It’s very easy, apparently, to set up your own server to run Shiny apps (Shiny is a web based method for interacting with R code). If you have set up Amazon Web Services, then it is even easier. Here is a detailed account of how to do this. Once I get my own Shiny server going, I will let you know. Continue reading

# Tag Archives: R software

# PMean: Obnoxious use of red text in RStudio

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

# Recommended: Text Mining with R

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

# Recommended: R and SQL Server 2016

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

# PMean: How to run your first Bayesian analysis using jags software in R

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

# PMean: Another example of pipes in R

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

# PMean: When differing versions of R packages matter

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

# PMean: A simple example of pipes in R

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

# PMean: Measuring pixels in an R graph

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

# PMean: Rotating text in an R graph

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