R refers to the statistical package developed by the R Project for Statistical Computing1. It has gained traction recently as a viable alternative to many of the most popular statistics packages such as SPSS, SAS, and S+2. This popularity is strongly related to a few factors, namely that is is free and open source, available for Windows, OSX and Linux, and that it is extremely robust and extensible. Because of the free nature of R a new generation of academic researchers and grad students are learning and developing on it, whom are now filtering into industry. R has built in capabilities to perform linear models, graphics and many other statistical needs. Because R is a language, it is relatively easy to write custom functions in R to tailor an analysis to a specific type of data, automate frequently run tasks or to develop a brand new analysis.
Specific Sensory Science Add-ons
There are hundreds of add-on packages available for R which provide functionality ranging from multivariate statistics to time series analysis, genetic algorithms and neural networks. The Comprehensive R Archive Network (CRAN)3 provides a thorough database for each package. In the past few years, some packages have been developed which are specifically designed to aid the sensory practitioner. These include:
|| Exploratory data analysis and multivariate statistics, including MFA, Generalized Procrustes Analysis (GPA) and PCA.|
||Sensory DOE, Projective Mapping analysis, and other sensory functions|
||Thurstonian models for sensory discrimination.|
The RCmdr7 package provides a rudimentary GUI interface to basic R statistical functions including basic linear models. It also aids in the importing and exporting of data to and from .xls or .csv files.
ggplot28 provides extremely advanced graphics capabilities. Sweave9 provides integration of R code into LaTeX (a typesetting language) documents, and in the newer versions of R is built-in. This allows the creation of high quality dynamic statistical reports on-the-fly from raw data, vastly increasing the efficiency of running and reporting routine sensory data.