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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.
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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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For the latest information on pricing, visithttps://posit.co/pricing
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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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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 77)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
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.
July 18, 2022
RStudio for Business Analysis
We use RStudio as an analysis tool to perform complex data analysis problems and scenarios. We build different statistical models to understand business data and perform forecasts. It has good visualizations and is a very flexible tool. As Business Analyst it is a good tool to understand big data in the organization.
- Visualization tool
- Statistical Analysis
- Forecasting
- More flexibility to import tamplates for the visuals
- More documentation about the formulas
- More coding automation
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 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 25, 2022
RStudio - a cheap and effective statistics program
RStudio is currently used to analyze data. It makes using R much easier for us researchers and allows us to test our hypotheses. It is used by researchers across the department to do quantitative analyses using data we have collected. We use it for social network analyses that include friendship nominations.
- Quantitative analyses.
- Descriptive analyses.
- Graphs.
- The point and click functions of the program could be better.
- Updating the program could be an easier process.
- Other programs make it easier to read in data.
January 21, 2022
RStudio - Perfect for the Low Budget Statistician
We use RStudio for statistics-related endeavors on our research projects. We use it frequently for accessing and analyzing our data in descriptive and predictive type analyses. It helps us address issues such as underperformance in schools and other education settings, or even issues of inequity and exclusion of vulnerable populations.
- Descriptive analyses.
- Predictive analyses.
- Accessing data.
- Replicating syntax.
- The interface.
- A more beginner-friendly walkthrough.
- Have also had issues with program versions impacting syntax execution.
January 18, 2022
RStudio is wonderful!
I am the primary statistician on my team and I use RStudio almost exclusively to perform the product efficacy analyses. I use RStudio to automate many of our data cleanup processes and also run dynamic analyses to answer our research questions.
- Automate processes
- Statistical Analyses
- Portable Code
- A very good IDE for R programming
- Can be intimidating to non-programmers
- I wish I could copy data to the clipboard easier
- I never have a big enough screen to see all of the data I want to see
January 13, 2022
A must have tool for data analysts
I am working with an Australian supermarket giant and helping them analyze data for their e-commerce business. RStudio helps me in getting the raw data from various sources and cleaning them up so that they can be aggregated and visualized in a BI tool for insight generation to improve the business performance.
- It's super quick.
- It has inbuilt functions for most of the analytics procedures.
- It has great visualizations.
- It has lots of libraries and sometimes it shows errors while importing them.
- Its UI can be improved.
- It takes a lot of time exporting files.
January 13, 2022
RStudio Is the Best R GUI for any price, let alone free!
I currently use RStudio to create and develop 3D maps for ground-mounted solar arrays to better account for the terrain where they will be placed. It is also used for statistical analysis within the company to determine where the best placement for the solar arrays will be within the topography.
- Troubleshooting of Code.
- Color Coding of different elements of code.
- Adding new packages.
- Infinitely customizable.
- Better Resource Analysis.
- Better progress bars.
- Debugging could use a little improvement.
December 05, 2021
An almost one-stop shop for your analytics needs
RStudio is the only way in which I interact with R. We're primarily a SQL-driven team but sometimes you need a tool that's a little more powerful; enter R. When I need this added firepower, RStudio is where I always go. There aren't any second thoughts. I'm primarily a fan of the UI shortcuts that make interacting with my local directory and managing my packages a breeze.
- Keyboard shortcuts
- Integrating multiple programming languages
- Providing creators with the resources to develop gold-standard packages
- I have never been able to successfully replace my SQL editor with RStudio since the SQL drivers are so hard to set up. I would eventually love to be able to use RStudio as a one-stop-shop.
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 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 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.