Indispensable tool for any serious data scientist (especially using Python and R)
December 14, 2020
Indispensable tool for any serious data scientist (especially using Python and R)
Score 10 out of 10
Vetted Review
Verified User
Overall Satisfaction with RStudio
I'm using RStudio in developing and delivering supporting course materials for machine learning and big data. It's a great resource, and the fact that I can use both R and Python in the same familiar IDE has made this a killer app for me. The majority of course material for Python has been developed and distributed via Jupyter Notebooks, which is not an IDE. The ability to do end-to-end development in one place, using the best of both worlds (plus C and C++), make RStudio the best choice for anyone who want to develop robust ML/AI applications.
- Great IDE
- Multiple language support
- Github integration
- Shiny integration
- Python integration
- C and C++ integration
- Project tools
- Graphics
- Integrated debugging tools
- Multiple versions of R can be confusing to maneuver
- Quick view of library locations relevant to the R version in use would be a good resource and reduce confusion
- Better online publication options for quick release, small apps by students
- Deploying tools to students externally
- Access to state-of-the-art analysis tools with easy-to-use package tools
- Ability to use NVIDEA Cuda C directly in parallel processing via integrated RCPP functionality
Far better integrated and easy to use. The only full-blown Python IDE is PyCharm, and it is a monolith. I used Spyder instead. I was very happy when RStudio added Python support so I can ditch Jupyter Notebooks, which really isn't an IDE but is more like RMarkdown, a small piece of RStudio functionality.