hvega-0.4.1.2: Create Vega-Lite visualizations (version 3) in Haskell.

Graphics.Vega.Tutorials.VegaLite

Description

This tutorial is inspired by - in that it starts off as a close copy of - the Elm Vega-Lite walkthrough created by Jo Wood, and converted as necessary for the differences between hvega and elm-vegalite. The Elm tutorial is based on the talk given by Wongsuphasawat et al at the 2017 Open Vis Conf.

The tutorial targets version 3 of the Vega-Lite specification and the functionality provided in version 0.4.0.0 of hvega.

Synopsis

# A Grammar of Graphics

hvega is a wrapper for the Vega-Lite visualization grammar which itself is based on Leland Wilkinson's Grammar of Graphics. The grammar provides an expressive way to define how data are represented graphically. The seven key elements of the grammar as represented in hvega and Vega-Lite are:

Data
The input to visualize. Example functions: dataFromUrl, dataFromColumns, and dataFromRows.
Transform
Functions to change the data before they are visualized. Example functions: filter, calculateAs, and binAs. These functions are combined with transform.
Projection
The mapping of 3d global geospatial locations onto a 2d plane . Example function: projection.
Mark
The visual symbol, or symbols, that represent the data. Example types, used with mark: Line, Circle, Bar, Text, and Geoshape. There are also ways to specify the shape to use for the Point type, using the MShape setting and the Symbol type.
Encoding
The specification of which data elements are mapped to which mark characteristics (commonly known as channels). Example functions: position, shape, size, and color. These encodings are combined with encoding.
Scale
Descriptions of the way encoded marks represent the data. Example settings: SDomain, SPadding, and SInterpolate.
Guides
Supplementary visual elements that support interpreting the visualization. Example setings: AxDomain (for position encodings) and LeTitle (for legend color, size, and shape encodings).

In common with other languages that build upon a grammar of graphics such as D3 and Vega, this grammar allows fine grain control of visualization design. Unlike those languages, Vega-Lite - and hvega in turn - provide practical default specifications for most of the grammar, allowing for a much more compact high-level form of expression.

The Vega-Lite Example Gallery provides a large-number of example visualizations that show off the capabilities of Vega-Lite. Hopefully, by the end of this tutorial, you will be able to create most of them.

## How many Haskell extensions do you need?

The VegaLite module is long, but does not use any complex type machinery, and so it can be loaded without any extensions, although the extensive use of the Text type means that using the OverloadedStrings extension is strongly advised.

The module does export several types that conflict with the Prelude, so one suggestion is to use

import Prelude hiding (filter, lookup, repeat)

## A note on type safety

The interface provided by hvega provides limited type safety. Various fields such as PmType are limited by the type of the argument (in this case Measurement), but there's no support to check that the type makes sense for the particular column (as hvega itself does not inspect the data source). Similarly, hvega does not stop you from defining properties that are not valid for a given situation - for instance you can say toVegaLite [] even though the output is not a valid Vega-Lite specification (i.e. it does not validate against the Vega-Lite schema).

## Comparing hvega to Elm Vega-Lite

hvega started out as a direct copy of elm-vegalite, and has been updated to try and match the functionality of that package. However, hvega has not (yet?) followed elm-vegalite into using functions rather than data structures to define the options: for example, elm-vegalite provides pQuant n which in hvega is the combination of PName n and PmType Quantitative in hvega. The top-level functions - such as dataFromUrl, encoding, and filter - are generally the same.

# What data are we using?

Rather than use the Seattle weather dataset, used in the Elm walkthrough (if you go through the Vega-Lite Example Gallery you may also want to look at different data ;-), I am going to use a small datset from the Gaia satellite, which has - and still is, as of Summer 2019 - radically-improved our knowledge of our Galaxy. The data itself is from the paper "Gaia Data Release 2: Observational Hertzsprung-Russell diagrams" preprint on arXiV NASA ADS link. We are going to use Table 1a, which was downloaded from the VizieR archive as a tab-separated file (aka TSV format).

