Using Python with R

The motivation for this comes from the fact that it is easier now more than ever to use Python with R via the reticulate package.

It does make a lot of sense too! I rely on packages to upload client data to S3 buckets quite a bit using R scripts and RStudio Connect for automation. Unfortunately, the aws.s3 r-package has been wonky and does not seem to be maintained.

Enter, boto3 - a Python package which lets you interface AWS S3. It is cleaner and easier to handle in terms of the set-up.


You first set up your R-script like you usually would (assuming you have Python installed and set up) with the addition to the reticulate library added.


## Do stuff and write to file

path <- "./file_name.csv"
s3_path <- "directory/file_name.csv"

Once you’re done processing data, etc. and writing results to a file, you move on to the Python section - you might have even seen the function below in some shape or form before.

# import boto3
# def upload_to_aws(local_file, bucket, s3_file):
#     s3 = boto3.client('s3', aws_access_key_id = r.ACCESS_KEY,
#                       aws_secret_access_key = r.SECRET_KEY)
#     try:
#         s3.upload_file(local_file, bucket, s3_file, ExtraArgs={'ACL':'bucket-owner-full-control'})
#         print("Upload Successful")
#         return True
#     except FileNotFoundError:
#         print("The file was not found")
#         return False
#     except NoCredentialsError:
#         print("Credentials not available")
#         return False
# uploaded = upload_to_aws(r.path, 'bucket_name', r.path_s3)

The beauty of this is that you can reference r-objects easily by prepending r., which allows you to not only feed your key/secret pair into the Python script but also the file paths for both the local file and the S3-path.

That’s it!!

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