RStudio helps data scientists get things done faster
Updated September 04, 2021

RStudio helps data scientists get things done faster

Ethan Kang, FCAS, CSPA | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with RStudio

RStudio is being used in the Underwriting and Analytics Department. We publish our analytics artifacts through RStudio Connect, then users from other department can consume with ease.

We help our colleagues to better understand the financial health of the company through profit and loss evaluation, risk underwriting and portfolio review. RStudio helps us bridge the gap between Data and Solution, we tell story through data visualization and reproducible analytical documents that are easy to grasp by our colleagues.
  • Great selection of libraries to do statistics, machine learning, data visualization, interactive dashboards
  • RStudio Connect makes sharing your work with your colleague a breeze using one-click publishing.
  • The ability to connect R with other languages like Julia and Python all within the same working session helps generate more creative ways to solve problems. We can use Julia in R to speed up intensive calculation, or we can use Python Tensorflow or Pytorch in R to do deep learning.
  • R has a great community on social media and stackoverflow so it's very easy to learn from the other users.
  • The learning curve may be a little steep for new R users
  • There are multiple ways to solve a problem. For example, there are mlr3 and tidymodels to build predictive models, and there are tidyverse and data.table to perform data cleaning. It could be confusing and overwhelming for new users to decide which libraries to learn and use.
  • Our work in RStudio helps us negotiate better pricing contract with third-party players and in the end resulted in 1.5 millions saved.
RStudio is free like Jupyter Notebook and VS Code. The others all require additional cost to use.

RStudio has more features than Jupyter Notebook. VS Code doesn't support R natively and require plugins, and it's not as mature as RStudio yet.

Compare to CDSW, Domino and others, RStudio Connect is much less expensive and can perform the same responsibilities and more.

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RStudio is great at reproducible research, data visualization, dashboard and REST API, and build predictive models.

R is single-threaded, so it may not be suitable when you need to scale your application to many users. For example, if you have a shiny app with R, the performance may slow down when multiple users are in the app. People are addressing this issue in several ways, however, so this may not be a deal-breaker.

Using RStudio

5 - 2 in Marketing Data Science/Analytics

3 in Actuarial / UW / Data Analytics
2 - We have DevOps engineers who help create RStudio Connect and RStudio Workbench on AWS servers.
  • Create Actuarial Reserving processes and inform senior stakeholders financial results on a monthly basis
  • Create Underwriting and Pricing models to drive profitable growth and appropriately price small commercial risks
  • Create Marketing data analytics to help target customers who are good risks and more likely to switch to or buy their commercial insurance with us.
  • RStudio Connect has been instrumental in helping us provide analytics to senior management by leveraging shiny and dash apps. We don't have to rely on Data Engineering team to create the same visuals on Looker because it usually takes less time from us to produce the results.
  • Through the use of RStudio's professional database drivers, we can seamlessly connect RStudio with our snowflake databases and do many of the data transformation processes within R. Using dplyr let us use code we are familiar with and still get the processing power of the cloud database.
  • We have also been trying to leverage reticulate to allow Python and R work together more often. We can do many of the preprocessing in R first then have the data object be used in a Python process (PyMC3 for example).
  • As we grow, we want RStudio to scale with us, which may mean allowing Shiny and Dash to be run concurrently by many users. This would mean we have to think about how to dynamically add/remove servers based on the number of users active.
  • RStudio can also be used in production using plumbr. We need to think about how we can utilize it to help us bring more models in production without the translation from modeling code to production code.
  • Continue to bring documentation innovation using Rmarkdown. We can improve many reports and visualization we produce today using Rmarkdown and that allows us to schedule reports to be run, and share results automatically via email, and continue to be proactive in bringing analytics to the different stakeholders.
We can do pretty much all of the modern data analytics in R, from data ingestion to final data products to be consumed by senior management. Yes, you can do an analysis in other tools, but RStudio is unique in being open-source licensed, its seamless integration with other data and programming tools (SQL/Python/C++/etc), allowing you to reliably complete your analysis all in one tool, and with reproducible research in mind. It cannot be replaced easily by other tools.

Evaluating RStudio and Competitors

  • Price
  • Product Features
  • Product Usability
  • Product Reputation
  • Prior Experience with the Product
The single most important factor of choosing RStudio over other products is its open-source tools that help students pick up the language without upfront cost. The new graduates nowadays know more about R and Python packages than SAS, so it is much easier for them to continue to use what they know already in a work setting.
I would look at how responsive they are helping customers accomplish their tasks. The turnaround time for an issue to be resolved is vastly more important in an enterprise setting