TrustRadius Insights
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
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
- 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
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
Would you like us to let the vendor know that you want pricing?
11 people also want pricing
Alternatives Pricing
What is MATLAB?
MatLab is a predictive analytics and computing platform based on a proprietary programming language. MatLab is used across industry and academia.
What is Rational BI?
Rational BI provides analytics, data science and business intelligence in an analytical platform that connects to databases, data files and cloud drives including AWS and Azure data sources, enabling users to explore and visualize data. Users can build real-time notebook-style reports directly in a…
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
Return to navigation
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
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
Compare with
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
(1-25 of 37)Companies can't remove reviews or game the system. Here's why
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.
April 23, 2022
My very personal RStudio R&D journey
I have used the R language since around 2010 and before (along with S-Plus). RStudio as soon as it was available, also around 2010. Example use cases: 1. bionanoengineering - descriptive statistics (describing biological motility or nano surfaces), in parallel with image analysis in ImageJ and MATLAB; 2. bioinformatics - producing descriptive statistics for the motility of Neurospora crassa (filamentous fungus) to prove that how one use statistics matters and how it impacts business decisions; 3. pharma - benefit-risk analysis and data visualizations along with Spotfire 4. healthcare - clinical programming along with Stata and Python (one suggestion: it would be nice to have R interface in Stata and improved R interface in Spotfire); 5 - in product development for creating data monitoring & evaluation apps in RShiny. RStudio has been with me since the very beginning of my professional career. I could easily write up a Ph.D. on the use cases of R in life sciences, pharma, healthcare, and computer science. I would highly recommend RStudio for those who need to deliver fast tailored, customized applications, attractive visualizations or need to use Bayesian statistics, for example, to validate pharmacovigilance scores.
- 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 10 out of 10
Vetted Review
Verified User
We license the RStudio Connect (RSC) product from RStudio. We also use, for free, the open source packages and development environment offered by RStudio. Without going into specifics, our Connect license is about 1/6th the cost of our QlikView license, which we will discontinue once we are done porting legacy dashboards off of it. A direct comparison between Qlik and RSC is unfair. Products such as QlikView and PowerBI are BI tools which licensed users use to build dashboards. I refer to RSC as a content management platform for data science. We use it to: Validate our data and alert us to problems. Email reports to clients in PDF and Excel. Upload data to FTP servers and to send HL7 messages (via a HL7 engine). Hosts our internal API. Host machine learning models. Host custom-built dashboards. And, staff love developing against it.I could calculate an ROI for everything, except staff satisfaction. But the value add is there and it is valuable.
- Deliver data/insights to customers
- Multi-language (R, Python)
- Empower staff
- The name causes people to incorrectly think it is an R-focused product. It isn't.
April 13, 2022
RStudio analytics perspective for product reporting
We are using RStudio for quick querying, reading, and writing tables on the backend for the sole purpose of product analytics. RStudio is the go-to interface, and with easy installation of ODBC drivers on Windows machines, it provides great utility to connect to the Amazon Redshift database. It is an important piece in our analytics framework, as the custom tables created through this interface are used for visualizations on other software.
- 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
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
April 13, 2022
Everyday Statistical Workbench
We use RStudio for analytics, data science, reporting, and statistical modeling for business clients in all enterprise functional groups. The system is extensible to a wide array of use cases, including quality, machine reliability, finance, supply chain, marketing, and business intelligence. RStudio connects to our Azure and on-premise data assets.
- Data visualization
- Big data
- Statistical modeling
- R
- Python
- Web sites
- Databricks
- Azure DevOps
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 20, 2022
Hooray RStudio.
Our internal analytical platform is deeply connected with a series of RStudio products, from RStudio Connect to RSPM. These products provide not only great development environment, but they also create the excellent user experience for customers. Importantly, the RStudio support team is very responsive. The team takes customer's request very seriously, and if there is no immediate solution, they usually follow up with a long-term plan. Shout out to our main contact Colin.
- Update.
- Support.
- Create an active community.
- RStudio Connect interface could be more flashy.
- More curated education sessions.
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 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.
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.
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.
July 14, 2021
RStudio for data maturity
It is used to deliver data focused clinical research covering a wide range of clinical areas. It is also being used to develop automated documents and analytics using r markdown documents. This is covering areas of interest within both the university and also our clinical collaborators including hospital, primary care and other healthcare stakeholders.
- User friendly.
- Good customer support.
- Integration of more than R.
- Good documentation.
- Automatically advising of updates.
- The pricing model for RStudio Connect makes it an unfeasible prospect for large public sector organisations.
- The sales yes can sometimes feel quite pushy.
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.
July 06, 2021
Makes life easier for analytics developers
We employ dozens of R users and install RStudio on our machines so that everyone has a familiar integrated development environment (IDE). We also purchase the commercial product RStudio Connect for our analytics teams to develop and share interactive tools with non-technical users. It allows our more experienced R users to create more advanced tools than on our other intelligence platforms.
- Being a complete IDE
- Integration with RStudio Connect
- Easy to install and administrate
- Could have better integration with free hosting solutions
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 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.
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.
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
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 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