Eine Einführung in plotly.js - eine Open-Source-Grafikbibliothek

Plotly.js ist eine Bibliothek, die sich ideal für JavaScript-Anwendungen eignet, die Grafiken und Diagramme verwenden. Es gibt einige Gründe, es für Ihr nächstes Datenvisualisierungsprojekt zu verwenden:

  1. Plotly.js verwendet sowohl D3.js (SVG) als auch WebGL für das Rendern von Grafiken
  2. Plotly.js ist ein "All-in-One-Bundle" mit den Modulen d3.js und stack.gl
  3. Es funktioniert mit dem JSON-Schema
  4. Plotly.js unterstützt grundlegende, statistische, wissenschaftliche, finanzielle und Kartendiagramme.

Darüber hinaus sind mehr als 9000 Sterne auf seinem Open-Source-Github ein starker Indikator für das Wachstum seiner Community.

Verwendung und Beispiele

Schauen wir uns das Setup und einige Beispiele zum besseren und praktischen Verständnis an.

Fügen Sie zunächst die Datei aus dem CDN hinzu.

Als nächstes zeichnen wir ein kleines Diagramm, das die Zahlen und ihre Quadrate zeigt:

Der Code zum Generieren dieses Diagramms lautet wie folgt:

 var trace = { x: [1, 2, 3, 4, 5, 6, 7, 8], y: [1, 4, 9, 16, 25, 36, 49, 64], mode: 'line' };
var data = [ trace ]; Plotly.newPlot('myDiv', data);

Die Grundeinstellung kann mit einem Dateieinschluss, einem DOM-Element und einem Skript zum Plotten erfolgen.

Nach der Aufnahme der Plotly.js-Bibliothek in ad>, we have defined an empty to plot the graph.

Plotly.new()draws a new plot in theiv> element, overwriting any existing plot and in this case we used myDiv. The input will be a element and some data.

Notice the inclusion of mode in the trace variable. It can be any combination of "lines", "markers", "text" joined with a "+" OR "none".

Examples include "lines", "markers", "lines+markers", "lines+markers+text", "none".

Here we have used markers. Notice that you only get points marked in the graph coordinates anddo not see the connected line across all points.

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Plot multiple lines now just by adding values to the data variable:

 var trace1 = { x: [1, 2, 3, 4], y: [10, 15, 13, 17], mode: 'lines', type: 'scatter' };
var trace2 = { x: [2, 3, 4, 5], y: [16, 5, 11, 9], mode: 'marker', type: 'scatter' };
var trace3 = { x: [1, 2, 3, 4], y: [12, 9, 15, 12], mode: 'lines+markers', type: 'scatter' };
var data = [trace1, trace2, trace3];
Plotly.newPlot('myDiv', data); 

The legendin a graph is linked to the data being graphically displayed in the plot area of the chart.

As of now we don’t have any labels, and the legend looks like:

Let’s update them by using options such as text,textfont ,textpostion for customization of our data labels. These should be passed with individual data sets.

 var trace1 = { x: [1, 2, 3, 4, 5], y: [100, 60, 30, 60, 10], mode: 'lines+markers+text', type: 'scatter', name: 'Beta', text: ['Mobile A', 'Mobile B', 'Mobile C', 'Mobile D', 'Mobile E'], textposition: 'top center', textfont: { family: 'Raleway, sans-serif' }, marker: { size: 12 } };
var trace2 = { x: [1.5, 2.5, 3.5, 4.5, 5.5], y: [100, 10, 70, 150, 40], mode: 'lines+markers+text', type: 'scatter', name: 'Alpha', text: ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'], textfont : { family:'Times New Roman' }, textposition: 'bottom center', marker: { size: 12 } };
var data = [ trace1, trace2 ];
var layout = { xaxis: { range: [ 0.75, 5.25 ] }, yaxis: { range: [0, 200] }, legend: { y: 0.5, yref: 'paper', font: { family: 'Arial, sans-serif', size: 20, color: 'black', } }, title:'Data Labels on the Plot' };
Plotly.newPlot('myDiv', data, layout); 

The layout of other visual attributes such as the title and annotations will be defined in an object usually called layout.

By now we have seen some examples of line, let’s quickly plot a bar chart using 'bar' as type.

var data = [{ x: ['Company X', 'Company Y', 'Company Z'], y: [200, 140, 230], type: 'bar'}];
Plotly.newPlot('myDiv', data);

You can also change the typein the above data shown for products and mobile by changing scatter to bar.

var trace = { x: [1.5, 2.5, 3.5, 4.5, 5.5], y: [100, 10, 70, 150, 40], mode: 'lines+markers+text', type: 'bar', name: 'Alpha', text: ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'], textfont : { family:'Times New Roman' }, textposition: 'top', marker: { size: 12 } };

Here is one example which changes the opacityof bar:

var trace2 = { x: ['Alpha', 'Beta', 'Gamma'], y: [100, 200, 500], type: 'bar', name: 'Opacity Example', marker: { color: 'rgb(204,204,204)', opacity: 0.5 }};

We have created some basic scatter charts and talked about few options which can be easily tweaked to get different variations of the same chart.

Let’s continue by plotting a meteor datasetusing only few lines of code.

I am using dataset from bcdunbar’s githuband will try to break down entire process into multiple steps.

Let’s get started.

Step 1. Initial Setup

Add plotly.js in your HTML file. This includes the JavaScript file, empty div element and placeholder for scripts.

 // JS code for plot 

Step 2. Dataset

Since our dataset is in CSV format, we can use Plotly.d3.csv.It internally reads the CSV data from an AJAX call.

Wrapper code for plotting:

Plotly.d3.csv('//raw.githubusercontent.com/bcdunbar/datasets/master/meteorites_subset.csv', function(err, rows){
Plotly.plot('mapDiv', data, layout);
});

Step 3. Access Token

Get the Mapbox access token we would be using from here.

Plotly.plot needs two main things: data and layout which defines what type of data will be used and how it should be plotted on screen.

Step 4. Map Layout

var layout = { title: 'Demonstration of Meteorite Landing using Plotly.js', font: { color: 'white' }, dragmode: 'zoom', mapbox: { center: { lat: 38.03697222, lon: -90.70916722 }, style: 'light', zoom: 2 }, paper_bgcolor: '#191A1A', plot_bgcolor: '#191A1A', showlegend: true, annotations: [{ x: 0, y: 0, text: 'NASA', showarrow: false }]};

Notice that we are using mapboxto define all map configs including center, zoom level, color and legends.

Next add the token we created in Step 3 by using:

Plotly.setPlotConfig({ mapboxAccessToken: 'your token here'});

Step 5. Process Data

Last thing we need is to add our data object from the source CSV:

var classArray = unpack(rows, 'class'); var classes = [...new Set(classArray)];
function unpack(rows, key) { return rows.map(function(row) { return row[key]; }); }
var data = classes.map(function(classes) { var rowsFiltered = rows.filter(function(row) { return (row.class === classes); }); return { type: 'scattermapbox', name: classes, lat: unpack(rowsFiltered, 'reclat'), lon: unpack(rowsFiltered, 'reclong') }; });

Now we have data, layout, token and map… Here’s the end result:

This was a plotting demonstration with step by step approach on plotting a map dataset using plotly.js. You can find a lot of examples on the Plotly documentation to get started with.

Hope this gave you a good introduction to Plotly js.

Make sure to drop your feedback below, and code for this can be found on my Github.