Scripts in R

Scripts in R are simple files which can be used to store and run code. The code stored is in the form of simple text. It is especially useful when you have worked really hard on a code and want to store it. It can be pulled later to show all your work. It is more basic than the RMarkdown file and cannot be used as it is to display results.

The analysis shown here is done using the R language for Statistical Computing in RStudio.

The R Project for Statistical Computing website: https://www.r-project.org/
The CRAN repository: https://cran.r-project.org/mirrors.html
The RStudio website: https://posit.co/products/open-source...
How to cite R in publications: R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

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Introduction to R (https://www.r-project.org/about.html):
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

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. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

This tutorial shows how statistical analysis can be done free of cost, by anyone, irrespective of their mathematical aptitude. These tutorials are made especially for medical students and residents or any researcher in health-care, who need data quickly analyzed, with tables, graphs, and plots conveying maximum information, and also reporting those results in a standard manner, so that writing the 'materials and methods' and 'results' section of the manuscript becomes a cake-walk.

Why healthcare professionals, especially medical students and residents should care about biostatistics:
Statistics, especially biostatistics, remains an enigma all throughout undergraduate days for medical students. Applying a few formulae which didn't even make sense during a 15 minute biostatistics exam or during your USMLE Step 1/Step 2 CK was all that was expected. However, the beauty of statistics and probability lies in its practicality and the power that it wields to influence decision making for the benefit of all mankind.

When it comes to clinical research, statisticians are the ones who are in charge of foraying and deriving meaningful conclusions from painstakingly obtained data. Most of the times, clinicians are left clueless and dumbfounded after looking at complex statistical techniques applied. Usually, clinicians have not explored the world of hypothesis testing beyond applying chi-square and t-tests. Moreover, it takes quite a lot of latent time to communicate to statisticians what clinicians really want; valuable time which can be spent elsewhere.

My goal is to enable everyone, including students, to be able to go out of their comfort zones, and delve into the exciting world of working with clinical data, thinking about the best strategy to analyze and present the data, plotting beautiful captivating graphs which have the power to explain what a thousand words can, read, critique, and review crucial research methodology, and overall, be better evidence-based clinicians and scientists.

The following series is going to focus on how to use R to perform statistical analyses and data visualization, quite unlike what is taught in most medical schools. These are easy tutorials on R which will help novices to easily write and develop their own codes in R. I also hope that this will change the perspective of biostatistics that medical students have, which is inculcated by the syllabus studied during their undergraduate days.