RStudio, the best IDE for R Progamming !!
July 21, 2019

RStudio, the best IDE for R Progamming !!

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with RStudio

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