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-...
Actually, I have never left a comment on TH-cam 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.
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!
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.
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.
I just discovered your TH-cam 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.
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/
@@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.
Actually, I have never left a comment on TH-cam 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.
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!
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.
The name exactly corresponds to the content. Great job!
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.
This video is so useful and well presented. I'm watching it both to learn and relax at the same time 🤓
Best advanced tutorial. Thanks so much
Kelsey, thanks for sharing this with us; it's an awesome course on introduction to R.
This video is a gem. 💎
Hi Kelsey,
It waa very helpful to understand how I'm able to apply to my work. Thank you!
I just discovered your TH-cam 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.
Thank you for the guide!
amazing - can you talk more about these advanced data wrangling techniques - it will be helpfull if you can make some practise sessions for viewers
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/
This was great. across() is so beautiful & useful.
It really is! Takes a bit to master and then it's indispensable.
@@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.
Thanks a lot. This is great.
Thank you very much
where are you! and why no update?
Nice
14:41
starwars %>%
distinct(hair_color) %>%
nrow()