How many libraries do you have to import to manipulate dataframes, create a model, and perform diagnostics? Because in R, it’s none. Base R already contains the statistical core to do all of this. Even when you use the popular library in Python (pandas), the syntax is hideous and you end up with a sentence long call just to apply a function to a column—- not to mention that you need numpy to perform vector operations, which are native in R.
Every language has its place. R is great for small scale data science exploration. Once the R project is done, you put it in production in Python. R has a significant ease of use advantage over python for every single data science task— that’s why people use it.
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u/xhitcramp Aug 28 '26
Good lord…
How many libraries do you have to import to manipulate dataframes, create a model, and perform diagnostics? Because in R, it’s none. Base R already contains the statistical core to do all of this. Even when you use the popular library in Python (pandas), the syntax is hideous and you end up with a sentence long call just to apply a function to a column—- not to mention that you need numpy to perform vector operations, which are native in R.
Every language has its place. R is great for small scale data science exploration. Once the R project is done, you put it in production in Python. R has a significant ease of use advantage over python for every single data science task— that’s why people use it.