The file contains basic measurements for a number of stars in nine open clusters that all lie within 250 parsecs of the Earth (please note, a parsec is a measure of distance, not time, no matter what some ruggedly-handsome ex-carpenter might claim). The downloaded file is called gaia-aa-616-a10-table1a.no-header.tsv, although I have manually edited it to a "more standard" TSV form (we Astronomers like our metadata, and tend to stick it in inappropriate places, such as the start of comma- and tab-separated files, which really mucks up other-people's parsing code). The first few rows in the file are:

Source Cluster RA_ICRS DE_ICRS Gmag plx e_plx
49520255665123328 Hyades 064.87461 +21.75372 12.861 20.866 0.033
49729231594420096 Hyades 060.20378 +18.19388 5.790 21.789 0.045
51383893515451392 Hyades 059.80696 +20.42805 12.570 22.737 0.006
... ... ... ... ... ... ...

The Source column is a numeric identifier for the star in the Gaia database, in this particular case the "DR2" release, the Cluster column tells us which Star Cluster the star belongs to, RA_ICRS and DE_ICRS locate the star on the sky and use the Equatorial coordinate system (the ICRS term has a meaning too, but it isn't important for our purposes), Gmag measues the "brightness" of the star (as in most-things Astronomical, this is not as obvious as you might think, as I'll go into below), and the plx and e_plx columns give the measured parallax of the star and its error value, in units of milli arcseconds. And yes, I do realise after complaining about popular-culture references confusing distances and time, I am now measuring distances with angles. I think I've already mentioned that Astronomy is confusing...

# Creating the Vega-Lite visualization

The function toVegaLite takes a list of grammar specifications, as will be shown in the examples below, and creates a single JSON object that encodes the entire design. As of hvega-0.4.0.0 this targets version 3 of the Vega-Lite schema, but this can be over-ridden with toVegaLiteSchema if needed.

There is no concept of ordering to these specification lists, in that [ dataFromUrl ..., encoding ..., mark ...]; [ encoding ..., dataFromUrl ..., mark ... ]; and [ encoding ..., mark ..., dataFromUrl ... ] would all result in the same visualization.

The output of toVegaLite can be sent to the Vega-Lite runtime to generate the Canvas or SVG output. hvega contains the helper routines:

• fromVL, which is used to extract the JSON contents from VegaLite and create an Aeson Value;
• toHtml, which creates a HTML page which uses the Vega Embed Javascript library to display the Vega-Lite visualization;
• and toHtmlFile, which is like toHtml but writes the output to a file.

# A Strip Plot

In this section we shall concentrate on creating a single plot. Later on we shall try combining plots, after branching out to explore some of the different ways to visualize multi-dimensional data sets.

In the examples I link to symbols that have not been used in previous visualizations, to make it easier to see the use of new functionality.

## Our first hvega plot

We could encode one of the numeric data fields as a strip plot where the horizontal position of a tick mark is determined by the value of the data item. In this case I am going to pick the "plx" column:

Open this visualization in the Vega Editor

toVegaLite
[ dataFromUrl "https://raw.githubusercontent.com/DougBurke/hvega/master/hvega/data/gaia-aa-616-a10-table1a.no-header.tsv" [TSV]
, mark Tick []
, encoding (position X [ PName "plx", PmType Quantitative ] [])
]


Notice how there is no explicit definition of the axis details, color choice or size. These can be customised, as shown in examples below, but the default values are designed to follow good practice in visualization design.

Three grammar elements are represented by the three functions dataFromUrl, mark, and encoding.

The encoding function takes as a single parameter, a list of specifications that are themselves generated by other functions. In this case we use the function position to provide an encoding of the "plx" field as the x-position in our plot. The precise way in which the data value (parallax) is mapped to the x-position will depend on the type of data we are encoding. We can provide a hint by delcaring the measurement type of the data field, here Quantitative indicating a numeric measurement type. The final parameter of position is a list of any additional encodings in our specification. Here, with only one encoding, we provide an empty list.

As we build up more complex visualizations we will use many more encodings. To keep the coding clear, the idiomatic way to do this with hvega is to chain encoding functions using point-free style. The example above coded in this way would be

let enc = encoding
. position X [ PName "plx", PmType Quantitative ]

in toVegaLite
, mark Tick []
, enc []
]


## Backgrounds

The default background color for the visualization, at least in the Vega-Embed PNG and SVG output, is transparent. In many cases this is perfectly fine, but an explicit color can be specified using the Background configuration option.

