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Intuitive User Interface: Users have found RStudio to have an intuitive user interface that allows them to quickly test and debug code. …
Overview
What is Posit?
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Recent Reviews
Awards
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Popular Features
- Visualization (26)8.484%
- Connect to Multiple Data Sources (25)8.181%
- Extend Existing Data Sources (26)7.474%
- Automatic Data Format Detection (25)6.464%
Reviewer Pros & Cons
Video Reviews
2 videos
RStudio Review: It Proves To Be A Reliable Statistical Tool W/ Support Avenues In Place If Needed
02:53
RStudio Review: Works As An Useful Tool But User Finds Free Version Could Be More Competitive
02:13
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What is Posit?
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
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- Setup fee optional
For the latest information on pricing, visithttps://posit.co/pricing
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- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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Product Demos
What is Posit Workbench? Build Data Products in R & Python using Jupyter, VSCode, and RStudio.
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Posit Connect | Host all of the data products you create
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Product Details
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- Tech Details
- FAQs
What is Posit?
Posit, formerly RStudio, provides a modular data science platform that combines open-source and commercial products.
their open source offerings, such as the RStudio IDE, Shiny Server, rmarkdown and the many packages in the tidyverse, boast users among data scientists around the world to enhance the production and consumption of knowledge by everyone, regardless of economic means.
Their commercial software products, including Posit Workbench, Posit Connect, and Posit Package Manager, are available as a bundle in Posit 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 Posit 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.
Posit’s open-source software and commercial software form what the vendor describes as a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for Posit’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.
Posit Features
Platform Connectivity Features
- Supported: Connect to Multiple Data Sources
- Supported: Extend Existing Data Sources
- Supported: Automatic Data Format Detection
Data Exploration Features
- Supported: Visualization
- Supported: Interactive Data Analysis
Data Preparation Features
- Supported: Interactive Data Cleaning and Enrichment
- Supported: Data Transformations
Platform Data Modeling Features
- Supported: Multiple Model Development Languages and Tools
- Supported: Single platform for multiple model development
- Supported: Self-Service Model Delivery
Model Deployment Features
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
Additional Features
- Supported: Share Data Science insights in the form of Shiny applications, Quarto content, R Markdown reports, Plumber APIs, dashboards, Jupyter Notebooks, and interactive Python content.
Posit Screenshots
Posit Videos
Open Source Software for Data Science - CEO J.J. Allaire provides an overview of Posit's mission, and why Posit has become a Public Benefits Corporation.
Posit Integrations
- Amazon SageMaker
- Kubernetes
- Apache Spark
- Jupyter Notebook
- Streamlit
- Tableau Desktop
- Azure Machine Learning
- Databricks Lakehouse Platform
- Microsoft Visual Studio Code
- Bokeh
- Slurm
- Dash applications
- SAML Marketplaces
Posit Competitors
Posit Technical Details
| Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Windows, Linux, Mac |
| Mobile Application | No |
Frequently Asked Questions
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Reviewers rate Security, Governance, and Cost Controls highest, with a score of 8.9.
The most common users of Posit are from Enterprises (1,001+ employees).
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Pros
- Cons
- Recommendations
Intuitive User Interface: Users have found RStudio to have an intuitive user interface that allows them to quickly test and debug code. This has been mentioned by numerous reviewers, highlighting the ease of use and convenience it offers in coding tasks.
Seamless Integration with Git: The seamless integration of RStudio with Git has been praised by users, making it easy for them to manage version control. Several reviewers have specifically mentioned this as a major advantage of using RStudio for their coding projects.
Powerful Statistical Analysis Tool: Many users appreciate RStudio's capabilities as a powerful tool for statistical analysis and data exploration. They mention its ability to import data from multiple sources, apply machine learning models easily, and export data into various channels.
Confusing and Outdated User Interface: Several users have expressed dissatisfaction with the user interface of RStudio, finding it confusing, unattractive, and outdated compared to other tools. They feel that the interface is too technical for business people.
Frequent Crashes with Large Datasets: Some users have mentioned that RStudio frequently crashes when loading large amounts of data. This can be frustrating and disrupt their workflow.
