In our early days as a biostatistical consultancy, we were often approached by early-stage drug development or biotechnology companies for assistance with study design, statistical analyses, and bioinformatics. Our clients generally did not have in-house capacity for analytics, and waiting for bespoke outsourced results for each experiment was a huge expense and bottleneck in their research. We saw and solved the same problems repeatedly: integrating disparate sources of data, building pipelines and databases, implementing analytics, and producing regular reports of results.
R was an obvious tool of choice since much of our team is made up of data scientists who have known and loved R for years. It was a revelation when we first began using Shiny, and saw the powerful capabilities R had for web development. Our software developers were quick to pick up R and see that the language can actually be used for web development in addition to analytics. Having a common language within our company has facilitated a lot of learning and growth.