R demo | Deep Exploratory Data Analysis (EDA) | explore your data and start to test hypotheses

In this video, I'll provide the simplest and the most effective ways to explore data in R, which will significantly speed up your work. Moreover, we'll go one step beyond EDA by starting to test our hypotheses with simple statistical tests... that's what I call "deep" here.
If you only want the code (or want to support me), consider join the channel (join button below any of the videos), because I provide the code upon members requests.
You of coarse don't need to see the whole thing:
Timetable:
00:50 - automated reports as a separate video ( • R demo | Automated Exp... )
02:44 - exploring categorical variables
05:00 - descriptive statistics with some basic tests, like Fishers and Chi-Square, Mann-Whitney and Kruskal-Wallis tests
09:15 - explore distribution with skewness and kurtosis tests
13:03 - explore normality with Quantile-Quantile plots and Shapiro-Wilk normality test
17:20 - compare groups with box-plots and non-parametric tests, like Mann-Whitney and Kruskal-Wallis
19:12 - explore and visualize correlations and get correlation coefficients, confidence intervals and p-values
24:03 - explore linearity of data with non-linear models and ggplot2 package
24:30 - explore and impute NAs as a separate video ( • R demo | How to impute... ) and outliers (part of this video)
Here is the code and article about or instead of this video: yuzar-blog.net...
By the way, one of the songs playing in the background is called ”Hypothesis” by the artist Vincent Rubinetti 😉
Music by Vincent Rubinetti
Download the music on Bandcamp:
vincerubinetti...
Stream the music on Spotify:
open.spotify.c...
Enjoy! 🥳

[@jeromebradenbaugh2637]

why you don't have millions of subs is beyond me. this and your other videos are exceptional. i've used R for several years and not been exposed to some of these packages. your style is clear and concise. thank you for quality content.

[@yuzaR-Data-Science]

Thanks for such a nice feedback, Jerome! That means the world to me! Well, I think, Stats is not as sexy of a topic for KZbin algorithm, R is less popular as python, and my channel is still young and has not enough videos. So, if you could spread the message and share videos, I would highly appreciate this. But the best support from you is - watching my videos (there are more to come)! Kind regards!

[@muhammedhadedy4570]

What an amazing tutorial. Your KZbin channel deserves to have millions of subscribers. The way you present the topic is simple and focused. No much talking and directed to the point. I really love the way you explain your tutorials. I wish you all the best in your life, my dear professor.

[@yuzaR-Data-Science]

Thanks a lot, Muhammed! This is the best feedback I have ever got! :) And thank you for watching - this is the best support. And if you can spread the message by sharing my videos with people who might find them useful, that would be amazing! Cheers

[@yeah_reee]

i hope the creator of this videos reads this because i THANK YOU SO SO MUCH!!! You did more than a spectacular job describing everything in just 28 minutes! you've earned my sub just from the introduction.

[@yuzaR-Data-Science]

Wow, thank you for such a nice feedback! I am very glad my content is useful! Thanks for watching!

[@famahsnazer3494]

Nice and fantastic; Exceptional! Clear and easy and funny way to explain thinks. I found this tutorial like a gold! Waou

[@yuzaR-Data-Science]

Glad you enjoyed it! It’s one of my oldest videos though. The newest might also be useful. Thanks for watching!

[@sn9751]

You are gifted and very good tutor, I really enjoyed watching this video , your way to present the information is clear, short and hit the point, besides, you provide some useful links to further info about any topics that needs more time to be explained. Thank you

[@yuzaR-Data-Science]

Thanks a ton! It was one of my oldest videos, and I think the newest are better, especially in terms of editing. So, feel free to check them out and thank you for watching

[@oluremiabayomi5924]

Great job. From one professor to another.

[@yuzaR-Data-Science]

Thanks, Oluremi! I am glad it’s useful!

[@juanfederos1969]

Wow, so many valuable information that I keep on pausing the video every two minutes to jot stuff down. Thank you for this great piece of instruction, and thank you for your other great videos, too! It really is well appreciated!

[@yuzaR-Data-Science]

Thanks for such a nice feedback, Juan! There is a link to a blog article below the video, where you can more conveniently copy-paste the R code. But I also wouldn't mind if you rewatch the video from time to time ;) that helps the channel grow. Cheers

[@t.n.strijker5114]

This is gold, great job! It will help me make beautiful graphs as I study the correlation between anti-oxidants levels of plants in response to abiotic stress. Thank you Mr. Zablotski.

[@yuzaR-Data-Science]

Thanks a lot! Glad it’s useful!

[@Zhu-lw4dn]

amazing demo! Thank you :)

[@yuzaR-Data-Science]

Glad you liked it! This one is old. Hope the newest videos are even better! 😉

[@harryporter9965]

This is an amazing EDA video I have ever watched!

[@yuzaR-Data-Science]

Thanks Harry! I am happy it is useful :) !

[@dmitrykozhevnikov6196]

Юрий, спасибо за видео!

[@yuzaR-Data-Science]

На здоровье! Рад что оказалось полезным 😊 Спасибо за фидбэк!

[@angezoclanclounon1751]

Great tutorial! Thanks indeed, Dr. Yury.

[@yuzaR-Data-Science]

Appreciate your feedback Ange!

