It looks most of the material from epicalc has been moved into epiDisplay. Full ‘ epicalc’ package with data management functions is available at the author’s. Suggests Description Functions making R easy for epidemiological calculation. License GPL (>= 2) URL Epidemiological calculator. Contribute to cran/epicalc development by creating an account on GitHub.
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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.
Do it Yourself: Essential Epidemiological Data Analysis
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. 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.
It was first launched as a Disk Operating System DOS based version, which was command driven and difficult to learn by the medical researchers. Inwindows-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. R is a relatively new and freely available programing language and software environment for statistical computing and graphics. 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. 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.
Analysis of epidemiological data using R and Epicalc
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. 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. One eoicalc 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 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. R is highly extensible through the use of usersubmitted packages for specific functions or specific areas of study. It requires some effort to find which package contains the statistical techniques that we require. 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. Epicalc, epivalc add-on package of R enables R to deal eplcalc easily with epidemiological data.
Epicalc, written by Virasakdi Chongsuvivatwong epicac Prince of Songkla University, Hat Yai, Thailand epicacl 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.
R is provided with a command line interface CLIwhich 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.
Install epicalc in R
The learning curve is typically longer than with a graphical user interface GUIalthough it is recognized that the effort is profitable and leads to better practice finer understanding of the analysis; command easily saved and replayed. 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.
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. National Center for Biotechnology InformationU.
Indian J Community Med. Author information Article notes Copyright and License information Disclaimer. Khan Amir Maroof, Room No. Received Jan 24; Accepted Feb 3. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3. This article has been cited by other articles in PMC. 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.
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.
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 well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities. Epicalc Package Epicalc, an add-on package of R enables R to deal more easily with epidemiological data.
Limitations of R R is provided with a command line interface CLIwhich is the preferred user interface for power users because it allows direct control on calculations and it is flexible.
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 spicalc also an advantage.