merge two episodes into one

Author: bast

content/customizing-plots.md

@@ -1,3 +1,203 @@
+(gapminder)=
+
+# Plotting the Gapminder dataset
+
+
+## Loading and plotting a dataset
+
+In this lesson will work with one of the
+[Gapminder](https://www.gapminder.org/tools/) datasets.
+
+Let us together read and plot the data and then we explain what is happening
+and we will improve the figure together.  First we read and inspect the data:
+```python
+# import necessary libraries
+import altair as alt
+import pandas as pd
+
+# read the data
+url_prefix = "https://raw.githubusercontent.com/plotly/datasets/master/"
+data = pd.read_csv(url_prefix + "gapminder_with_codes.csv")
+
+# print overview of the dataset
+data
+```
+
+With very few lines we can get the first plot:
+```python
+alt.Chart(data).mark_point().encode(
+    x="gdpPercap",
+    y="lifeExp",
+)
+```
+
+```{figure} img/gapminder/all-data.svg
+:alt: First raw plot with all countries and all years.
+
+First raw plot with all countries and all years.
+```
+
+Observe how we connect (encode) **visual channels** to data columns:
+- x-coordinate with "gdpPercap"
+- y-coordinate with "lifeExp"
+
+The following code would have the same effect but the above version might be
+easier to read:
+```python
+alt.Chart(data).mark_point().encode(x="gdpPercap", y="lifeExp")
+```
+
+```{discussion} Let us pause and explain the code
+- `alt` is a short-hand for `altair` which we imported on top of the notebook
+- `Chart()` is a function defined inside `altair` which takes the data as argument
+- `mark_point()` is a function that produces scatter plots
+- `encode()` is a function which encodes data columns to visual channels
+```
+
+
+## Filtering data directly in [Vega-Altair](https://altair-viz.github.io)
+
+In [Vega-Altair](https://altair-viz.github.io) we can chain functions. Let us
+add two more:
+```{code-block} python
+---
+emphasize-lines: 4
+---
+alt.Chart(data).mark_point().encode(
+    x="gdpPercap",
+    y="lifeExp",
+).transform_filter(alt.datum.year == 2007).interactive()
+```
+
+```{figure} img/gapminder/only-2007.svg
+:alt: Now we only keep the year 2007.
+
+Now we only keep the year 2007.
+```
+
+
+## Using color as additional channel
+
+A very neat feature of [Vega-Altair](https://altair-viz.github.io) is that it
+is easy to add and modify visual channels. Let us try to add one more so that
+we do something with the "continent" data column:
+```{code-block} python
+---
+emphasize-lines: 4
+---
+alt.Chart(data).mark_point().encode(
+    x="gdpPercap",
+    y="lifeExp",
+    color="continent",
+).transform_filter(alt.datum.year == 2007).interactive()
+```
+
+```{figure} img/gapminder/color.svg
+:alt: Using different colors for different continents.
+
+Using different colors for different continents.
+```
+
+
+## Changing to log scale
+
+For this data set we will get a better insight when switching the x-axis from
+linear to log scale (we changed two lines to show both the "method syntax" and
+the "attribute syntax"):
+```{code-block} python
+---
+emphasize-lines: 2-3
+---
+alt.Chart(data).mark_point().encode(
+    x=alt.X("gdpPercap").scale(type="log"),
+    y=alt.Y("lifeExp"),
+    color="continent",
+).transform_filter(alt.datum.year == 2007).interactive()
+```
+
+```{figure} img/gapminder/log-scale.svg
+:alt: Changing the x axis to log scale.
+
+Changing the x axis to log scale.
+```
+
+
+## Improving axis titles
+
+```{code-block} python
+---
+emphasize-lines: 2-3
+---
+alt.Chart(data).mark_point().encode(
+    x=alt.X("gdpPercap").scale(type="log").title("GDP per capita (PPP dollars)"),
+    y=alt.Y("lifeExp").title("Life expectancy (years)"),
+    color="continent",
+).transform_filter(alt.datum.year == 2007).interactive()
+```
+
+```{figure} img/gapminder/axis-titles.svg
+:alt: Improving the axis titles.
+
+Improving the axis titles.
+```
+
+
+## Faceted charts
+
+To see what faceted charts are and how easy it is to do this, add the following
+line:
+```{code-block} python
+---
+emphasize-lines: 5
+---
+alt.Chart(data).mark_point().encode(
+    x=alt.X("gdpPercap").scale(type="log").title("GDP per capita (PPP dollars)"),
+    y=alt.Y("lifeExp").title("Life expectancy (years)"),
+    color="continent",
+    row="continent",
+).transform_filter(alt.datum.year == 2007).interactive()
+```
+
+Guess what happens when you change `row="continent"` to `column="continent"`?
+
+
+## Changing from points to circles
+
+Let us add one more visual channel, mapping size of the circle to the
+population size of a country:
+```{code-block} python
+---
+emphasize-lines: 1,5
+---
+alt.Chart(data).mark_circle().encode(
+    x=alt.X("gdpPercap").scale(type="log").title("GDP per capita (PPP dollars)"),
+    y=alt.Y("lifeExp").title("Life expectancy (years)"),
+    color="continent",
+    size="pop",
+).transform_filter(alt.datum.year == 2007).interactive()
+```
+
+```{figure} img/gapminder/population-size.svg
+:alt: Circle sizes are proportional to population sizes.
+
+Circle sizes are proportional to population sizes.
+```
+
+---
+
+```{discussion} Where to go from here?
+In few steps and few lines of code we have achieved a lot!
+
+These plots are perhaps not publication quality yet but we will learn how to
+customize and improve in {ref}`customizing-plots`.
+```
+
+```{keypoints}
+- Avoid manual post-processing, try to script all steps.
+- 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. More about that later.
+```
 (customizing-plots)=
 
 # Customizing plots