RStudio - Very Powerful Statistical Tool
Updated April 11, 2022

RStudio - Very Powerful Statistical Tool

Kunal Sonalkar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with RStudio

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.
  • 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.
  • Statistics is very simple to use in RStudio and not in Tableau.
  • Tableau is very expensive and RStudio is open source.
  • Geo Coding in R is much better than Tableau.
  • Tableau is better in data visualization and easy to use and much more interactive.( as it is more of a drag and drop tool).
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.

Using RStudio

30 - Customer Discovery, Fraud Prevention, and Improving Search.
The data analysis helps in these domains to make data driven decisions.