Articles

This guest article was written by author and consultant Tristan Yates (see his bio below). It emphasizes R's data object manipulation and scoring capabilities via a detailed financial analysis example.

Scoring and ranking systems are extremely valuable management tools. They can be used to predict the future, make decisions, and improve behavior – sometimes all of the above. Think about how the simple grade point average is used to motivate students and make admissions decisions.

R is a great tool for building scoring and ranking systems. It’s a programming language designed for analytical applications with statistical capabilities. The capability to store and manipulate data in list and table form is built right into the core language.
 

There is a massive amount of data available on the web. Some of it is in the form of precompiled, downloadable datasets which are easy to access. But the majority of online data exists as web content such as blogs, news stories and cooking recipes. With precompiled files, accessing the data is fairly straightforward; just download the file, unzip if necessary, and import into R. For "wild" data however, getting the data into an analyzeable format is more difficult. Accessing online data of this sort is sometimes reffered to as "webscraping".

In a previous article I provided a list of R programming resources. As a complement to that post, I've compiled a list of statistically oriented websites that colleagues and I have found useful below. For the most part, these sites focus on statistics and quantitative research methods rather than programming.

R offers several ways to spatially orient multiple graphs in a single graphing space. The layout() function and mfrow/mfcol parameter settings are adequate solutions for many tasks and allow the graphing space to be broken up into tabular or matrix-based arrangements. For more fine grained manipulation, the fig and fin parameter settings are available. This article illustrates the capabilities and use of fig and fin.

First we'll create some simulation data to work with:

R can legitimately be called both a programming language and a statistical package. Many books address both the programming and statistical components of R, but invariably the discussion of statistical topics is more detailed than the discussion of programming capabilities. As a supplement, I've started the list of links below. Each of these sources deals specifically and almost exclusively with the the programming aspects of R: objects, arrays, loops and conditional statements, custom functions, debugging, and so on. I'll add to this list as I become aware of other sites.

When creating graphs, we're usually most concerned with what happens near the center of our displays, as this is where most of the important information is generally held. But sometimes, either for aesthetics or clarity, we want to adjust what's outside of the box - in the margins, labels or tick marks. The par() function offers several ways to do this and I'll discuss two that deal primarily with spatial orientation - rather than content - below.

The oma, omd, and omi options