Reviews (1-25 of 87)
- Data analysis and development of statistical / machine learning models.
- Development of information dashboards in the form of shiny applications, which are being deployed using RStudio Connect
- Provides both R and python development environments, which can be deployed to RStudio Connect
- Authentication integrated with enterprise solutions
- Well documented for end users and administrators
- Git integration for code versioning
- Project sharing is a great feature, but only works if RStudio Server is configured to use local accounts, not when using other authentication methods
- Excellent integration of both R and Python IDEs in one.
- Simple publishing of dashboards and applications from RStudio IDE to RStudio Connect.
- Integration of package management with projects to support collaboration.
- Excellent contributors to the R Open Source community, really invested in its health.
- Support integration with Enterprise AD environments for security.
- Python integration is newer and still can be rough, especially with when using virtual environments.
- RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
- Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.
So far have been addressing several business problems concerning HR analytics, sales optimization, stock optimization, database automatic consolidation, utility expenditure forecast. Many other projects are ongoing exploiting the APIs provided by the platform
- Easy to use. Not only for power user but also for people who need a reliable platform to deliver contents.
- Very versatile. There are many tools that can serve the scope of communicating results.
- Constant updates and newsletter keeps you on the track.
- Management of some deeper aspects of the platform is not a so straight-forward, especially when it comes to deal to customization (connections, packages management...).
- Administration console may be a bit richer, making available of some operations that you may be interested on doing by user interface and not by shell.
- Deploying apps is still a bit problematic for some particular (rare!) packages, make it easier to install packages not from the CRAN.
On the other hand, when it comes to structuring a more complex architecture in which RStudio Connect is only a part of it, it becomes more complicated. Of course we must say that we have received a lot of support in doing that!
- Great IDE
- Multiple language support
- Github integration
- Shiny integration
- Python integration
- C and C++ integration
- Project tools
- Integrated debugging tools
- Multiple versions of R can be confusing to maneuver
- Quick view of library locations relevant to the R version in use would be a good resource and reduce confusion
- Better online publication options for quick release, small apps by students
- Big data model generation
- Financial time-series applications
- Hybrid R/Python development
- Cluster analysis
- AWS cloud
- Rapid prototyping/rapid development
- New analysis tool development and distribution
- Software project management
- Software package development
- Report publishing
- Real-time collaborative editing
- More responsive RStudio Sever UI
- Launcher integration directly with Spark clusters (especially via third parties like Databricks)
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.
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.
- Excellent Documentation
- Well-designed Features
- RStudio Connect could really benefit from containerized environments to enable isolated, reproducible content.
- RStudio Connect's pricing model is a little frustrating at times. Infrequent consumers of content cost the same as heavy users who publish content regularly. This limits our ability to share the work of our data scientists at a reasonable cost. We would much rather pay more for each "publisher" seat and have much cheaper or free "viewer" seats. This would also likely lead to a greater investment in RStudio Connect on our part, as we would be able to expose the platform to more team members and key funding decision makers.
- RStudio excels at customer engagement.
- RStudio is very responsive to customer needs.
- RStudio cultivates one of the best tech communities that is safe and inclusive.
- RStudio could do more to provide easily consumable and sharable enterprise use cases that demonstrate the benefits of the enterprise apps.
- Organizing code - via projects
- Developing reproducible reporting - via seamless integration of RMarkdown
- Increasing efficiency of analysis - via "Find in Files", code reformatting, etc.
- It's gotten better, but code debugging still feels substandard (cf. Visual Studio Code)
- The workspace layout feels a bit stale compared to other environments, I spend a lot of time resizing panes.
- Addins seem powerful, but difficulty with discovery and use has kept me from using them much
- Data Organization
- Multi-Linear Regression
- Data Visualization
- Time-Series Forecasting
- As a scripting language, it is not a pick up and go platform. You need to spend the time to learning the program.
- Platform versions and Package versions often do not align.
- Would love to see standard templates that would generate a basic code for statistical models. This could save time and help newer users learn how to operate the program.
This is NOT a pick up and go platform as we are used to. It has hundreds of advantages and can be customized to near perfection. Yet, it will require many hours of investment. I would suggest looking at other pre-built platforms if the team is smaller.
- Data manipulation, easy and handy.
- Exploratory analysis, nice plots.
- Simple stats analysis, t test.
- Output format, it could be better so we can easily add output to a doc/ppt for sharing.
- Error message, it could be more informative.
- Data processing, it gets slow when data is big (e.g. millions of rows).
- R Studio is particularly good at performing quality assurance checks on data sets.
- R Studio is better than some other software at allowing the user to quickly test the data for coding errors.
- R Studio allows the user to reduce the number of lines of code to perform functions.
- More support for packages.
- Faster loading times.
- No suggestions.
- RStudio staff is very knowledgeable and supportive.
- The product documentation compared to other products we use is very good.
- Product roadmap is interesting and suits our future needs.
- For me the RStudio Launcher documentation (slurm/kubernetes) is not as clear as the rest. I had to put serious effort and a lot of trial and error to get all parts working.
- Admin web interface should provide clusterwide information - not per server.
- Developers are struggling to find a good way of working with tools like plumber & postman (web api) that start a locale service within RStudio server.
- Similar while switching from local IDE to RStudio Server Pro some developers ran into issues using oauth authentication flows.
