Monthly Archives: June 2013

A comprehensive guide to time series plotting in R

June 25, 2013
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A comprehensive guide to time series plotting in R

As R has evolved over the past 20 years its capabilities have improved in every area. The visual display of time series is no exception: as the folks from Timely Portfolio note that: Through both quiet iteration and significant revolutions, the volunteers of R have made analyzing and charting time series pleasant. R began with the basics, a simple...

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Getting started with R

June 25, 2013
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Getting started with R

I wanted to avoid advanced topics in this post and focus on some “blocking and tackling” with R in an effort to get novices started.  This is some of the basic code I found useful when I began using R just over 6 weeks ago. Reading in data from a .csv file is a breeze with this command. > data =...

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The Dream 8 Challenges

June 25, 2013
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The Dream 8 Challenges

The 8th iteration of the DREAM Challenges are underway. DREAM is something like the Kaggle of computational biology with an open science bent. Participating teams apply machine learning and statistical modeling methods to biological problems, competing to achieve the best predictive accuracy. This year's three challenges focus on reverse engineering cancer, toxicology and the kinetics of...

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Three Ways to Run Bayesian Models in R

June 25, 2013
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Three Ways to Run Bayesian Models in R

There are different ways of specifying and running Bayesian models from within R. Here I will compare three different methods, two that relies on an external program and one that only relies on R. I won’t go into much detail about the differences in syntax, the idea is more to give a gist about how the different modeling languages...

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Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R

Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R

Introduction Continuing my recent series on exploratory data analysis (EDA), and following up on the last post on the conceptual foundations of empirical cumulative distribution functions (CDFs), this post shows how to plot them in R.  (Previous posts in this series on EDA include descriptive statistics, box plots, kernel density estimation, and violin plots.) I

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Predicting spatial locations using point processes

June 25, 2013
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Predicting spatial locations using point processes

I’ve uploaded a draft tutorial on some aspects of prediction using point processes. I wrote it using R-Markdown, so there’s bits of R code for readers to play with. It’s hosted on Rpubs, which turns out to be a great deal more convenient than WordPress for that sort of thing.

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-omics in 2013

June 24, 2013
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-omics in 2013

Just how many (bad) -omics are there anyway? Let’s find out. 1. Get the raw data It would be nice if we could search PubMed for titles containing all -omics: However, we cannot since leading wildcards don’t work in PubMed search. So let’s just grab all articles from 2013: and save them in a format

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Visualising Crime Hotspots in England and Wales using {ggmap}

June 24, 2013
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Visualising Crime Hotspots in England and Wales using {ggmap}

Two weeks ago, I was looking for ways to make pretty maps for my own research project. A quick search led me to some very informative blog posts by Kim Gilbert, David Smith and Max Marchi. Eventually, I Google'd the excellent crime weather map exa...

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Comparing the speed of pqR with R-2.15.0 and R-3.0.1

June 24, 2013
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Comparing the speed of pqR with R-2.15.0 and R-3.0.1

As part of developing pqR, I wrote a suite of speed tests for R. Some of these tests were used to show how pqR speeds up simple real programs in my post announcing pqR, and to show the speed-up obtained with helper threads in pqR on systems with multiple processor cores. However, most tests in

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Exploratory Data Analysis: Conceptual Foundations of Empirical Cumulative Distribution Functions

Exploratory Data Analysis: Conceptual Foundations of Empirical Cumulative Distribution Functions

Introduction Continuing my recent series on exploratory data analysis (EDA), this post focuses on the conceptual foundations of empirical cumulative distribution functions (CDFs); in a separate post, I will show how to plot them in R.  (Previous posts in this series include descriptive statistics, box plots, kernel density estimation, and violin plots.) To give you

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