[@TheBaudoing2007]

Hello, do you still have your website?

[@arseneadou9602]

Great Job 🎉🎉🎉

[@yuzaR-Data-Science]

Thanks 🙏

[@chacmool2581]

Excellent!

[@yuzaR-Data-Science]

Glad you liked it! Thanks!

[@saygindiler3928]

Thank you. Perfect video. Great explanation.

[@yuzaR-Data-Science]

Glad you liked it!

[@eyadha1]

So helpful. great video. Many thanks !!

[@yuzaR-Data-Science]

Glad it was helpful! You might have a look at the blog article, for your convenience in copy-pasting R Code. Thanks for the feedback! yuzar-blog.netlify.app/posts/2021-01-09-exploratory-data-analysis-and-beyond-in-r-in-progress/

[@rihanamsadek480]

Thank you!

[@yuzaR-Data-Science]

Very welcome, Rihanna!

[@dinohadjiyannis3225]

brilliant stuff. Very easy to follow. Can you create a dedicated playlist regarding machine learning models ?

[@yuzaR-Data-Science]

Great suggestion! I'am on it, just made some videos on linear regression in R. Logistic will follow and then the rest of ML one day. Thanks for nice feedback and for watching!

[@dinohadjiyannis3225]

@@yuzaR-Data-Science you have no idea how easy these are to follow. thanks again. keep it up.

[@yuzaR-Data-Science]

thanks! do my best to upload more, but this summer is pretty busy.

[@bjrh1052]

Great video as always. I have a question. How do you visualize several graphs in the code editor? Wich addin or package do you use?? Thanks in advance

[@yuzaR-Data-Science]

Thanks 🙏 I am not sure, what you mean with the code editor, but ggplot2 package with facet_wrap or sjPlot package with grid … something can do that.

[@rashawnhoward564]

Thank you for this great video! You have a new subscriber!

[@yuzaR-Data-Science]

You are very welcome!

[@Dominus_Ryder]

The code link in the description does not seem to be working anymore, do you have an updated one by any chance?

[@yuzaR-Data-Science]

Hi Dominus, unfortunately it was blocked for too much traffic. I’ll try to reopen it ASAP with free alternative, but in the meanwhile please just rewatch the videos, because my blog is literally the script for them, so you won’t miss anything. I know that it might be cumbersome to write down the code from the script, so, I am sorry for that! But if you wanna get the written whole code now, consider to join my channel to become a member, because I post the code to members upon request. And members can see other code-posts I've posted on other videos already. Cheers

[@johnkaruitha2527]

This is a serious video

[@yuzaR-Data-Science]

Thanks a lot John. If you like this one, you might also like the newest on my channel. Cheers

[@dominiccomtois7850]

try view(dfSummary(data)) to get an html version of the table; its graphs are much prettier. Documentation provides instruction on how to integrate this in Rmd documents, too.

[@yuzaR-Data-Science]

Thanks, Dominic! I use it now and then too.

[@kapilrana1153]

Thankyou so much

[@yuzaR-Data-Science]

You are very welcome ;)

[@dmitry503]

Юрий, спасибо большое за такой контент! Приходится конспектировать )) Я понимаю, что вы организовали видео по логике анализа, но все-таки хотелось бы как-то организовать часть про пакеты тоже. Их много, они полезные, но как-то путаются с функциями по ходу повествования. Прошу прощения за критику, но только потому что очень интересно )

[@yuzaR-Data-Science]

Спасибо, Дмитрий! Согласен. Надо было делать несколько малых видео, вместо одного большого. Про отдельные полезные пакеты видео тоже есть, например performance, janitor, dlook, data-explorer etc. . Так же отдельные видео про автоматические анализы и imputating missing values. Если вы знаете очень полезные пакеты, пишите, попытаюсь сделать ревью.

[@yuzaR-Data-Science]

кстати, забыл сказать, в описании к видео почти всегда есть линк к блогу, где всё то же самое что и в видео, только в можно сразу брать и использовать код

[@dmitry503]

@@yuzaR-Data-Science Спасибо за ответ, Юрий! Буду смотреть и напишу, если что обнаружу. Уже впечатлился smartEDA после вашего обзора.

[@chacmool2581]

8:11 Those plots much better with 'browser', 'viewer' or 'render' option.

[@yuzaR-Data-Science]

hey Chac, thanks, but I could not use those options with dfSummary() command. Or did you meant some other command?

[@chacmool2581]

Sorry, you are right. To get dfSummary() to look good, you need to wrap it around the print() function with the print() function's method parameter set to 'render' (for RMarkdown), 'viewer' (for RStudio) or 'browser' to export it to a web browser. Like so: print(dfSummary(StudentData, ...), method = "render")

[@yuzaR-Data-Science]

All right, now I see what you mean. Thanks! Good to know, since I also often produce HTML with all the results. However, last time I caught myself using only describe() from dlookr package and tbl_summary() from gtsummary. They are amazing!

[@siriyakcr]

Super 🥰🥰🥰

[@yuzaR-Data-Science]

Thanks!

[@titlov123]

выкладывайте пожалуйста , код по видео ...спс

[@yuzaR-Data-Science]

самого кода слишком много, поэтому в описание под видео линк к блог посту со всем кодом. Спасибо за комментарий! 😊