- Integration with slurm, ability to run jobs that could not be run on a local workstation/laptop.
- Not have to troubleshoot local installations (dependency issues), sort out once on a central installation.
- Integration with external authentication.
- HA setup.
- Less suited for developers who are used to have full freedom to do whatever they want on their workstation.
- Brilliant IDE for coding.
- Easy publishing of apps and documents.
- Ease of use for data engineering team.
- It's consistently growing Python support, but there is still some room to grow here to make it a truly bilingual platform for data science. That said, it does server our Python users fairly well, even in its current form.
- 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.
It still needs to catch-up in terms of cloud platform integration and ML pipelines.
- Data wrangling
- See the data and the environment in IDE
- Rmarkdown output in many forms
- Readable code
- Rainbow parentheses
- Encourages open community & diversity
- Fun conference
- Segregation between high end and open source
- Need a clear Shimano-Style trickle down timeline of technology - so that what is available for Pro premium now should be in the open source version in N (5? 10?) years.
- Help teach new, diverse community to become coders and package-makers - pretty good now, but still fairly jargon-y and a steep learning curve. More resources to reach more people & move them from Excel to reproducible research.
- Make writing functions in the tidyverse cleaner and easier. Tidyeval is still a bit of a mess. Much better with curly-curly, but still many exceptions. still a ways to go.
- Ggvis is a neglected appendage. Should it be retired? is there a newer, better framework for interactive plots that can be used?
- Embrace open source package developers who do great stuff, like flextable. Too often RStudio uses its bully pulpit to overrun existing packages (patchwork > cowplot, gt > flextable). Embrace these folks and bring them into the fold (well done with Claus Wilke. Would like to see something like that with David Gohel.
- Would like to see a semi-automated workflow to take a dataset and generate oxygen documentation for each variable.
Not great (lots of barriers to entry) for Excel users. They can "code" - lots of complex formulas. But lots of entry processes are not great. Just installing Rstudio has ~14 screens of yes/no/default clicks. Better to have an option for "just give me the standard install" with a lot fewer clicks.
- Git tab
- Project options
- This could be because I simply don't know how to do it, and may not be a RStudio issue, but when I try to read files that are behind the firewall, everything runs very slow. Also, I cannot have Rmd files behind the firewall: they simply don't run. Also, I cannot get the Git tab to work if I am working behind the firewall.
- Unstable: It crashes without knowing why.
- I don't think RStudio has the capability of coding at the same time with your coworkers on the same script/project.
Not that good for working collaboratively simultaneously.
- Simplicity in managing code and projects.
- Easy git integration.
- Deployment of services both on .io and on Connect.
- Editable and downloadable dataframes in the viewer.
- More flexible file management in the file tab.
- More deeper dataframe exploration options in the viewer.
I don’t see scenarios where it is inappropriate when using R.
- It is a cross-platform IDE supporting R language
- Enhanced support for statistical computing
- It is an open-source scripting tool allowing simple scripts to do the work instead of long programs.
- Interface is not very overwhelming; GUI can be improved
- Support for gaming is limited
- Analysis of big data using RStudio is challenging
- It makes collaborative work easy.
- It makes it simple to develop and reuse code.
- It have a simple to use interface.
- More tutorial on how to use the interface.
- Help for predictive code writing.
- A better place to visualize images.
- Data visualization
- Reporting and dashboarding (e.g., Rmarkdown, RShiny)
- Ability to easily publish outputs to the web
- Best IDE for R programming.
- Good ecosystem for R Markdown and R Shiny.
- RStudio Connect is very useful for publishing and user authentication.
- It could have its own consulting team to support company to build R related products instead of partnering.
- It could also offer tailored paid training for small and large companies.
I do sometimes find RStudio to get stuck and slow when the code became long, so that is a place for enhancement.
RStudio Scorecard Summary
What is RStudio?
RStudio is a modular data science platform, combining open source and commercial products.
The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to enhance the production and consumption of knowledge by everyone, regardless of economic means.
Their commercial software products, including RStudio Server Pro, RStudio Connect, and RStudio Package Manager, are available as a bundle in RStudio Team. These products aim to give organizations the confidence to adopt R, Python and other open-source data science software at scale. This enables data science teams using R and Python to deliver interactive reports and applications to decision makers, leverage large amounts of data, integrate with existing enterprise systems, platforms, and processes, and be compliant with security practices and standards.
The platform is complemented by online services, including RStudio Cloud and shinyapps.io, to make it easier to do, teach and learn data science, and share data science insights with others, over the web.
Together, RStudio’s open-source software and commercial software form a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.
RStudio Videos (2)
- Has featureFree Trial Available?Yes
- Has featureFree or Freemium Version Available?Yes
- Does not have featurePremium Consulting/Integration Services Available?No
- Entry-level set up fee?Optional
For an overview of our pricing philosophy, please see https://rstudio.com/pricing/approach/. For pricing options for RStudio Cloud, please see https://rstudio.cloud/plans/free. For consulting and integration services, we work with our Certified Partners: https://rstudio.com/certified-partners/.
RStudio Support Options
|Free Version||Paid Version|
|Video Tutorials / Webinar|
RStudio Technical Details
|Deployment Types:||On-premise, SaaS|
|Operating Systems:||Windows, Linux, Mac|