What are some of the best way to integrate R and Python in a Data Science Project?

I need suggestions how to incorporate both R and Python a project, so when I learn one I don't forget the other. I'm currently trying to learn Python. It would be nice to practice a project that involved both.

I have learned some R, now I want to learn Python. I'm afraid of forgetting things. From what I have seen so far. Perhaps R is better for visualization of the data? On the other hand Python, by the rumors and ballads of bards that the snake is faster in ML/DL.

Any Rpubs or Notebooks example that use both to fulfill an objectives? Or perhaps your experience?

Thank you.

We are developing a shiny application for microscopy image analysis. The point is that most image processing libraries are in python while many statistical packages to analyse objects spatially are in R and python.

We are trying to combine them not out of choice but to use the simplest packages for the job. Python is very easy in most cases to incorporate into R with the reticulate package. I found that the other way round to work with R in python is not as comfortable. If I have to use a class in both languages I make a python class.

The comment that we often get is why we are not just using python instead of python and R. I don't really have a good answer for this because it could probably work. But then shiny is very comfortable to use and there are packages in R that have been around a long time and seem to make the job easier for us.

Think about the core of your project and check where you get more community support and packages and go with that.

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I see. Thank you for your input.