The avatar for @tashapiro
Tanya Shapiro
Independent Data Analytics Consultant, passionate about data visualization and storytelling · she/her 🏳️‍🌈
Public
Edited
Dec 18, 2023
7 forks
17 stars
4
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
tswift_pivoted.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

Insert cell
Insert cell
Insert cell
features = ["acousticness","danceability","energy", "speechiness", "instrumentalness", "liveness", "valence"]
Insert cell
Insert cell
albums = [...new Set(songs.map(d => d.album_name))].sort()
Insert cell
facet_features = ['valence','danceability','energy']
Insert cell
Insert cell
song_features_subset = song_features.filter(d => facet_features.includes(d.feature))
Insert cell
Insert cell
correlations = d3.cross(features, features).map(([a, b]) => ({
a,
b,
correlation: corr(Plot.valueof(songs, a), Plot.valueof(songs, b))
}))
Insert cell
Insert cell
Insert cell
Select a data source…
Type SQL, then Shift-Enter. Ctrl-space for more options.

Insert cell
songs
SELECT
key_name
,count(*)/263::double as songs
,count(case when mode_name='major' then track_name end)/263::double as major_songs
,count(case when mode_name='minor' then track_name end)/263::double as minor_songs
FROM songs
WHERE key_mode != 'NA'
GROUP BY 1
ORDER BY 1
Insert cell
Insert cell
songs
SELECT
track_name
,album_name
,round(liveness,2) as liveness
,round(valence,2) as valence
,round(energy,2) as energy
,round(acousticness,2) as acousticness
FROM songs
WHERE
valence >= ${sliders.valence}
and energy >= ${sliders.energy}
and acousticness >= ${sliders.acousticness}
Insert cell
Insert cell
import {corr} from "@observablehq/plot-correlation-heatmap"
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
<style>

@import url('https://fonts.googleapis.com/css?family=Yomogi&display=swap');
@import url('https://fonts.googleapis.com/css?family=Roboto&display=swap');
@import url('https://fonts.googleapis.com/css?family=Roboto+Mono&display=swap');

div > h1, div> h2, div>h3 {
font-family:Yomogi!important;
}

body {
font-family:Roboto;
}

h1, h2 {
text-transform:uppercase;
}

h2 {
margin-bottom:10px;
}

code {
background:#D4D7F5!important;
color:#401487!important;
padding:2px 4px!important;
border-radius:3px;
}
p, h1, h2 {
max-width:100%;
}

ul {
max-width:90%;
}

li {
margin-bottom:2px;
}

label {
font-weight:bold!important;
}
</style>
Insert cell
viewof select = Inputs.select(["A", "B"], {label: "Select one"})
Insert cell

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.
Learn more