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I really enjoy using RStudio, but one thing about it drives me bats. It seems to use red text for some very innocuous error messages. Continue reading
This page is moving to a new website.
I really enjoy using RStudio, but one thing about it drives me bats. It seems to use red text for some very innocuous error messages. Continue reading
This page is moving to a new website.
This is an O’Reilly book (cute animal on the cover is a rabbit) that is available online for free. It’s a great resource for someone just getting started with text mining. Continue reading
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I have not viewed this video yet, but it comes from a good friend. There is a substantial effort at Microsoft to better integrate the R programming language and their flagship database produce, SQL Server. Continue reading
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Someone wanted to know how to run a Bayesian data analysis for a two group longitudinal study. There are several ways you can do this, but I had to confess I did not have an immediate answer. So I took some time to figure out how to do this using jags software inside of R. I’ve done a fair amount of stuff in jags, but not anything close to a longitudinal design. The general principle is to start with something easy and work your way slowly up to the final analysis. Continue reading
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I am using pipes in R (the magrittr package) a lot recently. It reduces the number of errors due to nested functions, among other things. I’ve given a simple example before, and here’s another. Continue reading
When you use R, you are using a program that is constantly evolving. The user-contributed packages are also evolving as well. Normally this is not that big a deal. But sometimes it is. Continue reading
At the Joint Statistical Meetings this year, I learned a lot about recent developments in R, and not so recent developments that I was totally clueless about. One of those developments was the use of pipes in R. I wanted to show a simple example of how pipes can simplify your code. Continue reading
I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates unit conversions. If you want to measure the length of a diagonal line segment in an R graph, you need to calculate the size of the plotting region in pixels, compare that to the range of the plotting region in the x and y directions, and then apply the Pythagorean Theorem. Continue reading
I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates how you can use some of the commands described in that cheat sheet to rotate text to match a diagonal line in an R graph. It’s trickier than it seems. Continue reading
I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates how you can use some of the commands described in that cheat sheet to draw a perfect circle. Continue reading