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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)8.484%
- 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
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What is Posit?
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
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- Setup fee optional
For the latest information on pricing, visithttps://posit.co/pricing
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- 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.
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Posit Connect | Host all of the data products you create
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Product Details
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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).
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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 56)Companies can't remove reviews or game the system. Here's why
August 01, 2023
Posit, the Best ever Data Science Software
I use Posit software RStudio Pro to analyze, modelling and visualize dataset related to healthcare, medical affairs and pharma. There are lots of R packages available mainly dplyr, stringr, ggplot2, tidyr which we usually use in our day-to-day data management, data wrangling, cleaning, pre-processing tasks. Also, we use lots of other machine learning packages such as caret, tidymodels for statistical modelling and prediction. Our client network is integrated with AWS cloud platform so that we can use Posit software seamlessly and efficiently.
Business problems like patient analytics, feasibility studies are done using Posit Workbench. Based on clients' requirements and requests we use RStudio and R packages for data visualization including Bar plots, Line Plots for various kind of statistical analysis viz. Correlation analysis, LASSO regression, Elasso or Network analysis and Graph.
We have used RStudio for parallel computing with the R package VSURF to handle big data like millions of rows and columns (mostly patient churn and history data). We also used ggplot2 and plotly library for stunning graphs and plots.
Last but not the least, we have used Rmarkdown (or now Quarto) for generating PDF, Word reports to clients for data validation and case studies according to business requirements.
Business problems like patient analytics, feasibility studies are done using Posit Workbench. Based on clients' requirements and requests we use RStudio and R packages for data visualization including Bar plots, Line Plots for various kind of statistical analysis viz. Correlation analysis, LASSO regression, Elasso or Network analysis and Graph.
We have used RStudio for parallel computing with the R package VSURF to handle big data like millions of rows and columns (mostly patient churn and history data). We also used ggplot2 and plotly library for stunning graphs and plots.
Last but not the least, we have used Rmarkdown (or now Quarto) for generating PDF, Word reports to clients for data validation and case studies according to business requirements.
- Efficient coding
- Clean IDE
- Help page and large community
- Data view support for all kinds of data formats
- More organized help page
- Installation packages of older version as well as latest one
October 28, 2022
Everything you need in data science
We use Posit to process large amounts of data, both for importing into other systems and after exporting back from them. Other uses cases include performing statistical analysis and creating visualizations. Posit makes it easy to perform complex manipulations on large datasets and automate long complicated processes, saving us a ton of time and removes the potential of human error.
- 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
September 01, 2022
Great Product for Data Analysis
RStudio helps our large team of conservation researchers address problems relating to data management, cleaning, and processing. In addition, it also helps our team with database management as we often manage large and historical sets of data. In many cases, our teams are using RStudio for the analysis of field data to assist with international conservation programs.
- Data analysis
- Data sharing
- Graphs
- User interface
- Cleaner file storage in desktop
- Collaboration
May 31, 2022
Rstudio - The most convenient ML tool
We use RStudio to build data science and machine learning pipelines for AI models. The pipeline that we create on R studio help in end-to-end data processing, cleaning, RDA, model training, and prediction. The scripts that we write on RStudio are also used for automation and creating machine learning tools using R shiny as well.
- Data processing
- Data visualization
- Machine learning
- Tool development (Rshiny)
- User interface
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 19, 2022
RStudio is great but needs some improvements
RStudio is used in my organization to build machine learning models, such as linear regression, logistic regression, decision trees, random forest, k-mean clustering, and more. It solves our business problem of having a low-cost, open-source tool for building statistical models and running models for data analysis. We can also use this for data visualization and data cleaning.
- Data cleaning
- Statistical packages
- Machine learning algorithms
- Installation process is a bit confusing
- Steep learning curve for non technical person
- Better UI
April 14, 2022
RStudio from a Grad Student's POV
RStudio is used as a supporting program for graduate-level courses, such as Experimental Research Methods. It helps students understand how to clean, analyze, and visualize quantitative data. The scope of my use case is 10-week courses that have used RStudio in different ways, i.e., information visualization and data transformation.
