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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)6.363%
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
Pricing
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What is Posit?
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Entry-level set up fee?
- 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.
YouTube
Posit Connect | Host all of the data products you create
YouTube
Features
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Product Details
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- Competitors
- 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).
Comparisons
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Reviews and Ratings
(237)
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
(51-75 of 122)Companies can't remove reviews or game the system. Here's why
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
RStudio is primarily used by the analytics department, creating reports, analytical dashboards, and general data analysis for the wider company. We have recently had interest within the department to use Python as well as R, and have enabled Jupyter Notebooks on our instance of RStudio Server Pro. We host all of our reports and dashboards on RStudio Connect, which is integrated with our active directory. We also have a few proprietary packages that are available on our RStudio Package Manager.
- Amazing IDE for RStudio
- Ability to publish dashboards with just a few clicks--little server knowledge needed
- A lot of the DevOps are easy to understand
- The RStudio ecosystem means we don't need a lot of other products
- Decreased speed loss when RStudio Server is connecting to a hosted drive
February 22, 2021
A scalable and robust data science lab and collaboration platform
We are using RStudio Teams as central lab and prototyping environment for our Data Scientists. We use RStudio Connect to share plots, prototype dashboards and APIs with our colleagues, stakeholders and software developers.
RStudio is part of our internal data science platform and it is being used across product lines within our division (Industrial Applications).
RStudio Teams addressed the problem of having a prototyping environment for everyone. Users can decide which IDE would like (JupyterLab, RStudio, VScode) instead of forcing them to go with the default classical cloud Jupyter environment.
RStudio is part of our internal data science platform and it is being used across product lines within our division (Industrial Applications).
RStudio Teams addressed the problem of having a prototyping environment for everyone. Users can decide which IDE would like (JupyterLab, RStudio, VScode) instead of forcing them to go with the default classical cloud Jupyter environment.
- Close to open source.
- Kubernetes integration.
- Run on-premises.
- Cloud agnostic.
- A bit hard to set up.
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
January 06, 2021
RStudio has great tools for BI and Data Science Teams
The main user of RStudio is the business analytics team. The risk team has started using the product a couple of months ago. There are two use cases for RStudio inside the organization:
- 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
January 05, 2021
light user
It's being used as preferred tool by some data analysts/scientists. I use it for data transformation and simple stats analysis (e.g. a/b testing, linear regression, exploratory analysis, etc.).
- 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).
December 28, 2020
RStudio free IDE data analysis to AI
It’s used for data analysis, data visualization and data visualization app and machine learning.
It is used across whole organization.
It is used across whole organization.
The type of Uses from analyzing the injuries, and violence data. The death data from different sources and visualization. Also, analyzing disease data like HIV, STD and other Infectious diseases and visualization analysis!
Developing Machine Learning Analysis on predicting/ identify cases from the free text fields. Performing Natural Language Processing and other AI analysis!
Developing Machine Learning Analysis on predicting/ identify cases from the free text fields. Performing Natural Language Processing and other AI analysis!
- The tool provides vie of Code, log, data and library , plots on same places!
- Lots of predestined code , projects and samples available to ready to use with little modifications!
- Lots of Machine Learning algorithms, Artificial Intelligence related samples, and projects available.
- RShiny is great tool to develop a web applications and data visualization.
- Free of cost tool, easy to install and manage libraries. Does not take too much expertise to install or maintain RStudio tool!
- The dropdown menus or tool in order to test or select some of basic Machine Learning algorithms for analysis. Which requires No to very little coding.
- Some ready to use tool or menus in order to create different types of charts, GIS maps or other data visualization like MS Excel!
December 26, 2020
Best way to do R and much more!
RStudio is used regularly by my team in helping build and maintain custom systems tools that support a multitude of business-critical tasks across functions, from pre-sales and account management to program operations and settlement. The recent addition of Python script compatibility is a real gem and very useful for collaborative work between our analysts who prefer either R or Python.
- Great autocompletion
- Easy debug mode
- Useful Markdown templates
- Auto alignment
- Suggestions pop up for clean coding style
- Improvements in document outlining
December 23, 2020
Software that adds value to any data platform.
R Studio is used in conjunction with R Package Manager and R ShinyApps.io for data cleaning, preparation, and sharing with partners.
- 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.
December 23, 2020
RStudio - Best data science and machine learning tool
RStudio is used as primary tool for data science activities. It is used by multiple business units in our organization. It addresses multiple business problems few of them are identifying the engine performance, to proactively identify engine failure and intimate customers for service, etc.
- Reticulate package makes python developers life easy and adds more options for resolving an issue.
- Shiny and Markdown applications provides rich visual for business.
- Professional version has easy driver installation/upgrade modules.
- RStudio is one of the best GUI in market.
- Better Usage Tracking for all activities that are performed in both RStudio Server and Connect.
- Better alert mechanism for anomaly in resource usage.
- Better query governance with GUI.
December 23, 2020
Good experience with RStudio, hoping it evolves faster
Build statistical testing into dashboards. Mainly used by the Data Science team, though other teams have the option to upskill themselves.
- Easier loading of packages without needing to search depositories.
- Neat layout when graph is being ouput.
- Pathway management is messy.
December 23, 2020
RStudio is the leading IDE for R
RStudio is the leading tool for running R code interactively. We use it for bioinformatics and data science research. While RStudio is free, RStudio the company also offers a series of paid offerings with additional features. They do a good job supporting their paid products. Overall, RStudio is the top tool for running R code.
