Item: Posit
Rating: 9
Author: Verified User

Overall Satisfaction with RStudio

Use Cases and Deployment Scope

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.

Pros and Cons

Pros

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.

Cons

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.

Return on Investment

High Impact.
Our ML platform is built on RStudio and R.

Alternatives Considered

Visual Studio IDE

RStudio has multiple products like desktop, server and server pro. Within RStudio, one can create multiple tabs of R code, and it is easier to work in this development environment.
Another advantage that I see in RStudio is saving the environment variables. Environment variables are saved locally when I close RStudio, and I can continue the same session once I restart RStudio later. All the variables will be stored, and I just need to reload the library/packages.

Other Software Used

EditPlus

Likelihood to Recommend

We mainly use R Studio for performing some statistical analysis and running our ML platform.
For Example:
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.

Comments

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RStudio, the best IDE for R Progamming !!