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Great Platform for Data Analysis and Data Visualization along with Statistical Computing.RStudio is a powerful application for data analysis and statistical computing. It provides an integrated platform to develop scripts in R which can be used to automate repetitive tasks or to mine data. It is aggressively used in our organisation for data mining. Being open source in nature it has huge user support base. Full featured text editor, graphical workspace, cross-platform integration are some of the useful features of R which helps to work faster and efficiently.,Integrated Environment for statistical computing, pre-installed modules, cross-platform integration makes RStudio one of the best applications in this space. Being open source, a lot of help can be found on the net. The full text editor helps to manipulate data which is one of the most time-consuming tasks for any automation. Seamless R-markdown is one of the great features of RStudio. It helps you to document what exactly you are performing.,Stiff competition from Python. Python is more integrated with other applications as compared to R. Seems to crash more often as compared to R platform. Sometimes you run into weird bugs which are very difficult to debug.,9,License for Rstudio is very cheap as compared to the value it adds to the business. Have huge return of investment if power of Rstudio is utilised properly. Data is secured on the local machine which is very valuable.,Visual Studio IDE,Anaconda, Microsoft YammerRStudio - Very Powerful Statistical ToolWe 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.,9,Positive impact is when you automate excel reports using Shiny applications, it ends up saving a lot of time and money. It's easy to catch on so with a little training and sound math background you can start coding right away. Its compatibility with other platforms like SQL databases, Salesforce, Tableau , etc is amazing and makes it worth the investment. It doesn't have any negatives as such.,Tableau Desktop,Tableau Desktop, DB2, Oracle Advanced Analytics, IBM Analytics EngineRStudio works!Since RStudio is an open source statistical software system, we are teaching it to our statistics students. It provides them with the skills necessary to enter into the business industry where RStudio is being more widely used over SAS. It's cost effective and has many more resources available to learners. It has allowed the university to decrease our SAS licensing contract and save money that way.,It's well organized library of resources and documentation. It's cost. It's free! It has excellent computational power given it's size.,It's graphics could be improved. It has a high learning curve. As with any open source programming language, there could be bugs and errors throughout.,10,Since it's open source, it has decreased our software cost. We've had greater interest from prospective hiring company's since our students know RStudio. Negative impact: Higher learning curve and thus takes more time to teach in class.,JMP Statistical Discovery Software from SAS,JMP Statistical Discovery Software from SAS, Amazon Relational Database Service, SAP Financial Statement InsightsRStudio is THE standard for exploratory data analysis on large data setsRStudio is used as a an R development environment for cleaning, manipulating, and analyzing large data sets. It is used in conjunction with Python for data science tasks. RStudio is used across the entire organization as a complement to other technologies and to support data science and analysis projects. In my role, I gather large data sets (>500,000 or million rows) from different platforms, and rely on RStudio to prepare data for further analysis. It's an excellent platform for conducting preliminary / exploratory data analysis: to get an understanding of trends and behaviors exhibited by the data set, and to guide later analytic decisions.,Create and manipulate data frames: syntax is intuitive, terminal lets you see results / behaviors immediately. Visualization (especially using shiny or other visualization packages): so many different kinds of graphs and viz available. Sharing results and community documentation: extensive information is available on use and applications of different packages, making RStudio (and R) very versatile for a variety of analysis projects.,R has a fairly steep learning curve and can be intimidating for new users. RStudio's package, swirl, is useful as an introductory tutorial for use and capabilities, but it is limited. RStudio sometimes has stability problems when it comes to working with very large / big data sets. This is because RStudio relies on the computer's memory to process the data. A quick calculation can be used to determine if the data set's size exceeds the computer's memory capabilities, though.,8,Quickly analyze data to determine validity, and if further exploration is needed (basically as a triage to assess data trends/behavior/usefulness). Code can be re-used and redeployed to save time and improve organization efficiency.,PyCharm,PyCharm, Gitlab, Tableau Public,Ingesting data from common file types (CSV, XLSX). Performing basic visualization or analysis. swirl - can't recommend the built-in tutorials enough!RStudio for quick prediction prototypingVery 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.,9,RStudio is free and it's easy to start using it It's easy to install new libraries and start using them seamlessly The installation of some libraries is challenging, especially when they depend on a lot of other libraries. RStudio crashes when there is a clash between libraries somehow.,,10,10,Running quick predictions based on the data at hand Representing data using graphs and charts Exploratory data analysis using RStudio,We use it for scatterplot matrices We use it to quickly see the dependencies of various predictors We check multicollinearity between our input columns,We hope to use it on a production run basis on cloud We need to be able to scale our prototype solution to larger sets of data We wish to have stable models, using Rstudio, which can be dynamic based on new data,9,No,Price Vendor Reputation Third-party Reviews,If we had to do it again, we would like consider a product which is cloud first. We currently use RStudio Cloud, which is close to what we want in the future. But how much can we scale is the question. We have not really tested that yet. We would assume there are options to use it on cloud vendors such as Azure and AWS.,Generation of HTML reports out of the RMD quick help files for any functions A quick view of data files,The loading of files with lot of data takes a lot of time Generation of pdf report from RMD is not very easy.,No,9
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RStudio
49 Ratings
Score 8.8 out of 101
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RStudio Reviews