Lack of Integration with Other Applications: Users have pointed out that RStudio is not as integrated with other applications as Python. This limitation makes it less convenient for users who rely on seamless integration between different software tools.
Users commonly recommend RStudio for beginners in R programming and data analytics. They believe that RStudio is a good tool for learning machine learning and recommend using it for data work, programming R code for machine learning, implementing R software, data analysis, and data science. Users consider RStudio to be a great resource for analyzing data and necessary for anyone who wants to get into R programming. While considering other suites and languages like Python, they still recommend taking a look at RStudio for data analysis. Additionally, users find RStudio to be useful for doing statistics and creating professional plots and figures. They suggest familiarizing oneself with common libraries in the field and doing online tutorials before starting to use RStudio. Users warn about the steep learning curve but believe it is worth investing the time to learn it. Moreover, they recommend using RStudio for big data and epidemiological research.
Attribute Ratings
Reviews
(1-25 of 46)Companies can't remove reviews or game the system. Here's why
August 01, 2023
Posit, the Best ever Data Science Software
I use Posit software RStudio Pro to analyze, modelling and visualize dataset related to healthcare, medical affairs and pharma. There are lots of R packages available mainly dplyr, stringr, ggplot2, tidyr which we usually use in our day-to-day data management, data wrangling, cleaning, pre-processing tasks. Also, we use lots of other machine learning packages such as caret, tidymodels for statistical modelling and prediction. Our client network is integrated with AWS cloud platform so that we can use Posit software seamlessly and efficiently.
Business problems like patient analytics, feasibility studies are done using Posit Workbench. Based on clients' requirements and requests we use RStudio and R packages for data visualization including Bar plots, Line Plots for various kind of statistical analysis viz. Correlation analysis, LASSO regression, Elasso or Network analysis and Graph.
We have used RStudio for parallel computing with the R package VSURF to handle big data like millions of rows and columns (mostly patient churn and history data). We also used ggplot2 and plotly library for stunning graphs and plots.
Last but not the least, we have used Rmarkdown (or now Quarto) for generating PDF, Word reports to clients for data validation and case studies according to business requirements.
Business problems like patient analytics, feasibility studies are done using Posit Workbench. Based on clients' requirements and requests we use RStudio and R packages for data visualization including Bar plots, Line Plots for various kind of statistical analysis viz. Correlation analysis, LASSO regression, Elasso or Network analysis and Graph.
We have used RStudio for parallel computing with the R package VSURF to handle big data like millions of rows and columns (mostly patient churn and history data). We also used ggplot2 and plotly library for stunning graphs and plots.
Last but not the least, we have used Rmarkdown (or now Quarto) for generating PDF, Word reports to clients for data validation and case studies according to business requirements.
- Efficient coding
- Clean IDE
- Help page and large community
- Data view support for all kinds of data formats
- More organized help page
- Installation packages of older version as well as latest one
June 30, 2023
All-in with RStudio
RStudio products are used across multiple departments in our organization, including the research, IT, and data science business units. Our work largely involves analyzing clinical and supply-chain data to improve quality of patient care and to reduce excess cost in the healthcare system. This work can include predicting patient outcomes based on clinical and demographic data, predicting supply shortages, and estimating under- or over-utilization of hospital resources.
- RStudio provides an integrated product suite for both model development and deployment to end users.
- RStudio provides an open-source version of its products to ensure accessibility to all users
- The RStudio Connect (Shiny) platform is an incredible tool for quickly making statistical models, documents, apis, and enriched data available to end users.
- RStudio could consider an archetype tool within the RStudio IDE. This would be similar to a Maven archetype, where the user identifies the type of work they are doing and the tool would generate a series of directories and the project scaffolding (based on best practices) to springboard a larger project.
- It would be nice to see the files in a directory tree similar to Intellij. This would prevent the constant drilling in and out of directories in a larger project.
July 18, 2022
RStudio for Business Analysis
We use RStudio as an analysis tool to perform complex data analysis problems and scenarios. We build different statistical models to understand business data and perform forecasts. It has good visualizations and is a very flexible tool. As Business Analyst it is a good tool to understand big data in the organization.
