Histograms and Density Plots with {ggplot2}

Histograms display the shape of the distribution of continuous numeric data. The distribution can be symmetrical, right-skewed, left-skewed, unimodal, or multimodal? Knowing the shape of the distribution helps us decide which statistical test is appropriate. For example, if the distribution is symmetrical, we could use a t-test or linear regression. However, if the distribution is skewed, we’d need to use the Mann-Whitney test or median regression. Moreover, when the data has several peaks, we might need to transform the data before analyzing it. Otherwise, when we calculate central tendencies like the average, we will heavily misrepresent reality. Histograms also help to identify outliers, which is very useful for cleaning the data. So, visualizing the distribution with histograms and density plots helps us avoid these pitfalls.
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[@ibrahimlawan9663]

Great Job. I always learn a lot from your videos

[@yuzaR-Data-Science]

Glad to hear it! Thanks for watching!

[@GFXHDTV]

love this thank you so much and please do more and keep it up

[@yuzaR-Data-Science]

Thank you! Will do!

[@hikeaway1596]

thanks for another cool video!

[@yuzaR-Data-Science]

Thanks for watching! And for your continuous support!

[@yominorwemajor7235]

I really enjoy your videos! Do you consider doing a video for the use of flextable package?😊

[@yuzaR-Data-Science]

Great suggestion! I will put it on the list. But not too long ago, I made a video on {gtsummary} package. It's also about tables. And, to be honest, I never needed another one. So, if you did not see gtsummary, give it a chance. And you did, thanks you for watching!

[@aram5704]

Great video! Keep going.

[@yuzaR-Data-Science]

Thanks, will do!

[@GreenManXY]

Good work! Recently learned about the after_stat function, really useful to enhance the information in the graph. One suggestion for a video would be how to enhance some of the graphs that are drawn for the whole data set by packages like DataExplorer. It's really useful to get a whole set of graphs by just writing plot_bar(mtcars, by = "cyl") but I found it hard to easily modify the resulting graphs, for example to add labels or percentages.

[@yuzaR-Data-Science]

Great suggestion! I also have realised that some packages can actually accept the ggplot2 function on top of them. For example sjPlot or effects packages have no problem. But thanks, I'll have a look at it, and if I find a meaningful solution and enough material for the video, I'll produce one. Thanks for watching!

[@yuzaR-Data-Science]

by the way, I have to explore "after_stat()" more, it's a new function as far as I know.

[@chacmool2581]

Do a vid on violin plots which combine boxplots with probability density plots. I rather prefer those to histograms.

[@yuzaR-Data-Science]

I have actually done a whole video on box-violin-plots earlier: All you need to Know Box-Plots. So, I am not sure I need another one. Let's me know if you thing so and tell why?

[@chacmool2581]

@@yuzaR-Data-Science No, probably not. I'll have to check out that video. Thanks.

[@yuzaR-Data-Science]

it's an old one: kzbin.info/www/bejne/hWXaqWCAasxnhMU so, don't be surprised about the quality and delivery ;)

[@maicelmonzonperez]

great

[@yuzaR-Data-Science]

glad you liked it!