Overview
What is Posit?
Recent Reviews
How Posit Differs From Its Competitors
Awards
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Popular Features
View all 12 features- Visualization (24)8.585%
- Connect to Multiple Data Sources (23)8.484%
- Extend Existing Data Sources (24)8.181%
- Automatic Data Format Detection (23)7.171%
Reviewer Pros & Cons
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Pricing
View all pricingEntry-level set up fee?
- Setup fee optional
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
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Product Details
- About
- Integrations
- 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
Posit Integrations
- Amazon SageMaker
- Kubernetes
- Apache Spark
- Jupyter Notebook
- Streamlit
- Tableau Desktop
- Azure ML
- Bokeh
- Slurm
- Dash applications
- SAML Marketplaces
- Databricks Lakehouse Platform (Unified Analytics Platform)
- VS Code
Posit Competitors
- Anaconda
- Dataiku DSS
- Cloudera Data Science Workbench
- IBM SPSS Statistics
- Domino Data Labs
- SAS
- STATA
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
Comparisons
View all alternativesCompare with
MATLAB
Jupyter Notebook
Anaconda
PyCharm
Spyder
IBM SPSS Statistics
Microsoft R Open / Revolution R Enterprise
IBM Watson Studio
DataRobot
RapidMiner
KNIME Analytics Platform
TIBCO Data Science (including Team Studio and Statistica)
Dataiku DSS
Reviews and Ratings
Attribute Ratings
Reviews
(1-25 of 116)- Popular Filters
Everything you need in data science
- Time saved
- More accurate data
- Greater reporting capacity
Great Product for Data Analysis
- Speeds up data processing
- Improved data quality
- Improved ability to run advanced statistics
RStudio for Business Analysis
- It was easy to access and share models
- We create good models to explain business behavior
- The open source version is powerful
Rstudio - The most convenient ML tool
- Faster data processing
- consistent pipeline creation
My very personal RStudio R&D journey
- I landed a good job every single time I showed my RStudio implementations during my interviews
- every single time I developed RShiny app, it has been a success business-wise
- R helps to validate some of the other commercial systems, thus helping to make more informed business decisions, for example when choosing a commercial supplier or making an investment decision or planning R&D strategies...also simply because R helps to bring stakeholders together regarding communication of the scientific results
- good for advertising: a funny story - at some point in my career I was prevented to show my app to more customers as they started asking whether this is a product and whether they can buy it together with another product (perhaps R could create some type of fast-track legal pipeline for commercialization of the R-based apps)
There is no WORK without R and no R without RStudio
- Seamless workflow that enables quick turnaround for creating end-to-end analytics
- Easy sharing with the business audience in a wide selection of formats (Shiny, RMarkdown, Dash, Jupyter Notebook, etc.)
- Provides more options for Python developers and a platform for collaboration between Python and R
- Robust technical support for all products
- We will save money replacing legacy dashboard tools.
RStudio is great but needs some improvements
- Better visualization output
- Quick implementation of statistical models
- Improved analytical capacity
RStudio from a Grad Student's POV
- Cutting down on time
- Lower costs of data exploration
- Facilitating data insights
RStudio analytics perspective for product reporting
- Helps speed up the process of new report creation and provides the organization with better visibility into data
- Iterative analysis run through RStudio helps in decision making in meetings
- Makes the organization data-centric and enhances possibilities to go granular into data for better business strategy
- I am an end-user and won't know from Org's perspective, but yes, switching from Alteryx to RStudio saved a lot of money and raised eyebrows on our budget.
- The simple fact that not only the IDE is free but also the entire ecosystem makes it really powerful.
- The community is really welcoming and helpful.
RStudio: An all-purpose way to interact with R
- It enabled our organization to have a common platform for data analysis and for running R, which led to higher efficiencies between groups.
- It provided us with the ability to develop GUIs through RShiny for users who preferred to work with a different user interface which allowed us to reach a higher user base and extend the efficiencies further.
- It expedited some of our analysis workstreams by helping analysts and developers develop scripts faster through its tools and open source offerings.
Everyday Statistical Workbench
- Data mining to discover customer needs
- Modeling causal inference
- Making predictions
RStudio - Very Powerful Statistical Tool
- Positive impact is when you automate excel reports using Shiny applications, it ends up saving a lot of time and money.
- It's easy to catch on so with a little training and sound math background you can start coding right away.
- Its compatibility with other platforms like SQL databases, Salesforce, Tableau , etc is amazing and makes it worth the investment. It doesn't have any negatives as such.
Enhancing Data science capabilities with RStudio
- Much cheaper option then SAS.
- Data science and machine learning- used to solve complex business problems.
- Enables to launch Data Science project in Cloud-based environment.
- RStudio has enabled us to launch data science projects in the cloud at scale, which has brought in at least 10x more money than we spent on the licences.
- RStudio products are nice to use for my staff and I think are good to retain good people.
- RStudio allows us to put analytic products in the hands of managers rapidly, which is vitally important for us.
RStudio - a cheap and effective statistics program
- Allowed us to publish findings.
- Expanded our interest in social network analyses.
- Grew our knowledge of python-type languages.
RStudio - Perfect for the Low Budget Statistician
- Allows us to reach our statistics goals.
- Low cost.
- Allows us to invest in other parts of our project due to the low cost.
Hooray RStudio.
- Great product to distribute results to broader team.
RStudio: Best Bang for Your Buck
- Business decisions based on empirical data promotes growth
- Collaboration across teams saves time
- RStudio's integration with other languages eliminates [the] need for other software, saving money
RStudio is wonderful!
- Very positive impact on report automation
- Very positive impact on reproduceable code
- Very positive impact on sharing code with others. The only limit has been colleagues' experience and competence.
A must have tool for data analysts
- It has reduced the cost of operations as the tool is free.
- It has helped clean data to better visualize it.
- It has made ML operation implementation easy.
RStudio Is the Best R GUI for any price, let alone free!
- Quicker development of maps.
- Easy Statistical analysis saving time and money.
- Customer satisfaction.
RStudio is Very Useful but Needs Improvements
- Brings to light the data that cannot be managed/visualized by Excel. This helps decision making using data.
- Learning curve in R is steep. Using Tidyverse packages is a HUGE help.
An almost one-stop shop for your analytics needs
- I've been able to do analyses and build models that I would have otherwise been unable to do using SQL and BI tools alone.