vue-declarative-plots

This repository will serve as a place for reusable Vue plot components (built with D3). These components were developed for use in iMuSE.

View demo Documentation

Installation

Coming soon

Development

Install dependencies:

yarn

Serve for development at http://localhost:8080:

yarn run serve

Build for production (generates /dist and /examples):

yarn run build

Run tests with jest:

yarn run test

Compile documentation with documentationjs:

yarn run docs

Plot Components

  • BarPlot :white_check_mark:
  • StackedBarPlot :white_check_mark:
  • ScatterPlot :white_check_mark:
  • GenomeScatterPlot :white_check_mark:
  • GenomeStackedBarPlot
  • GenomeTrackPlot
  • TrackPlot :white_check_mark:
  • MultiTrackPlot
  • BoxPlot :white_check_mark:
  • MultiBoxPlot :white_check_mark:
  • ViolinPlot

Axis Components

  • Axis :white_check_mark:
  • GenomeAxis :white_check_mark:
  • DendrogramAxis

Other Components

  • PlotContainer :white_check_mark:
  • Legend
  • SortOptions :white_check_mark:

Goals:

  • [x] props should declare visual encodings of data
    • example: the StackedBarPlot component will accept the following props:
      x="sample_id"
y="exposure"
c="signature" // color
  • [x] props should declare where to find the data
    • data will never be passed directly to a plot - instead it will be globally accessible by a key
    • for now, assume data is stored in a global JSON object with <key>: <data> mappings
    • example: for a dataset with key exposures_data, the following prop would specify to a plot component that this dataset should be used:
      data="exposures_data"
  • [x] props should declare where to find the scales
    • [x] scales will be ES6 classes with different APIs depending on whether categorical, continuous, etc...
    • [x] scales will always expose a domain
    • [x] scales will always expose a domain-var-to-color function
    • [ ] the color scale (or even individual colors) should also be able to be changed programmatically
    • [x] scales will always expose a domain-var-to-human-readable-text function
    • [x] categorical scales will always expose a sort function that takes in a specification of the data that will define the ordering
    • [x] scales will always expose a filter function (and a corresponding filter-reset function)
      • if categorical, this will accept an array of new values
      • if continuous, this will accept a [min, max] array
      • if binary, this will accept a boolean value
    • scales should contain all of the information necessary to draw a legend
    • scales will never be passed directly to a plot - instead they will be globally accessible by a key
    • for now, assume scales are stored in a global JSON object with <variable>: <scale> mappings
    • example: for a variable sample_id, the following prop would specify to a plot component that this scale object should be used for the x axis:
      x="sample_id"
  • [x] plots should assume which type of scale is on which axis
    • for example, a bar plot (with vertical bars) would assume a continuous y scale and a categorical x
  • [x] events should alert plots when a scale has been mutated
    • these alerts should specify which scale has been updated using a key
    • plot components should listen for these updates and re-draw if necessary
    • scales may be mutated upon filter or zoom
  • [x] data should be immutable
    • even small variations of data sets should be stored in a separate DataContainer instance
    • however, plots may need to subscribe to data updates for asynchronous loading reasons
  • [x] plots should NOT draw their own axes
    • axes should be independent of plots
    • axes should be contained in their own components
  • [x] axes should accept props specifying which scale to use, and where to draw
    • example:
      variable="sample_id"
side="bottom"
  • [x] axes should be brush-able
    • but also should have the option to disable brushing
    • brushing along an axis should trigger a zoom event
    • axes, if their corresponding scale has been "zoomed", should display a "zoomed out"/full view beside the "zoomed-in" view in order to show context
    • categorical axes should snap
  • [x] plots and axes should accept width and height props
    • container components should be responsible for keeping plot and axis props in sync if they are dynamic
    • example:
      :pWidth="windowWidth"
:pHeight="300"
  • [x] plots should accept margin props
    • container components should be responsible for keeping margin props in sync if they are dynamic
    • example:
      :pMarginTop="10"
:pMarginLeft="50"
:pMarginRight="20"
:pMarginBottom="0"
  • [x] plots should emit click events, specifying variables in a predefined order to a prop-supplied callback
    • example:
    :clickHandler="chooseSample" // will be called with chooseSample(x, y, c)
  • [x] plots should have tooltips
    • tooltips should obtain human-readable variable names from the appropriate scale
  • [x] plots should dispatch applicable hover events
    • dispatching should be done through the scale
    • example:
      • hovering on a section of a bar on a stacked bar plot would cause dispatches for (at least) the x and color variables
  • [x] the internals of the drawing of the plots should be abstracted away as much as possible
    • details of SVG, canvas, etc. implementation should be contained
  • [x] all meaningful interactions will be stored in a history stack
    • meaningful interactions: scale filter/zoom/sort
    • will allow backward(undo)/forward(redo) operations
    • will allow "replaying" of the user's interactions
    • will allow sharing of a user session, so that others may replay or step through the interactions
    • but this should also be optional, for example if the user chooses not to supply the stack to a plot via prop

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