Įvadas į plotly.js - atvirojo kodo grafikų biblioteką

Plotly.js yra biblioteka, idealiai tinkanti „JavaScript“ programoms, naudojančioms grafikus ir diagramas. Yra keletas priežasčių, kodėl verta apsvarstyti galimybę jį naudoti kitame duomenų vizualizavimo projekte:

  1. „Plotly.js“ grafiniam vaizdavimui naudoja ir D3.js (SVG), ir „WebGL“
  2. „Plotly.js“ yra „viskas viename“ paketas su d3.js ir stack.gl moduliais
  3. Jis veikia su JSON schema
  4. Plotly.js palaiko pagrindines, statistines, mokslo, finansines ir žemėlapių diagramas.

Be to, daugiau nei 9000 žvaigždžių atvirame šaltinyje „Github“ yra stiprus bendruomenės augimo rodiklis.

Naudojimas ir pavyzdžiai

Pažvelkime į sąranką ir keletą pavyzdžių, kad galėtume geriau ir praktiškai suprasti.

Pirmiausia įtraukite failą iš savo CDN.

Tada nubrėžkime nedidelį grafiką, kuriame rodomi skaičiai ir jų kvadratai:

Kodas, skirtas generuoti šią diagramą, yra žemiau:

 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);

Pagrindinę sąranką galima atlikti įtraukiant failą, DOM elementą ir scenarijų braižymui.

Įtraukus „Plotly.js“ biblioteką į ad>, we have defined an empty to plot the graph.

Plotly.new()draws a new plot in the iv> element, overwriting any existin g 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.

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.