This library renders visualizations of Open mHealth structured data in a web browser. It currently generates line charts and bar charts, with default settings included for the following measures:
- body weight (
body_weight) - heart rate (
heart_rate) - blood pressure (
systolic_blood_pressure,diastolic_blood_pressure) - physical activity (
step_count,minutes_moderate_activity)
The charting functions of the library are built on top of Plottable.js, which is built on top of D3. You can play with a live demo here.
You can learn more about the design principles behind these visualisations on our website, and learn about how design became implementation on our blog.
If you'd like to use the charts in your own project, simply pull the library into your project as a Bower dependency using
bower install omh-web-visualizations
If you don't have Bower, install it using
npm install -g bower. If you don't have npm, you'll need to install Node.js.
If you'd like to experiment with the library using a demonstration page,
- Clone this repository
git clone https://github.com/openmhealth/web-visualizations.git
- Navigate to the cloned repository and install the project's dependencies with Bower
bower install
- Install the development dependencies using npm
npm install
- Make your changes
- To publish your changes to the
distdirectory, run gulpgulp
- To see the results on the example page
gulp watchand open http://localhost:8080/examples/charts.html in your browser
- If you leave it running, gulp will watch for changes in the background and update
distas needed
You can create a chart by calling:
chart = new OMHWebVisualizations.Chart( data, element, measureList, settings );The arguments passed to the constructor are:
| Argument | Description |
|---|---|
| data | An array of Open mHealth structured data points. |
| element | A dom element, such as a <div> containing an <svg> node. This can also be a D3 selection. For backward compatibility, it can also be a jQuery object, however this functionality is deprecated and may be removed in a future release. |
| measureList | A string containing a comma-separated list of Open mHealth measures to display. |
| settings | An object with settings for the chart. If this is omitted or if an empty object is passed in, the function uses the default settings explained below. |
The easiest way to create data points to pass to the data parameter is to use our sample data generator. You can either use a pre-generated data set, or download the generator itself to create data that fits your needs.
A chart is considered initialized if the constructor OMHWebVisualizations.Chart(...); completes. If, for example, no measures specified in the measureList argument can be found in the data argument, the constructor will not complete, and the chart will not be initialized. Initialization state is tracked by the Chart.initialized property, which can be used as a condition for rendering a chart or requesting its components after construction.
The settings parameter of the OMHWebVisualization.Chart(...) function is divided into two sections. The interface section controls the UI of the chart as a whole. The measures section contains settings that customize charts for specific measures.
The following object is the default settings object used by the OMHWebVisualization.Chart(...) function when its settings parameter is empty. You can specify any subset of these settings to override them:
{
'interface': {
'toolbar': {
'visible': true,
'timespanButtons': { 'visible': true },
'zoomButtons': { 'visible': true },
'navigationButtons': { 'visible': true }
},
'tooltips': {
'visible': true,
'timeFormat': 'M/D/YY, h:mma',
'decimalPlaces': 0,
'contentFormatter': OMHWebVisualizations.ChartStyles.formatters.defaultTooltip.bind( this ),
'grouped': true
},
'panZoomUsingMouse': {
'enabled': true,
'hint':{
'visible': true
}
},
'axes': {
'yAxis': {
'visible': true
},
'xAxis': {
'visible': true
}
}
},
'measures': {
'body_weight': {
'data': {
'yValuePath': 'body.body_weight.value',
},
'yAxis': {
'range': { 'min': 0, 'max': 100 },
'label': 'kg'
}
},
'heart_rate': {
'data':{
'yValuePath': 'body.heart_rate.value'
},
'yAxis': {
'range': { 'min': 30, 'max': 150 },
'label': 'bpm'
}
},
'step_count': {
'data': {
'yValuePath': 'body.step_count',
'xValueQuantization': {
'period': OMHWebVisualizations.DataParser.QUANTIZE_DAY,
'aggregator': parent.DataParser.aggregators.sum
}
},
'chart': {
'type': 'clustered_bar',
'daysShownOnTimeline': { 'min': 7, 'max': 90 }
},
'legend': {
'seriesName': 'Steps',
'seriesColor': '#eeeeee'
},
'yAxis': {
'range': { 'min': 0, 'max': 1500 },
'label': 'Steps'
}
},
'minutes_moderate_activity': {
'data':{
'yValuePath': 'body.minutes_moderate_activity.value',
'xValueQuantization': {
'period': OMHWebVisualizations.DataParser.QUANTIZE_DAY,
'aggregator': parent.DataParser.aggregators.sum
}
},
'chart': {
'type': 'clustered_bar',
'daysShownOnTimeline': { 'min': 7, 'max': 90 }
},
'legend': {
'seriesName': 'Minutes of moderate activity',
'seriesColor': '#4a90e2'
},
'yAxis':{
'range': { 'min': 0, 'max': 300 },
'label': 'Minutes'
}
},
'systolic_blood_pressure': {
'data': {
'yValuePath': 'body.systolic_blood_pressure.value'
},
'yAxis': {
'range': { 'min': 30, 'max': 200 },
'label': 'mmHg'
}
},
'diastolic_blood_pressure': {
'data': {
'yValuePath': 'body.diastolic_blood_pressure.value'
},
'yAxis':{
'range': { 'min': 30, 'max': 200 },
'label': 'mmHg'
}
}
}
}
For example, using these default settings to graph heart_rate data will generate a chart that looks like this:
If you look carefully at the default settings object, you'll also notice that some measure settings have more properties than others. When a property is missing, the following default settings are assumed.
