Tip 1: Tidy evaluation Tip 2: Pipe into later arguments of a function using . Tip 3: Function conflicts workaround (no more dplyr::select()) Tip 4: geom_col(): you’ll never have to specify “stat = identity” for your bar plots ever again! Tip 5: Using show_col() for viewing colour palettes This was my second year attending rstudio::conf() as a diversity scholar (and my first time as a speaker), and I was yet again blown away by the friendliness of the community and the quality of the talks.
Map functions: beyond apply Simplest usage: repeated looping with map The tilde-dot shorthand for functions Applying map functions in a slightly more interesting context Maps with multiple input objects List columns and Nested data frames Nesting the gapminder data Advanced exercise Additional purrr functionalities for lists Keep/Discard: select_if for lists Reduce Logical statements for lists Answer to advanced exercise “It was on the corner of the street that he noticed the first sign of something peculiar - a cat reading a map” - J.
Data shaping: tidyr Gathering and spreading Combining and separating variables Replacing loops: purrr Loading data: readr Storing data: tibbles Dates, factors and strings: lubridate, forcats and stringr Handling dates and times: lubridate Handling factors: forcats Handling strings: stringr If you’re new to the tidyverse, I recommend that you first read part one of this two-part series on transitioning into the tidyverse. Part 1 focuses on what I feel are the most important aspects and packages of the tidyverse: tidy thinking, piping, dplyr and ggplot2.