You are right - the language itself can be a bit messy. R’s advantage is the massive amount of packages available. Almost no matter what niche problem you are trying to solve - someone probably made a package for it. And if not all the functionality is there: you can just add.
Nope. R's advantage is that the language is extremely expressive and makes many things about it's implementation extremely transparent to it's users. The huge package count for a niche language is a direct result of that.
200% agreed. I switched from python to R for data science machine learning and data visualization and found that python is far behind .
I am able to generate and test and make machine learning models easily and in standardised manner using its tidymodels libraries while I am unable to generate models in python scikit-learn etc libraries
Comments
You are right - the language itself can be a bit messy. R’s advantage is the massive amount of packages available. Almost no matter what niche problem you are trying to solve - someone probably made a package for it. And if not all the functionality is there: you can just add.
Nope. R's advantage is that the language is extremely expressive and makes many things about it's implementation extremely transparent to it's users. The huge package count for a niche language is a direct result of that.
200% agreed. I switched from python to R for data science machine learning and data visualization and found that python is far behind . I am able to generate and test and make machine learning models easily and in standardised manner using its tidymodels libraries while I am unable to generate models in python scikit-learn etc libraries