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Filter 69 vetted RStudio reviews and ratings
Reviews (1-25 of 32)
February 25, 2020
Rstudio statistician's best friend
I have used RStudio for various data analysis at my company from data cleaning to data visualization and model selection. The entire organization widely uses RStudio. RStudio helps in ad-hoc analysis and enables users to run line by line and easily create R markdown files as well as a shiny app.
- Stats best tool
- Easy exploration data analysis
- Package building
- Package version selection
January 17, 2020
RStudio is preferable in my view for data science
RStudio is the main tool we use in advanced analytics for predictive modeling development, maintenance, and deployment into production. We run it on a separate Linux server that connects to our enterprise data warehouse. The business problem it addresses is risk modeling on our patient population for outreach and operational efficiency purposes. In addition we use it for data visualization exercises in GGPLOT2 which are more novel than simple excel plots so is a favorite among my team.
- Packages are really easy to install and load.
- Models are really easy to deploy in this language. The functions are simple and relatively straightforward which democratizes the process.
- Links very well with the Oracle data warehouse both to read data and write tables in the EDW.
- Errors in R and RStudio are almost impossible to interpret.
- Updating R and RStudio can lead to packages no longer working which can be frustrating since I have to reinstall all of them from scratch.
- The visual interface is not as good as Jupyter notebook it's tough to read the font especially when projecting onto a screen with projector for meetings.
January 28, 2020
The open source alternative to Matlab
RStudio is used as a free, open-source alternative to MATLAB. We can generate custom codes to solve a variety of data analysis problems. These scripts can be easily shared with other users with little hassle in trying to purchase additional software.
- Easy to learn language
- Clean UI for R software
January 21, 2020
RStudio - a beginner's perspective
Most analysts in my organisation use Excel for simple tasks and for advanced analysis, use SPSS or SAS. The license costs for SPSS and SAS are substantial and I along with few other analysts are now transitioning to using R with its integrated development environment RStudio. This has cut down cost for us as RStudio is free and open source and does not require annual license costs. With a bit of learning RStudio can be equally good as SAS or SPSS, although there's a steep learning curve involved especially for users who have not used any scripting language in the past and are used to drop-down menus and pre-formatted statistical output.
- Data manipulation in RStudio is a breeze. It is an exceptional product when it comes to creating data subsets from an existing data, creating calculating columns and storing data and plots as objects.
- Everything in RStudio is done via writing script. It can be tedious to begin with but once you have written all your data manipulation and analysis in a script, it becomes very easy to maintain and edit it and run it again and again without having to remember the steps like in other statistical software.
- When you first start with RStudio, you need to install and then reference them in your script. In my view RStudio should come with pre-installed packages that most fundamental to any data analysis. Few example packages are 'dplyr' and 'ggplot.'
- While everything in RStudio is achieved via writing script, it should include more point and click tasks such as right-clicking temporary datasets and removing them.
March 30, 2020
Good tool for statistical data scientists
Since my company is a large financial institution, tools used with some groups might not be shared with other groups and some tools used in some geographical locations might not be used in other locations. R and Rstudio is such a tool. RStudio is basically the default option if you are using R.
- Easy.
- Clear.
- Free.
- No multi-language.
- Version control is hard.
- Manage environment.
RStudio is our go-to IDE for using R in our firm. It is used across our large data science community (1000+ people) and for prototyping in our capital modeling and asset management arms. The business problem addressed is an efficient and clean environment for coding in R (and a few other languages).
- The interface is clear and customizable.
- Importing and interacting with data is clear and intuitive.
- There is a wide range of clever tools that make using RStudio actively pleasurable.
- Would love to see integration with other languages such as VB, Julia.
October 29, 2019
My 2 bits of experience with RStudio
RStudio is the tool of choice for helping us with the data analysis. We are using it for time series analysis and forecasting, etc. What makes RStudio best fit for us that it is the very user-friendly and ready availability of the whole history of the commands and data you have worked on to date.
- The simple user interface leads to almost zero training time for the user.
- RStudio provides the flexibility to generate reports in a number of formats depending on the use case.
- The interoperability of RStudio because it is available for almost every OS Ecosystem.
- There are countless packages available for almost every kind of analysis you can imagine.
- To be fair, there are hardly any negatives or even shortcomings with RStudio with it being free. But, I have to be nitpicky, it can be argued that in some cases, the input data files have to manipulated or arranged in a certain way for it to perform analysis.
- It can be made a bit more attractive by upgrading the UI a bit.
It's being used in the Data Analytics team and several business and functional units to create automated and reproducible data analyses.
- Ease of integration across common programming languages
- Free tier offers much of functionalities as paid version
- Great support
- Intermittent crashing
- Improved UX for new users
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.
June 12, 2019
Statistical Modeling at its best
RStudio is a powerful application when it comes to analyzing data using statistical modeling. It is used by different departments within the organization, especially the data science group. In the production department, statistical modeling is helpful in extrapolating the production volume and rate. RStudio is intuitive and user-friendly. The documentation is very informative and gives deep dives into the functionality. It's also very well integrated with other applications.
- Very Intuitive and user-friendly.
- Can perform statistical modeling for extrapolating and also automating repetitive tasks.
- Good for people with less coding experience.
