Monthly Archives: August 2013

JSM 2013 – Wednesday

August 8, 2013
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I was able to attend a continuing education short course workshops at the JSM conference that proved to be quite insightful.  The discussion was on data mining and was titled “Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets”.  The presentation was given by Dan Steinberg and the examples that he gave were

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Understanding the *apply() functions and then others in R by asking questions

August 7, 2013
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Understanding the *apply() functions and then others in R by asking questions

The *apply() functions are powerful but designed poorly. The R documents are written poorly. They combined make R very hard to learn. All the time, it seems there are some explanations missing. (Another bad example of documentation is Python docs. I cannot retrieve information from the long long pages with multi-level bullets. Why don’t they

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methods of calling differential region of ChIP-seq

August 7, 2013
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Related papers to read:Model-based Analysis of ChIP-Seq (MACS)MACS can also be applied to differential binding between two conditions by treating one of the samples as the control. Since peaks from either sample are likely to be biologically meaningful...

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Data Transformations – from R to PMML – The pmmlTransformations Package

August 7, 2013
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Data Transformations – from R to PMML – The pmmlTransformations Package

We are very excited to announce the availability of the R pmmlTransformations package. This package allows you to export data transformations together with your model from R into a PMML file, which you can then be deployed in the Zementis ADAPA or UPPI...

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K-means Clustering (from “R in Action”)

August 7, 2013
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K-means Clustering (from “R in Action”)

In R’s partitioning approach, observations are divided into K groups and reshuffled to form the most cohesive clusters possible according to a given criterion. There are two methods—K-means and partitioning around mediods (PAM). In this article, based on chapter 16 of R in Action, Second Edition, author Rob Kabacoff discusses K-means clustering. Read more »

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The power, and danger, of visualizations

August 7, 2013
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The power, and danger, of visualizations

I recently posted about visualizing the voting patterns of senators. In the post, I scraped voting data for each senator on every vote in the 113th Congress from the Senate website, and then assigned a code of 0 for a no vote on a particular issue, 1 for a yes vote, 2 for abstention, and 3 if the...

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The power, and danger, of visualizations

August 7, 2013
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The power, and danger, of visualizations

I recently posted about visualizing the voting patterns of senators. In the post, I scraped voting data for each senator on every vote in the 113th Congress from the Senate website, and then assigned a code of 0 for a no vote on a particular issue, 1 for a yes vote, 2 for abstention, and 3 if...

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ggplot2 meet d3

August 7, 2013
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With great libraries, just a couple lines of code can do amazing things.  For instance, let’s limit ourselves to less than 10 lines of code and see what ggplot2 and d3 can do.  We will use gridSVG as discussed in yesterday’s post I Want ggplot2/lattice and d3 (gridSVG–The Glue) to expose ggplot2 to d3.  Thanks

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Disrupting the Traditional Analytics Ecosystem

August 7, 2013
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This guest post is by Punit Kulkarni. Punit is the Director of Marketing at Symphony Analytics and a marketing technology enthusiast. He has helped Fortune 500 retailers and brands in building their customer loyalty programs, direct marketing and business analytics. As a trusted co-marketing partner of Revolution Analytics, Symphony Analytics is committed to developing predictive analytics solutions built upon...

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Downloading SP500 stock price data with R

August 7, 2013
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Downloading SP500 stock price data with R

Using R, we show how to download historic stock prices for all S&P500 components from Yahoo!Finance. We visualize missing data, and process stock prices to get clean daily logarithmic returns. The data then could readily be used in financial applications like risk management or asset management.

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