I’ve been working on a long-term (25+yr) longitudinal study of rheumatoid arthritis with my boss. He just walked in and asked if I could create a plot showing the trajectory of pain scores over time for each subject, separated by educational level (4 groups). Having now worked with ggplot2 for a while, and learning more at the last two DC useR meetups, I realized that I could formulate this in ggplot very easily and in short order.
Forest plots are most commonly used in reporting meta-analyses, but can be profitably used to summarise the results of a fitted model. They essentially display the estimates for model parameters and their corresponding confidence intervals.
Matt Shotwell just posted a message to the R-help mailing list with his lattice-based solution to the problem of creating forest plots in R. I just figured out how to create a forest plot for a consulting report using ggplot2.
The useR! 2010 R users conference just finished up this afternoon with a thought-provoking, controversial, and sometimes hilarious talk by Richard Stallman of GNU fame. It started on Tuesday with great tutorials (I took ones on MICE for multiple imputation and Frank Harrell’s excellent regression modeling). In between these bookends was a wonderful conference where I got the chance to put faces to names (from their online presence), make many new friends, hopefully no enemies, and learn quite a bit.
JD Long (at Cerebral Mastication) posted a question on Twitter about an artifact in ESS, where typing “” gets you “<-“. This is because in the early days of S+, “” was an allowed assignment operator, and ESS was developed in that era. Later, it was disallowed in favor of “<-” and “=”, so ESS was modified to map “_” to “<-“. Now I like the typing convenience of this map, and I don’t use underscores in my variable names, so I was fine.
R has some powerful tools for parallel processing, which I discovered while searching for ways to fully utilize my 8-core computer at work. What surprised me is how easy it is…about 6 lines of code, if that. Given that I wasn’t allowed to install heavy duty parallel-processing systems like MPICH on the computer, I found that the library SNOW fit the bill nicely through its use of sockets. I also discovered the libraries foreach and iterators, which were released to the community by the development team at Revolution R.
On a lark, and to kill a bit of time, I was running the R fortune command looking for references to SAS. Here’s what two successive random fortunes turned up. Can there be two more antipodal opinions about the same product? I laughed out loud.
fortune(‘SAS’) There are companies whose yearly license fees to SAS total millions of dollars. Then those companies hire armies of SAS programmers to program an archaic macro language using old statistical methods to produce ugly tables and the worst graphics in the statistical software world.
I seem to be doing more and more with Python for work over and above using it as a generic scripting language. R has been my workhorse for analysis for a long time (15+ years in various incarnations of S+ and R), but it still has some deficiencies. I’m finding Python easier and faster to work with for large data sets. I’m also a bit happier with Python’s graphical capabilities via matplotlib, which allows dynamic updating of graphs _a la _Matlab, another drawback that R has despite great graphical capabilities.