
This post originally appeared in Social Media Marketing Magazine, Issue 1, Number 6
Statistics. For many current and former students, just the sound of this word evokes nightmares. However, once you learn to master some basic tasks using statistical analysis software, the world becomes your data playground. Thus, the purpose of this column is to introduce you to one of the most powerful statistical analysis software packages (open source!) and to provide examples of how it can be used to build your social media marketing analytics capabilities.
R is one of the most popular statistical analysis software programs available. It is used by statisticians, financial analysts, marketing researchers and social media researchers. To download and install R, go to the Comprehensive R Archive Network (CRAN) site.
You’ll find versions of R built for Linux, MacOS X and Windows. In my opinion, the versions for Linux and Windows work best. Issues for Mac users related to installing and updating additional statistical analysis packages (3311 are currently available – and like R, all for free) are known to exist. Mac users experiencing problems are encouraged to consider installing Virtual Box in order to install and run the Linux version of the R statistical program. After selecting, downloading and installing the base program, visit the Quick-R site for examples of how to use R for statistical analysis.
Two recent additional (or add-on) social media statistical packages are available to be installed in R: RGoogleTrends and twitteR. The former is one of the most difficult R add-ons to install in R (it took me a couple of hours to make it work) and the latter performs many of the same functions of one of the earlier software packages recommended in this column, NodeXL.
RGoogleTrends:
RGoogleTrends is not available for download within the R “install packages” option. To install RGoogleTrends, one must visit this site to download the .tar.gz file (I use 7-zip for Windows to unzip the file). Hyunyoung Choi and Hal Varian provide a unique perspective of the power of RGoogleTrends in their 2009 white paper “Predicting the Present with Google Trends”. Joe Rothermich developed an interesting presentation (2011) that illustrates the power of using RGoogleTrends to measure market sentiment and events. Download and read both to get an idea of the power of R from a social media marketing perspective.
twitteR:
The easier of the two R add-on packages to download and install (can be done within the “install packages” option included in the R base package), twitteR provides users with access to the networks of businesses or people who are on Twitter. Using twitteR, one can perform network analysis tasks that include basic statistics. Jeffrey Breen recently presented (2011) an incredible example of how to use R and twitteR to mine Twitter for consumer attitudes. His presentation includes advanced R code for how to replicate/duplicate his research.
Admittedly, none of this is easy. But spending the time to master R, RGoogleTrends and twitteR will make you a better social media marketing researcher. R you up to the challenge?








The final chart presented is social media share of voice. The inner ring contains the data from 2006 and the outer ring presents the data from 2011.

In a recent article co-written with Dr. Godwin C. Ariguzo (@ariguzo), we predict that by the end of 2013, total U.S. e-commerce sales will reach a level of $254.7 billion. This represents a compound annual growth rate (CAGR) of 52.94 percent for the period 2010-2013. Overall, the CAGR for the period 2000-2013 is projected to be 18.65 percent, which is less than the CAGR for the 2000-2010 period (19.70 percent). The table below presents the quarterly growth of U.S. e-commerce since 2000 and the projected growth per quarter through 2013. The projected growth was calculated using a Holt-Winters technique in time series analysis.
Graphically, the projected annual growth for U.S. E-Commerce sales looks like this:
The recovery in U.S. e-commerce growth from 2009 to 2010 is impressive. We project the trend to continue through the end of 2011 and then to begin to decline slightly. The actual and projected growth rates are presented graphically below.