- Visualization tool
- Statistical Analysis
- Forecasting
- More flexibility to import tamplates for the visuals
- More documentation about the formulas
- More coding automation
April 22, 2022
There is no WORK without R and no R without RStudio
It is used by the Data Science team within the department. It helps the team with the reporting functions, tackles business problems from an analytical perspective, and builds up quick interactive tools.
- Well-designed UI
- Full support of R
- Great technical support
- Better support for the desktop version
April 13, 2022
RStudio: An all-purpose way to interact with R
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
My team uses RStudio products, but we distribute reports and dashboards to 100+ users. The business problem it addresses is how to get the data science work that we're doing in R and Python (for example, text mining), as well as more day-to-day reporting based on some of the data structures that we have written in R/SQL.
- The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
- The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
- Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.
- I'm only a data scientist, I'm not DevOps, but I did find RStudio Connect hard to install. I have also tried and failed to get proxy authentication with Apache working, I'm sure it's me being thick but I have never gotten there with it.
- Another thing I don't like is some of the abstractions in RStudio Connect. There isn't really a file system at all, and data refresh is done with a scheduled RMarkdown report, which is okay but it's a bit round the houses and it's very different from the code on my local machine.
- I don't love the pricing model of RStudio Connect where you pay just as much for publishers as you do for consumers. I wish we could have, say, 5 publishers and more consumers on our current licence.
January 21, 2022
RStudio - Perfect for the Low Budget Statistician
We use RStudio for statistics-related endeavors on our research projects. We use it frequently for accessing and analyzing our data in descriptive and predictive type analyses. It helps us address issues such as underperformance in schools and other education settings, or even issues of inequity and exclusion of vulnerable populations.
- Descriptive analyses.
- Predictive analyses.
- Accessing data.
- Replicating syntax.
- The interface.
- A more beginner-friendly walkthrough.
- Have also had issues with program versions impacting syntax execution.
September 11, 2021
RStudio for R!
RStudio is the go to tool in our team for data analytics workflow, from pulling and wrangling data, modeling and visualization.
Through the products - e.g., web app, API, reports - that were built using the publishing platform (RStudio Connect), every users in the company were able to access analytics applications designed specifically for individual use cases.
Through the products - e.g., web app, API, reports - that were built using the publishing platform (RStudio Connect), every users in the company were able to access analytics applications designed specifically for individual use cases.
- Integration with databases.
- User community.
- Integration with other software/languages.
- Lacks stability.
- Memory management.
September 10, 2021
RStudio: Difficult Learning Curve with Matching Pay Off
RStudio is being implemented by our analytics department to help solve complex client problems by utilizing the data and statistical packages available through RStudio. The ability to perform far more accurate multi-linear regression models and time-series forecasts has helped our clients not only see where they are going in terms of sales but also what affects their sales.
- 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.
September 10, 2021
The R-Studio suite is a well thought out solution to development, control and publishing r apps and services.
RStudio is a great way to allow teams to develop r-shiny apps without needing to go and install lots of software on each developer's individual machine. It helps people to get ideas together much faster that you can traditionally. And by pairing it with other products in the suite you can then deploy them to non-devs too for quick feedback.
- Centralised admin
- Ability to manage allocations of CPU / RAM per user
- SSO
- Set up can be complex
- Automated updates via the admin screens
September 06, 2021
RStudio for quick prediction prototyping
Very few of us are getting into predictions using Machine Learning and Data Science. We use Rstudio to program our algorithms. There are only a handful of people in the whole organization who use Rstudio right now. We use it in pockets, and do the proof of concepts with Machine Learning using R.
- We use it for a quick visual representation of data
- We do exploratory data analysis to understand data
- We do predictions using RStudio
- When we have to run 100 iterations using more than 10000 records, RStudio gets stuck or takes a very very long time to respond
- Generating a pdf report from an RMD file is very difficult from RStudio.
- Generating a pdf report in RStudio cloud is straightforward and inbuilt.
September 05, 2021
RStudio - Dire wolf of "Game of Data Science"
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.
