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.
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).
When I went to school we were always taught the “i before e, except after c” rule for spelling. But how accurate is this rule? Kevin Marks tweeted today the following:
»@uberfacts: There are 923 words in the English language that break the “I before E” rule. Only 44 words actually follow that rule.« Science
— Kevin Marks (@kevinmarks) March 25, 2013 Not sure where he came up with that result, but seems simple enough to verify.