R's come a long way in the last decade. The tibble and data.table packages both address this issue. data.table (https://github.com/Rdatatable/data.table https:/

R's come a long way in the last decade. The tibble and data.table packages both address this issue. data.table (https://github.com/Rdatatable/data.table https://github.com/Rdatatable/data.table) is the more strongly-typed of the two, by default it fails loudly when it encounters data that doesn't conform to the column type. It's also quite fast--binds to C code that parallelizes with OpenMP. It has very terse and expressive syntax, I find it so much more intuitive and easy to work with than pandas. If you're happy with Python, by all means keep using it. I use both languages. Just suggesting that if you gave up on R that long ago, you might be pleasantly surprised by how much better it's gotten since then.