xgboost in R have different results compared to boosted decision tree in Azure ML
It's hard to say, without being able to know exactly what Azure is doing.  


From what they do share, they bin continuous features; you could try tree_method='hist' in xgb to be more similar there. 
I can't tell how Azure deals with categoricals or missing values. 
Be sure to set xgb's max_depth=0 and grow_policy='lossguide', since you want to use max_leaves instead for a direct comparison.


https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree#usage-tips
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree#module-parameters