Customer Verified
Top Rated
About TrustRadius Scoring
Score 8.9 out of 100
Top Rated


Recent Reviews

My very personal RStudio R&D journey

April 23, 2022
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. …
Read full review

Open Source IDE for R and Python

April 22, 2022
RStudio provides the IDE to be more productive with Python and R. It has a console, a syntax highlighter that supports direct code …
Read full review

RStudio from a Grad Student's POV

April 14, 2022
RStudio is used as a supporting program for graduate-level courses, such as Experimental Research Methods. It helps students understand …
Read full review

Everyday Statistical Workbench

April 13, 2022
We use RStudio for analytics, data science, reporting, and statistical modeling for business clients in all enterprise functional groups. …

Reviewer Sentiment

Positive ()
Negative ()
Learn how we calculate reviewer sentiment


TrustRadius Award Top Rated 2022
TrustRadius Award Top Rated 2021
TrustRadius Award Top Rated 2020
TrustRadius Award Top Rated 2019

Popular Features

View all 12 features

Visualization (21)


Connect to Multiple Data Sources (20)


Extend Existing Data Sources (21)


Automatic Data Format Detection (20)


Reviewer Pros & Cons

View all pros & cons

Video Reviews

Leaving a video review helps other professionals like you evaluate products. Be the first one in your network to record a review of RStudio, and make your voice heard!


View all pricing

What is RStudio?

RStudio is a modular data science platform, combining open source and commercial products. The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to enhance…

Entry-level set up fee?

  • Setup fee optional
For the latest information on pricing, visit


  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

Would you like us to let the vendor know that you want pricing?

3 people want pricing too

Alternatives Pricing

What is IBM SPSS?

SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and…

What is Anaconda?

Anaconda is an open source Python distribution / data discovery & analytics platform.

Features Scorecard

Platform Connectivity


Data Exploration


Data Preparation


Platform Data Modeling


Model Deployment


Product Details

What is RStudio?

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

The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to enhance the production and consumption of knowledge by everyone, regardless of economic means.

Their commercial software products, including RStudio Workbench, RStudio Connect, and RStudio Package Manager, are available as a bundle in RStudio 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 RStudio Cloud and, to make it easier to do, teach and learn data science, and share data science insights with others, over the web.

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

RStudio 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, R Markdown reports, Plumber APIs, dashboards, Jupyter Notebooks, interactive Python content, and more.

RStudio Screenshots

RStudio runs on most desktops or on a server and accessed over the webRStudio supports authoring HTML, PDF, Word Documents, and slide showsRStudio supports interactive graphics with Shiny and ggvisShiny combines the computational power of R with the interactivity of the modern webRemote Interactive Sessions: Start R and Python processes from RStudio Workbench within various systems such as Kubernetes and SLURM with Launcher.Use Jupyter: Author and edit your Python code with Jupyter using the same RStudio Workbench infrastructure.RStudio Connect makes it easy to deploy Interactive Python Applications (including Dash, Bokeh and Streamlit), in the same place you share your Shiny apps.

RStudio Videos

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

Watch Overview of RStudio Connect

RStudio Integrations

RStudio Competitors

RStudio Technical Details

Deployment TypesOn-premise, SaaS
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo


View all alternatives

Frequently Asked Questions

What is RStudio's best feature?

Reviewers rate Visualization highest, with a score of 8.9.

Who uses RStudio?

The most common users of RStudio are from Enterprises (1,001+ employees) and the Hospital & Health Care industry.


(1-25 of 118)
Companies can't remove reviews or game the system. Here's why
Score 10 out of 10
Vetted Review
Verified User
Review Source
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
Review Source
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.
Score 9 out of 10
Vetted Review
Verified User
Review Source
RStudio provides the IDE to be more productive with Python and R. It has a console, a syntax highlighter that supports direct code execution. And it has various tools for viewing history, debugging, and managing the workspace. RStudio Desktop is Free having integrated tools for R and in order to use advanced features then need to purchase the upgraded versions.
  • RStudio IDE makes it easy to combine R and Python in a single data science project.
  • RStudio Workbench launches and manages jupyter notebooks.
  • RStudio Package Manager makes it easy to control and distribute python and r packages.
  • There should be some more featured that needs to be added in Rstudio desktop free version.
  • In compititive market they should focus on more used friendly unique features.
  • User interface needs to be updated
RStudio provides a reticulate package that provides a comprehensive set of tools for interoperability between Python and R.
Andrew Choens | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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.
RStudio Workbench helps scale for a team of R users there are number of useful features such as project sharing, collaborative editing, session management, and IT administration tools like authentication, audit logs, and server performance metrics.
I continue to learn and use RStudio and this is really working for analytical and reporting purpose.
Chris Beeley | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with.

Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.
First class support. Friendly, helpful, and they very often help me with stuff that isn't really anything to do with their product but just issues that I am having with the configuration of the server (for example, the problems I had when I upgraded to Ubuntu 20.04)
Kenton Woods | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
I think that RStudio scales pretty well based on the size of the datasets I'm using. It has multithreading capabilities unlike some other statistical analysis programs which is very useful in cutting down on time. The format of RStudio's syntax also makes it very easy to replicate regardless off the scale of the analysis and data set.
Bobbi Woods | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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
Review Source
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.
Jacob Benzaquen | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
RStudio is perfect for the initial writing and troubleshooting of your code, as well as running it, 3D modeling it, and debugging it. In all honesty, after using other R GUIs I have not found one that does everything as well as RStudio, and it is remarkably better than base R in terms of writing code and adding packages.
Paul Pulley | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
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?
I like the user-friendliness and RStudio's ability to accommodate the use of R. For scenarios where it is less appropriate, see my comment above about rendering a plot to copy/paste to a slide deck, and the resolution that changes between laptop-mode and desktop mode. I can't figure out how to fix this! There is also the issue I mentioned above where when a program hangs, the red stop sign in the console process not only ends the process running, but also kills the whole RStudio program. [As a result], I need to completely restart and reload all the packages, variables, datasets, etc. into memory.
Auggie Heschmeyer | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
In my mind, there is no other way to interface with R than using RStudio.
September 11, 2021

RStudio for R!

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
  • Integration with databases.
  • User community.
  • Integration with other software/languages.
  • Lacks stability.
  • Memory management.
RStudio is the de facto IDE for R language.

As RStudio continues to expand its adoption of other languages and IDE - e.g., Python, VS Code - and leverages its seamless develop-test-deploy workflow, the platform is suitable for analytics team who desire full control and flexibility of product development with little overhead when publishing to production.
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
RStudio is a fantastic program for anyone looking to do data organization, visualization, or statistical analysis. It excels if your team is looking to take a heavier investment into a complex platform. RStudio does not have a native spreadsheet editor and newer users will have to learn how to edit their data in the platform.

This is NOT a pick up and go platform as we are used to. It has hundreds of advantages and can be customized to near perfection. Yet, it will require many hours of investment. I would suggest looking at other pre-built platforms if the team is smaller.