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RStudio
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Rajat Wadhwani profile photo
December 27, 2018

RStudio Review: "Great Platform for Data Analysis and Data Visualization along with Statistical Computing."

Score 9 out of 10
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RStudio is a powerful application for data analysis and statistical computing. It provides an integrated platform to develop scripts in R which can be used to automate repetitive tasks or to mine data. It is aggressively used in our organisation for data mining. Being open source in nature it has huge user support base. Full featured text editor, graphical workspace, cross-platform integration are some of the useful features of R which helps to work faster and efficiently.
  • Integrated Environment for statistical computing, pre-installed modules, cross-platform integration makes RStudio one of the best applications in this space.
  • Being open source, a lot of help can be found on the net. The full text editor helps to manipulate data which is one of the most time-consuming tasks for any automation.
  • Seamless R-markdown is one of the great features of RStudio. It helps you to document what exactly you are performing.
  • Stiff competition from Python. Python is more integrated with other applications as compared to R.
  • Seems to crash more often as compared to R platform.
  • Sometimes you run into weird bugs which are very difficult to debug.
RStudio is considered to be primarily a statistical software. Due to its very nature, it is well suited for data analysis and data visualization. Data analysis plays an important part in making business decisions which directly impacts the organization. Having data wouldn't be useful unless and until value is generated from it. Also to extract valuable meta data from various type of documents RStudio provides a great platform to develop scripts in R.
Read Rajat Wadhwani's full review
Kunal Sonalkar profile photo
December 13, 2018

Review: "RStudio - Very Powerful Statistical Tool"

Score 9 out of 10
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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.
Read Kunal Sonalkar's full review
Kyle Moninger profile photo
December 08, 2018

User Review: "RStudio works!"

Score 10 out of 10
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Since RStudio is an open source statistical software system, we are teaching it to our statistics students. It provides them with the skills necessary to enter into the business industry where RStudio is being more widely used over SAS. It's cost effective and has many more resources available to learners. It has allowed the university to decrease our SAS licensing contract and save money that way.
  • It's well organized library of resources and documentation.
  • It's cost. It's free!
  • It has excellent computational power given it's size.
  • It's graphics could be improved.
  • It has a high learning curve.
  • As with any open source programming language, there could be bugs and errors throughout.
RStudio is perfect for intermediate and advanced statistical computing. Whether it's predictive analytics, descriptive statistics, or graphical summaries it is a tool that can deliver. It is especially useful if the user has programming language experience.

It is less useful for a user who has no programming language experience and only needs simple statistical calculations. Minitab or Excel may be better suited. It is also less appropriate when higher resolution graphs are needed as it's graphics are less than optimal.
Read Kyle Moninger's full review
Leah Jakaitis profile photo
November 29, 2018

Review: "RStudio is THE standard for exploratory data analysis on large data sets"

