There are several comments below that suggest not using tidyverse because "base R" is the foundation for everything.
I think it is important to use tidyverse because of the many quirks, surprises, and inconsistencies in base R. It would be helpful if others share their reasoning, or at least point to their favorite blog explanation, so that beginners can understand the problems they will face.
Unfortunately 5 minutes of Googleing failed a to produce a reference for me --- the start of some advanced R book that begins by asking "do you need to read this?" and showing examples whose results are predicted incorrectly by most people. Perhaps another user can provide the info.
This depends on what you are using R for. Tidyverse is focused on handling data.frame objects and everything that comes with them. Even ggplot2 uses a data.frame as a default input. And tidyverse has a competitor - data.table, which can be substituted instead (given that you are familiar with base R).
However, some data are better suited to be represented in the form of matrices. Putting matrix-like data in a data.frame is silly, since performance will suffer and you would have to convert it back and forth for many matrix-friendly operations like PCA, tSNE, etc. The creator of data.table shares this opinion [1]. And similar opinions are generally given by people who are familiar with problems that fall outside the data.frame model [2].
I think it is important to use tidyverse because of the many quirks, surprises, and inconsistencies in base R. It would be helpful if others share their reasoning, or at least point to their favorite blog explanation, so that beginners can understand the problems they will face.
Unfortunately 5 minutes of Googleing failed a to produce a reference for me --- the start of some advanced R book that begins by asking "do you need to read this?" and showing examples whose results are predicted incorrectly by most people. Perhaps another user can provide the info.
* Reference to good HN thread: https://news.ycombinator.com/item?id=20362626
* Particularly pointed notes on base R problems: https://news.ycombinator.com/item?id=20363806