example = FileAttachment("example.json").json()
data = example.data
data
Type Table, then Shift-Enter. Ctrl-space for more options.
data1
X
year
Y
—
mean
Color
series
Size
—
Facet X
—
Facet Y
—
Mark
line
Type Chart, then Shift-Enter. Ctrl-space for more options.
Select a data source…
Type Table, then Shift-Enter. Ctrl-space for more options.
Plot.plot({
style: "overflow: visible;",
y: { grid: true },
color: {
type: "ordinal",
legend: true,
legendPosition: "right",
range: [
"#009e73",
"#ffb000",
"#dc267f",
"#648fff",
"#d55e00",
"#cc79a7",
"#0072b2",
"#f0e442",
"#785ef0",
"#fe6100"
]
},
marks: [
Plot.lineY(data1, {
x: "year",
y: "mean",
stroke: "series",
tip: true
}),
Plot.text(
data1,
Plot.selectLast({
x: "year",
y: "mean",
z: "series",
text: "series",
textAnchor: "start",
dx: 3
})
)
]
})
//compute the average of the column called mean
average = data1.reduce((total, d) => total + d.mean, 0) / data1.length
//set the minimum of axis=minimum of data - x*average. Adjust x
yaxisMin = Math.min(...data1.map((d) => d.mean)) -0.2 * average
//set the maximum of axis=maximum of data + x*average. Adjust x
yaxisMax = Math.max(...data1.map((d) => d.mean)) + 0.15 * average
Plot.plot({
style: "overflow: visible;",
y: { grid: true, domain: [yaxisMin, yaxisMax] }, //here add the y axis min/max
marks: [
Plot.lineY(data1, {
x: "year",
y: "mean",
stroke: "series",
tip: true
}),
Plot.text(
data1,
Plot.selectLast({
x: "year",
y: "mean",
z: "series",
text: "series",
textAnchor: "start",
dx: 5
})
)
]
})
Plot.plot({
//size
height: 543,
width: 900,
//space around
marginLeft: 100, // space to the left of the chart
marginRight: 50,
marginTop: 50,
marginBottom: 50, // space below the chart
//space within
insetTop: 0,
insetBottom: 10, // space between the x-axis and the marks
insetLeft: 20, // space between the y-axis and the marks
insetRight: 47,
x: { label: "Year " },
y: { label: "$" },
style: { fontSize: "20" }, //changes the fontsize of line label
y: { grid: true },
marks: [
Plot.axisX({ fontSize: 12 }), //fontsize of x axis marks
Plot.axisY({ fontSize: 12, label: "$" }),
Plot.lineY(data1, {
x: "year",
y: "mean",
stroke: "series",
tip: true
}),
Plot.text(
data1,
Plot.selectLast({
x: "year",
y: "mean",
z: "series",
text: "series",
textAnchor: "start",
dx: 5
})
)
]
})
Plot.plot({
x: {
label: "Year ",
tickFormat: d3.format("$.2f"),
ticks: 2,
line: true,
nice: true
},
y: { label: "$" },
style: { fontSize: "14" },
y: { grid: true },
marks: [
Plot.axisX({ fontSize: 16 }),
Plot.axisY({ fontSize: 12, label: "$" }),
Plot.lineY(data1, {
x: "year",
y: "mean",
stroke: "series",
tip: true
}),
Plot.text(
data1,
Plot.selectLast({
x: "year",
y: "mean",
z: "series",
text: "series",
textAnchor: "start",
dx: 5
})
)
],
// Layout adjustments
marginTop: 50,
marginRight: 10,
marginBottom: 50,
marginLeft: 80
})
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.