- 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
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
R is primarily used as a data cleaning tool (in our team) which is agnostic to user machines, thus creating a repeatable workflow. Earlier, we used both Power Query and Alteryx for it. Power Query used to take a lot of time, and Alteryx turned out to be a pretty expensive affair. For our reporting purpose, we had to collate many files, and after doing some manipulation by removing duplicates and other process-related activities, we had to create some metrics. All were done in RStudio, and then the output is used to upload in DWH.
- 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.
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
April 11, 2022
RStudio - Very Powerful Statistical Tool
We are using RStudio to develop shiny web applications and develop predictive data models. We perform statistical analysis on the data and try to gain insights from it.
With the shiny apps, we are automating routine excel reports which saves a lot of time for database and business analysts.
We have written numerous algorithms in RStudio like Naive Bayesian Classification, K-Means Clustering and ARIMA modelling.
RStudio is an amazing platform for statistical data analysis.
With the shiny apps, we are automating routine excel reports which saves a lot of time for database and business analysts.
We have written numerous algorithms in RStudio like Naive Bayesian Classification, K-Means Clustering and ARIMA modelling.
RStudio is an amazing platform for statistical data analysis.
- 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.
January 26, 2022
Enhancing Data science capabilities with RStudio
I have extensively used RStudio, when I was seconded to the Department of Health in Victoria to assist with the surge in COVID19 Delta response. In my day to day, I used R mostly for Descriptive and Graphical analysis and data management. Most of the analysis is used to provide insights to reduce road trauma and promote road safety.
- 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.
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 19, 2022
Great software; I rely on it almost every day
It's used by my small team. We do econometric analysis using R.
- 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
December 23, 2021
RStudio is Very Useful but Needs Improvements
I use RStudio to manipulate, munge, analyze data for ad hoc projects, and create visualizations. [My main use for the platform] is for projects that are too big or slow for Excel.
- good visual design environment
- Allows you to quickly see your data set in tabular form
- Manages package list/download well
- When rendering a plot, there are several issues bringing it more smoothly to copy/paste it to a slide deck, etc. It is very frustrating that the aspect ratio, etc. visual quality is not consistent from when you originally render to when copy/paste or downloading as a file to later be put in a slide.
- My resolution changes between laptop mode and desktop mode (plugging into external monitors). In desktop mode I literally have to shut RStudio down and restart/reload/rerun to continue my project. HOW DO I fix this? Is there a way. I've seen a toggle that says it can bounce back and forth depending on device type but it doesn't seem to work!
- When a program hangs, there is a red stop sign ( I think) in console corner to end the process. This requires, however that I need to complete restart RStudio and restart/reload/rerun etc. Can't it just start but keep all packages, datasets, variables in memory?
September 10, 2021
RStudio: Difficult Learning Curve with Matching Pay Off
RStudio is being implemented by our analytics department to help solve complex client problems by utilizing the data and statistical packages available through RStudio. The ability to perform far more accurate multi-linear regression models and time-series forecasts has helped our clients not only see where they are going in terms of sales but also what affects their sales.
- Data Organization
- Multi-Linear Regression
- Data Visualization
- Time-Series Forecasting
- As a scripting language, it is not a pick up and go platform. You need to spend the time to learning the program.
- Platform versions and Package versions often do not align.
- Would love to see standard templates that would generate a basic code for statistical models. This could save time and help newer users learn how to operate the program.
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.
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.
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 is amazing, the future of Data Science
Score 10 out of 10
Vetted Review
Verified User
I use RStudio for research & teaching purposes (I'm a professor in a business school). I know 5-6 of my colleagues also use it. All of my courses are entirely focused on coding and I use RStudio all the time in class. Additionally, all of my research revolves around uses of R code, thus I spend 5-6h/day working with RStudio.
- Amazing user interface
- Great package development
- Incredible simple to use
- Joyful team & community
- NLP, a package (wrapper) for major models (ex: GPT3), just as for Keras-Tensorflow
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.
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