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Formerly RStudio


What is Posit?

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

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Recent Reviews

TrustRadius Insights

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

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

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  • Visualization (26)
  • Connect to Multiple Data Sources (25)
  • Extend Existing Data Sources (26)
  • Automatic Data Format Detection (25)

Reviewer Pros & Cons

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Video Reviews

2 videos

RStudio Review: It Proves To Be A Reliable Statistical Tool W/ Support Avenues In Place If Needed
RStudio Review: Works As An Useful Tool But User Finds Free Version Could Be More Competitive
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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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Product Demos

What is Posit Workbench? Build Data Products in R & Python using Jupyter, VSCode, and RStudio.


Posit Connect | Host all of the data products you create

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Platform Connectivity

Ability to connect to a wide variety of data sources

Avg 8.5

Data Exploration

Ability to explore data and develop insights

Avg 8.4

Data Preparation

Ability to prepare data for analysis

Avg 8.2

Platform Data Modeling

Building predictive data models

Avg 8.5

Model Deployment

Tools for deploying models into production

Avg 8.6
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Product Details

What is Posit?

Posit, formerly RStudio, provides a modular data science platform that combines open-source and commercial products.

their open source offerings, such as the RStudio IDE, Shiny Server, rmarkdown and the many packages in the tidyverse, boast users among data scientists around the world to enhance the production and consumption of knowledge by everyone, regardless of economic means.

Their commercial software products, including Posit Workbench, Posit Connect, and Posit Package Manager, are available as a bundle in Posit Team. These products aim to give organizations the confidence to adopt R, Python and other open-source data science software at scale. This enables data science teams using R and Python to deliver interactive reports and applications to decision-makers, leverage large amounts of data, integrate with existing enterprise systems, platforms, and processes, and be compliant with security practices and standards.

The platform is complemented by online services, including Posit Cloud and, to make it easier to do, teach and learn data science, and share data science insights with others, over the web.

Posit’s open-source software and commercial software form what the vendor describes as a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for Posit’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.

Posit Features

Platform Connectivity Features

  • Supported: Connect to Multiple Data Sources
  • Supported: Extend Existing Data Sources
  • Supported: Automatic Data Format Detection

Data Exploration Features

  • Supported: Visualization
  • Supported: Interactive Data Analysis

Data Preparation Features

  • Supported: Interactive Data Cleaning and Enrichment
  • Supported: Data Transformations

Platform Data Modeling Features

  • Supported: Multiple Model Development Languages and Tools
  • Supported: Single platform for multiple model development
  • Supported: Self-Service Model Delivery

Model Deployment Features

  • Supported: Flexible Model Publishing Options
  • Supported: Security, Governance, and Cost Controls

Additional Features

  • Supported: Share Data Science insights in the form of Shiny applications, Quarto content, R Markdown reports, Plumber APIs, dashboards, Jupyter Notebooks, and interactive Python content.

Posit Screenshots

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

Posit Videos

Open Source Software for Data Science - CEO J.J. Allaire provides an overview of Posit's mission, and why Posit has become a Public Benefits Corporation.

Watch Overview of Posit Connect

Posit Technical Details

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

Frequently Asked Questions

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

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

Reviewers rate Security, Governance, and Cost Controls highest, with a score of 8.9.

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


Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Intuitive User Interface: Users have found RStudio to have an intuitive user interface that allows them to quickly test and debug code. This has been mentioned by numerous reviewers, highlighting the ease of use and convenience it offers in coding tasks.

Seamless Integration with Git: The seamless integration of RStudio with Git has been praised by users, making it easy for them to manage version control. Several reviewers have specifically mentioned this as a major advantage of using RStudio for their coding projects.

Powerful Statistical Analysis Tool: Many users appreciate RStudio's capabilities as a powerful tool for statistical analysis and data exploration. They mention its ability to import data from multiple sources, apply machine learning models easily, and export data into various channels.

Confusing and Outdated User Interface: Several users have expressed dissatisfaction with the user interface of RStudio, finding it confusing, unattractive, and outdated compared to other tools. They feel that the interface is too technical for business people.

Frequent Crashes with Large Datasets: Some users have mentioned that RStudio frequently crashes when loading large amounts of data. This can be frustrating and disrupt their workflow.

Lack of Integration with Other Applications: Users have pointed out that RStudio is not as integrated with other applications as Python. This limitation makes it less convenient for users who rely on seamless integration between different software tools.

