Monthly Archives: August 2013

R/gridSVG/d3 Line Reverse Data Bind

August 8, 2013
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R/gridSVG/d3 Line Reverse Data Bind

I veer from finance to tech, so let’s use some data from FRED/OECD this time.  I do not think I need to comment much on what has happened to New Car Registrations in Greece. Reverse data binding a line plot from ggplot2 or lattice is slightly more difficult than what we saw in the last post I Want...

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Absolute Deviation Around the Median

August 8, 2013
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Absolute Deviation Around the Median

Median Absolute Deviation (MAD) or Absolute Deviation Around the Median as stated in the title, is a robust measure of central tendency. Robust statistics are statistics with good performance for data drawn from a wide range of non-normally distributed probability distributions. Unlike the standard mean/standard deviation combo, MAD is not sensitive to the presence of outliers. This

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R on Your iPhone (Not the Way You Think)

August 8, 2013
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R on Your iPhone (Not the Way You Think)

If you really love R, you should put it on your iPhone.  Apple gives the measurements for its products here. Let's use a little grid magic with ggplot2 to make a chart for the back of your iphone similar to this. require(grid)require(ggplot2)# thanks for the Apple measurements# https://developer.apple.com/resources/cases/x11( height = as.numeric(convertX(unit(58.55, "mm"), "in")),...

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Bio7 1.7 for Windows Released!

August 8, 2013
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Bio7 1.7 for Windows Released!

07.08.2013 A new Windows version of Bio7 is available. This version comes with a lot of new features and improvements for Java, R and ImageJ. One highlight is that you can now interpret Jython (Python) code with Bio7. In addition a new console implementation is available which offers access to a native shell, different Java

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Summarize content of a vector or data.frame every n entries

August 8, 2013
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Summarize content of a vector or data.frame every n entries

I imagine that the same result can be achieved by a proper use of quantile, but I like to have an easy way to obtain summary statistics every n entries of my dataset be it a vector or data.frame. The function takes three parameters: the R object on which we need to obtain statistics (x),

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Data Science MD July Recap: Python and R Meetup

August 8, 2013
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Data Science MD July Recap: Python and R Meetup

For July’s meetup, Data Science MD was honored to have Jonathan Street of NIH and Brian Godsey of RedOwl Analytics come discuss using Python and R for data analysis. Jonathan started off by describing the growing ecosystem of Python data … Continue reading → The post Data Science MD July Recap: Python and R Meetup appeared first on...

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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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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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