{
'yAxis': {
'range': { 'min': 0, 'max': 100 },
'label': 'Units',
},
'data':{
'xValueQuantization': {
'period': OMHWebVisualizations.DataParser.QUANTIZE_NONE,
'aggregator': OMHWebVisualizations.DataParser.aggregators.mean,
}
},
'chart': {
'type': 'line',
'daysShownOnTimeline': { 'min': 1, 'max': 1000 },
},
'legend': {
'seriesName': 'Series',
'seriesColor': '#4a90e2'
}
}Additionally, default styles are provided for rendering each measure's plot.
To override the defaults, specify the new interface settings and styles in the settings object passed to OMHWebVisualization.Chart(...).
For example, if you would like to graph heart_rate data with a blue line and no tooltips, you'd use the following settings object:
{
'interface': {
'tooltips': {
'visible': false,
}
},
'measures': {
'heart_rate': {
'chart': {
'styles': [
{
'name': 'blue-lines',
'plotType': 'Line',
'attributes': {
'stroke': '#4a90e2'
}
}
]
}
}
}
}This will produce a chart that looks something like the following screenshot:
The Y axis range can be set to adapt to the data by setting the yAxis.range property of a measure's settings to undefined e.g. settings.measures.heart_rate.yAxis.range = undefined_
Quantization reduces the dataset's size by summarizing each group of points that fall into a common time range, or "bucket," with a single point that represents their bucket's range.
Currently, quantized data point values within each subsequent quantization bucket are averaged (mean) for most measures and summed for step_count and minutes_moderate_activity.
If you wish to configure the timeQuantizationLevel for a measure, you will need the following constants:
OMHWebVisualizations.DataParser.QUANTIZE_YEAROMHWebVisualizations.DataParser.QUANTIZE_MONTHOMHWebVisualizations.DataParser.QUANTIZE_DAYOMHWebVisualizations.DataParser.QUANTIZE_HOUROMHWebVisualizations.DataParser.QUANTIZE_MINUTEOMHWebVisualizations.DataParser.QUANTIZE_SECONDOMHWebVisualizations.DataParser.QUANTIZE_MILLISECONDOMHWebVisualizations.DataParser.QUANTIZE_NONE
These can be used in an settings object as follows:
// an example of some settings for a distance chart
var settings = {
'measures': {
'distance': {
'seriesName': 'Distance',
'yAxis':{
'range': { 'min': 0, 'max': 200000 },
'label': 'm'
},
'data': {
'yValuePath': 'body.distance.value',
'xValueQuantization': {
'period': OMHWebVisualizations.DataParser.QUANTIZE_MONTH,
'aggregator': OMHWebVisualizations.DataParser.aggregators.sum
}
},
'chart': {
'type': 'clustered_bar',
'daysShownOnTimeline': { 'min': 90, 'max': 365 }
}
}
}
};Here is a chart of some unquantized data:
As an example, the data will be quantized by hour using OMHWebVisualizations.DataParser.QUANTIZE_HOUR. Thus all points in the hour from 04:00 to 05:00 will be summed into a single point. The unquantized points in this hour are shown below in a zoomed-in view of the minutes just before 05:00:
And here is a chart of the same data quantized by hour. The points before 05:00 in the zoomed-in view above have been accumulated into a single point, shown in dark blue:
Horizontal lines (e.g. representing safe thresholds) can be drawn on charts. Each line is labelled with a custom label or its y value, unless that label will overlap another gridline's label. Here are two maximum gridlines with default appearance:
Gridlines can be specified as gridlines in the chart property of a measure in the measures section of the configuration settings object, eg: settings.measures.heart_rate.chart.gridlines. It should be an array of gridline objects, as detailed below:
| Property | Description |
|---|---|
| value | The y value of the horizontal line. |
| label | The label to show above the line (optional). If no label is specified, the value property is used. |
| visible | Whether to show the gridline (optional). |
On a chart of type line, a labeled horizontal rule is drawn all the way across the chart for each gridline. Gridlines are not drawn on bar charts.
