Data-scientist who loves to use #datascienceforgood, especially in ecology, energy and the environment. Bonsai, gardening, bikes and music when I'm not at a keyboard.
If you need to you can learn the differences later as under the hood a tibble is a data.frame with bells and whistles. Also, a data.table is just a data.frame too, but with different bells and whistles. I hope that's helpful rather than confusing :)
Another way of looking at it: data.frame is baked into the language at a fundamental level, even more so than numpy and pandas. It's native to base R. These other packages extend it in specific, opinionated, and usually pretty useful ways.
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Might be a bit late, but my advice:
Use tidyverse.
If you need to you can learn the differences later as under the hood a tibble is a data.frame with bells and whistles. Also, a data.table is just a data.frame too, but with different bells and whistles. I hope that's helpful rather than confusing :)
Another way of looking at it: data.frame is baked into the language at a fundamental level, even more so than numpy and pandas. It's native to base R. These other packages extend it in specific, opinionated, and usually pretty useful ways.