- Not as integrated as Python is with other applications.
- Objects are generally stored in physical memory, which hogs the memory.
- RStudio is slower than many other statistical modeling packages.
We are a service based company. For most of the clients, we work in data analytics. So we use this product department-wide where we have to apply data models, EDAs, etc. Generally, the business problems deal in drawing the statistical inferences out from the data and applying various machine learning models for the predictions and sometimes we also use this product to clean the data.
- RStudio provides good data visualization while doing exploratory data analysis
- We can import the data from multiple sources for processing the data.
- Its syntax is pretty much easy to use and learn. Also applying machine learning models are very easy in it.
- Downloading the packages/modules very easily and we can use them very comfortably.
- We can export the data into multiple channels from it, which I think is a major boost for it.
- Since its freely available, we always need good RAM to support it
- While loading the big size of data (millions of records), it crashes many times.
- Its user interface doesn't look attractive.
- We can not apply any major artificial intelligence framework in it which I think is a major con it. It's more into drawing statistical inferences from the data.
July 20, 2019
RStudio, the best IDE for R Progamming !!
Currently, this product is being used by all the users in the software development team in our organization. Almost all of our development activity is done using RStudio. We are a data company, and we use a lot of data in a variety of formats. We use RStudio for data cleaning, performing statistical analysis, data visualization, and machine learning.
- They have a variety of readily available packages (data cleaning, machine learning, statistics).
- A convenient IDE with coding and console in the same window.
- It easily integrates with other software.
- They have a continuous support team.
- It may need improvement in job scheduling. Currently, R scripts has to be scheduled separately as batch jobs.
- Running jobs in multiple clusters/cores. There are some R packages to do parallel processing, but it would be great to see some in-built parallel processing features.
It is just used by me. I used it during my education for data analysis. I still use it for quick data analysis for CSV files. It can also create great graphics quickly that are easy to read and are very simple. RStudio has been great when Excel crashes easily and can go through mass amounts of data easily.
- Easy to use
- Can handle large amount of data
- Creates graphics
- Old interface
- Add more data analysis features
- Faster processing speed
December 27, 2018
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.
December 13, 2018
RStudio - Very Powerful Statistical Tool
We are using RStudio to develop shiny web applications and develop predictive data models. We perform statistical analysis on the data and try to gain insights from it.
With the shiny apps, we are automating routine excel reports which saves a lot of time for database and business analysts.
We have written numerous algorithms in RStudio like Naive Bayesian Classification, K-Means Clustering and ARIMA modelling.
RStudio is an amazing platform for statistical data analysis.
With the shiny apps, we are automating routine excel reports which saves a lot of time for database and business analysts.
We have written numerous algorithms in RStudio like Naive Bayesian Classification, K-Means Clustering and ARIMA modelling.
RStudio is an amazing platform for statistical data analysis.
- Performing Statistical Analysis is very efficient. With a lot of open source packages available in R programming, data analysis becomes very easy.
- Publishing web applications and deploying predictive data models is very easy if you have R Server in your firm using Shiny. It can handle large sets of data.
- Writing data science algorithms like Clustering, Classification and Apriori Analysis is very efficient. The open source nature of this programming language allows everyone to contribute packages to the environment.
- There are some packages in RStudio which aren't very well known hence its very difficult to get help if you get stuck using them.
- If the dataset size crosses 20 million rows, then you need extremely high RAM otherwise the processing gets very slow. So in such a case R Server is a must. Cloud storage can be a good alternative though.
- The graphs which are plotted in the console aren't very intuitive and labels, colors, axis, etc have to be manually written to make the visuals look more appeasing.
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 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.
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 has been used by most students who are in statistical classes dealing with data analysis. It has been installed in the statical computer labs for students to solve their class problems or conduct research studies including estimation of the time to failure of a structural/mechanical component, determining the probability of failure under certain conditions, and planning a reliability demonstration test, etc.
- The data file can be imported from text files and multiple data files can be imported and processed in one R command window.
- R commands and functions are embedded, so getting familiar with them would make coding in R easier.
- The way of coding in R is not complex. If a beginner just started using R but has some background in other coding languages, it would help with coding in R as well.
- Unlike other statistical software, RStudio does not display results at every coding step unless a command is made.
- If your functions are not in the database of RStudio, users need to make their own by coding, which is not that easy to do for beginners with no previous experience.
January 19, 2019
RStudio for quick prediction prototyping
Very few of us are getting into predictions using Machine Learning and Data Science. We use Rstudio to program our algorithms. There are only a handful of people in the whole organization who use Rstudio right now. We use it in pockets, and do the proof of concepts with Machine Learning using R.
- We use it for a quick visual representation of data
- We do exploratory data analysis to understand data
- We do predictions using RStudio
- When we have to run 100 iterations using more than 10000 records, RStudio gets stuck or takes a very very long time to respond
- Generating a pdf report from an RMD file is very difficult from RStudio.
- Generating a pdf report in RStudio cloud is straightforward and inbuilt.
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
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.
June 05, 2018
RStudio - the biggest analytics platform
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.
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.
June 04, 2018
RStudio the best for statistical data
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.
RStudio Scorecard Summary
About RStudio
Categories: Predictive Analytics
RStudio Technical Details
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| Mobile Application: | No |