Well Suited for Data Visualization and Modeling
Updated September 02, 2021

Well Suited for Data Visualization and Modeling

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

Overall Satisfaction with RStudio

We use RStudio as an GUI interface for R, which we use to visualize and model data. For modeling data, we use lots of machine learning techniques like Regression, and R provides an excellent package to implement various flavors of regression like lasso and ridge regression. For data visualization we also use Shiny apps.
  • Debugging
  • Front-end interface to R
  • Provide shortcuts to some R commands
  • RStudio connect experience was not very smooth
  • Web service configuration for the RStudio server is not very intuitive
  • Some Visual DataGrid GUI would be beneficial
  • Web service configuration
  • RStudio connect interface is not easily configurable
  • No widows server option for R Connect server, so IT department needs to be familiar with Linux server administration
JMP is more customizable. It also has very good drag and drop graphing capabilities, which are not present in RStudio. Data exploration is much more convenient with JMP. However, the analysis work is better with RStudio since it is a bit hard to tweak the JMP built-in models.
I have not used customer support.
I chose to use the open-source tools like Shiny and IDE because of some specific reasons:
  1. RStudio: The best GUI available to the R compiler. Has nice debug capabilities. Also, RStudio takes care of package dependency, as it offers to install any additional package that needs to be installed after installing a package mentioned inside the script.
  2. Shiny: Interactive GUI Apps.
I have used RStudio Server Pro, which allows you to use R via a web interface without installing anything to a particular machine. I find this very useful to demonstrate some R result to any colleague who does not have RStudio already installed on his/her laptop but would like to occasionally run an RScript. This web-based service also takes care of any issues that arise with different versions of the installed packages.
I am not using any RStudio-hosted offerings.
RStudio is well suited for individual data visualization and modeling work. R has some very good modeling packages like glmnet. R also has some very good data manipulation package like tidyverse. Data visualization capabilities are also great. RStudio provides a great user interface to R for harnessing the capabilities that I mentioned above.

Using RStudio

7 - Write R scripts for machine learning and data visualization . Data visualization is used for control charting and monitor production yield. It is also used to follow up on debugging other manufacturing issues by using data visualization. Machine learning is used to create statistical models for the products that are used to convert raw data to a format understood by the relevant clients.
2 - R syntax knowledge is the most important skill needed to use RStudio. In addition these people are also capable with configuring the R compiler in a Windows environment. One of them is also knowledgeable with RStudio web version which is available with the Pro version of R studio. Network configuartion knowledge is helpful in this area.
  • Statistical modeling
  • Data visualization
  • Yield monitoring
  • Use RStudio to create web based reports.
  • Write script in RStudio and call it from JMP software environment.
  • Use RStudio for Python integration
  • Develop shiny apps using R studio to create interactive apps.
  • Use RStudio debug functionalities to root out bugs.
  • Use Rstudio as as IDE.
RStudio visual environment is essential for developing R scripts. R by itself does not provide any visual environment for development , so some sort of IDE is required to develop larger and complicated R scripts. Compared to other IDE environments like R Commander , R studio is way more user friendly. It can show the data tables that are loaded into the memory.
N/A as we do not use RStudio for Python development at this time. We find that Pycharm is most appropriate for Python development. This might change in future but at this time we prefer Pycharm as it provides belter debug environments like watch functionalities for variables compared to Rstudio for Python.