TrustRadius

RStudio One of the best and freely available tool for data analysis.2019-05-13T19:56:11.930ZWe 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.,9,Since it is available in a free license ROI is very good.
We take very less time to train people in it which gives us a good ROI.
For working with the huge amount of data, we need to upgrade the configuration of our systems. Which involves high costs,SAS Enterprise Guide, SAS Enterprise Miner and IBM SPSS,JMP Statistical Discovery Software from SAS, IBM SPSS Modeler, MongoDB, Amazon Redshift, Microsoft SQL Server, Tableau Server, IBM InteractAkshaya BhardwajGreat Platform for Data Analysis and Data Visualization along with Statistical Computing.2018-12-27T16:46:07.156ZRStudio 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 YammerRajat WadhwaniRStudio - Very Powerful Statistical Tool2018-12-13T19:10:17.157ZWe 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 EngineKunal SonalkarRStudio works!2018-12-08T17:18:41.902ZSince 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 InsightsKyle MoningerRStudio is THE standard for exploratory data analysis on large data sets2018-11-29T17:59:26.452ZRStudio 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!Leah Jakaitis

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RStudio

56 Ratings

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