- Interactive usage
- Good community support
- Active development
- Better documentation on new offerings
- Better debugging of community offerings
- Less closed ecosystem
December 22, 2020
RStudio is a great end user product
We mostly use it for R Shiny Apps and R Markdown reports. I am more of a python user so I have a mixture of dash, flask and jupyter notebooks on RStudio. I like it because I don't have to configure a server in order to run dash or flask. I just upload and it works.
- R Shiny Apps - Great solution for fast prototyping
- Flask or Plumber - can implement an api very quickly
- Jupyter notebook or R Markdown doc - great for a dynamic report that you want to have refresh periodically
December 22, 2020
RStudio for life sciences data science
We use RStudio for our Data Science work in cancer research. It is used by our Informatics department. We have multiple external systems that house our research data, and RStudio addresses the business problem of providing an environment for doing interactive analysis on that data using a controlled package environment, as well as a place for publishing dashboards for non-technical users to explore our data.
- Interactive programming environment in R
- Controlled package environment with docker containers
- Management of users with authentication
- Management of user sessions with clear options and flexibility
- Management of docker containers for programming environments (ease of use)
- Process of deploying dashboards to RStudio Connect (specifically package versioning and management using packrat and RStudio Package Manager is difficult)
- Occasional lags and bugs in spinning up sessions or working interactively
December 20, 2020
A must-have expansion to R core!
We use RStudio as a way to quickly develop and deploy proofs of concept that we can deploy to a core group of users before embarking in a broader industrialization process. It allows us to own the entire process and rapidly bring in code fixes or improvements as requested by the power users.
- Deploying known processes already built in R.
- Easily producing UI's for triggering known processes in R or SQL.
- Creating dashboards.
- Creating landing pages / managing the environment as a whole.
- Monitoring app usage.
December 20, 2020
Reflection of being a three-years RStudio user.
The decision of technology has been divided by corporate and teams. RStudio is a department-based decision, which has been selected by our director and carried out through. However, we also have team members use Python predominantly. We used RStudio to prepare markdown notes, slides, reports, and ranges of analysis around the organization, including admission, human resource, finance budgeting, etc. Currently, the most important outcome is reporting to senior management teams.
- Reproducibility for repetitive reports.
- Endless choices of open source packages supporting integration with data lake and data warehouse.
- Flexibility of customise visualisation.
- Python is quite popular in the industry, though the package rticulate is available to support integration, there is still room to improve.
- Environment management (package and R versions) is complex, or need to purchase paid functionality on Mac.
December 18, 2020
RStudio helps reluctant researchers with its intuitive IDE
RStudio is used in my company to provide reproducible research in the form of scripted data analysis.
- Script-driven research which allows for provenance and reproducibility.
- Intuitive IDE that is constantly improving.
- Tight integration with git / GitHub.
- Tighter integration to other proprietary tools such as REDCap, SAS, SPSS.
- More point-and-click to code tools for reluctant coders.
December 18, 2020
RStudio is a world of possibilities
We use RStudio Connect for deliverying data science product (dashboard and documents) across all companies and areas of the group.
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
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.
December 18, 2020
Sysadmin's review
RStudio is used by multiple departments in our organisation mainly in the R&D area, quantitative genetics, breeding and bioinformatics.
- 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.
December 16, 2020
Build and Share Data Science Products with RStudio
It is used by our Advanced Analytics department. We use it both as an IDE for individuals as well as using RStudio Connect and RStudio Server Pro. RStudio Connect allows us to easily deploy shiny apps, rmarkdown documents and jupyter notebooks. RStudio Server Pro allows us to easily use RStudio in a server environment. Essentially RStudio lets data scientists do their work and share their work without needing an entire team of data engineers to support them.
- 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.
December 16, 2020
RStudio suite is amazing!
We do use across the whole company in both serving apps and reports that are available to and from wet lab colleagues and directors.
- Keeping things simple and tidy.
- Integration of different languages (R, Pytho, Bash, SQL).
- Testing code directly in the same IDE.
- Shiny apps deployment to RStudio connect may be tricky with repositories that are not CRAN (e.g. Bioconductor).
December 15, 2020
Amazing open source tools
[Used for] Data management and data analysis in research.
- Data wrangling
- See the data and the environment in IDE
- Rmarkdown output in many forms
- Readable code
- Addins
- 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.
December 15, 2020
RSC review
Our main use is with RSC for deployment of Shiny dashboards and Rmarkdown reports. It used to communicate our work with both internal and external teams across the whole organization and company leadership. We help to optimize cost with the use of statistical tools (descriptive and predictive analysis, data visualization, etc.).
- Easy to deploy and share with other teams.
- RStudio support.
- Ability to incorporate open source tools.
- Ability to scale across the company is limited based on the users license, cannot share a dashboard to the general view of the company.
- Ability to retain session - not simple method to customize view per user (e.g., once session is ended, the users will return next time to the baseline view).
- Ability to enable communication between multiple users - leave notes, tag other users, or share specific view.
I'm using RStudio in developing and delivering supporting course materials for machine learning and big data. It's a great resource, and the fact that I can use both R and Python in the same familiar IDE has made this a killer app for me. The majority of course material for Python has been developed and distributed via Jupyter Notebooks, which is not an IDE. The ability to do end-to-end development in one place, using the best of both worlds (plus C and C++), make RStudio the best choice for anyone who want to develop robust ML/AI applications.
- Great IDE
- Multiple language support
- Github integration
- Shiny integration
- Python integration
- C and C++ integration
- Project tools
- Graphics
- 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