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Iris to penguins #2231

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Iris to penguins #2231

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ff2f3f7
horizontal concatenation
baldwint Jul 8, 2020
3be03af
repeated charts
baldwint Jul 8, 2020
dcf139b
faceted charts
baldwint Jul 8, 2020
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91 changes: 47 additions & 44 deletions doc/user_guide/compound_charts.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -124,21 +124,21 @@ distribution of its points:
import altair as alt
from vega_datasets import data

iris = data.iris.url
penguins = data.penguins.url

chart1 = alt.Chart(iris).mark_point().encode(
x='petalLength:Q',
y='petalWidth:Q',
color='species:N'
chart1 = alt.Chart(penguins).mark_point().encode(
x=alt.X('Flipper Length (mm):Q', scale=alt.Scale(zero=False)),
y=alt.Y('Body Mass (g):Q', scale=alt.Scale(zero=False)),
color='Species:N'
).properties(
height=300,
width=300
)

chart2 = alt.Chart(iris).mark_bar().encode(
chart2 = alt.Chart(penguins).mark_bar().encode(
x='count()',
y=alt.Y('petalWidth:Q', bin=alt.Bin(maxbins=30)),
color='species:N'
y=alt.Y('Body Mass (g):Q', bin=alt.Bin(maxbins=30)),
color='Species:N'
).properties(
height=300,
width=100
Expand Down Expand Up @@ -224,20 +224,23 @@ showing how ``repeat`` can be used to build the chart more efficiently:
import altair as alt
from vega_datasets import data

iris = data.iris.url
penguins = data.penguins.url

base = alt.Chart().mark_point().encode(
color='species:N'
color='Species:N'
).properties(
width=200,
height=200
).interactive()

chart = alt.vconcat(data=iris)
for y_encoding in ['petalLength:Q', 'petalWidth:Q']:
chart = alt.vconcat(data=penguins)
for y_encoding in ['Flipper Length (mm):Q', 'Body Mass (g):Q']:
row = alt.hconcat()
for x_encoding in ['sepalLength:Q', 'sepalWidth:Q']:
row |= base.encode(x=x_encoding, y=y_encoding)
for x_encoding in ['Beak Length (mm):Q', 'Beak Depth (mm):Q']:
row |= base.encode(
x=alt.X(x_encoding, scale=alt.Scale(zero=False)),
y=alt.Y(y_encoding, scale=alt.Scale(zero=False)),
)
chart &= row
chart

Expand All @@ -252,18 +255,18 @@ method, makes this type of chart a bit easier to produce:

import altair as alt
from vega_datasets import data
iris = data.iris.url
penguins = data.penguins.url

alt.Chart(iris).mark_point().encode(
alt.X(alt.repeat("column"), type='quantitative'),
alt.Y(alt.repeat("row"), type='quantitative'),
color='species:N'
alt.Chart(penguins).mark_point().encode(
alt.X(alt.repeat("column"), type='quantitative', scale=alt.Scale(zero=False)),
alt.Y(alt.repeat("row"), type='quantitative', scale=alt.Scale(zero=False)),
color='Species:N'
).properties(
width=200,
height=200
).repeat(
row=['petalLength', 'petalWidth'],
column=['sepalLength', 'sepalWidth']
row=['Flipper Length (mm)', 'Body Mass (g)'],
column=['Beak Length (mm)', 'Beak Depth (mm)']
).interactive()

The :meth:`Chart.repeat` method is the key here: it lets you specify a set of
Expand Down Expand Up @@ -291,20 +294,20 @@ concatenation:
import altair as alt
from altair.expr import datum
from vega_datasets import data
iris = data.iris.url
penguins = data.penguins.url

base = alt.Chart(iris).mark_point().encode(
x='petalLength:Q',
y='petalWidth:Q',
color='species:N'
base = alt.Chart(penguins).mark_point().encode(
x=alt.X('Flipper Length (mm):Q', scale=alt.Scale(zero=False)),
y=alt.Y('Body Mass (g):Q', scale=alt.Scale(zero=False)),
color='Species:N'
).properties(
width=160,
height=160
)

chart = alt.hconcat()
for species in ['setosa', 'versicolor', 'virginica']:
chart |= base.transform_filter(datum.species == species)
for species in ['Adelie', 'Chinstrap', 'Gentoo']:
chart |= base.transform_filter(datum.Species == species)
chart

As with the manual approach to :ref:`repeat-chart`, this is straightforward,
Expand All @@ -314,27 +317,27 @@ Using ``alt.facet`` it becomes a bit cleaner:

.. altair-plot::

alt.Chart(iris).mark_point().encode(
x='petalLength:Q',
y='petalWidth:Q',
color='species:N'
alt.Chart(penguins).mark_point().encode(
x=alt.X('Flipper Length (mm):Q', scale=alt.Scale(zero=False)),
y=alt.Y('Body Mass (g):Q', scale=alt.Scale(zero=False)),
color='Species:N'
).properties(
width=180,
height=180
).facet(
column='species:N'
column='Species:N'
)

For simple charts like this, there is also a ``column`` encoding channel that
can give the same results:

.. altair-plot::

alt.Chart(iris).mark_point().encode(
x='petalLength:Q',
y='petalWidth:Q',
color='species:N',
column='species:N'
alt.Chart(penguins).mark_point().encode(
x=alt.X('Flipper Length (mm):Q', scale=alt.Scale(zero=False)),
y=alt.Y('Body Mass (g):Q', scale=alt.Scale(zero=False)),
color='Species:N',
column='Species:N'
).properties(
width=180,
height=180
Expand All @@ -348,10 +351,10 @@ layered chart with a hover selection:

hover = alt.selection_single(on='mouseover', nearest=True, empty='none')

base = alt.Chart(iris).encode(
x='petalLength:Q',
y='petalWidth:Q',
color=alt.condition(hover, 'species:N', alt.value('lightgray'))
base = alt.Chart(penguins).encode(
x=alt.X('Flipper Length (mm):Q', scale=alt.Scale(zero=False)),
y=alt.Y('Body Mass (g):Q', scale=alt.Scale(zero=False)),
color=alt.condition(hover, 'Species:N', alt.value('lightgray'))
).properties(
width=180,
height=180,
Expand All @@ -362,12 +365,12 @@ layered chart with a hover selection:
)

text = base.mark_text(dy=-5).encode(
text = 'species:N',
text = 'Species:N',
opacity = alt.condition(hover, alt.value(1), alt.value(0))
)

alt.layer(points, text).facet(
'species:N',
'Species:N',
)

Though each of the above examples have faceted the data across columns,
Expand Down