Score 8 out of 10
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RStudio is used as a an R development environment for cleaning, manipulating, and analyzing large data sets. It is used in conjunction with Python for data science tasks. RStudio is used across the entire organization as a complement to other technologies and to support data science and analysis projects. In my role, I gather large data sets (>500,000 or million rows) from different platforms, and rely on RStudio to prepare data for further analysis. It's an excellent platform for conducting preliminary / exploratory data analysis: to get an understanding of trends and behaviors exhibited by the data set, and to guide later analytic decisions.
  • Create and manipulate data frames: syntax is intuitive, terminal lets you see results / behaviors immediately.
  • Visualization (especially using shiny or other visualization packages): so many different kinds of graphs and viz available.
  • Sharing results and community documentation: extensive information is available on use and applications of different packages, making RStudio (and R) very versatile for a variety of analysis projects.
  • R has a fairly steep learning curve and can be intimidating for new users. RStudio's package, swirl, is useful as an introductory tutorial for use and capabilities, but it is limited.
  • RStudio sometimes has stability problems when it comes to working with very large / big data sets. This is because RStudio relies on the computer's memory to process the data. A quick calculation can be used to determine if the data set's size exceeds the computer's memory capabilities, though.
RStudio is well suited for ingesting and analyzing large data sets in a variety of formats, including CSV files. A large number of packages are supported to enable all kinds of projects: time series analysis, visualization, table-building, advanced statistical analysis are all examples of RStudio's application. There is exhaustive community documentation available online about how and when to deploy different packages (and their functions), and also how to troubleshoot different issues users may run into.

For more extensive analysis and polished visualization, Python is generally the recommended language. It's also where the industry (data science, data analysis, etc) is heading overall. R is still extensively used in-field, and is a standard part of a statistics curriculum in academia.
Read Leah Jakaitis's full review
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January 19, 2019

Review: "RStudio for quick prediction prototyping"

Score 9 out of 10
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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.
RStudio is a very nice tool to do exploratory data analysis. Generating an HTML report of the RMD file is straightforward. However, the generation of pdf is not so. It is best for quick prototyping. However, dealing with a lot of data is not very good with this IDE. The cloud version of RStudio is also very good.
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January 11, 2019

RStudio Review: "Comprehensive R Package"

Score 10 out of 10
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We use RStudio for all instances we might use R for. It is not used across the whole organization but among users of R, this is our preferred IDE for accomplishing any of our R work. The main reason we use RStudio is that it provides a very easy to understand platform for our users that may not necessarily come from a coding background. These issues are exacerbated when we use the command line version so it is much preferred to utilize this IDE.
  • Organizes R in a fashion that is understandable
  • Provides a console to quickly test or run scripts
  • Easily understandable error prompts
  • Good documentation and consistent updates
  • Open source
  • Will run slower on larger projects than on command line
  • Different from the traditional command line so has a very slight learning curve
  • Open source
Would highly recommend RStudio in almost all instances except when running intensive tasks; I would recommend using RStudio. For huge tasks, it would be best to run those on the command line but we have yet to encounter a situation where we would prefer to use an alternative to RStudio.
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January 10, 2019

RStudio Review: "My choice of IDE for R"

Score 8 out of 10
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RStudio is used mostly by the Data Science team of our company to code in R. We implement both single-time analyses and also full-scale projects for internal usage with Shiny applications. We analyze financial time-series and perform forecasting, do clustering and segmentation of customers, train models in terms of Machine Learning for predictive analysis and data extrapolation. RStudio helps us with these tasks.
  • "Publish" tools, so that Shiny applications and code can be shared instantly from the RStudio window.
  • Customizable workspace, code styling tools availability.
  • Git. RStudio's extension works significantly slowly with it, considering that our corporate laptops are pretty good.
  • Terminal. Same issue as above.
  • Debugging. It is not intuitive for users (especially in large projects) of how to debug the code.
RStudio is suitable to perform analyses in terms of Data Science projects. Nowadays, it is still the best option for developers to write code in R programming language. You can easily manipulate the data, however, if you have a project involving BigData, then R and RStudio are not appropriate for such large tasks. Try using Python.
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December 13, 2018

User Review: "RStudio for R!"

