Why is R one of the best Statistical programming languages for Biomedical and Pharmaceutical industries

Why is R one of the best programming languages for Biomed and Pharma industries? I could write books about this... so for this article ill try to concise and write the most important aspects that come to my mind. Here they are:

R - The programming language made by Statisticians for Statistical programming and and has been evolving to become one of the most advanced and most popular languages for Statistics today. One path of its development is specifically focused on pharmaceutical and biomedical industry with hundreds and hundreds of packages related to these areas. In essence large portion of R packages is focused on Life Sciences.

R - In my experience no other programming language covers such a wide spectrum of implementable methods for Biomedical and Pharmaceutical Research. During years of my Statistical programming experience, i was frequently in a situation to find the implementation and implement the method that most other Statisticians don't use and its implementation wouldn't not be that easy. Almost every time R implementations were the solution for me. This tells enough i believe. R has one of the largest pools of method implementations for specific areas of Biology, Biomedicine, Pharma analytics, Bioinformatics, Genetics, Case studies, other Preclinical studies, Clinical trials, Biometrics, Adaptive designs, Innovative designs, Bayesian Clinical designs, Cross-over designs, Anthropometrics, Oncology specific designs, Cardiovascular research, Immunology specific designs, Microbiology Research, Manufacturing analysis, in vitro studies, IVF, Ecology, Systematic Literature Reviews, Specific Meta-analysis implementations and many others.

R - Compliance, validation, regulatory and standardization in Biomedical and Pharmaceutical industries. R is one of the Statistical programming languages with strong validation and standardization background and framework developed in the past period and here is R project document about these areas - link. Here is another link about the validation performed in using R in late phase clinical trials - R consortium.

Successful FDA test is important aspect of using R in Biomedical and Clinical settings and here are some additional materials of R consortium for using R in regulatory Review. Most of well adapted R packages have options to comply with Journals standards for visualization. Publication ready plots are also one of the characteristics of many R packages.

R - One of the most documented languages out there. This means that compliance procedures can be well documented and transparent. Publications arising from the Research in these areas will also benefit from being well documented. Good explainabaility is another related aspect of R packages. R packages tend to be so well documented that some packages have implementation documentation larger than a book. Documentation most often contains easily reproducible examples and open source code.

R- Powerful, fully customizable and standardized Data visualization. One of my favorite things about R for years has been high resolution data visualization. Any visualization can be fully customized to have highest possible resolution. In fact in R we can create graphs as vector graphics and have virtually limitless resolution, meaning we can zoom in as much as needed into the graph with absolutely no loss in resolution. Wide spectrum of specific areas of Biomed and Pharma industries covered with customization and quality level that makes R absolute number 1 in this area. Read more about the Visualization packages in R here.

R - Being open source contributes to standardization and regulatory aspects. Most people think that open source means free. Open source means its open and transparent. Almost every block of R code and its packages is visible to everyone, which makes it very transparent and reproducible. This is the key in Biomedical and Pharmaceutical areas where compliance and documentation is the key aspect of reproducible Research.

R - Financial aspects for a Biomedical or Pharmaceutical company. R is indeed free in addition to being open source. While being one of the most advanced and complete Statistical programming languages, being free means it can be a crucial aspect for both small and large academic institutions and pharmaceutical companies. For smaller institutions and companies affording a good programming languages for the whole team can be problematic, which is why R is one of the ideal choices. For large eg. pharma companies expenses for large teams can be gigantic, which is again why R is an ideal choice for these companies and their teams too, while enabling probably most advanced and complete Statistical programming languages out there . In addition to being open source and free R offers most advanced and most popular programing language in Biomedicine and Pharma Analytics.

R - Files from majority of other platform's and programs can be easily used in R. R is compatible will almost every environment and can be easily used on almost any computer. Easily installed, IDEs for ease of use present, such as RStudio and many others. Almost any document can be important into R, text docs, excel, other spreadsheet formats, json files, h5 files, feather files, parquet files, fasta, fastq, bam, spss, jasp, stata and alsmot any other statistically related format. Also exporting any analysis into any of these formats is very fast and easy. R is so flexible that majority of Data Science and Statistics frameworks can today be implemented into R, se we can use open a David database for Bioinformatics, implement BLAST algorithms , create data according to CDISC standards, use TensorFlow and Keras for Biomedical AI studies and many other areas.

R - Using other programming languages in R. Of of my favorite characteristics of R which has been developed over the past few years is i can use R to implement other languages. For example if i were to implement stan for probabilistic parts of Bayesian Adaptive design, i can easily implement all in R. Python, java, C++, C, Ruby in addition to Stan can be effectively implemented using R. Fantastic characteristic...

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Darko Medin, Statistician, Researcher and a Data Scientist