Best all-in-one IDE for R
Updated May 24, 2021

Best all-in-one IDE for R

Maike Holthuijzen | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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Overall Satisfaction with RStudio

RStudio is used by several working groups within a larger project for the University of Vermont. It is used mainly for statistical analyses, manipulating spatial data, spatial analyses, and other programming/statistical tasks. I use my personal version of Rstudio as well as Rstudio server for analyses for this project. Rstudio is one of the best IDEs I have come across for R. I can keep track of variables within my workspace, view the files in my working directory, run the code and inspect output, and look at plots on different panels of the Rstudio interface. This helps keep my work organized and efficient. Rstudio has helped increase the overall productivity of the working group in which I work. Also, Rstudio interfaces with GitHub, which has been used for collaborative coding efforts.
  • Rstudio is very customizable. You can easily change font colors, sizes, and screen layout. I am particular about how I like my IDE setup, so this is a big plus for me.
  • Rstudio allows you to look at datasets in your workspace with the click of a button. I do a lot of data manipulation, so I am constantly having to look at datasets after operations to make sure they look correct. The view option in Rstudio makes checking datasets very fast.
  • Finally, I love the way Rstudio manages plotting. Your plots can be viewed in one of the panels. Those plots can easily be copy/pasted or exported into a variety of file types. You can also magnify the plots and scroll between plots to look at previous plots.
  • Sometimes Rstudio crashes when you work with big datasets.
  • I've had some issues installing packages, which is very annoying. Sometimes I can install packages on my PC but not on my Mac, and vice versa.
  • Rstudio is not exactly a lightweight IDE, so it is not ideal for computationally intensive tasks.
  • Increases rate of publishing in research journals.
  • Specific packages in R (not available elsewhere) have allowed me to progress on a new climate downscaling technique I am working on.
  • On the negative side, it is not very unusual to spend 2+ hours figuring package install errors.
I like the simplicity of Rstudio, and besides the obvious point that PyCharm is an IDE for python, I find Rstudio much more intuitive. Plotting is better, Rstudio is much easier to customize, and PyCharm tends to take a long time to load. However, I have not experienced as much crashing with PyCharm as with Rstudio.
Well suited for spatial data analysis, statistical analyses, plotting and working with collaborators through Github. It can also compile Latex files and supports Rmarkdown, a markup language similar to Latex. Packages are constantly being added, so it's great for using novel analytical techniques that may not be available elsewhere.

Not as well suited for any big data tasks or deep learning or image processing.

Using RStudio

50 - All of the employees I work with use Rstudio for research. They use Rstudio for data analysis, plotting results, doing spatial analyses, and utilizing novel techniques to process their data. Rstudio is well suited for interdisciplinary collaborations--my colleagues work in a variety of fields, from climate science, statistics, and computer science to hydrology and geology.
1 - We have access to an Rstudio Server, and sometimes there are issues with installing packages and importing data. A technical support person should have a background in computer science, be familiar with R, and also be able to quickly trouble shoot technical problems.Since Rstudio is slower than other programs, it is helpful to have tech support who knows how to easily vectorize code and parallelize code.
  • analyzing data
  • plotting data and results
  • data processing/formatting
  • geospatial analysis
  • implementing new techniques (Bayesian spatial analysis via the spBayes package) on research data
  • process large amounts of data easily
  • writing up research results with Rmarkdown/Latex
  • make use of the 'project' feature in Rstudio which integrates with GitHub
  • integrate other languages into R code (python, C++)
Rstudio is my go - to language for anything involving analysis. It has far more capabilities for data analysis than Python (in my opinion). New packages for novel analytical techniques are often published. Rstudio continues to implement great updates every few months as well. Finally, I really like that I can write LateX documents in Rstudio and integrate R code into them.