ElixirZone VegaLite
Section
Mix.install([
{:vega_lite, "~> 0.1.3"},
{:kino, "~> 0.5.0"},
{:jason, "~> 1.2"}
])
alias VegaLite, as: Vl
# Initialize the specification, optionally with some top-level properties
Vl.new(width: 400, height: 400)
# Specify data source for the graphic, see the data_from_* functions
|> Vl.data_from_series(iteration: 1..100, score: 1..100)
# |> Vl.data_from_values([%{iteration: 1, score: 1}, ...])
# |> Vl.data_from_url("...")
# Pick a visual mark for the graphic
|> Vl.mark(:line)
# |> Vl.mark(:point, tooltip: true)
# Map data fields to visual properties of the mark, like position or shape
|> Vl.encode_field(:x, "iteration", type: :quantitative)
|> Vl.encode_field(:y, "score", type: :quantitative)
# |> Vl.encode(:color, "country", type: :nominal)
# |> Vl.encode(:size, "count", type: :quantitative)
Vl.new() |> inspect()
Vl.new()
|> Vl.data_from_values([
%{a: "C", b: 2},
%{a: "C", b: 7}
])
|> inspect()
Vl.from_json("""
{
"data": { "url": "https://vega.github.io/editor/data/cars.json" },
"mark": "point",
"encoding": {
"x": { "field": "Horsepower", "type": "quantitative" },
"y": { "field": "Miles_per_Gallon", "type": "quantitative" }
}
}
""")
|> inspect()
# https://vega.github.io/vega-lite/tutorials/getting_started.html
Vl.new()
|> Vl.data_from_values([
%{a: "C", b: 2},
%{a: "C", b: 7},
%{a: "C", b: 4},
%{a: "D", b: 1},
%{a: "D", b: 2},
%{a: "D", b: 6},
%{a: "E", b: 8},
%{a: "E", b: 4},
%{a: "E", b: 7}
])
|> Vl.mark(:point, tooltip: true)
|> Vl.encode_field(:x, "a", type: :nominal)
|> Vl.encode_field(:y, "b", type: :quantitative)
# |> inspect()
|> tap(&IO.inspect/1)
# https://vega.github.io/vega-lite/tutorials/getting_started.html
Vl.new()
|> Vl.data_from_values([
%{a: "C", b: 2},
%{a: "C", b: 7},
%{a: "C", b: 4},
%{a: "D", b: 1},
%{a: "D", b: 2},
%{a: "D", b: 6},
%{a: "E", b: 8},
%{a: "E", b: 4},
%{a: "E", b: 7}
])
|> Vl.mark(:bar)
|> Vl.encode_field(:y, "a", type: :nominal)
|> Vl.encode_field(:x, "b", type: :quantitative, aggregate: :mean, title: "avg(b)")
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:tick)
|> Vl.encode_field(:x, "precipitation",
type: :quantitative,
title: "Daily precipitation in Seattle"
)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:bar)
|> Vl.encode_field(:x, "precipitation", bin: true, title: "Daily precipitation in Seattle")
|> Vl.encode(:y, aggregate: :count)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:line)
|> Vl.encode_field(:x, "date", time_unit: :month)
|> Vl.encode_field(:y, "precipitation", aggregate: :mean)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:line)
|> Vl.encode_field(:x, "date", time_unit: :yearmonth)
|> Vl.encode_field(:y, "temp_max", aggregate: :max)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:line)
|> Vl.encode_field(:x, "date", time_unit: :year)
|> Vl.encode_field(:y, "temp_max", aggregate: :mean)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.transform(calculate: "datum.temp_max - datum.temp_min", as: "temp_range")
|> Vl.mark(:line)
|> Vl.encode_field(:x, "date", time_unit: :month)
|> Vl.encode_field(:y, "temp_range", aggregate: :mean)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:bar)
|> Vl.encode_field(:x, "date", type: :ordinal, time_unit: :month)
|> Vl.encode(:y, aggregate: :count, type: :quantitative)
|> Vl.encode_field(:color, "weather", type: :nominal)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/seattle-weather.csv")
|> Vl.mark(:bar)
|> Vl.encode_field(:x, "date", type: :ordinal, time_unit: :month, title: "Month of the year")
|> Vl.encode(:y, aggregate: :count, type: :quantitative)
|> Vl.encode_field(:color, "weather",
type: :nominal,
title: "Weather type",
scale: [
domain: ["sun", "fog", "drizzle", "rain", "snow"],
range: ["#e7ba52", "#c7c7c7", "#aec7e8", "#1f77b4", "#9467bd"]
]
)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/weather.csv")
|> Vl.transform(filter: "datum.location == 'Seattle'")
|> Vl.concat([
Vl.new()
|> Vl.mark(:bar)
|> Vl.encode_field(:x, "date", time_unit: :month, type: :ordinal)
|> Vl.encode_field(:y, "precipitation", aggregate: :mean),
Vl.new()
|> Vl.mark(:point)
|> Vl.encode_field(:x, "temp_min", bin: true)
|> Vl.encode_field(:y, "temp_max", bin: true)
|> Vl.encode(:size, aggregate: :count)
])
Vl.from_json("""
{
"data": { "url": "https://vega.github.io/editor/data/cars.json" },
"mark": "point",
"encoding": {
"x": { "field": "Horsepower", "type": "quantitative" },
"y": { "field": "Miles_per_Gallon", "type": "quantitative" }
}
}
""")