To create a new gridline without using the settings object, you can alternatively call chart.addGridline() before the chart is rendered.
To change the colors and other visual attributes of points on the chart, you can specify a chart.styles section in each measure in the measures block of the configuration settings object.
You can alternatively customize the chart's ChartStyles object before rendering the chart by calling chart.getStylesForPlot() and chart.setStylesForPlot(). The Plottable plot you wish to affect must be passed into these functions.
Below is an example of what can be done. See examples/charts.html for code samples.
var dangerValue = 120;
// these filter functions are used to determine which
// points will be rendered with the style's attributes
var dangerFilter = function ( d ) {
// a filter function takes a datum and returns a boolean
return d.y >= dangerValue;
};
var dangerSettings = {
'measures': {
'systolic_blood_pressure':{
'chart':{
'styles': {
'name': 'danger',
'plotType': 'Scatter',
'filters': [ dangerFilter ],
'attributes': {
'fill': 'red'
'stroke': 'red'
}
}
}
}
}
}
//builds a new plottable chart with the danger settings
chart = new OMHWebVisualizations.Chart( data, element, measureList, dangerSettings );
if ( chart.initialized ) {
chart.renderTo( element.select( "svg" ).node() );
}The code above changes the color of points above the gridline:
You could also add a range in the chart that is colored differently:
Tooltips can be enabled, disabled, and configured using the userInterface.tooltips property of the settings object passed into the constructor (see 'Configuring a Chart'). The properties of userInterface.tooltips are explained in the following table:
| Property | Description |
|---|---|
| enabled | Whether to show tooltips when the user hovers on a point. |
| timeFormat | A string representing the time format for the time field in the tooltip. |
| decimalPlaces | The number of decimal places to show the datum's y value with by default when rendering a data point value in the tooltip. |
| contentFormatter | A function that takes a D3 datum and returns a string. Used to render the datum in the tooltip. If undefined, the datum's y value will be truncated by a default formatter to the number of decimal places specified in the decimalPlaces parameter and converted to a string. |
| grouped | Whether to show a single common tooltip for data points of different measure types that are found together in the body of a single data point. |
In the following chart, we see a tooltip that has been colored light orange to match its point in the diastolic blood pressure series:
And here is the CSS used for the tooltip:
.omh-tooltip.above .value {
color: #e8ac4e;
}You can restrict the tooltip colors to only diastolic_blood_pressure as follows:
.omh-tooltip.diastolic_blood_pressure.b .value {
color: #e8ac4e;
}In the same chart, we see a tooltip that has been colored red to match its point below the gridline in the systolic blood pressure series:
Here is the CSS used for the systolic tooltip:
.omh-tooltip.systolic_blood_pressure.below .value {
color:#ce5050;
}And again, in the same chart, we see a tooltip that has been colored light orange to match its point in the first measure:
The point shown above has matched a chart style named warning:
In order for this to work, the corresponding chart style's name property is set to warning as follows:
{
'name': 'warning',
'filters': chartStyles.filters.above( 120 ),
'attributes':{ 'fill': '#e8ac4e', 'stroke': '#745628' }
}(where chartStyles is an instance of OMHWebVisualizations.ChartStyles)
And here is the CSS used to style tooltip
.omh-tooltip.warning .value {
color:#e8ac4e;
}See examples/charts.html for js and css code samples.
Once a chart has been constructed, it must be rendered to an <svg> element. Render the chart by calling:
chart.renderTo( svgElement );After a chart has been constructed, but before it is rendered, you may choose to get the Plottable components and make further modifications that are not afforded by the constructor's settings parameter. Get the Plottable components, modify them, and render the chart as follows:
// construct chart here...
if (chart.initialized) {
var components = chart.getComponents();
// modify plottable components here...
chart.renderTo( svgElement );
}To see an example of component modification, check out the examples/charts.html file in this repository.
In order to free up resources or re-use an element for a new chart, the chart and all of its interactive features can be destroyed with:
chart.destroy();To contribute to this repository
- Open an issue to let us know what you're going to work on.
- This lets us give you feedback early and lets us put you in touch with people who can help.
- Fork this repository.
- Create your feature branch from the
developbranch. - Commit and push your changes to your fork.
- Create a pull request.