Stat 412 6: Advanced Data Wrangling with dplyr

This is a recording of American University's Statistics 412/612 course on Introduction to R Programming.
You can find the material from this meeting here:
american-stat-...

[@izuuburgher8704]

Actually, I have never left a comment on RUclips but after going through this video and how you explained function syntax~ , girl I couldn't stop myself from telling you that you are good. Thank you.

[@jl2696]

Thanks Ms. Gonzalez! This is exactly the way an intro to R course should be taught. The sad reality is most people majoring in statistics or data science unfortunately WILL NOT have the chance to be cross-trained as software engineers and merely need to know how to wrangle the data to get it into a "model ready" format (since modeling tends to be the easier part of the analysis once the data is preprocessed correctly). If my old professors had taught R this way life would've been so much easier. Instead I was given links to sites or texts that either treated you like an idiot with the most basic examples or read like a reference manual. This is exactly what turns a lot of people away from R. I especially enjoyed the following:
- Ms. Gonzalez uses interactive coding sessions where she prompts the students to help finish lines and checks understanding
- Focused on up-to-date software (tidyverse / dplyr in this case) instead of relying solely on base R
- Multiple use cases of functions were shown to help students understand nuance and detail
Great work!

[@KelseyCodes]

This is wonderful feedback, thank you! I'm glad it helped you. I follow the coding pedagogy of Software Carpentries, so thanks to the countless people who have researched how to teach coding well that in turn taught me.

[@jameswhitaker4357]

This video is a gem. 💎

[@jamieamc]

This video is so useful and well presented. I'm watching it both to learn and relax at the same time 🤓

[@viniciussilva2997]

Kelsey, thanks for sharing this with us; it's an awesome course on introduction to R.

[@EsinaViwn9]

The name exactly corresponds to the content. Great job!

[@osoriomatucurane9511]

Kelsey, Awesome tutorial. df%>%summarise(across(everything(), ~ is.na(.x), useful to display all rows with missing values and investigate the pattern or the missiness and decide what to do with them.

[@PaoloCondo]

Thank you for the guide!

[@djangoworldwide7925]

Best advanced tutorial. Thanks so much

[@jaeyeon2229]

Hi Kelsey,
It waa very helpful to understand how I'm able to apply to my work. Thank you!

[@muhammedhadedy4570]

I just discovered your RUclips channel. It's amazing. Please keep up the great work. And I wish if you do a tutorial on inferential statistics with R.
Thanks in advance.

[@fit_sarthi]

amazing - can you talk more about these advanced data wrangling techniques - it will be helpfull if you can make some practise sessions for viewers

[@KelseyCodes]

Hi Sahil - I'm glad you enjoyed it. Here are some practice questions you can use!
Basic data wrangling: american-stat-412612.netlify.app/assignment/03-lab/
Advanced data wrangling: american-stat-412612.netlify.app/assignment/05-lab/

[@annaczgli2983]

This was great. across() is so beautiful & useful.

[@KelseyCodes]

It really is! Takes a bit to master and then it's indispensable.

[@annaczgli2983]

@@KelseyCodes Could you do a video on Meta-Programming, if possible? I recently discovered the new operators, curly-curly, walrus, & bang-bang-bang. Not only did I find them useful, but they also made my code so elegant & pretty. I would be thrilled if you did a deep dive on them. Thanks again for your lovely videos.

[@bassamabdelnabi3117]

Thanks a lot. This is great.

[@niceday2015]

Thank you very much

[@arshad1781]

Nice

[@khalidyaseen]

where are you! and why no update?

[@georgekaplan8158]

14:41
starwars %>%
distinct(hair_color) %>%
nrow()