The configure function allows a large number of configuration options to be configured, each one introduced by the configuration function. Here I set the color to be a light gray (actually a very-transparent black; the Color type describes the various supported color specifications, but it is generally safe to assume that if you can use it in HTML then you can use it here).

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "plx", PmType Quantitative ]

conf = configure
. configuration (Background "rgba(0, 0, 0, 0.1)")

in toVegaLite
, mark Tick []
, enc []
, conf []
]


## Challenging the primacy of the x axis

There is nothing that forces us to use the x axis, so let's try a vertical strip plot. To do so requires changing only one character in the specifiction, that is the first argument to position is now Y rather than X:

Open this visualization in the Vega Editor

let enc = encoding
. position Y [ PName "plx", PmType Quantitative ]

in toVegaLite
, mark Tick []
, enc []
]


## Data sources

Since we are going to be using the same data source, let's define it here:

gaiaData =
let addFormat n = (n, FoNumber)
cols = [ "RA_ICRS", "DE_ICRS", "Gmag", "plx", "e_plx" ]
opts = [ Parse (map addFormat cols) ]



The list argument to dataFromUrl allows for some customisation of the input data. Previously I used [TSV] to specify the data is in tab-separated format, but it isn't actually needed here (since the file name ends in ".tsv"). However, I have now explicitly defined how to parse the numeric columns using Parse: this is because the columns are read in as strings for this file by default, which actually doesn't cause any problems in most cases, but did cause me significant problems at one point during the development of the tutorial! There is limited to no feedback from the visualizer for cases like this (perhaps I should have used the Javascript console), and I only realised the problem thanks to the Data Viewer tab in the Vega Editor (after a suggestion from a colleague).

Although not used in this tutorial, data can also be defined algorithmically - using dataSequence and dataSequenceAs - or inline - with dataFromColumns or dataFromRows - or directly from JSON (as a Value) using dataFromJson.

## Adding color as an encoding

One question would be how the parallaxes vary by cluster: as parallax is measuring distance, then are the clusters similar distances away from us, or are there a range of values? A first look is to use another "channel" to represent (i.e. encode) the cluster:

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "plx", PmType Quantitative, PAxis [ AxTitle "Parallax (mas)" ] ]
. color [ MName "Cluster", MmType Nominal ]

in toVegaLite
, mark Tick []
, enc []
]


Now each tick mark is colored by the cluster, and a legend is automatically added to indicate this mapping. Fortunately the number of clusters in the sample is small enough to make this readable! The color function has added this mapping, just by giving the column to use (with MName) and its type (MmType). The constructors generally begin with P for position and M for mark, and as we'll see there are other property types such as facet and text.

Vega-Lite supports several data types, represented by the Measurement type. We have already seen Quantitative, which is used for numeric data, and here we use Nominal for the clusters, since they have no obvious ordering.

The labelling for the X axis has been tweaked using PAxis, in this case the default value for the label (the column name) has been over-ridden by an explicit value.

## Comparing Ordinal with Nominal data types

It is instructive to see what happens if we change the mark type for the color encoding from Nominal to Ordinal.

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "plx", PmType Quantitative, PAxis [ AxTitle "Parallax (mas)" ] ]
. color [ MName "Cluster", MmType Ordinal ]

in toVegaLite
, mark Tick []
, enc []
]


As can be seen, the choice of color scale has changed to one more appropriate for an ordered set of values.

While the strip plot shows the range of parallaxes, it is hard to make out the distribution of values, since the ticks overlap. Even changing the opacity of the ticks - by adding an encoding channel like opacity [ MNumber 0.6 ], or by setting the MOpacity property of the mark - only helps so much. Adding a second axis is easy to do, so let's see how the parallax distribution varies with cluster membership.

