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

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.

Do you think Posit delivers good value for the price?


Are you happy with Posit's feature set?


Did Posit live up to sales and marketing promises?


Did implementation of Posit go as expected?


Would you buy Posit 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