Run Many Calculations All at Once With Map Functions | Step-by-Step R Tutorial

[@rappa753]

Thank you for watching my latest video. ♥ If you enjoyed this video, I'm sure you're also going to love my Data Cleaning Master Class. Clean your data and find insights much faster at data-cleaning.albert-rapp.de/

[@thespaniardinme]

Keep doing the good work, Sir. Always a good feeling when I get a notification that a new video has been uploaded on your channel, Dr. Can you kindly consider doing a video on Web scraping, sir. There are none to very few resources on things like async web scraping using R (be it using httr2, mirai, async). As always, thank you for your good work, sir!

[@rappa753]

Thank you, I'm Happy to hear that 🤗 As for your topics, I'll add it to the backlog. Have you checked out my tutorial on {httr2} and {rvest} yet? 🤔

[@thespaniardinme]

@@rappa753 thank you so much, Dr. I have indeed watched both of them. My first introduction to httr2 was actually by way of your video! I had not even known that the package existed prior to that, really.

[@rappa753]

Awesome! It's always delightful to hear that my videos made an impact. Thank you for sharing that. ♥️

[@ambhat3953]

+1

[@adan8657]

Great thanks. And cool new logo!

[@rappa753]

Thank you. That's exactly what I wanted to hear ❤️🤗

[@albertocabrera4297]

Your KZbin lesson could also be used to understand the powerful concept of multilevel modeling regression (MLM), which rests on nested data. I imagine your map( ) could be used to pull out the estimates generated by MLM. Thanks again for another superb lesson.

[@rappa753]

Glad you like it 🤗 and yeah the map() functions can help with loads of analyses 🥳

[@enocharthur4322]

A simpler example would have drum home the point faster. Great work though

[@rappa753]

I like to start with an elaborate example to show people what's possible first (with explanations of course) 🤗 the simpler example is also available in the video

[@haraldurkarlsson1147]

I prefer the more explict expressions rather than short-hand because it may be difficult for someone (inlcuding one self) to interpret the code down the line. I like the ~.x versions e.g. map_dbl(coeffs, ~.x[1]) but I suppose with \(x) x[1] things are clear. Lastly I would have liked to have seen r-squared and the p value for the slope.

[@rappa753]

Yeah I write \(x) almost instinctively so I never use the short-hand either 😃 you can add additional map calls to extract these stats from the lm object as well 😊