reticulate

Misc

• {reticulate}, {rpy2}
• Reticulate-based R packages (Thread)
• Before interactively running python in RStudio, start REPL: reticulate::repl_python()
• Get current python versions installed: py --list-paths (windows)
  • FYI python --list-paths doesn’t work
• Avoid conversion whenever possible. Achieved by using the import(<library>, convert = FALSE)
  • When converting objects between R and Python, a new copy of the object is created in the target language. This slows down execution and increases memory use.
  • Useful when working with large objects in Python that do not require manipulation in R

Python in R Scripts

• Create Virtual Environment, Install Libraries, Activate VE, Import Modules (source)

  library(reticulate)
  
  # Step 1: Create a virtual environment 
  virtualenv_create(envname = "gemini")
  
  ## Step 1.1: Install the appropriate modules
  py_install(
    c("google-generativeai",
      "langchain",
      "langchain-community",
      "pypdf",
      "python-dotenv"), 
    pip = T, 
    virtualenv = "gemini"
  )
  
  # Step 2: Use the virtual environment
  use_virtualenv("gemini")
  
  # Step 3: Import installed modules
  dotenv <- import("dotenv")
  genai <- import("google.generativeai")
  langchain_community <- import("langchain_community")

• Via source_python

  • Example

    library(tidyverse)
    library(reticulate)
    source("funs.R")
    use_virtualenv("../../")
    source_python("funs.py")
    
    # stuff
    
    for (r in 1:nrow(res)) {
      cat(r, "\n")
    
      tmp_wikitext <- get_wikitext(res$film[r], res$year[r]) # r fun
    
      # skip if get_wikitext fails
      if (is.na(tmp_wikitext)) next
      if (length(tmp_wikitext) == 0) next
    
      # give the text to openai
      tmp_chat <- tryCatch(
        get_results(client, tmp_wikitext), # py fun
        error = \(x) NA
      )
    
      # if openai returned a dict of 2
      if (length(tmp_chat) == 2) {
        res$writer[r] <- tmp_chat$writer
        res$producer[r] <- tmp_chat$producer
      }
    }

    • get_results is a python function defined in funs.py

RMarkdown

• Also see Quarto >> R and Python

• Basic set-up

  ---
  title: "R Notebook"
  output: html_notebook
  ---
  
  
  ```{r}
  knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE
  )
  ```
  ```{r}
  library(reticulate) 
  ```
  ```{python}
  import pandas as pd 
  import numpy as np
  ```