RStudio is great but needs some improvements

RStudio is used in my organization to build machine learning models, such as linear regression, logistic regression, decision trees, random forest, k-mean clustering, and more. It solves our business problem of having a low-cost, open-source tool for building statistical models and running models for data analysis. We can also use this for data visualization and data cleaning.

I don't really have experience using customer success support from RStudio before. Still, I have used the documentation provided for R Shiny, which I found pretty useful when developing interactive visualization. I felt other tools like Plotly for Python had better documentation, so RStudio could learn from them.

There are many resources out there for open sources packages, and it is free to use. The good thing about open source in data science is transparency and flexibility. We can see how the packages are written so we know the underlying assumptions in the models and how we can customize them for our needs. Data science is a quickly developing field. We need the flexibility and adaptability of open source tools to implement changes quickly when new research comes out and not rely on large corporations to update their products.

I don't have experience using these tools.