I use both R and Python extensively, and also was confused for a long time why the conventional wisdom is that one of Python's strengths is a data cleaning and

I use both R and Python extensively, and also was confused for a long time why the conventional wisdom is that one of Python's strengths is a data cleaning and preprocessing. I too always found data cleaning to actually be one of R's strongest features. My hypothesis: I think there is an inconsistency between what is meant by "cleaning/processing" between stats- and CS-oriented people. To the latter, it often just means turning unstructured or tree-like data (JSON/XML) into a tabular format. As a statistician, I think of it being a much larger set of operations; specifically those supported by packages such as dplyr, tidyr, and stringr... I'm not sure if that interpretation is correct, but Python does excel in the XML/JSON => CSV step and R is great at all the processing that happens after that.