Score 10 out of 10
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RStudio is the go to tool in our team for data analytics workflow, from pulling and wrangling data, modeling and visualization.
  • Integration with databases.
  • User community.
  • Integration with other software/languages.
  • Lacks stability.
  • Memory management.
RStudio is the de facto IDE for R language.
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Gabriel Chiararia profile photo
June 05, 2018

Review: "RStudio - the biggest analytics platform"

Score 10 out of 10
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RStudio is being used by analysts and managers in both marketing and IT departments. In some cases we do ad-hoc analysis, in other cases, we try to streamline data process with R. The IT department comes in when we need more complex analysis and integration with Python. The marketing department uses for basic data analysis (exploratory, regression, and we are planning to use it for segmentation as well).
  • 1: RStudio is a great tool for organizing your R code - coding, executing and seeing the results on the same page!
  • 2: RStudio (and R in general) is great because it is an open source tool! So it receives new packages and updates constantly. It's also one of the most used analytics tools, so you are likely to find all of the models you need here!
  • 3: Did I mention it is free? This is great if your IT department (or company) has budget constraints.
  • 1: Coding background! Even though I think coding with R is much easier than any other tool (C++, Python, VBA...), you still need to know how to code to get an analysis done. Other tools (like Azure ML or JMP), you don't need a coding background.
  • 2: User interface: There are some better user interfaces out there. RStudio is not bad, but it's not the greatest.
  • 3: Saving files: It always confuses me when I need to save a file or a project. I never know when or how to save which.
Well suited: For anyone interested in data analysis. R can help you do a simple exploratory analysis to increase your R Square with a boosted decision tree! It's probably one of the most comprehensive analytics tools.

Less appropriate: Maybe if you have a team more focused on business and less on data analysis (marketers, salespeople, for instance), RStudio might not be the best, since the learning curve is complicated.
Read Gabriel Chiararia's full review
Maike Holthuijzen profile photo
April 25, 2018

RStudio Review: "Best all-in-one IDE for R"

Score 10 out of 10
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RStudio is used by several working groups within a larger project for the University of Vermont. It is used mainly for statistical analyses, manipulating spatial data, spatial analyses, and other programming/statistical tasks. I use my personal version of Rstudio as well as Rstudio server for analyses for this project. Rstudio is one of the best IDEs I have come across for R. I can keep track of variables within my workspace, view the files in my working directory, run the code and inspect output, and look at plots on different panels of the Rstudio interface. This helps keep my work organized and efficient. Rstudio has helped increase the overall productivity of the working group in which I work. Also, Rstudio interfaces with GitHub, which has been used for collaborative coding efforts.
  • Rstudio is very customizable. You can easily change font colors, sizes, and screen layout. I am particular about how I like my IDE setup, so this is a big plus for me.
  • Rstudio allows you to look at datasets in your workspace with the click of a button. I do a lot of data manipulation, so I am constantly having to look at datasets after operations to make sure they look correct. The view option in Rstudio makes checking datasets very fast.
  • Finally, I love the way Rstudio manages plotting. Your plots can be viewed in one of the panels. Those plots can easily be copy/pasted or exported into a variety of file types. You can also magnify the plots and scroll between plots to look at previous plots.
  • Sometimes Rstudio crashes when you work with big datasets.
  • I've had some issues installing packages, which is very annoying. Sometimes I can install packages on my PC but not on my Mac, and vice versa.
  • Rstudio is not exactly a lightweight IDE, so it is not ideal for computationally intensive tasks.
Well suited for spatial data analysis, statistical analyses, plotting and working with collaborators through GitHub. It can also compile Latex files and supports Rmarkdown, a markup language similar to Latex. Packages are constantly being added, so it's great for using novel analytical techniques that may not be available elsewhere.

Not as well suited for any big data tasks or deep learning or image processing.
Read Maike Holthuijzen's full review
Jennifer Lamas profile photo
June 04, 2018

User Review: "RStudio the best for statistical data"

Score 10 out of 10
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Basically, I can simplify the steps when updating a database, and reduce working time. Once it is scheduled as a statistical platform, it offers me all the techniques of data analysis. In addition to programming new methods and routines in an easy and robust way, I can do any database immediately. I can perform all the data analysis and even read files of different formats.

