Meeting chat log
00:04:35 Ryan Woodbury: https://rfordatascience.slack.com/files/UQ4DR12BY/F01QUFD8V5H/dsieur_ch7_slides
00:04:50 Ryan Woodbury: https://rfordatascience.slack.com/files/UQ4DR12BY/F01RJ4ENF4Y/desieur_ch7_scripts.r
00:04:55 Ryan Woodbury: Slides, then script
00:10:33 Ryan Woodbury: Is it like skimr?
00:14:57 Isabella Velásquez: super clear! love the color coding
00:16:26 Edgar Zamora: https://www.garrickadenbuie.com/project/tidyexplain/ I use these GIFs to help me visualize the different kind of joins. Get confused
00:17:31 Rob Lucas: Thanks for sharing that Edgar! The semi and anti joins were new to me. I think this will help me visualize them.
00:26:26 Mark LaVenia: you are doing great! Thanks!
00:29:29 Ryan Woodbury: The `mutate_*()` functions are being superseded by using the `across()` function within `mutate()`.
00:30:03 Isabella Velásquez: here's some documentation on the mutate_* functions. https://dplyr.tidyverse.org/reference/mutate_all.html but like Ryan said, they've been superseded. I am still learning across()!
00:30:05 Ryan Woodbury: Line 69 would be: mutate(across(q1:q10, as.numeric)) in the "new" format
00:30:37 Alyssa Ibarra: Thanks, Ryan! I didn't know it was changing
00:30:49 Ryan Woodbury: I was just getting used to the mutate_*() functions too!
00:37:57 Alyssa Ibarra: when would you use mutate versus transmute?
00:38:35 Ryan Woodbury: The issue with the psych::reverse.code() function that Yukie is talking about is that it is not tidyverse friendly, *but* is a function that is already made and does a great job. (I love the psych package, BTW.)
00:45:41 Mark LaVenia: I love that
01:00:27 Ryan Woodbury: Great advice on imagining the joined datasets.
01:01:19 Isabella Velásquez: I've got to hop off at 5. Thank you SO much Yukie! That was fantastic !
01:01:39 Ryan Woodbury: Thank you! Great work.
01:02:58 Mark LaVenia: Great job Yukie!
01:03:00 Alyssa Ibarra: Thank you so much! It was so great!
01:03:09 Edgar Zamora: Great job! Thank you!