Users commonly recommend RStudio for beginners in R programming and data analytics. They believe that RStudio is a good tool for learning machine learning and recommend using it for data work, programming R code for machine learning, implementing R software, data analysis, and data science. Users consider RStudio to be a great resource for analyzing data and necessary for anyone who wants to get into R programming. While considering other suites and languages like Python, they still recommend taking a look at RStudio for data analysis. Additionally, users find RStudio to be useful for doing statistics and creating professional plots and figures. They suggest familiarizing oneself with common libraries in the field and doing online tutorials before starting to use RStudio. Users warn about the steep learning curve but believe it is worth investing the time to learn it. Moreover, they recommend using RStudio for big data and epidemiological research.

Attribute Ratings


(1-25 of 122)
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Samrit Pramanik | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
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.
  • 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
I will highly recommend Posit to anyone who works in Advanced analytics because of its high computing power and seamless delivery of model output of various analytical case studies and problems.

Based on my experience, I use Posit software aka RStudio Pro and Posit Workbench for almost everything in our company as well as clients network. From data preparation to statistical and predictive model building, I use RStudio Pro exclusively. In addition to this, data visualization and data manipulation are also done by Posit software.
I have used multiple R packages for various kind of data analysis from logistic regression, classification to LASSO and elasso (Network Analysis).

Only one scenario I would like to say that it is less appropriate is to view the data of formats other than data frame. I really wish to see this issue will be solved in the next major updates of Posit.

Overall Posit is really a good software and platform for any kind of data analysis and visualizations. Thanks.
June 30, 2023

All-in with RStudio

Score 10 out of 10
Vetted Review
Verified User
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.
RStudio is the only feature-rich R IDE in the industry, so for the majority of our work, we will use the IDE. There are times when we deploy our scripts outside of the IDE. A recent example was when we had a large amount of data that was too much for the IDE itself. We used the IDE to write a script and then deployed that script outside of the IDE to multiple servers. It would be helpful if we could run a single script across multiple servers based on defined partition.
Score 9 out of 10
Vetted Review
Verified User
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
Posit is well-suited to just about any scenario you can throw at it. Basic data manipulation, statistical analysis, visualizations, machine learning, simulation - there is a function or library out there for everything. It's also lightning fast, which is a blessing when computing large calculations. Less well-suited to Posit is its learning curve - syntax is relatively unique and picking up a new package usually means learning new behaviours.
Score 8 out of 10
Vetted Review
Verified User
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
RStudio is well suited for professionals who need to interpret and analyze large sets of data. It can have a step-learning curve though so maybe less functional and not appropriate for an inexperienced user. Additionally, if users are trying to collaborate on a set of data, additional programs or software may be needed in addition to RStudio in order to collaborate.
Score 9 out of 10
Vetted Review
Verified User
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
RStudio is appropriate to perform complex analysis and data modeling exercises while is not that useful where the analysis is simple due to complexity where Excel will better suit. Also, if your organization is not used to it, probably, is better to use other software. Any kind of statistical analysis like regressions or decision trees would be a very good option to model with R Studio.
Akshat Garg | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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
RStudio is well suited for data processing and visualization. The tool provided a very interactive and user-friendly environment to understand each step in the data processing. However, RStudio lacks in adoption among the data science community as python is not available and most of the machine learning libraries are custom built for python.
Score 10 out of 10
Vetted Review
Verified User
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
well suited: creating and delivering apps for multi-disciplinary teams, for example, or less appropriate: Kaggle competitions, multi-community collaborations, Google collab...scenarios when the whole communities decide to work on a specific problem in Python and R is left behind, e.g. in 2015 my colleague delivered better results with Bayesian statistics simply cause he decided to go for Python to visualize joint distributions (priors and posteriors) ...even if I had way more knowledge on the algorithmic side, I was simply slower because I chose R; what I have learned over the years is that when it comes to the stakeholders, a good visualization (==communicating the results and effectively advertising) is everything as without it there is no funding and without funding no science, no R&D
Score 10 out of 10
Vetted Review
Verified User
It is used by the Data Science team within the department. It helps the team with the reporting functions, tackles business problems from an analytical perspective, and builds up quick interactive tools.
  • Well-designed UI
  • Full support of R
  • Great technical support
  • Better support for the desktop version
Any analytical problems that start with data could be tackled with R in an agile and flexible manner, and since RStudio does such a good job of embedding R to the product itself, it is great for industries/companies with such problems.
Andrew Choens | TrustRadius Reviewer
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.
Well Suited: Doing data science. Not Well Suited: It isn't a rapid application tool/environment for CRUD applications.
Score 8 out of 10
Vetted Review
Verified User
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
Based on my experience, I would like to recommend RStudio to anyone that needs to run small to medium-sized statistical analysis quickly and cost-effectively. Many packages are written pretty friendly for producing readable output for regressions results. However, it is less suited to large-scale big data projects that require large processing power.
Score 6 out of 10
Vetted Review
Verified User
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
RStudio is the best option I've seen for data cleaning, prep, and transformation. Other tools, such as Tableau or Excel, are not easily transferrable to other formats or are manual and take too much time. RStudio is less appropriate for small datasets and academic courses that won't dedicate as much time to learning the fundamentals of R.
Score 8 out of 10
Vetted Review
Verified User
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
RStudio is the best studio for statistical analysis, reporting with graphs and charts, or building custom dashboards that would refresh the data from backend tables at a set frequency. It's good for data visualization, data modeling, and data visualization. I have limited experience in machine learning using R (I have mostly been using other software), so I cannot comment on its capabilities for that.
Score 10 out of 10
Vetted Review
Verified User
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.
In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition.

