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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
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Popular Features
- Visualization (26)8.585%
- 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
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
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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
- About
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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 9.
The most common users of Posit are from Enterprises (1,001+ employees).
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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
(1-5 of 5)Companies can't remove reviews or game the system. Here's why
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.
- RStudio is free and it's easy to start using it
- It's easy to install new libraries and start using them seamlessly
- The installation of some libraries is challenging, especially when they depend on a lot of other libraries.
- RStudio crashes when there is a clash between libraries somehow.
I have used Jupyter notebooks. I have used the cloud version of RStudio extensively. I program mainly in R as we have some libraries on Microstrategy which are in R. So, R was a natural choice for prototyping. I also use Jupiter Notebook for python programming. But, I use this less often than R.
10
We are a varied group of individuals coming from different backgrounds. Some are data scientists, some are Ph.D. doctors, some are programmers like me. All of us work on business problems, which present a lot of data which does not have immediate meaning to the business. We try to run predictions based on that data.
10
We are a bunch of programmers who use Rstudio. We do not really support the software, but we use it. We do help each other when we run into issues or get stuck into specific programming needs using R. Most of us have some kind of programming experience. There are some Ph.D. scientists who also program on RStudio.
- Running quick predictions based on the data at hand
- Representing data using graphs and charts
- Exploratory data analysis using RStudio
- We use it for scatterplot matrices
- We use it to quickly see the dependencies of various predictors
- We check multicollinearity between our input columns
- We hope to use it on a production run basis on cloud
- We need to be able to scale our prototype solution to larger sets of data
- We wish to have stable models, using Rstudio, which can be dynamic based on new data
No
- Price
- Vendor Reputation
- Third-party Reviews
It's free and easy to use. That's most important, as it gives us the flexibility to switch to something else for our prototyping needs.
If we had to do it again, we would like consider a product which is cloud first. We currently use RStudio Cloud, which is close to what we want in the future. But how much can we scale is the question. We have not really tested that yet. We would assume there are options to use it on cloud vendors such as Azure and AWS.
- Implemented in-house
No
Change management was minimal
I did not purchase premium support. I just use the cloud-based and RStudio IDE.
No
None
- Generation of HTML reports out of the RMD
- quick help files for any functions
- A quick view of data files
- The loading of files with lot of data takes a lot of time
- Generation of pdf report from RMD is not very easy.
No
September 04, 2021
RStudio helps data scientists get things done faster
RStudio is being used in the Underwriting and Analytics Department. We publish our analytics artifacts through RStudio Connect, then users from other department can consume with ease.
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.
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.
- Our work in RStudio helps us negotiate better pricing contract with third-party players and in the end resulted in 1.5 millions saved.
RStudio is free like Jupyter Notebook and VS Code. The others all require additional cost to use.
RStudio has more features than Jupyter Notebook. VS Code doesn't support R natively and require plugins, and it's not as mature as RStudio yet.
Compare to CDSW, Domino and others, RStudio Connect is much less expensive and can perform the same responsibilities and more.
RStudio has more features than Jupyter Notebook. VS Code doesn't support R natively and require plugins, and it's not as mature as RStudio yet.
Compare to CDSW, Domino and others, RStudio Connect is much less expensive and can perform the same responsibilities and more.
5
2 in Marketing Data Science/Analytics
3 in Actuarial / UW / Data Analytics
3 in Actuarial / UW / Data Analytics
2
We have DevOps engineers who help create RStudio Connect and RStudio Workbench on AWS servers.
- Create Actuarial Reserving processes and inform senior stakeholders financial results on a monthly basis
- Create Underwriting and Pricing models to drive profitable growth and appropriately price small commercial risks
- Create Marketing data analytics to help target customers who are good risks and more likely to switch to or buy their commercial insurance with us.
- RStudio Connect has been instrumental in helping us provide analytics to senior management by leveraging shiny and dash apps. We don't have to rely on Data Engineering team to create the same visuals on Looker because it usually takes less time from us to produce the results.
- Through the use of RStudio's professional database drivers, we can seamlessly connect RStudio with our snowflake databases and do many of the data transformation processes within R. Using dplyr let us use code we are familiar with and still get the processing power of the cloud database.
- We have also been trying to leverage reticulate to allow Python and R work together more often. We can do many of the preprocessing in R first then have the data object be used in a Python process (PyMC3 for example).
- As we grow, we want RStudio to scale with us, which may mean allowing Shiny and Dash to be run concurrently by many users. This would mean we have to think about how to dynamically add/remove servers based on the number of users active.
- RStudio can also be used in production using plumbr. We need to think about how we can utilize it to help us bring more models in production without the translation from modeling code to production code.
- Continue to bring documentation innovation using Rmarkdown. We can improve many reports and visualization we produce today using Rmarkdown and that allows us to schedule reports to be run, and share results automatically via email, and continue to be proactive in bringing analytics to the different stakeholders.
No
- Price
- Product Features
- Product Usability
- Product Reputation
- Prior Experience with the Product
The single most important factor of choosing RStudio over other products is its open-source tools that help students pick up the language without upfront cost. The new graduates nowadays know more about R and Python packages than SAS, so it is much easier for them to continue to use what they know already in a work setting.
I would look at how responsive they are helping customers accomplish their tasks. The turnaround time for an issue to be resolved is vastly more important in an enterprise setting
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.
