The first line told R where to find the right Python executable in a Conda environment where I had installed all the packages I needed (namely pandas, scipy, scikit-learn, and XGBoost). Whenever you start a project in R, this file will execute its contents on startup.

Rprofile file in your R project directory. I also put together a Github repo with this example.įor these methods to work, you will need to work inside an R project and point to a Python executable in a Conda environment or Virtualenv that contains all the Python packages you need. I determined that it was pretty easy to do this working in RStudio, so I’ve written up the ‘how’ here. I opted to try a k-fold cross-validated XGBoost model. So I recently decided to see if I could easily run a Python ML algorithm in R. That said, I much prefer R for the tidying and preparation of data and would love to be able to import Python algorithms into R so I can have the best of both worlds. There’s no doubt that Python has the widest range of ML algorithms of all the programming languages and in general Python is my first port of call if I am intending to do any form of predictive modeling.

Run python code in rstudio