I am trying to develop a new data visualization/graphic, and the bubble plot available here is very similar to what I am trying to make in shape:
https://altair-viz.github.io/gallery/table_bubble_plot_github.html
However, the graph I am trying to make involves some shaded bubbles and some filled in. Is there a way to edit this graph so that the bubble marks are not always filled?
Thank you!
You could use the fillOpacity encoding linked to a field in your data and then set the domain and range of its scale, so that only the values you want have a completely transparent fill:
import altair as alt
from vega_datasets import data
source = data.github.url
fill_threshold = 12
alt.Chart(source).mark_circle(
stroke='black'
).encode(
x='hours(time):O',
y='day(time):O',
size='sum(count):Q',
fillOpacity=alt.FillOpacity(
'sum(count):Q',
scale=alt.Scale(
domain=[fill_threshold, fill_threshold + 0.01],
range=[0 ,1]
)
)
)
You can use the fillOpacity and stroke mark properties to make the marks into circles with no fill. For example:
import altair as alt
from vega_datasets import data
source = data.github.url
alt.Chart(source).mark_circle(
fillOpacity=0,
stroke='black'
).encode(
x='hours(time):O',
y='day(time):O',
size='sum(count):Q',
)
Related
I like the way tooltip looks way more than when I add text as labels for the points in my plot, is there a way to make it visible wether the mouse is on it or not?
I looked it up but haven't found any solutions, maybe with messing around with conditions?
example code from doc if you have ideas you'd like to test out :
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_circle(size=60).encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']
).interactive()
thank u :)
As per my comment above, I think the easiest way to do this is adding a styled text box. You can see an example of how to style it in this issue, which I also pasted below:
import altair as alt
from vega_datasets import data
cars = data.cars()
chart = alt.Chart(cars).mark_circle().encode(
alt.X('Miles_per_Gallon', scale=alt.Scale(domain=(5,50))),
y='Weight_in_lbs')
corl = cars[['Miles_per_Gallon','Weight_in_lbs']].corr().iloc[0,1]
text = alt.Chart({'values':[{}]}).mark_text(
align="left", baseline="top"
).encode(
x=alt.value(5), # pixels from left
y=alt.value(5), # pixels from top
text=alt.value([f"r: {corl:.3f}", 'Line 2']))
box = alt.Chart({'values':[{}]}).mark_rect(stroke='black', color='orange').encode(
x=alt.value(3),
x2=alt.value(50),
y=alt.value(3),
y2=alt.value(30))
chart + box + text + chart.transform_regression('Miles_per_Gallon','Weight_in_lbs').mark_line()
I'm working on a project for a class where I've been creating faceted scatterplots in Altair where each chart is a different type of food. I was wondering if it would be possible to still use these faceted charts but only display one at a time and give the user the ability to toggle between graphs instead of having each graph displayed one after the other?
Here is a rough diagram of what I'm trying to do and here's the code I have now (and what the output looks like at the moment):
import altair as alt
from vega_datasets import data
hover = alt.selection_single(on='mouseover', nearest=True, empty='none')
base = alt.Chart("Food Nutrition Info Compiled.csv").encode(
x='Energ_Kcal:N',
y='Water_(g):Q',
color=alt.condition(hover, 'Type:N', alt.value('lightgray'))
).properties(
width=180,
height=180,
)
points = base.mark_point().add_selection(
hover
)
text = base.mark_text(dy=-5).encode(
text = 'Shrt_Desc:N',
opacity = alt.condition(hover, alt.value(1), alt.value(0))
)
alt.layer(points, text).facet(
'Type:N',
)
Thanks and I hope this makes sense!
