A case for using R for the statistical computation of my data

As a usage-based study my project involves quantitative data analyses. This post makes a brief case for the use of R as the chosen statistical computation program for the quantitative analyses of my data.

R is:
- a language and environment for statistical computing and graphics
- a program providing a variety of statistical and graphical techniques
- a free open-source program

The use of R is rapidly growing in the fields of statistics, engineering and science. This article from The New York Times (07/01/2009) provides an overview of the various uses of R by data analysts from differing professional backgrounds.

In corpus linguistics, the use of R is confidently spreading as it allows analysts to carry out multifactoral searches and approach data with fine degrees of granularity. Stefan Gries is actively contributing to the development of R and its application to the field of corpus linguistics and is the author of recently published Quantitative Corpus Linguistcs with R. As an open-source program, R is continually being improved and updated with new codes. In that respect, Gries provides linguists using R with downloadable updated codes on a regular basis.

Generally, the use of R has been praised in the literature concerned with analysis of linguitsic data. As Larson-Hall writes in her review of Baayen's (2008) Analysing linguistic data: A practical introduction to statistics using R : "(...) the statistical program you use guides the way you think about statistical analysis, and I do think R is far superior to any menu-driven program in this way"(p.472).

In the field of cognitive semantics, Dogmar Divjak and Stefan Gries (2008) (Clusters in the mind? Converging evidence from near synonymy in Russian) (The Mental Lexicon 3.2:188-213) provide illustrations of the use of R. Further, in her CMLLP-2008 [Corpus Methods in Linguistics and Language Teaching] Masterclass material used at the University of Chicago, Dogmar Divjak provides a suggested procedure to approach semantic issues via the use of R. Divjak uses the case of the semantics of be and have as a case study. The suggested methodology is as follows:

1. Identify problem
2. Come up with a list of variables
3. Operationalize variables: ensure assigning unique value during manual annotation process
4. Annotate corpus extractions
5. ? hypothesis:
   

• no > exploratory analysis
• yes > confirmatory analysis

Considering all of the above, the use of R, for the purpose of my project, would methodologically place my investigation in line with other recognised current studies . However, it should be noted that the actual use of R is not recognised as straight forward. As Larson-Hall notes in her above-mentioned review:

"While I myself have become fairly familiar with R and think it is an excellent statistical program, I have to admit that there is something of a learning curve when it comes to using it for one's own data. (...) Although R is elegant and useful, I would not label it as an 'easy to learn' program (...)" (p. 472)