Monthly Archives: January 2013

Introducing the BH package

January 31, 2013
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Earlier today a new package BH arrived on CRAN. Over the years, Jay Emerson, Michael Kane and I had numerous discussions about a basic Boost infrastructure package providing Boost headers for other CRAN packages (and yes, we are talking packages usin...

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Flowchart: How to learn survey analysis with R

January 31, 2013
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Flowchart: How to learn survey analysis with R

In a recent talk to the DC R User Group, Anthony Damico presented the following handy flowchart for learning to do survey analysis with R (actually, it's a pretty good flowchart for learning R for any application): Since they're not clickable above, here are the resource links: Learn R by watching two‐minute videos on http://twotorials.com Read the “Getting Started...

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Data analysis approaches to modeling changes in primary metabolism

January 31, 2013
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Data analysis approaches to modeling changes in primary metabolism

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Taking Expectations to the Next Level

January 31, 2013
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Taking Expectations to the Next Level

Higher Expectations I came across this post on Thursday and found it to be quite interesting. Clearly rental prices vary according to where you live. That isn't too surprising. I started thinking a bit more about it and thought that Boston and the nearby communities would have to...

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Using R: writing a table with odd lines (again)

January 31, 2013
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Using R: writing a table with odd lines (again)

Let’s look at my gff track headers again. Why not do it with plyr instead? d_ply splits the data frame by the feature column and applies a nameless function that writes subsets to the file (and returns nothing, hence the ”_” in the name). This isn’t shorter or necessarily better, but it appeals to me.

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Using Line Segments to Compare Values in R

January 31, 2013
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Using Line Segments to Compare Values in R

Sometimes you want to create a graph that will allow the viewer to see in one glance:The original value of a variableThe new value of the variableThe change between old and newOne method I like to use to do this is using geom_segment and geom_poin...

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Scatterplot Matrices

January 31, 2013
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Scatterplot Matrices

Scatterplot matrices are a great way to roughly determine if you have a linear correlation between multiple variables. This is particularly helpful in pinpointing specific variables that might have similar correlations to your genomic or proteomic data. If you already have data with multiple variables, load it up as described here. If not, no worries

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How to install packages on R + screenshots

January 31, 2013
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How to install packages on R + screenshots

Have no fear, the screenshots are here! (For the original tutorial, click here) Method 1 (less typing) Part 1-Getting the Package onto Your Computer Open R via  your preferred method (icon on desktop, Start Menu, dock, etc.) Click “Packages” in the top menu then click “Install package(s)”.  Choose a mirror that is closest to your geographical location. Now

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Soup up your R environment: how to install packages

January 31, 2013
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Soup up your R environment: how to install packages

Today we are going to make additions to our R environment in a common process called installing packages. The transition won’t be as long, drastic nor emotional as an episode of Extreme Makeover: Home Edition, but it does add on more capabilities to your R environment. A package is a bunch of codes combined and distributed

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Using SPARQL Query Libraries to Generate Simple Linked Data API Wrappers

January 31, 2013
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Using SPARQL Query Libraries to Generate Simple Linked Data API Wrappers

A handful of open Linked Data have appeared through my feeds in the last couple of days, including (via RBloggers) SPARQL with R in less than 5 minutes, which shows how to query US data.gov Linked Data and then Leigh Dodds’ Brief Review of the Land Registry Linked Data. I was going to post a

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