RStudio - Dire wolf of "Game of Data Science"
Updated September 05, 2021

RStudio - Dire wolf of "Game of Data Science"

Heramb Gadgil | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with RStudio

Most of the DNA teams within our organization are using RStudio right from data pulls to visualization & reporting. UI/UX for shiny applications has been phenomenal and has been utilized in broader initiatives that enabled huge dollar savings. 'RMarkdown' has eased report generation to a great extent and 'odbc' drivers have made connecting to databases an easy task.
  • Abundant development on statistical and data science libraries.
  • Interaction with other programming languages and BI tools.
  • Customized application building and reporting framework through shiny and markdown.
  • Simple IDE with competent and robust functionalities.
  • Strong and active community.
  • Highly approachable core members and teams @Rstudio.
  • Integration with Google Cloud Platform.
  • Flexibility of choosing a remote R-interpreter (as is present in IntelliJ/PyCharm).
  • Memory issues and slowdowns when it comes to working with large datasets.
  • Orchestration of production workflows with Airflow.
  • Production pipelines for RStudio Connect content.
  • Shiny applications enabled huge dollar savings along with ease of access for statistical solutions.
  • Single platform (RStudio Connect) to deploy Pins, Shiny, Dash, Plumber and Flask has increased collaboration opportunities within R-Python community.
  • Project management and version control was enabled and eased within R-initiatives.
RStudio is the only R-friendly IDE. None of the IDEs, even though they offer R-plugins, are as intuitive as RStudio.
RStudio has the 'Best In Class' customer support. They are empathetic, patient and problem solvers with a TAT of 1-day in almost all the cases. It can be marked as one of the strong points of the organization. The documentations are detailed, organized and comprehensive. Customer Success representatives often schedule a check-in meeting to understand 'What are the road-blockers?' with definitive timelines to address the issues.
Open source has a huge community associated with it. This makes the development of a solution easy, as there are numerous SMEs to help you out. Also, the issues/errors are resolved quickly.

The Public Package manager (open-source) has been an enormous success for installing packages that required compilation earlier.
RStudio Connect has enabled statisticians to deploy and share the analysis with the stake-holders. It has also enabled the stake-holders to tweak and play with the offered solutions allowing them a degree of flexibility.
RStudio pro gives a common platform for R-developers and brings homogeneity in the resource (R-versions, Server configurations) utilization and sharing.
Public package manager is huge relief for installing packages that needed compilation earlier. It has reduced the errors due to change/mis-match in compiler versions.

Do you think RStudio delivers good value for the price?


Are you happy with RStudio's feature set?


Did RStudio live up to sales and marketing promises?


Did implementation of RStudio go as expected?


Would you buy RStudio again?


Most of the DnA use-cases are handled perfectly well with RStudio eco-system. Tidyverse, tidytext, ggplot2, shiny, Rcpp, rJava and numerous other statistical libraries are robust to handle all the stages of a data analysis pipeline. Seamless integration with Javascript, CSS and JSON enriches the visualizations in shiny application. If your project involves moderate sized data pulls, R (RStudio) is a go-to solution without much of a thought.

It still needs to catch-up in terms of cloud platform integration and ML pipelines.

Using RStudio

500 - RStudio is leveraged by almost all the major functions within the organization. The usage ranges from occasional to critical. Although there hasn't been well established production workflows in many departments, RStudio acts as a go-to tool for data cleaning, analysis and reporting. With the expansion of RStudio connect' support for hosting various contents, it is anticipated to affect the usage positively!
10 - The support is required at two stages - System and Product. While each department relies on their own resources to handle the product level tickets, system support is something which needs a central team. System requirements often see requests for installation of packages at an OS-level and debugging of issues related the connection drivers.
  • Data Cleaning
  • Model APIs
  • Dashboards for change review of systems and alerts
  • Report generations
  • Enabling login-screens for Shiny applications thus allowing for use of anonymous users while keeping the content secured
  • System command calls through R allows for RDS files refresh through applications thus addressing the data connection latency.
  • Integrations with GCP to allow for familiar environment for R-developers within the cloud platform
We are in a process of building some of our production workflows on R. This will be a significant achievement for analytic processes that still need manual interventions at various stages. The shiny applications have been assimilated in various critical processes that now rely heavily on them for future action items
We are not using python through RStudio heavily. Its still in its nascent phase of exploration and getting comfortable.