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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12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4. @DillikiBiili

Share for the benefit of everyone.

Here are the setups from @Pathik_Trader Sir first.

1. Open Drive (Intraday Setup explained)


Bactesting results of Open Drive


2. Two Price Action setups to get good long side trade for intraday.

1. PDC Acts as Support
2. PDH Acts as


Example of PDC/PDH Setup given