The stripPlotWithColor visualization can be changed to show two variables just by adding a second position declaration:

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "plx", PmType Quantitative, PAxis [ AxTitle "Parallax (mas)" ] ]
. position Y [ PName "Cluster", PmType Nominal ]
. color [ MName "Cluster", MmType Nominal ]

in toVegaLite
, mark Tick []
, enc []
]


I have left the color-encoding in, as it makes it easier to compare to stripPlotWithColor, even though it replicates the information provided by the position of the mark on the Y axis.

## Creating a value to plot: aggregating data

We can also "create" data to be plotted, by aggregating data. In this case we can create a histogram showing the number of stars with the same parallax value (well, a range of parallaxes).

Since sensible (hopefully) defaults are provided for unspecified settings, it is relatively easy to write generic representations of a particular visualization. The following function expands upon the previous specifications by:

• taking a field name, rather than hard coding it;
• the use of PBin [] to ask for the x-axis values to be binned;
• the addition of a second axis (Y) which is used for the aggregated value (Count, which means that no column has to be specified with PName);
• and the change from Tick to Bar for the mark.

Note that we did not have to specify how we wanted the histogram calculation to proceed - e.g. the number of bins, the bin widths, or edges - although we could have added this, by using a non-empty list of BinProperty values with PBin, if the defaults are not sufficient.

simpleHistogram field =
let enc = encoding
. position X [ PName field, PmType Quantitative, PBin [] ]
. position Y [ PAggregate Count, PmType Quantitative ]

in toVegaLite
, mark Bar []
, enc []
]


With simpleHistogram it becomes easy to get a histogram of the parallax values:

Open this visualization in the Vega Editor

parallaxHistogram = simpleHistogram "plx"

We can see that although parallaxes around 20 to 25 milli-arcseconds dominated the earlier visualizations, such as stripPlotWithColor, most of the stars have a much-smalled parallax, with values in the range 5 to 10.

A different column (or field) of the input data can be viewed, just by changing the name in the specification:

Open this visualization in the Vega Editor

gmagHistogram = simpleHistogram "Gmag"

Here we can see that the number of stars with a given magnitude rises up until a value of around 18, and then drops off.

## Changing the scale of an axis

In the case of parallaxHistogram, the data is dominated by stars with small parallaxes. Changing the scale of the Y axis to use a logarithmic, rather than linear, scale might provide more information:

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "plx", PmType Quantitative, PBin [], PAxis [ AxTitle "Parallax (mas)" ] ]
. position Y [ PAggregate Count, PmType Quantitative, PScale [ SType ScLog ] ]

in toVegaLite
, mark Bar [ MFill "orange", MStroke "gray" ]
, enc []
, height 300
, title "Gaia Parallaxes" []
]


There are four new changes to the visualization created by simpleHistogram (since PAxis has been used above):

1. an explicit choice of scaling for the Y channel (using PScale);
2. the fill (MFill) and edge (MStroke) colors of the histogram bars are different;
3. the height of the overall visualization has been increased;
4. and a title has been added.

If you view this in the Vega Editor you will see the following warning:

A log scale is used to encode bar's y. This can be misleading as the height of the bar can be arbitrary based on the scale domain. You may want to use point mark instead.


## Stacked Histogram

A color encoding can also be added. When used with the Tick mark - stripPlotWithColor - the result was that each tick mark was colored by the "Cluster" field, but for the Bar mark the result is that the bars are stacked together. I have also taken the opportunity to widen the plot (width); define the binning scheme used, with Step 1; and configure the location of the x axis tick marks, using AxValues.

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "Gmag", PmType Quantitative, binning, axis ]
. position Y [ PAggregate Count, PmType Quantitative ]
. color [ MName "Cluster", MmType Nominal ]

binning = PBin [ Step 1 ]
axis = PAxis [ AxValues (Numbers [ 0, 5 .. 20 ]) ]

in toVegaLite
, mark Bar []
, enc []
, height 300
, width 400
]


Note that hvega will allow you to combine a color encoding with a ScLog scale, even though a Vega-Lite viewer will not display the resulting Vega-Lite specification, saying

Cannot stack non-linear scale (log)

Notice how we never needed to state explicitly that we wished our bars to be stacked. This was reasoned directly by Vega-Lite based on the combination of bar marks and color channel encoding. If we were to change just the mark function from Bar to Line, Vega-Lite produces an unstacked series of lines, which makes sense because unlike bars, lines do not occlude one another to the same extent.