September 04, 2021
RStudio is the Swiss Army knife of R Solutions
RStudio provides a number of products and services, from their best-in-class IDE for R to their collaboration and publication platform, RStudio Connect. Our Data Scientists leverage RStudio Server on a daily basis to do analysis, develop dashboards and Shiny applications. They deploy these to either our Shiny Server Pro environment or, more commonly, our RStudio Connect environment. Others at the company use the RStudio IDE to do analysis on their local machines. R, as a statistical programming language, is mostly commonly used by our data scientists who support the whole organization, often in a paired environment. By using RStudio Server we can ensure consistent environments for deployment of assets and ease of managing security. There are pockets of other scientists, marketing and logistics analysts who use R to amplify their work and they use the desktop IDE because they have no need for collaboration.
- 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.
We use RStudio for development within bioinformatics group. The product (R Shiny) from our group is used across the whole organization, it is used for data integration, both clinical and pre-clinical biomarker data and other types of data integration.
- 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.
September 02, 2021
Well Suited for Data Visualization and Modeling
We use RStudio as an GUI interface for R, which we use to visualize and model data. For modeling data, we use lots of machine learning techniques like Regression, and R provides an excellent package to implement various flavors of regression like lasso and ridge regression. For data visualization we also use Shiny apps.
- Debugging
- Front-end interface to R
- Provide shortcuts to some R commands
- RStudio connect experience was not very smooth
- Web service configuration for the RStudio server is not very intuitive
- Some Visual DataGrid GUI would be beneficial
September 01, 2021
RStudio provides stable and trusted open source tools in a market frequently flooded by trendy and soon-to-be abandoned software
We use RStudio Connect as a publishing platform for R and Python documents, apps, and APIs. It provides us with a professional, clean looking interface to share our work with internal clients and stakeholders. Our usage began within a relatively small team, but the popularity of RStudio Connect and its features saw that usage grow to a broader group of users that spans multiple departments and business units.
- 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.
August 31, 2021
RStudio--Standing on the shoulders of giants
We model economic indicators for cities in Mexico. Traffic congestion and other demographics.
- Coordinate data wrangling with visualizations
- Interactions with other software
- Project management
- Function name autofill
- Speed
- More and clearer detail on dashboard bugs
Score 10 out of 10
Vetted Review
Verified User
All courses in the statistics degree, the data science certificate, and quantitative courses in other sciences, such as biology, ecology, and economics that use the programming language R do so using the RStudio IDE. This potentially reaches 1,000 students annually. We use R/RStudio to teach students how to do modern statistics and data analysis.
- Seamless integration with professional quality reporting packages (R Markdown, xaringan, Sweave), other coding languages (Markdown, SQL, Python, C++), version control, and the terminal
- Environment management (using Rprojects)
- Pop-up tool tips for function arguments
- Tab completion!
- Change the default global options to not save Rdata sets between instances; this is very unfriendly for beginner users
- Better GUI support for exporting images out of the viewer pane
As an analytics organization embedded within the marketing team, we support roughly 90-100 marketing professionals. While we have an extensive BI and analytics stack, RStudio Connect really helps us with serving insights to these stakeholders at the right moment. We utilize parameterized reporting to cut down time it takes to report, schedule processes that previously relied on cumbersome crontab processes, and host bespoke, highly customized dashboards that allow non-technical users to interact with data better than they would in a BI environment.
- The CSMs are fantastic - they solve our problems with the platform rather than being salespeople in disguise.
- Ease of use - The entire environment is very easy to understand and use, both for developers and non-technical end users.
- Scheduling - We replaced our entire crontab based scheduling system, saving us time through efficiency.
- Git-backed deployment - if you're working with version control already, RStudio Connect works flawlessly with it.
- Python support is still new, though we haven't run into large issues.
- To get Python support, we had to sign up for the mid-pricing tier that comes with 100 users, while we really only needed 10-15 developer seats, so I think some more flexibility in the pricing model would be nice. That said, overall the product seems very(!) reasonably priced.
