Performance

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The diff usion of R is certainly an interesting phenomenon on many counts. First,
it encompasses many di erent elds of academic as well as applied research.
Second, it’s been developing as one of the dominating analytics tools in areas
such as mining of big data sets and nance, for example. Third, it has tickled a
large, active, and enthusiastic global community that has been adding useful and
interesting functionalities (called packages) to the primary packages provided by
the core development team of R-Project. Fourth, it is open source software with
all its obvious bene ts (and drawbacks, as well) that it implies.
Though Excel runs to a good extent Wall Street, the advent of quantitative
nance in the early nineties has marked the entry of other more sophisticated
computer coding and charting tools. Uncountable are the initiative under which
falls R with its extensive libraries (again packages). It is easy to check the
size of its nancial extensions by looking at CRAN comprehensive catalog on
nance.

By stressing its basic plotting functions while at the same time highliting
the incredible extensions provided by the ggplot2 package, this article wants
to highlight the major graphics capabilities of R that can supplement and
complement e ectively some of the typical tasks performed in performance
analysis without claiming to provide an extensive and detailed range of all its
possible applications in nance.

Without even scratching the surface of the R language (though the following

chunks of code should whet the reader’s appetite to go through the works, cited
in [1] and [3], and try using it), certainly most of the relevant tasks in applied
nance, that can be achieved by using R, rely on a data structure called data
frame. A data frame is fundamental to the use of R, see Maindonald in [2], and
is a generalization of a matrix where its components (the columns) do not have
to have the same mode. For those who are not familiar with R at all, suce it to

 

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