R-software: A Newer Tool in Epidemiological Data Analysis

Background

Analyzing epidemiological data has always been a matter of concern especially for those researchers who have a background of biological sciences and not of mathematics. As the dataset is usually large in epidemiology, calculating even simple statistics like mean or standard deviation is quite cumbersome to be done manually. For many, even finding a statistician becomes difficult in their setting. So many datasets remain unexplored, sometimes forever waiting to be analyzed even by simple exploratory and descriptive data analysis.

Softwares in Data Analysis

With the introduction of softwares for statistical computations, things changed and data analysis came to be thought of something within the realm of possibility by the medical researchers. But for developing countries, the scenario did not change as expected because of the very high cost of the statistical packages.

The World Health Organization and Centers for Disease Control promoted free software known as Epi Info to be used by medical researchers. It was first launched as a Disk Operating System (DOS based) version, which was command driven and difficult to learn by the medical researchers. In 2001, windows-based version, which was menu driven, was launched and it became very popular among the medical researchers. Epi Info is also not suitable for data manipulation for longitudinal studies and its regression analysis facilities cannot cope with repeated measures and multilevel modeling. Also the graphing facilities are limited. Other statistical softwares such as Statistical Package for Social Sciences (SPSS), Stata, etc., are upgrading with newer dimensions in statistical analysis but they are not affordable to most institutions in developing countries.

What is R-software?

R is a relatively new and freely available programing language and software environment for statistical computing and graphics. The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and concept being partly taken from the name of the Bell Labs language ‘S’.(1) It compiles and runs on a wide variety of UNIX platforms, Windows, and MacOS.(2) It has almost everything that an epidemiological data analyst needs. R is an environment that can handle several datasets simultaneously. R is also a programming language with an extensive set of built-in functions. One can write their own code to build their own statistical tools. Advanced users can even incorporate functions written in other languages, such as C, C++, and Fortran.(3) R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible. R is available as a Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form. One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.(4)

The R Environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

• An effective data handling and storage facility,

• A suite of operators for calculations on arrays, in particular matrices,

• A large, coherent, integrated collection of intermediate tools for data analysis,

• Graphical facilities for data analysis and display either on-screen or on hardcopy, and

• A well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term environment is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software. R is not a typical statistics system but an environment within which statistical techniques are implemented. R can be extended via packages.(4)

What is CRAN?

CRAN stands for Comprehensive R Archive Network.(2) It is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. One can use the nearest (with respect to geographical location) CRAN mirror to minimize network load. Apart from the packages which automatically come with R; there are more than 2000 packages available at CRAN. So depending on the type of statistical analytical techniques, one can download the package required. CRAN does not have Windows systems and therefore cannot check for viruses. It is important to use the normal precautions that is taken while downloading data on our hard disk.(5)

Packages in R

The functions of R and its datasets are stored in “packages,” whose contents are available only after it has been downloaded. R is highly extensible through the use of usersubmitted packages for specific functions or specific areas of study. There are about 25 packages supplied with R (called “standard” and “recommended” packages) and many more are available through the CRAN family of Internet sites (via http://CRAN.R-project.org) and elsewhere. It requires some effort to find which package contains the statistical techniques that we require. For example, the “survfit” function from the “survival” package computes the Kaplan-Meier estimator for truncated and/or censored data and various confidence intervals and confidence bands for the Kaplan-Meier estimator are implemented in the “km.ci” package.

There is an important difference between R and the other main statistical systems. In R, a statistical analysis is normally done as a series of steps, with intermediate results being stored in objects. Thus whereas SAS and SPSS will give all the details in the output from a regression or discriminant analysis, R will give the desired and minimal output and store the results in a fit object for subsequent interrogation by further R functions.(6)

Epicalc Package

Epicalc, an add-on package of R enables R to deal more easily with epidemiological data. Epicalc, written by Virasakdi Chongsuvivatwong of Prince of Songkla University, Hat Yai, Thailand has been well accepted by members of the R core-team and the package is downloadable from CRAN which is mirrored by 69 academic institutes in 29 countries. The main advantage of using this package is that it gives rise to display which is more understandable by most epidemiologists. On one hand, it assists data analysts in data exploration and management. On the other hand, it has the potential to help young epidemiologists to learn the key terms and concepts based on numerical and graphical results of the analysis. For basic biostatistical and epidemiological purposes Epicalc package is sufficient to start with and then to go on for other packages as and when required.

Limitations of R

R is provided with a command line interface (CLI), which is the preferred user interface for power users because it allows direct control on calculations and it is flexible. However, good knowledge of the language is required. CLI is thus intimidating for beginners. The learning curve is typically longer than with a graphical user interface (GUI), although it is recognized that the effort is profitable and leads to better practice (finer understanding of the analysis; command easily saved and replayed).(7) Therefore one has to understand what one is doing or else giving a certain command will be nearly impossible. The other limitation is that, being an open source software, hackers can easily know about the weaknesses or loopholes of the software more easily than closed-source software and so it is more prone to bug attacks.

Conclusions

Being free of cost, it is surely a boon for researchers in developing countries and resource scarce institutions The quality of this software in terms of handling large datasets, having hundreds of functions with ever increasing number of add on packages and the neat outputs is also an advantage. As R is command driven, learning R will by default make the user to attempt to understand what is going on in the analysis and thus learn the details of biostatistics and epidemiology. The steep learning of R is a serious disadvantage which if eased by the introduction of menu driven R can make it more popular among the non-mathematicians dealing with epidemiological data.