Welcome to the second edition of "R for Data Science"!
This is a major reworking of the first edition, removing material we no longer think is useful, adding material we wish we included in the first edition, and generally updating the text and code to reflect changes in best practices.
We're also very excited to welcome a new co-author: Mine Çetinkaya-Rundel, a noted data science educator and one of our colleagues at Posit (the company formerly known as RStudio).
It's a new set of chapters that goes beyond reading flat text files to now embrace working with spreadsheets, getting data out of databases, working with big data, rectangling hierarchical data, and scraping data from web sites.
- The "Program" part continues, but has been rewritten from top-to-bottom to focus on the most important parts of function writing and iteration.
Function writing now includes sections on how to wrap tidyverse functions (dealing with the challenges of tidy evaluation), since this has become much easier over the last few years.
We've added a new chapter on important Base R functions that you're likely to see when reading R code found in the wild.
We generally recommend using the [tidymodels](https://www.tidymodels.org/) packages and reading [Tidy Modeling with R](https://www.tmwr.org/) by Max Kuhn and Julia Silge.
- The communicate part continues as well, but features Quarto instead of R Markdown as the tool of choice for authoring reproducible computational documents.
