

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R: parallel processing using multicore package

April 14, 2010
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I have been meaning to look at adding some parallel processing to R as I have some scripts that are painfully slow and embarrassingly parallel. There seem to be a lot of packages around for doing parallel computing, listed here.I decided to look at mul...

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Plotting “time of day” data using ggplot2

April 14, 2010
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Plotting “time of day” data using ggplot2

William asks: How can I make a graph that looks like this, “tweet density” style, showing time intervals? He then helpfully describes his input data: a CSV file with headers “time started, time finished, date”. Here’s a simple CSV file, tasks.csv: task,date,start,end task1,2010-03-05,09:00:00,13:00:00 task2,2010-03-06,10:00:00,15:00:00 task3,2010-03-06,11:00:00,18:00:00 task4,2010-03-07,08:00:00,11:00:00 task5,2010-03-08,14:00:00,17:00:00 task6,2010-03-09,12:00:00,16:00:00 task7,2010-03-10,14:00:00,19:00:00 task8,2010-03-11,09:30:00,13:30:00 Read into R, calculate the

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In case you missed it: March Roundup

April 13, 2010
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In case you missed them, here are some articles from last month of particular interest to R users. We reviewed a special report in The Economist on the "Data Deluge" and the growing importance of statistical analysis in business. One section mentioned R specifically. We announced that Zack Urlocker, formerly responsible for engineering and marketing for the open-source database...

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formatR: farewell to ugly R code

April 13, 2010
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formatR: farewell to ugly R code

It is not uncommon to see messy R code which is almost not human-readable like this: # rotation of the word "Animation" # in a loop; change the angle and color # step by step for (i in 1:360) { # redraw the plot again and again plot(1,ann=FALSE,type="n",axes=FALSE) # rotate; use rainbow() colors text(1,1,"Animation",srt=i,col=rainbow(360),cex=7*i/360) #

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Efficient Mixed-Model Association in GWAS using R

April 13, 2010
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I recently did an analysis for the eMERGE network where I had lots of individuals from a small town in central Wisconsin where many of the subjects were related to one another. The subjects could not be treated as independent, but I could not use a fam...

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Repeated measures ANOVA with R (tutorials)

April 13, 2010
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Repeated measures ANOVA is a common task for the data analyst. There are (at least) two ways of performing “repeated measures ANOVA” using R but none is really trivial, and each way has it’s own complication/pitfalls (explanation/solution to which I was usually able to find through searching in the R-help mailing list). So for future reference, I am starting this page...

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Cherry Picking to Generalize ~ NASA Global Temperature Trends ~ enhanced w/ ggplot2

April 12, 2010
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Cherry Picking to Generalize ~ NASA Global Temperature Trends ~ enhanced w/ ggplot2

In a prior article, I tried to visualize the linear global temperatures trends for a grid of start and end years. The visual I created was confusing in that the specification of color scale was interdependent with the data values. I wanted a blue -> white -> red scale of the temperatures indicating cool ->

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