Wellll... A few weeks back I started working on a tutorial for our lab's Code Club on how to make shitty graphs. It was too dispiriting and I balked. A twitter workshop with figures and code:

Here's the code to generate the data frame. You can get the "raw" data from https://t.co/jcTE5t0uBT
Obligatory stacked bar chart that hides any sense of variation in the data
Obligatory stacked bar chart that shows all the things and yet shows absolutely nothing at the same time
STACKED Donut plot. Who doesn't want a donut? Who wouldn't want a stack of them!?! This took forever to render and looked worse than it should because coord_polar doesn't do scales="free_x".
More donuts. Let's get rid of all that messy variation in the data
One pie for @surt_lab, one for @watermicrobe, and one waiting to explode for @a2binny
Fine. Here's a pie for those of you that are still watching... This also took forever to render. The numbers are subject IDs
In all seriousness, here's the type of plot that I encourage for showing relative abundance by taxonomic data. Not fully polished, but you get the idea. Here each diagnosis has about 160 samples. With fewer samples, I'd use geom_jitter rather than geom_histogram
I prefer the boxplot/jitter plot because it allows the viewer to directly compare what I think is important. It also shows the variation in the data. Here's more polished version.
You can see how to do this for other taxonomic levels, incorporate statistical analysis to pick levels to show, and how to add a log scale on y-axis at https://t.co/U30ehefQPE. Thanks for attending my twitter workshop.

More from Data science

To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇

https://t.co/EwwOzgfDca : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks.

https://t.co/J4qMzPAZ6u Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs).

https://t.co/9IgKkSxPCq a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.

https://t.co/EAqn2YngIE : TensorFlow Java API (experimental)

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IMPORTANCE, ADVANTAGES AND CHARACTERISTICS OF BHAGWAT PURAN

It was Ved Vyas who edited the eighteen thousand shlokas of Bhagwat. This book destroys all your sins. It has twelve parts which are like kalpvraksh.

In the first skandh, the importance of Vedvyas


and characters of Pandavas are described by the dialogues between Suutji and Shaunakji. Then there is the story of Parikshit.
Next there is a Brahm Narad dialogue describing the avtaar of Bhagwan. Then the characteristics of Puraan are mentioned.

It also discusses the evolution of universe.(
https://t.co/2aK1AZSC79 )

Next is the portrayal of Vidur and his dialogue with Maitreyji. Then there is a mention of Creation of universe by Brahma and the preachings of Sankhya by Kapil Muni.


In the next section we find the portrayal of Sati, Dhruv, Pruthu, and the story of ancient King, Bahirshi.
In the next section we find the character of King Priyavrat and his sons, different types of loks in this universe, and description of Narak. ( https://t.co/gmDTkLktKS )


In the sixth part we find the portrayal of Ajaamil ( https://t.co/LdVSSNspa2 ), Daksh and the birth of Marudgans( https://t.co/tecNidVckj )

In the seventh section we find the story of Prahlad and the description of Varnashram dharma. This section is based on karma vaasna.