RStudio: An all-purpose way to interact with R
April 13, 2022

RStudio: An all-purpose way to interact with R

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with RStudio

Currently, we use RStudio within our group as the primary way to interact with R and particularly R scripts for automated analysis of large datasets. We've also used RStudio to develop Shiny GUIs to provide a user-friendly interface for these R scripts for others in our organization that may be less familiar with running scripts in RStudio.
  • Great statistical packages
  • Good code visualization (formatting/color coding options)
  • Decent integration with other languages
  • Documentation and versioning of the packages can be tedious to track and check for compatibility
  • Requires startup time from the user to learn to use/setup
  • Some features like RStudio Connect are a little buggy/not super smooth
  • It enabled our organization to have a common platform for data analysis and for running R, which led to higher efficiencies between groups.
  • It provided us with the ability to develop GUIs through RShiny for users who preferred to work with a different user interface which allowed us to reach a higher user base and extend the efficiencies further.
  • It expedited some of our analysis workstreams by helping analysts and developers develop scripts faster through its tools and open source offerings.
RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.
I haven't really used RStudio support but participated in some workshops around shiny and tidyverse, which were both extremely clear, enjoyable, and well-executed - our organization learned a lot from them, so I would highly recommend them.
The open-source offerings are well maintained. They offer a set of functionalities that would be difficult or time-consuming to implement on an individual basis, so they offer us major time savings. The main advantage I see in open source data science is that it'll reduce duplicate work and enable wider use of these tools and for these tools to be able to iteratively improve with the input of these users as well.
We've only used RStudio connect minimally, and so far, it has helped with onboarding other groups that are less familiar with RStudio and R in general. Using Rstudio connect was also super helpful for use in training sessions in general.
Shinyapps.io has been really helpful in hosting interactive data GUIs for users/consumers of our data analytics in the form of a separate user interface that is easier to understand, and it allows users who are less comfortable with R to use to view and consume the analyzed data in a consistent way.

Do you think RStudio delivers good value for the price?

Yes

Are you happy with RStudio's feature set?

Yes

Did RStudio live up to sales and marketing promises?

Yes

Did implementation of RStudio go as expected?

Yes

Would you buy RStudio again?

Yes

RStudio is well suited, particularly to providing an environment for the statistical analysis of datasets and leveraging various data analysis packages using R. Its user interface is highly customizable and provides all the information users need to script, run, and generate various GUIs and dashboards. Overall it's well suited to R and perhaps less well suited (although it does allow) for other languages such as Python. Overall it's well suited for analysis needs but probably less suited for other development needs, especially if they require the heavy use of other languages.

RStudio Feature Ratings

Connect to Multiple Data Sources
8
Extend Existing Data Sources
7
Automatic Data Format Detection
8
Visualization
9
Interactive Data Analysis
9
Interactive Data Cleaning and Enrichment
8
Data Transformations
9
Multiple Model Development Languages and Tools
8
Single platform for multiple model development
8
Self-Service Model Delivery
7
Flexible Model Publishing Options
8
Security, Governance, and Cost Controls
8