Applied R for the quantitative social scientist is a manual on R written specifically as an introduction for the quantitative social scientist. To my opinion, R-Project is a magnificent statistical program, ready to be accepted and implemented in the social sciences. The flexibility of this program and the way data are handled gives the user a sense of closeness to and control over the data. I think this inspires users to analyze their data more creatively and sometimes in a more advanced way.
The R-Sessions are a series of blog entries on using R. A large part consists of an R-manual I once wrote. Other posts include some tricks I found out, as well as entries detailing functions and packages I wrote for …
Recently, I was contacted with an question about R code. A befriended researcher was working with nested data, which was unbalanced. He was working with data in a ‘long’ format: all observations nested within the same group had the same …
There are various ways of getting your output from R to your publication draft. Most of them are highly efficient, but unfortunately I couldn’t find a function that combines the output from several (lmer) models and presents it in a …
It always takes some time to get a grip on a new dataset, especially large ones. The code-books are often as indispensable as they are massive, and not always as clear as one would want. Routings, and resulting and strange patterns of missing values are at times difficult to find.
I found a nice way to plot missing values, using R. Basically, I thought it would be nice to calculate the percentage of missings on each variable, and do so for each year represented in the data. These numbers could be visualized using a levelplot(), which resulted in the graph below.
Working with statistics can be quite time consuming. As anyone working with relatively advanced models and large amounts of data knows, especially the waiting can be excruciating. Your statistical software is locked up while crunching those numbers, while you’d actually prefer to run some minor procedures, such as post-estimations, testing some loops, or simply displaying the output of a previously estimated model. With Apple’s Mac OS X you now can run R-Project twice, making the most of your dual core processor.
Yesterday, I received my new Apple MacBook. It’s running a Core 2 Duo at 2.4 Ghz and it’s fast. Really fast! I tested it with using R-Project, doing some timings on matrix transformations.
Apparently, it’s very cool to show of the speed of R-Project on your system. Optimized .DLL files help to speed up your R on Windows systems (and possibly other systems as well) with respect to matrix transformations, which has led to enormous speed increases. So, let’s perform a speed-test of our own.
Since R-Project is essentially syntax based, one needs a good text editor to write some code before it is executed in R. And, since we are all writing high quality code, we need a high quality text editor. This is the first in a series on text editors for using with R-Project on MacOSX.
The first editor to look at, is the internal one. The Mac OS X version of R-Project comes with quite a strong, although basic, text editor.
Despite the reference to S and S-PLUS in the title of this book, it offers an excellent guide for the nlme-package in R-Project. Reason for this is the close resemblance between R and S. The nlme-package, available in R-Project for estimation of both linear and non-linear multilevel models, is written and maintained by the authors of this book.
