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Posit

Posit
Formerly RStudio

Overview

What is Posit?

Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.

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Recent Reviews

TrustRadius Insights

Intuitive User Interface: Users have found RStudio to have an intuitive user interface that allows them to quickly test and debug code. …
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All-in with RStudio

10 out of 10
June 30, 2023
Incentivized
RStudio products are used across multiple departments in our organization, including the research, IT, and data science business units. …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 12 features
  • Visualization (26)
    8.4
    84%
  • Connect to Multiple Data Sources (25)
    8.1
    81%
  • Extend Existing Data Sources (26)
    7.5
    75%
  • Automatic Data Format Detection (25)
    6.4
    64%

Reviewer Pros & Cons

View all 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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Pricing

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N/A
Unavailable

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

Offerings

  • 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
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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

7.3
Avg 8.5

Data Exploration

Ability to explore data and develop insights

8.3
Avg 8.4

Data Preparation

Ability to prepare data for analysis

8.2
Avg 8.2

Platform Data Modeling

Building predictive data models

8.2
Avg 8.5

Model Deployment

Tools for deploying models into production

8.6
Avg 8.6
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Product Details

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

Screenshot of Posit runs on most desktops or on a server and accessed over the webScreenshot of Posit supports authoring HTML, PDF, Word Documents, and slide showsScreenshot of Posit supports interactive graphics with Shiny and ggvisScreenshot of Shiny combines the computational power of R with the interactivity of the modern webScreenshot of Remote Interactive Sessions: Start R and Python processes from Posit Workbench within various systems such as Kubernetes and SLURM with Launcher.Screenshot of Jupyter: Author and edit Python code with Jupyter using the same Posit Workbench infrastructure.Screenshot of Posit Connect enables users to deploy Interactive Python Applications (including Dash, Bokeh and Streamlit), in the same place Shiny apps are shared.

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.

Watch Overview of Posit Connect

Posit Technical Details

Deployment TypesOn-premise, Software as a Service (SaaS), Cloud, or Web-Based
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo

Frequently Asked Questions

Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.

Anaconda, Dataiku, and Cloudera Data Science Workbench are common alternatives for Posit.

Reviewers rate Security, Governance, and Cost Controls highest, with a score of 8.8.

The most common users of Posit are from Enterprises (1,001+ employees).
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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!

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 122)
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June 30, 2023

All-in with RStudio

Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • 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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Data processing
  • Statistical analysis
  • Libraries for just about every use case
  • Improved debugging tools - breakpoints can be clunky
  • Better generalisation - creating for loops to handle dynamic data can be a pain
  • Garbage collection - when working with large datasets it can be necessary to manage memory yourself
Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • RShiny applications that are intuitive and help to communicate in the multidisciplinary teams
  • d3.js based visualizations
  • Bayesian statistics
  • calculating confidence intervals
  • merging tables by using SQL commands
  • using regular expressions
  • some of the machine learning implementations are best in R
  • way more hassle-free than SAS, in my opinion
  • open-source - RStudio does not discriminate against people & businesses based on their financial status, many small businesses cannot afford SAS, in many developing countries young people are willing to learn to program, and SAS platforms or other paid software is absolutely out of the question, those people/young programmers will be not able to afford even free cloud SAS due to the internet infrastructure...some of the best ideas come from those who face serious challenges in life and can speak several languages as their minds are often more creative ("necessity is the mother of invention"). I feel that platforms like RStudio or Jupyter connect me with the World, with other creative minds, and contribute to making the World a fairer, better place.
  • something like IronPython in Spotfire, but R equivalent would be great; the existing R interface is not fully functioning
  • something like Pyhon interface in Stata, but R equivalent would be awesome
  • in the pharma World deadlines are tight, pressure is very high - Stata lets manipulate data super fast compared to R
  • brining R and Python community together
  • in my opinion, Natural Language Processing pipelines are better than in R
  • catching up with some of the machine learning implementations - visualization aids in this field are better in Python, at least that is my intuition
Score 6 out of 10
Vetted Review
Verified User
Incentivized
  • Cleaning large datasets
  • Automating the data transformation process
  • Customizing datasets, plots, etc.
  • Unintuitive importing/exporting CSVs and/or datasets
  • Console errors are difficult to understand and not informative
  • Steep learning curve, especially for those unfamiliar with R and programming
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • Easy installation of library of requisite drivers
  • Great for statistical analysis
  • Quick querying and custom analysis to drill down
  • Better inbuilt training to decrease reliance on external resources
  • Parity for Mac and PC users, provide same set of features
  • Bundle R so that it doesn't have to be installed separately
Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • Programmable
  • Repeatable workflow
  • Consumes very less resources
  • Statistical analysis
  • Data cleansing
  • Data visualization
  • Modelling
  • Though the UI is far better than other IDEs available in the market yet, it looks more like an old DOS machine.
  • Packages. They are all over the place as there are no evident categories in which they can be arranged. So unless you know the name of the Package, it's really hard to get your hands on it.
  • A little overwhelming. At least for someone who comes from a low coding platform. Although the community is pretty strong.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • 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
Kunal Sonalkar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • Performing Statistical Analysis is very efficient. With a lot of open source packages available in R programming, data analysis becomes very easy.
  • Publishing web applications and deploying predictive data models is very easy if you have R Server in your firm using Shiny. It can handle large sets of data.
  • Writing data science algorithms like Clustering, Classification and Apriori Analysis is very efficient. The open source nature of this programming language allows everyone to contribute packages to the environment.
  • There are some packages in RStudio which aren't very well known hence its very difficult to get help if you get stuck using them.
  • If the dataset size crosses 20 million rows, then you need extremely high RAM otherwise the processing gets very slow. So in such a case R Server is a must. Cloud storage can be a good alternative though.
  • The graphs which are plotted in the console aren't very intuitive and labels, colors, axis, etc have to be manually written to make the visuals look more appeasing.
Suryaprakash Mishra | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • Dara Management.
  • Descriptive and Statistical analysis.
  • Data science and machine learning.
  • Text analysis.
  • Can not run concurrent sessions and sometimes freezes but can be due to local or virtual machine capacity.
  • RStudio has come a long way, and expect enhancements will continue to improve performance and ease to use.
Chris Beeley | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • 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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Visualizing data
  • Integration with other programming languages and tools
  • Variety of inbuilt functions and packages
  • Dashboard publishing
  • Processing is slow when working with large datasets
  • Description of bugs could be clearer
  • More tutorials on capabilities
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Notebooks, where you can run chunks and see the output
  • I can view data frames
  • Integration with git
  • Queries to external data warehouses (e.g., using RJDBC::dbGetQuery) are blocking things to the extent that Rstudio freezes and I need to force quit it to stop the query
  • I want to have tools to manage the variables by size
  • Sometimes I want to clean the memory and it would be nice if RStudio suggested an easy way to rank variables by size in the environment
January 18, 2022

RStudio is wonderful!

Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Automate processes
  • Statistical Analyses
  • Portable Code
  • A very good IDE for R programming
  • Can be intimidating to non-programmers
  • I wish I could copy data to the clipboard easier
  • I never have a big enough screen to see all of the data I want to see
Prashast Vaish | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • It's super quick.
  • It has inbuilt functions for most of the analytics procedures.
  • It has great visualizations.
  • It has lots of libraries and sometimes it shows errors while importing them.
  • Its UI can be improved.
  • It takes a lot of time exporting files.
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