Displaying one faceted chart at a time sounds the same as showing a single subset of the data at any one point. This is currently possible by creating a selection that is bound to e.g. a dropdown and the using that with transform_filter:
import altair as alt
from vega_datasets import data
source = data.cars()
dropdown_options = source['Origin'].unique().tolist()
dropdown = alt.binding_select(
options=dropdown_options,
name='Origin '
)
selection = alt.selection_multi(
fields=['Origin'],
value=[{'Origin': dropdown_options[0]}],
bind=dropdown
)
alt.Chart(source).mark_circle().encode(
x=alt.X('Weight_in_lbs:Q', title=''),
y='Horsepower',
color='Origin',
).add_selection(
selection
).transform_filter(
selection
)
I was playing around with the following example from the Altair Gallery:
https://altair-viz.github.io/gallery/airports_count.html
As of right now, the only way to display the actual count appears to be via the tooltip, as the example shows. However, I am trying to code a static visualization for which it would be very helpful if the exact value was displayed right next to the mark itself, without the user having to hover or interact in any way. Is there a way to achieve this?
You can do this by manually calculating offsets for text labels, though this is admittedly difficult when the points become crowded:
import altair as alt
from vega_datasets import data
airports = data.airports.url
states = alt.topo_feature(data.us_10m.url, feature='states')
# US states background
background = alt.Chart(states).mark_geoshape(
fill='lightgray',
stroke='white'
).properties(
width=500,
height=300
).project('albersUsa')
# airport positions on background
base = alt.Chart(airports).transform_aggregate(
latitude='mean(latitude)',
longitude='mean(longitude)',
count='count()',
groupby=['state']
).encode(
longitude='longitude:Q',
latitude='latitude:Q',
)
points = base.mark_circle().encode(
size=alt.Size('count:Q', title='Number of Airports'),
color=alt.value('steelblue'),
tooltip=['state:N','count:Q']
).properties(
title='Number of airports in US'
)
text = base.mark_text(
dx=15, dy=10
).encode(
text='count:Q'
)
background + points + text
Long-term, a better solution will be to use vega-label, which will be able to do this automatically once it's part of the Vega-Lite package. For Altair, this feature is tracked in this bug: https://github.com/altair-viz/altair/issues/1731
I can modify the classic Simple Scatter Plot with Tooltips, to add opacity to marks, but I'd like to legend colors to stay 100% opaque. In the chart I'm trying to make, I have a df with tens of thousands of rows.
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_circle(size=60, opacity=0.1).encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin'
)
I've tried alt.Legend's symbolOpacity and gradientOpacity to no avail
color=alt.Color(
'Origin:N',
legend=alt.Legend(
# symbolOpacity=1,
gradientOpacity=1,
)
)
As of now, this seems to be a bug with vega-lite. Per #jvp's suggestion, I've filed a bug report here
UPDATE -- IT'S FIXED
I'm trying to recreate something similar to this vega plot in Altair.
I've had luck building the mark_geoshape maps before when binding to something like color, which can be in encode function, but I cannot for the life of me figure out how to bind the slider to the rotate property of the chart, which lives in project.
I presumed I could do something like this, but no luck:
import altair as alt
from vega_datasets import data
# Data generators for the background
sphere = alt.sphere()
graticule = alt.graticule()
# Source of land data
source = alt.topo_feature(data.world_110m.url, 'countries')
slider = alt.binding_range(min=0, max=100, step=1, name='rotate:')
selector = alt.selection_single(name="SelectorName", fields=['rotate'],
bind=slider, init={'rotate': 180})
# Layering and configuring the components
alt.layer(
alt.Chart(sphere).mark_geoshape(),
alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),
alt.Chart(source).mark_geoshape(fill='ForestGreen', stroke='black')
).project(
'orthographic',
).encode(
rotate=['rotate',180,180]
).properties(width=600, height=400, selection=selector).configure_view(stroke=None)
Any help would be much appreciated.
Thanks
leo
There is currently no way to do this in Altair: the Vega-Lite schema only supports constant rotation values. If you wish, you can do this in Vega; there is an example at https://vega.github.io/vega/docs/projections/.