Software tools for data analysis – an overview

February 19, 2011 | Szilard

by Szilard Pafka Discussions on various software tools (C, C++, Perl, Python, Unix shell, R, Matlab, SAS, SPSS, Excel, databases, Hadoop etc.) used in data analysis. Szilard Pafka (founder and co-organizer of the Los Angeles R users group) presents an … Continue reading →
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What Social Network Analysis software do you use?

May 12, 2010 | Michal

See a the poll here by Gabriel Rossman at Code and Culture. I voted for R and ‘igraph’. If you use R you are getting access to all the other wonderful things that come with R. Using specialized package, like Pajek, UCINET etc requires constant going back and forth between ... [Read more...]

Bayes vs. SAS

May 6, 2010 | xi'an

Glancing perchance at the back of my Amstat News, I was intrigued by the SAS advertisement Bayesian Methods Specify Bayesian analysis for ANOVA, logistic regression, Poisson regression, accelerated failure time models and Cox regression through the GENMOD, LIFEREG and PHREG procedures. Analyze a wider variety of models with the MCMC ...
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R is an Epic Fail?

April 15, 2010 | Yihui Xie

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 ... [Read more...]

Validating credit card numbers in SAS

March 16, 2010 | heuristicandrew

Major credit card issuing networks (including Visa, MasterCard, Discover, and American Express) allow simple credit card number validation using the Luhn Algorithm (also called the “modulus 10″ or “mod 10″ algorithm). The following code demonstrates an implementation in SAS. The code also validates the credit card number by length and by checking ... [Read more...]

Example 7.4: A prettier jittered scatterplot

July 2, 2009 | Ken Kleinman

The plot in section 7.3 has some problems. At the very least, the jittered values ought to be between 0 and 1, so the smoothed lines fit better with them. Once again we use the data generated in section 7.2 as an example. For both SAS and R, we use conditioning (section 1.11.2) to make ...
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R in SAS

February 15, 2009 | Gregor Gorjanc

Another "proof" that R definitely is one of mainstream statistical packages is the news that SAS will provide an interface to R via SAS/IML Studio (today known as SAS Stat Studio).
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Merging data: A tutorial

May 7, 2006 | dataninja

The situation: you have two datasets with a common variable, and you want to incorporate both into one large dataset containing all of the variables. This is called merging data, and it’s easy to do in any standard statistical package. In these examples, I assume that there is only ... [Read more...]
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