Published
Edited
Apr 1, 2021
2 forks
genres = movies.rollup({genre: d => op.array_agg_distinct(d.Major_Genre)}).objects()[0]["genre"]
{
const selection = vl.selectSingle('Select') // name the selection 'Select'
.fields('Major_Genre') // limit selection to the Major_Genre field
.init({Major_Genre: genres[0]}) // use first genre entry as initial value
.bind(vl.menu(genres)); // bind to a menu of unique genre values
// scatter plot, modify opacity based on genre selection
return vl.markCircle()
.data(movies)
.select(selection)
.encode(
vl.x().fieldQ('Rotten_Tomatoes_Rating'),
vl.y().fieldQ('IMDB_Rating'),
vl.tooltip().fieldN('Title'),
vl.opacity().if(selection, vl.value(0.75)).value(0.05)
)
.height
(240)
.render();
}
{
// single-value selection over [Major_Genre, MPAA_Rating] pairs
// use specific hard-wired values as the initial selected values
const selection = vl.selectSingle('Select')
.fields('Major_Genre', 'MPAA_Rating')
.init({Major_Genre: 'Drama', MPAA_Rating: 'R'})
.bind({Major_Genre: vl.menu(genres), MPAA_Rating: vl.radio(mpaa)});
// scatterplot, modify opacity based on selection
return vl.markCircle()
.data(movies)
.select(selection)
.encode(
vl.x().fieldQ('Rotten_Tomatoes_Rating'),
vl.y().fieldQ('IMDB_Rating'),
vl.tooltip().fieldN('Title'),
vl.opacity().if(selection, vl.value(0.75)).value(0.05)
)
.height(240)
.render();
}
{
const brush = vl.selectInterval()
.encodings('x'); // limit selection to x-axis (year) values
// dynamic query histogram
const years = vl.markBar()
.data(movies)
.select(brush)
.encode(
vl.x().year('Release_Date').title('Films by Release Year'),
vl.y().count().title(null)
)
.width(500)
.height(40);
// ratings scatter plot
const ratings = vl.markCircle()
.data(movies)
.encode(
vl.x().fieldQ('Rotten_Tomatoes_Rating'),
vl.y().fieldQ('IMDB_Rating'),
vl.tooltip().fieldN('Title'),
vl.opacity().if(brush, vl.value(0.75)).value(0.05)
)
.width(500)
.height(350);
return vl.vconcat(years, ratings).render();
}
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.