styler packagescale()R became popular in biological data analysis in the early to mid 2000s, when microarray technology came into widespread use enabling researchers to look for statistical differences in gene expression for thousands of genes across large numbers of samples. As a result of this popularity, a community of biological researchers and data analysts created a collection of software packages called Bioconductor, which made a vast array of cutting edge statistical and bioinformatic methodologies widely available.
Due to its ease of use, widespread community support, rich ecosystem of biological data analysis packages, and to it being free, R remains one of the primary languages of biological data analysis. The language’s early focus on statistical analysis, and later transition to data science in general, makes it very useful and accessible to perform the kinds of analyses the new data science of biology required. It is also a bridge between biologists without a computational background and statisticians and bioinformaticians, who invent new methods and implement them as R packages that are easily accessible by all. The language and the package ecosystems and communities it supports continue to be a major source of biological discovery and innovation.
As a data science, biology benefits from the main strengths of R and the tidyverse when combined with the powerful analytical techniques available in Bioconductor packages, namely to manipulate, visualize, analyze, and communicate biological data.