May 24, 2021
Best all-in-one IDE for R
RStudio is used by several working groups within a larger project for the University of Vermont. It is used mainly for statistical analyses, manipulating spatial data, spatial analyses, and other programming/statistical tasks. I use my personal version of Rstudio as well as Rstudio server for analyses for this project. Rstudio is one of the best IDEs I have come across for R. I can keep track of variables within my workspace, view the files in my working directory, run the code and inspect output, and look at plots on different panels of the Rstudio interface. This helps keep my work organized and efficient. Rstudio has helped increase the overall productivity of the working group in which I work. Also, Rstudio interfaces with GitHub, which has been used for collaborative coding efforts.
- Rstudio is very customizable. You can easily change font colors, sizes, and screen layout. I am particular about how I like my IDE setup, so this is a big plus for me.
- Rstudio allows you to look at datasets in your workspace with the click of a button. I do a lot of data manipulation, so I am constantly having to look at datasets after operations to make sure they look correct. The view option in Rstudio makes checking datasets very fast.
- Finally, I love the way Rstudio manages plotting. Your plots can be viewed in one of the panels. Those plots can easily be copy/pasted or exported into a variety of file types. You can also magnify the plots and scroll between plots to look at previous plots.
- Sometimes Rstudio crashes when you work with big datasets.
- I've had some issues installing packages, which is very annoying. Sometimes I can install packages on my PC but not on my Mac, and vice versa.
- Rstudio is not exactly a lightweight IDE, so it is not ideal for computationally intensive tasks.
March 17, 2021
RStudio Review
RStudio is being used by our finance department. We have close 20 developers. R is the main language used for development. We use RStudio Connect for running the reports. Our RStudio enterprise edition gives us the best value for our investment. We are planning to upgrade to the latest version soon.
- Provides a great interface for our R language
- Easy to implement multiple packages
- Connect feature for viewing the reports.
- SAML integration has been little pain
- Provide support for latest R versions
- Phone support
March 16, 2021
Using RStudio to solve the pandemic
I have been using RStudio for almost a decade now for the analytic use of systems biology datasets. As a team leader, I made RStudio professional available for multiple data analysts and also used multiple programs offered by RStudio such as package manager and Shiny app. It has definitely improved over the years with use cases and has been widely used by scientists for top-notch research. We now have been using Rstudio for a COVID project and it has been a great experience especially with the functionality of python and other programming languages built into reticulate system. So far so good and I am very satisfied.
- Ease of use for Python users to be all in one platform
- Package manager for version control
- Shiny app functionality
- Interaction with AWS system
- Easier access login for multiple institution users
- Secure cognito access for users
March 11, 2021
RStudio Pro and RStudio Connect are really helpful.
The data scientists in our company have been using RStudio on a daily basis for years. We have seen this software kept improving for the past few years. RStudio Pro is an excellent tool for data analysis in R, and the RStudio Connect is super useful to host and present the analysis report to our collaborators in our company.
- RStudio Pro is an excellent tool for data analysis in R.
- RStudio Connect is super useful to host and present the analysis report to our collaborators in our company.
- It's very straigtforward to mange the users.
- The efficiency and stability of the shiny apps on RStudio connect could be improved.
March 11, 2021
RStudio just works out of the box
Our organization has devoted considerable resources to implement RStudio across the firm. We have dedicated servers, a team of specialists on staff to manage them, and many individuals who use the product. We use RStudio to analyze data in many forms--AMI data, utility participation data, survey response management, etc.
- Code completion is a life saver when I vaguely know the function or variable and the popup fills in the correct choice
- Help on function definitions right in the tool; no need to search the web
- Repositioning panels on the fly allows me to minimize parts that matter and get more room for my analysis in the console or in the R file
- Built in Git helps me remember to keep the repo current
- Code formatting sometimes rearranges code that was formatted the way I wanted
- There is a bit of a learning curve setting up projects, and it makes folders even if I already had them; perhaps modal on what each choice would do might help
- We do not save environment when closing, perhaps include a one time for all checkbox
February 20, 2021
De-facto for R programming.
It's being used in the Data Analytics team and several business and functional units to create automated and reproducible data analyses.
- Ease of integration across common programming languages
- Free tier offers much of functionalities as paid version
- Great support
- Intermittent crashing
- Improved UX for new users