starting with the vega-altair episode

Author: bast

content/index.rst

@@ -72,6 +72,7 @@ to learn yourself as you need to.
    60 min   ; :doc:`pandas`
    30 min   ; :doc:`xarray`
    60 min   ; :doc:`plotting-matplotlib`
+   60 min   ; :doc:`plotting-vega-altair`
    30 min   ; :doc:`data-formats`
    60 min   ; :doc:`scripts`
    40 min   ; :doc:`profiling`
@@ -97,6 +98,7 @@ to learn yourself as you need to.
    pandas
    xarray
    plotting-matplotlib
+   plotting-vega-altair
    data-formats
    scripts
    profiling

content/plotting-vega-altair.md

@@ -0,0 +1,97 @@
+# Plotting with [Vega-Altair](https://altair-viz.github.io/)
+
+:::{instructor-note}
+- 10 min: Introduction
+- 10 min: Type-along (creating a first plot)
+- 20 min: Exercise (using visual channels)
+- 20 min: Exercise (adapting a gallery example and customizing)
+- 10 min: Key points, discussion, and Q&A
+:::
+
+
+## Repeatability/reproducibility
+
+From [Claus O. Wilke: "Fundamentals of Data Visualization"](https://clauswilke.com/dataviz/):
+
+> *One thing I have learned over the years is that automation is your friend. I
+> think figures should be autogenerated as part of the data analysis pipeline
+> (which should also be automated), and they should come out of the pipeline
+> ready to be sent to the printer, no manual post-processing needed.*
+
+- **Try to minimize manual post-processing**. This could bite you when you need to regenerate 50
+  figures one day before submission deadline or regenerate a set of figures
+  after the person who created them left the group.
+- There is not the one perfect language and **not the one perfect library** for everything.
+- Within Python, many libraries exist:
+  - [Vega-Altair](https://altair-viz.github.io/gallery/index.html):
+    declarative visualization, statistics built in
+  - [Matplotlib](https://matplotlib.org/stable/gallery/index.html):
+    probably the most standard and most widely used
+  - [Seaborn](https://seaborn.pydata.org/examples/index.html):
+    high-level interface to Matplotlib, statistical functions built in
+  - [Plotly](https://plotly.com/python/):
+    interactive graphs
+  - [Bokeh](https://demo.bokeh.org/):
+    also here good for interactivity
+  - [plotnine](https://plotnine.readthedocs.io/):
+    implementation of a grammar of graphics in Python, it is based on [ggplot2](https://ggplot2.tidyverse.org/)
+  - [ggplot](https://yhat.github.io/ggpy/):
+    R users will be more at home
+  - [PyNGL](https://www.pyngl.ucar.edu/Examples/gallery.shtml):
+    used in the weather forecast community
+  - [K3D](https://k3d-jupyter.org/gallery/index.html):
+    Jupyter Notebook extension for 3D visualization
+  - ...
+- Two main families of libraries: procedural (e.g. Matplotlib) and declarative (e.g. Vega-Altair).
+
+
+## Why are we starting with [Vega-Altair](https://altair-viz.github.io/)?
+
+- Concise and powerful
+- "Simple, friendly and consistent API" allows us to focus on the data
+  visualization part and get started without too much Python knowledge
+- The way it **combines visual channels with data columns** can feel intuitive
+- Interfaces very nicely with [pandas](https://pandas.pydata.org/)
+- Easy to change figures
+- Good documentation
+- Open source
+- Makes it easy to save figures in a number of formats
+- Easy to save interactive visualizations to be used in websites
+
+
+## Exercise
+
+In this exercise we can try to adapt existing scripts to either **tweak how the
+plot looks** or to **modify the input data**.  This is very close to real life:
+there are so many options and possibilities and it is almost impossible to
+remember everything so this strategy is useful to practice:
+- Select an example that is close to what you have in mind
+- Being able to adapt it to your needs
+- Being able to search for help
+- Being able to understand help request answers (not easy)
+
+:::{challenge} Exercise Customization-1: Adapting a gallery example
+**This is a great exercise which is very close to real life.**
+
+- Browse the [Vega-Altair example gallery](https://altair-viz.github.io/gallery/index.html).
+- Select one example that is close to your current/recent visualization project
+  or simply interests you.
+- First try to reproduce this example, as-is, in the Jupyter Notebook.
+- Then try to print out the data that is used in this example just before the call of the plotting function
+  to learn about its structure.
+- Then try to modify the data a bit.
+- If you have time, try to feed it different, simplified data.
+  This will be key for adapting the examples to your projects.
+:::
+
+---
+
+:::{keypoints}
+- Browse a number of example galleries to help you choose the library
+  that fits best your work/style.
+- Figures for presentation slides and figures for manuscripts have
+  different requirements.
+- Think about color-vision deficiencies when choosing colors. There are color
+  palettes optimized for this.
+- Minimize manual post-processing and try to script all steps.
+:::