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One of the more interesting talks at this year’s useR! Conference was the heR Panel discussing the role of women in the R community. They estimate that fewer than 15% of package authors are women. One of the points brought up was that this is less than the percentage of women in statistics. Perhaps this is more related to the computer science aspect of R that that of statistics. By way of comparison, the United States Department of Labor estimates there are between 7.

PSAboot is an R package to assist with bootstrapping propensity score methods. I gave a talk today at the useR! 2014 Conference. The slides can be downloaded from the PSAboot Github page or directly here. The package is described at jason.bryer.org/PSAboot and maintained on Github at github.com/jbryer/PSAboot/.

Also, version 1.1 of the package was just released to CRAN.

The str function is perhaps the most useful function in R. It provides great information about the structure of some object. When I teach R, especially for those coming from SPSS, the str function for data frames provides the information they are use to seeing on the variable view tab. However, sometimes I want to display the information str returns in a better format (e.g. as an HTML or LaTeX table).

Last week I published an R script to interface with Gitbook. I received some positive feedback and decided to include all the code in an R package. This also allowed me to make some nice additions including default support for MathJax. It is currently available on Github and can be installed using devtools: devtools::install_github('jbryer/Rgitbook') I have only tested this on Mac OS X, so please provide suggestions or issues on other systems.

I have started an R Users Group for the Albany, NY area. Hopefully we get enough interest that we can host a meeting in the next couple of months. Please feel free to share with your colleagues and friends.

www.meetup.com/Albany-R-Users-Group

Feel free to email me or leave comment on this page or on the Meetup page if you are interested in giving a talk or hosting some future meeting.

One of the more tedious parts of working with R is maintaining my R library. To make my R scripts reproducible and sharable, I will install packages if they are not available. For example, the top of my R scripts tend to look something like this: if(!require(devtools) | !require(ggplot2) | !require(psych) | !require(lme4) | !require(benchmark)) { install.packages(c('devtools','ggplot2','psych','lme4','benchmark')) } This has worked fine for some time, but I felt there was a better approach.

I am happy to announce that version 1.0 of the PSAboot package has been released to CRAN. This package implements bootstrapping for propensity score analysis. This deviates from typical implementations such as boot in that it allows for separate sampling specifications for treatment and control units. For example, in the case where the ratio of treatment-to-control units is large, one can bootstrap only the control units while always using all available treatment units.

I am about to head home from my fifth time attending the North East Association for Institutional Research (NEAIR), this year in Newport, RI, which was just fantastic. Really great people, interesting talks, and good food. I again taught an Introduction to R and LaTeX for Institutional Research pre-conference workshop and also gave a talk on Propensity Score Analysis for Institutional Research which was an brief version of a workshop I taught at the 2013 useR!

I finally had an opportunity to play with Shiny, and I am very impressed. I have created a Github Project so head over there for the source code. There are a number of ways to distribute Shiny apps. If you are running R (and mostly likely you are if you are reading this), you can download and run Shiny apps using the runApp (if already downloaded), runGitHub, runGist, or runUrl functions.

Frequently I need to recode a date column to quarters. For example, at Excelsior College we have continuous enrollment so we report new enrollments per quarter. To complicate things a bit, our fiscal year starts in July so that July, August, and September represent the first quarter, January, February, and March are actually the third quarter. But sometimes we do need need to report out based upon calendar years (i.e. where January is in the first quarter).