# https://vega.github.io/vega-lite/examples/point_2d.html
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/cars.json")
|> Vl.mark(:point, tooltip: true)
|> Vl.encode_field(:x, "Horsepower", type: :quantitative)
|> Vl.encode_field(:y, "Miles_per_Gallon", type: :quantitative)
# https://vega.github.io/vega-lite/examples/line.html
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/stocks.csv")
|> Vl.transform(filter: "datum.symbol==='GOOG'")
|> Vl.mark(:line)
|> Vl.encode_field(:x, "date", type: :temporal)
|> Vl.encode_field(:y, "price", type: :quantitative)
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/weather.csv")
|> Vl.repeat(
["temp_max", "precipitation", "wind"],
Vl.new()
|> Vl.mark(:line)
|> Vl.encode_field(:x, "date", time_unit: "month")
|> Vl.encode_repeat(:y, :repeat, aggregate: :mean, type: :quantitative)
|> Vl.encode_field(:color, "location")
)
# https://vega.github.io/vega-lite/examples/repeat_layer.html
# Vl.new()
# |> Vl.data_from_url("https://vega.github.io/editor/data/movies.json")
# |> Vl.repeat(
# ["US Gross", "Worldwide Gross"],
# Vl.new()
# |> Vl.mark(:line)
# |> Vl.encode_field(:x, "IMDB Rating", type: :quantitative, bin: true)
# |> Vl.encode_repeat(:y, :repeat, aggregate: :mean, type: :quantitative)
# |> Vl.encode_field(:color, :repeat, type: :nominal)
# )
# https://vega.github.io/vega-lite/docs/repeat.html#repeated-histogram-wrapped
Vl.new(columns: 2)
|> Vl.data_from_url("https://vega.github.io/editor/data/cars.json")
|> Vl.repeat(
["Horsepower", "Miles_per_Gallon", "Acceleration", "Displacement"],
Vl.new()
|> Vl.mark(:bar)
|> Vl.encode_repeat(:x, :repeat, bin: true)
|> Vl.encode_field(:y, "count", aggregate: :count)
|> Vl.encode_field(:color, "Origin")
)
# https://vega.github.io/vega-lite/docs/repeat.html#scatterplot-matrix-splom
Vl.from_json("""
{
"data": {"url": "https://vega.github.io/editor/data/penguins.json"},
"repeat": {
"row": [
"Beak Length (mm)",
"Beak Depth (mm)",
"Flipper Length (mm)",
"Body Mass (g)"
],
"column": [
"Body Mass (g)",
"Flipper Length (mm)",
"Beak Depth (mm)",
"Beak Length (mm)"
]
},
"spec": {
"width": 150,
"height": 150,
"mark": "point",
"encoding": {
"x": {
"field": {"repeat": "column"},
"type": "quantitative",
"scale": {"zero": false}
},
"y": {
"field": {"repeat": "row"},
"type": "quantitative",
"scale": {"zero": false}
},
"color": {"field": "Species", "type": "nominal"}
}
}
}
""")
# https://vega.github.io/vega-lite/docs/repeat.html#scatterplot-matrix-splom
Vl.new()
|> Vl.data_from_url("https://vega.github.io/editor/data/penguins.json")
|> Vl.repeat(
[
row: [
"Beak Length (mm)",
"Beak Depth (mm)",
"Flipper Length (mm)",
"Body Mass (g)"
],
column: [
"Body Mass (g)",
"Flipper Length (mm)",
"Beak Depth (mm)",
"Beak Length (mm)"
]
],
Vl.new(width: 150, height: 150)
|> Vl.mark(:point)
|> Vl.encode_repeat(:x, :column, type: :quantitative, scale: [zero: false])
|> Vl.encode_repeat(:y, :row, type: :quantitative, scale: [zero: false])
|> Vl.encode_field(:color, "Species", type: :nominal)
)
# https://vega.github.io/vega-lite/examples/area_gradient.html
Vl.from_json("""
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"data": {"url": "https://vega.github.io/editor/data/cars.json"},
"mark": "rect",
"encoding": {
"y": {"field": "Origin", "type": "nominal"},
"x": {"field": "Cylinders", "type": "ordinal"},
"color": {"aggregate": "mean", "field": "Horsepower"}
},
"config": {
"axis": {"grid": true, "tickBand": "extent"}
}
}
""")
# https://vega.github.io/vega-lite/examples/arc_donut.html
Vl.from_json("""
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"description": "A simple donut chart with embedded data.",
"data": {
"values": [
{"category": 1, "value": 4},
{"category": 2, "value": 6},
{"category": 3, "value": 10},
{"category": 4, "value": 3},
{"category": 5, "value": 7},
{"category": 6, "value": 8}
]
},
"mark": {"type": "arc", "innerRadius": 50},
"encoding": {
"theta": {"field": "value", "type": "quantitative"},
"color": {"field": "category", "type": "nominal"}
}
}
""")
# https://vega.github.io/vega-lite/examples/stacked_bar_normalize.html
Vl.from_json("""
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"data": { "url": "https://vega.github.io/editor/data/population.json"},
"transform": [
{"filter": "datum.year == 2000"},
{"calculate": "datum.sex == 2 ? 'Female' : 'Male'", "as": "gender"}
],
"mark": "bar",
"width": {"step": 17},
"encoding": {
"y": {
"aggregate": "sum", "field": "people",
"title": "population",
"stack": "normalize"
},
"x": {"field": "age"},
"color": {
"field": "gender",
"scale": {"range": ["#675193", "#ca8861"]}
}
}
}
""")
