Notes: 31:30 = check for correlation across variables {corrr} 38:30 = recipe for PCA + graph contributors to PCA + perc variance explained by each PCA sdev + get PCA values juice() {tidymodels} 45:00 = reorder_within(), scale_y_reordered() {tidytext}
wow Julia, thank you very much, I have made these analyzes without Tidymodels and this blows my mind! Could you make a video with Multiple Correspondence Analysis? THANK YOU!
Hi Julia, great video. Would PLS make more sense in this case in order to see variability in respect to the target? Although it seems as step_pls does not exist within tidymodels
Hey Julia, thanks for sharing! I have a question. Aren't the variables mode and key qualitative? I got that they are INT in your data, but I can't see how they'd be quantitative. I'm looking forward to your answer!! I've been learning a lot with you!
I can see what you're saying here, yes, but the mode was already basically an indicator/dummy variable so not much different than what we'd do anyway. For key, I could definitely see your argument that it should be treated qualitatively (is that really a meaningfully continuous variable?) in which case we would want to create dummy/indicator variables with it.
For the last part in your linear model, would you be able to include the rater's gender or any demographic variable/phenotype variable of the rater into the linear model? Could one do a separate PCA of the rater variables then include those components in a lm as well?
Love these video's.
For me personally the best way to learn R is through these follow-along videos.
Love it Julia! This is a great service to the R community :D
Thanks Julia. These videos are truly excellent. Like another commenter posted, I too find videos like this immensely helpful in improving my R skills.
Cheers!
Always perfect your videos!
Thanks Julia. This is the best source to learn the tidy model frame work.
Notes:
31:30 = check for correlation across variables {corrr}
38:30 = recipe for PCA + graph contributors to PCA + perc variance explained by each PCA sdev + get PCA values juice() {tidymodels}
45:00 = reorder_within(), scale_y_reordered() {tidytext}
wow Julia, thank you very much, I have made these analyzes without Tidymodels and this blows my mind! Could you make a video with Multiple Correspondence Analysis? THANK YOU!
Great videos and explanations, cheers from Mexico!
Thank you for this video!!
Great stuff!! thanks a lot!!
Hi Julia, great video. Would PLS make more sense in this case in order to see variability in respect to the target? Although it seems as step_pls does not exist within tidymodels
Hey Julia, thanks for sharing!
I have a question.
Aren't the variables mode and key qualitative?
I got that they are INT in your data, but I can't see how they'd be quantitative.
I'm looking forward to your answer!!
I've been learning a lot with you!
I can see what you're saying here, yes, but the mode was already basically an indicator/dummy variable so not much different than what we'd do anyway. For key, I could definitely see your argument that it should be treated qualitatively (is that really a meaningfully continuous variable?) in which case we would want to create dummy/indicator variables with it.
@@JuliaSilge thanks for your attention, Julia!
I love your videos!
great tutorial. But I am unable to reproduce.
Package ‘spotifyr’ was removed from the CRAN repository. :(
You should be able to still install from GitHub: www.rcharlie.com/spotifyr/
@@JuliaSilge thank you for the link
For the last part in your linear model, would you be able to include the rater's gender or any demographic variable/phenotype variable of the rater into the linear model? Could one do a separate PCA of the rater variables then include those components in a lm as well?
Think I’m going to move my R console to the top right too now
Thanks alot for this
great coding skils! well the low % of variance for each PC anticipate poor model results ahah
Explanae linear Models
get those Arial labels outta here! :D
HA YES
Don't you make videos on python? Your videos are very comphrensive and I want you to make videos on python.