1659 search results for "Ggplot2"

A word cloud where the x and y axes mean something

April 17, 2012
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A word cloud where the x and y axes mean something

Ok so I have now done two iterations on a better way to visualize term frequencies using R, ggplot2 and plyr. The first was ok but ugly, the second was better but still ugly. How to read it: Frequency is segmented in to 20% quantiles The frequency is on the y axis Word size is

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Quickly Explore the Penn World Tables in R

April 17, 2012
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Quickly Explore the Penn World Tables in R

The Penn World Tables are one of the greatest source of worldwide macroeconomic data, but dealing with its web interface is somewhat cumbersome. Fortunately, the data is also available as a R package on CRAN. Having some tools at hand … Continue reading →

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The (Un)disputed Champion of Psychotherapy – Clinical psychologists and their theoretical orientations

April 17, 2012
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The (Un)disputed Champion of Psychotherapy – Clinical psychologists and their theoretical orientations

Cognitive Behavioral Therapy is the psychological treatment of choice for many, if not all, mental disorders. Nonetheless a majority of US clinical psychologist do not primarily identify themselves as either cognitive or behavioral therapists. Looking at data from PubMed publication counts a clear picture emerges; psychodynamic researchers might just be research loafers.

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Linguistic Notation Inside of R Plots!

April 14, 2012
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Linguistic Notation Inside of R Plots!

So, I've been playing around with learning knitr, which is a Sweave-like R package for combining LaTeX and R code into one document. There's almost no learning curve if you already use Sweave, and I find a lot of knitr's design and usage to be a lot nicer.I wasn't going to make a blog post or tutorial about...

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R’s continued growth in academia

April 13, 2012
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R’s continued growth in academia

Bob Muenchen has recently updated his report on the popularity of statistical software. With the updated analysis, we see that the R community remains as strong as ever: the number of contributed R packages continues its exponential growth rate, R maintains its dominance in online discussion, and has 20x the content of other statistics packages on social programming sites...

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In case you missed it: March 2012 Roundup

April 12, 2012
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In case you missed them, here are some articles from March of particular interest to R users. New features in the latest version of ggplot2 include choropleths, violin plots, and improved annotations. A video demonstration of big-data Naive Bayes and Classification Tree models with Revolution R Enterprise for IBM Netezza. A collection of two-minute video tutorials for R beginners....

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Statistical Software Popularity on Google Scholar

April 12, 2012
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Statistical Software Popularity on Google Scholar

Background (probably boring) Several months ago, my boss and I were discussing how he got the data for his software popularity article; the rest of the background discussion pertains to those plots, so I would recommend going over to take a look before continuing on (or just skip to the next section if you're impatient).  Specifically, we were talking...

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How to work with Google n-gram data sets in R using MySQL

April 12, 2012
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How to work with Google n-gram data sets in R using MySQL

In this R tutorial you will learn how to work with Google n-gram data sets with the help of MySQL. The complete R code is included in this post.

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Flying: Boredom and Terror

April 11, 2012
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Flying: Boredom and Terror

Data represents all planes (not just commercial planes) for the United States"There are only two emotions on a plane: boredom and terror." -- Orson Welles, interview to celebrate his 70th birthday, The Times of London, 6 May 1985....

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The lm() function with categorical predictors

April 8, 2012
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The lm() function with categorical predictors

What's with those estimates?By Ben OgorekIn R, categorical variables can be added to a regression using the lm() function without a hint of extra work. But have you ever look at the resulting estimates and wondered exactly what they were?First, let's define a data set.set.seed(12255)n = 30sigma = 2.0AOV.df <- data.frame(category = c(rep("category1", n)     ...

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