  • In the first place, because it is a language with a complex learning curve, but very robust and effective for the handling of statistical data, for developers, specialized in these languages, it can be simple.
  • R is a programming language in constant evolution and has extensive documentation, ease in data preparation, with this technique is very simple, largely because it automates many processes by programming sequences.
  • R works with any type of file, R is a language that allows the implementation of additional packages that provide a great capacity of data management, it is open source and free.
  • RStudio facilitates the work when entering RStudio, we see the screen divided into four windows, that multiplatform R, works on Mac, Windows and UNIX Numbers.
  • This means that you can work with your data, figures, analysis and, most importantly, with your instructions. It is free software, there is a large community of volunteers working to update it.
  • Allowing you to face specific problems. Programs like R-studio, Java GUI for R, R-commander, RKWard, among others, and with more than 6000 packages indexed in CRAN, Biocoductor, GitHub and R-Forge.
With RStudio you can review statistical databases in a quick way to help simplify work. If working with certain numbers is cumbersome, RStudio helps to improve the process.
Read Jennifer Lamas's full review
Eric Myers profile photo
May 07, 2018

RStudio Review: "An Essential Tool in Your Data Science Toolkit"

Score 8 out of 10
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RStudio is used in our organization for advanced statistical analysis and visualization of data. It also helps us to implement and use advanced forecasting and application modeling for our online and e-commerce data points. For the most part, RStudio is able to meet our needs. While there are other options and opportunities, the open-source community driven nature of the R Community and RStudio helps to greatly enhance the base capabilities available to the initial program.
  • Easy to Deploy
  • Inexpensive
  • Powerful analysis
  • Open-Source (Can lend itself to vulnerability)
  • Data Ownership Terms
  • Python quickly overtaking the R language as the data science programming language of choice
To get your feet wet in data science, you definitely should start with RStudio. It allows a low barrier to entry in terms of the learning and knowledge required to set it up and interact. As your analyses get larger, however, RStudio may not be your most efficient choice. It can quickly get bogged down when you begin breaching into extensive data sets that are larger (100000000+ rows of data) and will be dependent on the box you install it on (unless you can cloud-deploy and use Shiny). Be careful before you invest too heavily into this platform that you have truly considered the full costs.
Read Eric Myers's full review
Bronson Bullivant profile photo
April 25, 2018

Review: "RStudio goes a long way for open-source programs"

Score 10 out of 10
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RStudio was used by my organization to "clean" big-data projects while working in a private consulting setting. RStudio made the process of importing multiple datasets, creating arrays, and combining data extremely efficient due to the easy to understand the visual layout of the program. The added dictionary feature built into the interface was also very useful. Using the programs interface with others who are not familiar with the R language is more effective as each item defined will be visually identifiable.
  • Able to handle large amounts of data without storage issues
  • All-in-one user interface
  • Tabs for different worksheets is useful to stay organized
  • Codes can be saved as a project
  • Sometimes RStudio creates a problem in viewing data; does not show all the fields
  • Dictionary/package finder could be more intuitive
  • Large computational tasks will take longer than running them in command line
It is best for use in large data projects, or small ones with a lot of specific code which has to be saved in a common place. I would say it is less than ideal for tasks that are simple which are best saved for programs that can be replicated among many users, such as Excel/VBA.
Read Bronson Bullivant's full review
Jake Tolbert profile photo
April 24, 2018

User Review: "RStudio is the only IDE you need for R"

Score 10 out of 10
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I used RStudio to do the overwhelming majority of my data analysis, which includes general direct mail-style campaign selection, statistical analysis, predictive modeling, and reporting. It gives me a single environment to work in where I can do SQL-style work, statistical work and reporting--in essence, if it involves data, I'll do it in RStudio.
  • RStudio ticks most of the IDE boxes for R users: autocompletion, an overview of your current environment, an interface for files in the working directory and a way to interact with plots in the GUI.
  • Combined with the tidyverse set of packages, you can do most of your database work, plus work faster and smarter, in both the interactive environment and in scripts.
  • RStudio's snippets functionality allows you to quickly access the bits of boilerplate code you find yourself typing over and over and to paste them in with just a few keypresses.
  • Though they're currently developing ways to extend RStudio, ie. add-ons, the environment and hooks needed are still fairly limited.
  • Package management is available, but could be simplified even further.
  • Git integration is great and provides are really useful way to view diffs. However, I still run into a few bugs here and there that force me to drop back to the terminal.
RStudio is a must if you've doing any work at all in R--there's simply not a better tool. I've looked into other IDEs including Rodeo--they're just not nearly as polished or effective. RStudio is a mediocre SQL client, but can function as such if need be. The terminal support added recently is useful, but again, the heart of RStudio is semi-interactive work in R.
Read Jake Tolbert's full review
Jevgenijs Steinbuks profile photo
April 24, 2018

