RStudio Enables Computational Biology At Scale
December 04, 2020

RStudio Enables Computational Biology At Scale

Sean Corbett | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with RStudio

RStudio is presently used heavily across several departments in our organization. In Data Science, we utilize RStudio and its related products for analysis of both early- and late-stage biomedical data for drug development. Our Biostatistics group uses RStudio for analyses related to population-wide trends in results from our ongoing clinical trials.

Pros

  • Software project management
  • Software package development
  • Report publishing

Cons

  • Real-time collaborative editing
  • More responsive RStudio Sever UI
  • Launcher integration directly with Spark clusters (especially via third parties like Databricks)
  • RStudio makes analysis work dramatically more efficient
Because RStudio is more specifically focused on facilitating programming in R, whereas these other IDEs focus either on more general programming frameworks or a different language, it is the best choice for most of our analysis. Computational biology relies heavily on the Bioconductor package repository which is tightly tied to the R programming language, compelling us to use R and thus the best IDE for that language.
RStudio is generally well suited especially for exploratory analysis in a computational biology setting, as well as as a Python IDE for developing more robust production code that might need to integrate tightly with R. RStudio is less appropriate as an IDE for other languages beyond these two (for obvious reasons).

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