VegaLite({
data: {values: Cereals},
transform:[{
window: [{op: "rank", as: "id"}],
groupby: ["Type"]
}],
mark: "circle",
encoding: {
y: {field: "Type", type: "nominal"},
x: {field: "id", type: "ordinal"},
}
})
md `Let's look at the cereal data using a dot plot. It has strings that we need to convert to numbers. we can do that in vega-lite using the \`\`\`transform\`\`\` operator. We reference the object we are converting using the keyword \`\`\` datum\`\`\`. This is clearly not the right way to use colour!`
VegaLite({
data: {values: Cereals},
width: 600,
height: 400,
"transform": [
{"calculate": "+datum.calories", "as": "cal"},
{"calculate": "+datum.rating", "as": "rat"},
{"calculate": "+datum.shelf", "as": "sh"}
],
mark:
{
type: "circle",
opacity: 0.8,
stroke: "black",
strokeWidth: 1
},
encoding: {
y: {
field: "Type",
type: "nominal",
},
x: {
field: "cal",
type: "quantitative"
},
color: {
field: "rat",
type: "quantitative",
scale: {domain: [0, 100], range: ['#8AB9ED','#FDB813']}
},
size: {
field: "sh",
type: "ordinal"
}
}
})
invalidCereals = simpleCereals.filter(function(cereal) {
return ((isNaN(cereal.rating) || (cereal.rating==0)));
})
md `Now let's look at that same scatter plot. Note we still have values at 0, but Vega-Lite has filtered out the \`\`\ NaN\`\`\` vallues.`
VegaLite({
data: {values: Cars},
mark: "tick",
encoding: {
x: {field: "Horsepower", type: "quantitative"},
y: {field: "Cylinders", type: "ordinal"}
}
})
VegaLite({
data: {values: Stocks},
width: 600,
height: 400,
mark: {type: "line", opacity: 0.7, strokeWidth: 3},
encoding:{
x: {field: "date", type: "temporal"},
y: {field: "price", type: "quantitative"},
color: {field: "symbol", type: "nominal"}
}
})
VegaLite({
data: {values: Population},
transform: [
{filter: "datum.year == 2000"}
],
width: 20,
mark: "bar",
encoding: {
column: {field: "age", type: "ordinal", spacing: 10},
y: {aggregate: "sum", field: "people", type: "quantitative"},
x: {
field: "sex", type: "nominal",
},
color: {field: "sex", type: "nominal", scale: {range: ["#EA98D2", "#659CCA"]}},
},
})
md `Let's relabel our gender field to use M and F. I use a \`\`\`transform\`\`\` calculation in the data itself. Note that we have to do some tricky things with escaping string constants to make this work. I don't know how to turn off the \"gender\" label in the x axis.`
VegaLite({
data: {values: Population},
transform: [
{filter: "datum.year == 2000"},
{"calculate": "if(datum.sex==1,\"M\",\"F\")", "as": "gender"}
],
width: 20,
mark: "bar",
encoding: {
column: {field: "age", type: "ordinal", spacing: 10},
y: {aggregate: "sum", field: "people", type: "quantitative"},
x: {
field: "gender",
type: "nominal"
},
color: {field: "gender", type: "nominal", scale: {range: ["#EA98D2", "#659CCA"]}}
}
})
VegaLite({
data: {
values: [
{"task": "A", "start": 1, "end": 3},
{"task": "B", "start": 3, "end": 8},
{"task": "C", "start": 8, "end": 10}
]
},
mark: "bar",
encoding: {
y: {field: "task", type: "ordinal"},
x: {field: "start", type: "quantitative"},
x2: {field: "end", type: "quantitative"}
}
})
VegaLite({
data: {values: Gapminder},
width: 600,
height: 400,
mark: "circle",
encoding: {
y: {
field: "health",
type: "quantitative"
},
x: {
field: "income",
type: "quantitative",
scale: {type: "log"}
},
size: {field: "population", type: "quantitative"},
color: {value: "black"}
}
})
VegaLite({
data: {values: Weather},
mark: "rect",
encoding: {
x: {field: "date", timeUnit: "date", type: "ordinal", title: "Day", axis: {labelAngle: 0}},
y: {field: "date", timeUnit: "month", type: "ordinal", title: "Month"},
color: {field: "temp_max", aggregate: "max", type: "quantitative"}
}
})
Stocks = d3.csv("https://raw.githubusercontent.com/vega/vega-datasets/master/data/stocks.csv")
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.