Reviews (1-25 of 32)
- 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.
- Easy to learn language
- Clean UI for R software
- 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.
RStudio is a free product, therefore, you can utilise this tool without any requirement for a licence. It is excellent tool for data manipulation and performing tasks that can't be done by any other software. For example RStudio allows you to install packages that can directly analyse your Google Scholar profile data and predict your citation index over the next 10 years. Other examples include accessing and analysing movies data from www.imdb.com and creating a word cloud directly from a website. In my knowledge, no other software allows you to do that with an ease that can be done in RStudio and did I mention that 'it's free'!
R (language) and RStudio has a steep learning curve. Therefore, it is not ideal for people who are beginners in programming. Creating a statistical analysis output in RStudio is like putting together each statistical output value bit by bit. There are no nice outputs that can be generated by just clicking few options. The scripting and variables are case sensitive which can make it sometimes hard to diagnose an error in your script.
- 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.
- 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.
But, if you are not opposed to spending for more sophisticated solutions with more professional UI and somewhat better ease of use, then there are other tools in the market.
But nothing beats RStudio when it comes low-cost, fully equipped data analysis tool category.
- 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.
- 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.
We have suggested many tools in our company but they are pretty much expensive and also the quality of output is not that good.
- 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.
1) Statistics: to do correlation, t-tests.
2) Visualization: box plot, bar chart.
3) Machine learning: To build a model using available R packages, train the model, perform cv, and test the model.
4) To find the relationship between variables by creating a generalized linear regression model.
5) Data cleaning: to remove incorrect fields, subset data frames, and remove missing fields.
- 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.
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.
In a situation where you want to automate excel reports then shiny (user interface for R) comes in very handy.
- 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.
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.
- 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.
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.
- 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.
- 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.
- 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.
- 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
- "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.
- 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.
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.
- 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.
Not as well suited for any big data tasks or deep learning or image processing.
- 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
RStudio Technical Details