[ineedasername]

This comes across as just another comment on why one language is "bad" when, as always, it all comes down to trade offs & preference when choosing a tool for a job.

The thing is, it's easy to write bug-ridden sloppy code in any language. Bemoaning R as a language because of these flaws, occurring due to rapid adoption, ignores the reasons why R has seen wide-scale adoption.

R has had an extreme democratizing effect on access to tools that facilitate data science. Previously, tools for data science were either massively expensive or had a prohibitively high price tag attached.

This means that many non-programmers are coming to R, and I maintain that the problems the parent post sees with R stem from that fact. As a result, any language that achieved that sort of layman (to programming) appeal would have the exact same bug or sloppy code fallout. That you cannot separate the momentum that led to such an accessible tool without have the same consequences. Rather than demonize the tool for this, we should recognize the positive dynamic at play and simply help guide users to better practices or improvements that would fix the issues.