- Making data science content readily accessible on an intuitive platform has made the work we do less mysterious to our clients/stakeholders
- Clients/stakeholders find engaging with the company's data science function more enjoyable since they have greater visibility into our work via RStudio Connect
We use RStudio Connect over Domino for publishing content because it is much more flexible and user friendly. It also "just works" far more often than Domino. RStudio Connect has much better support for API-type content, such as OpenAPI/Swagger documentation. For interactive web apps, RStudio Connect offers the data scientist more controls in a more intuitive interface to manage processes, load balancing, and URL paths. Domino either has 1) no such functionality, 2) an unintuitive interface, or 3) functionality that is only available to administrators.
100
The heavy users of RStudio products in our organization are primarily Data Scientists, Product Analysts, and Business Intelligence Analysts. However, each of these groups have stakeholders located in just about every part of the business, and RStudio Connect is the platform we use to share our work with these audiences, resulting in a very wide range of backgrounds that ultimately interact with Connect.
1
A Linux administrator is necessary to support RStudio Connect. In our case, an experienced Data Scientist (myself) who has a lot of personal experience with Linux provides this support. In addition to Linux knowledge, a support person needs to be familiar with the types of computing challenges that Data Scientists and Analysts face in their day to day work, so that they can properly configure RStudio settings to best fit these needs.
- Scheduling jobs
- Deploying web apps
- Deploying APIs
- Using scheduled RMarkdown documents for data ETL
- Deploying a shiny app dashboard to monitor a competing product's performance
- Deploying product quality APIs
No
- Product Features
- Product Usability
- Product Reputation
Usability is a very important element of our decision to go with RStudio Connect. Many alternative software offerings are bloating with half-baked and ill-fitting features, often with lots of redundancy (e.g., many ways to do the same thing). Connect provides a single comprehensive and flexible path to accomplishing a task. This allows Data Scientists to focus on their work rather than learning the nuances of an overly complex tool.
If we had to evaluate these products again, we would add an additional criterion to our rubric that captures the ease of deployment across a federated cloud organization. At the time, we were a single team operating in a single AWS account, but the success and adoption of RStudio Connect has seen the incorporation of other teams across our company. These teams have their own AWS accounts with their own permission rule sets. We would now ask: "How easily can we deploy something like RStudio Connect either across these federated accounts or in such a way that content published to Connect can access other AWS accounts while adhering to security best practices?"
RStudio provides the same level of support for all of its paid offerings, so we did not need to choose whether to pay for support or go it alone.
Yes
When reporting bugs to RStudio the response is swift, as are all responses to support tickets we've opened. In the case of bugs, RStudio has been great at acknowledging the problem, working with us to identify alternatives and workarounds, and ultimately putting the fixes on their development roadmap. They have also been good at communicating the status of any potential fixes.
During an evaluation of RStudio Server Pro (now called Workbench), I opened a support ticket asking about whether an advanced feature described in the Admin documentation could be used to achieve a particular behavior. Not only did RStudio confirm that yes, this could be done, but they also acknowledged the value in using the feature in such a way and even went so far as to provide a working example of how to do it!
- Scheduling reports
- Setting up email notifications
- Configuring content runtimes
- Deploying RStudio onto a cluster
- Identifying environment differences between Connect and the client machine
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
- We can communicate insights in a very professional way
- We can scale up solutions
- We can adapt different solutions to our projects
- We save time in order to invest it in analysis
With RStudio I can easily deploy insightful information and I can update it. Moreover, it takes minutes normally to resolve most of the new requests or to scale if needed. I have the control of my code and I can translate it into digestible reporting.
1
Data Analytics
1
Small start-up
- Modeling new traffic congestion initiatives
- Historical traffic data
- Monitoring live traffic congestion data
- Dashboard capabilities for story telling
- Speed of implementation of new ideas
- Easy interaction with other softwares
- Report users metrics on traffic congestion
- Predict traffic bubbles
- Analyze alternative data and report the results - live
No
- Price
- Product Features
- Product Usability
- Product Reputation
- Prior Experience with the Product
I've tried Rstudio in the past and I knew its capabilities to simplify complex analysis
Difficult to tell at this stage
- Implemented in-house
No
Change management was minimal
- Deciding when to upgrade to more premium versions of products
No, I considered but it is still not cost effective for my company. We hope we can escalate next year
No
Not yet.
- Environment creation
- Link with Python
- Link with Rmarkdown might be unstable
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
- Allows novice users to use R
- Interactive feedback
- Good core feature set
RStudio gives a more integrated R experience compared to Jupyter. RStudio is the ideal tool for running R interactively.
100
RStudio is the major IDE for R, and R is used for statistical and bioinformatics analysis across multiple groups. R is the leading and often only tool for many different types of analysis.
100
There are a range of skills for RStudio users, from new fellows just starting biostatistics/bioinformatics work to senior users who have many years of experience.
- Biostatistic modeling - regression analysis etc
- Bioinformatics analysis - microbiome, RNA-seq, etc
- Machine Learning through caret
- Build dash boards to facilitate cooperation between groups.
- Use notebooks for interactive training.
- Use notebooks for sharing
- More dashboards in the future.
- More notebooks for cooperation.
- Better reproducibility through Rproj and Renv
No
- Product Features
- Product Usability
RStudio allows interactive R coding, which is a major language used by our institutions. It allows both novice and expert users to have a good experience with R.
I won't. RStudio products are the best for R
- Implemented in-house
No
Change management was minimal
More training and communication with users.
- Changing expectations from users.
Yes
Included in our purchase
Yes
Yes. It was handled quickly and promptly.
We were discussing some additional features, and the product support team helped us find equivalent functions.
- Environment reproduction
- Literate notebooks
- Rproj for building projects
- Running cluster or parallel jobs