A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.
Score 8 out of 10
Vetted Review
Verified User
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
RStudio is well suited, particularly to providing an environment for the statistical analysis of datasets and leveraging various data analysis packages using R. Its user interface is highly customizable and provides all the information users need to script, run, and generate various GUIs and dashboards. Overall it's well suited to R and perhaps less well suited (although it does allow) for other languages such as Python. Overall it's well suited for analysis needs but probably less suited for other development needs, especially if they require the heavy use of other languages.
Jim Gruman | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
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
RStudio is the premier statistical workbench and development environment for professionals. It is well suited for serious data science and statistical analysis on local compute hardware and in the cloud. RStudio is not a graphical toy.
Kunal Sonalkar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
  • 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.
RStudio is very well suited for data analysts and statisticians. Writing and designing predictive data models is very efficient and there is a lot of online help if you plan to use standard machine learning algorithms like Naive Bayesian, Apriori Analysis, Random Forest, DENCLUE,, etc.

In a situation where you want to automate excel reports then shiny (user interface for R) comes in very handy.
Suryaprakash Mishra | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
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.
RStudio is very easy to learn and learn. Lots of free resources and user groups and support is around to enhance individual capacity to solve problems. Most recently we used RStudio to geospatially map road infrastructure within 100 meters of crashes in Victoria.
Chris Beeley | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
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.
If you've got a bit of Linux skills in your organisation, and if you have the money to pay for RStudio Connect (or RStudio Workbench on the development side) then I think RStudio is definitely a sound investment. If you don't have Linux skills, or your analysts are not sufficiently advanced in R to need to deploy stuff running live (and are just emailing stuff around, basically) then you're probably not ready yet.
Kenton Woods | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
I would use RStudio if you need a cheap way to effectively analyze data using social network analyses. Linear regressions are also fairly easy to run in RStudio, but if you have the money I'd recommend going another direction for your statistics needs.
Bobbi Woods | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
RStudio is perfect for statisticians who want to run descriptive and predictive analyses but do not want to spend big money to acquire a license from a competing statistics software. It is less suited for scenarios in which a company will reimburse for a license, in which I would recommend IBM's SPSS over Rstudio.
January 20, 2022

Hooray RStudio.

Score 8 out of 10
Vetted Review
Verified User
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.
There are tons of examples which we feel great when we have RStudio. At whole, I extremely enjoy how RStudio encourages/brings update to the community. There are just lots of great packages coming to CRAN, which are easily accessible and loadable from RStudio.
Score 9 out of 10
Vetted Review
Verified User
I use RStudio to produce descriptive and predictive analytics surrounding various business products. My analytics help higher-ups understand the efficiencies and problem areas in business processes and make evidence-based decisions. The data visualizations I generate in [RStudio] are especially instrumental in presenting accurate, easily consumable metrics for lay audiences.
  • Visualizing data
  • Integration with other programming languages and tools
  • Variety of inbuilt functions and packages
  • Dashboard publishing
  • Processing is slow when working with large datasets
  • Description of bugs could be clearer
  • More tutorials on capabilities
I would definitely recommend RStudio for business analytics. The interface is extremely user-friendly and intuitive. There are a wide array of inbuilt functions and limitless packages available to support almost any analysis desired. The only caveat is processing tends to be slow for datasets larger than about 2 GB.
Score 9 out of 10
Vetted Review
Verified User
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
It works great for me. I heard that R ecosystem may be a bit behind Python ecosystem for machine learning but I personally don't feel restricted.
January 18, 2022

RStudio is wonderful!

Score 9 out of 10
Vetted Review
Verified User
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
I really like that RStudio has the ability to run code line by line. That is crucial in my work as I am constantly modifying and testing little things that would not be practical/desired to run the whole code.
Prashast Vaish | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
It's is best suited for data cleaning and analytics. It is an awesome tool if you want to apply some statistics operations. It can handle large amounts of data It is not the best tool if you want to start with coding in general as concepts are a little tough.
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