Tidyverse

As of tidyverse 1.2.0, the following packages are included in the core tidyverse:

• ggplot2
• dplyr
• tidyr
• readr
• purrr
• tibble
• stringr
• forcats

dplyr

https://bookdown.org/rdpeng/rprogdatascience/managing-data-frames-with-the-dplyr-package.html

• dplyr can work with other data frame "backends" such as SQL databases; there is an SQL interface for relational databases via the DBI package
• dplyr can be integrated with the data.table package for large fast tables

Useful tidbits:

• case_when()
• filter(row_number() == n())
• count(), tally(), add_count(), add_tally()
• distinct()
• subset <- select(chicago, ends_with("2")) # select based on a pattern
• subset <- select(chicago, starts_with("d")) # select based on a pattern, see ?select for more information
• chicago <- rename(chicago, dewpoint = dptp, pm25 = pm25tmean2)
• filter(rowSums(.[2:ncol(.)]) == 4) see https://stackoverflow.com/questions/32618744/dplyr-how-to-reference-columns-by-column-index-rather-than-column-name-using-mu

tidyr

The goal of tidyr is to help you create tidy data. Tidy data is data where:

1. Each variable is in a column.
2. Each observation is a row.
3. Each value is a cell.

The principles of tidy data provide a standard way to organise data values within a dataset.