RStudio Review: "Open Source Statistical Software ideal for Big Data Work"

Score 8 out of 10
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Our department extensively uses RStudio to conduct econometric analysis for development research. It is the second popular software after STATA. Rstudio is also occasionally used in other Departments in their knowledge products.
  • Open source and massively parallelizable makes it an ideal vehicle to work with Big data
  • There are many extensive libraries, which makes it easy to implement complex routines in R
  • RStudio is especially helpful to work with geospatial data, such as satellite nightlights or road traffic data.
  • The numerical libraries in R rely on open source solvers, which leads to stability issues for solving complex nonlinear problems
  • Many open source packages are unstable and poor quality
  • Less user-friendly than STATA
RStudio is very well suited for manipulating and organizing large scale geospatial data. It is less appropriate for a complex nonlinear econometric estimation.
Read Jevgenijs Steinbuks's full review
Mounika Chirasani profile photo
April 24, 2018

RStudio Review: "IDE to use with R programming"

Score 8 out of 10
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RStudio has been used by myself for my Research on machine learning algorithms support vector machines, neural networks, and singular spectrum analysis. It is used mainly for data cleaning and for predictive analysis.
  • Mainly used for data wrangling. Statistical knowledge of software coding skills can do wonders
  • It is used to analyze, process and manipulate data
  • Easy to use for a new learners
  • There are still missing packages for machine learning and deep learning. It has to be improved as python has many.
  • Processing large documents might struck the system while using this IDE.
  • Plotting and showing the graphs has to be improved.
It is best for analyzing large data and predicting and regressions of data.
Read Mounika Chirasani's full review
Robin Mattern profile photo
February 12, 2018

Review: "RStudio as a documentation tool for software development"

Score 9 out of 10
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We are software developers, not data scientists. We use RStudio for documentation. The .RMD pages allow us to document operational and development tasks with repeatable commands and/or scripts intermixed with explanations.
  • Document BASH and build scripts written in various languages.
  • Run Ad Hoc and initial SQL statements against our databases.
  • Easily publish the .RMD documents as HTML or PDF files.
  • Support for NodeJS and Javascript.
  • Better examples and documentation regarding PanDoc.
Read Robin Mattern's full review
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April 25, 2018

RStudio Review: "Most underappreciated IDE"

Score 10 out of 10
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Our entire team uses RStudio both for statistical analysis and application development purposes.
  • RStudio is probably one of the most underrated IDEs. The environment panel is probably the most useful one.
  • The help tab is also very useful, saves a lot of random Google search time.
  • It is also probably the only IDE I never had issues with while installing/upgrading.
  • The debugging feature is probably not the best designed one.
  • I would love to see a live shiny debugging feature in the future, maybe something similar to the environment panel for reactive values.
  • Big computational tasks are sometimes slower in RStudio.
Rstudio is a very well designed IDE. Especially in cases where the user is a beginner and needs to have a very clear view of his/her variables, Rstudio is very useful.
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Juan Francisco Tavira profile photo
November 03, 2017

Review: "Rstudio, all-you-can-imagine algorithms for your data"

Score 6 out of 10
Vetted Review
Verified User
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RStudio is used just in a couple of departments, mostly data analyst working with huge amounts of data and complex algorithms on statistics, trend prediction, and big data projects. All those process are directly impacting business opening new market oportunities
  • Complex mathematical/statistical algorithms on large amount of data
  • Pattern detection, trend prediction, market analysis
  • User interface feels a bit old and too technical for business people
  • It relies on R installation, that means a lot of the libraries are near "hobbist" work and difficult to install and operate
  • Documentation requires some improvements
RStudio is very well suited when your algorithms are very complex and / or your datasets are huge.
But the visualization tools require a bit more building than alternatives and bear in mind that huge amounts of data require that much memory and network transfer, newer big data tools based on Map-Reduce solve the transfer problem.
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About RStudio

RStudio is a free and open-source integrated development environment for R, a programming language for statistical computing and graphics.
Categories:  Predictive Analytics

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