For AzureML Python SDK we can use get_by_name() which returns the dataset.
import azuremlsdk
mydata = get_by_name(myworkspace, 'mydata')
And I can get the panda dataframe of mydata by the .to_pandas_dataframe() method
mydata.to_pandas_dataframe()
For R equivalent, I'm stuck here
mydata <- azuremlsdk::get_dataset_by_name(myworkspace, 'mydata')
The question is, what are the options for R so that I get the tables, say, in csv or tibble?
I notice R's AzureML SDK is not as well documented as Python's, which makes migrating to AzureML pretty challenging for our R code base.