Master Deep EDA in R: Quick & Effective Data Exploration 🚀

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 (https://youtu.be/sKrWYE63Vk4) 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 (https://youtu.be/Akb401i32Oc) and outliers (part of this video) Here is the code and article about or instead of this video: https://yuzar-blog.netlify.app/posts/2021-01-09-exploratory-data-analysis-and-beyond-in-r-in-progress/ 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: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u Enjoy! 🥳