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "Gmag", PmType Quantitative, binning, axis ]
. position Y [ PAggregate Count, PmType Quantitative ]
. color [ MName "Cluster", MmType Nominal ]

binning = PBin [ Step 1 ]
axis = PAxis [ AxValues (Numbers [ 0, 5 .. 20 ]) ]

in toVegaLite
, mark Line []
, enc []
, height 300
, width 400
]


## You don't have to just count

The previous histogram visualizations have taken advantage of Vega-Lite's ability to bin up (Count) a field, but there are a number of aggregation properties (as defined by the Operation type). For example, there are a number of measures of the "spread" of a population, such as the sample standard deviation (Stdev).

The aim for this visualization is to show the spread in the Gmag field for each cluster, so we now swap the axis on which the aggregate is being applied (so that the cluster names appear on the y axis), and hide the legend that is applied (using MLegend []) since we can read off the color mapping from the y axis.

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "Gmag", PmType Quantitative, PAggregate Stdev ]
. position Y [ PName "Cluster", PmType Nominal ]
. color [ MName "Cluster", MmType Nominal, MLegend [] ]

in toVegaLite
, mark Bar [ MOpacity 0.6 ]
, enc []
]


The bar opacity is reduced slightly with 'MOpacity 0.6' so that the x-axis grid lines are visible. An alternative would be to change the AxZIndex value for the X encoding so that it is drawn on top of the bars.

## Plotting with points

At this point we make a signifiant detour from the Elm Vega-Lite walkthtough, and look at the Point mark, rather than creating small-multiple plots. Don't worry, we'll get to them later.

Here I use the Point mark to display the individual Gmag, plx pairs, encoding by both color and 'shape. Since the encoding uses the same field of the data (the Cluster name), Vega-Lite is smart enough to only display one legend, which contains the point shape and color used for each cluster.

Since the parallax values are bunched together at low values, a logarithmic scale (ScLog) is used for the y axis, along with commands to define the actual axis domain - by turning off the IsNice support and listing the minimum and maximum values for the axis with SDomain.

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "Gmag", PmType Quantitative ]
. position Y [ PName "plx", PmType Quantitative, PScale scaleOpts ]
. color cluster
. shape cluster

scaleOpts = [ SType ScLog, SDomain (DNumbers [3.5, 32]), SNice (IsNice False) ]
cluster = [ MName "Cluster", MmType Nominal ]

, mark Point []
, enc []
, width 400
, height 400
]


We can see that each cluster appears to have a separate parallax value, and that it doesn't really vary with Gmag. What this is telling us is that for these star clusters, the distance to each member star is similar, and that they are generally at different distances from us. However, it's a bit hard to tell exactly what is going on around 5 to 6 milli arcseconds, as the clusters overlap here.

This line of thinking leads us nicely to map making, but before we try some cartography, I wanted to briefly provide some context for these plots. The parallax of a star is a measure of its distance from us, but it is an inverse relationship, so that nearer stars have a larger parallax than those further from us. The Gmag column measures the apparent brightness of the star, with the G part indicating what part of the spectrum is used (for Gaia, the G band is pretty broad, covering much of the visible spectrum), and the mag part is because optical Astronomy tends to use

so that larger values mean fainter sources. These are also apparent magnitues, so that they measure the flux of the star as measured at Earth, rather than its intrinsic luminosity (often defined as an object's absolute magnitude).

We can see that the further the cluster is from us - that is, as we move down this graph to smaller parallax values - then the smallest stellar magnitude we can see in a cluster tends to increase, but that there are stars up to the maximum value (20) in each cluster. This information can be used to look at the distribution of absolute magnitudes of stars in a cluster, which tells us about its evolutionary state - such as is it newly formed or old - amongst other things. However, this is straying far from the aim of this tutorial, so lets get back to plotting things.

# Making a map

We have some hint that the different clusters are distinct objects in space, in that they appear to be different distances from us, but where does the "cluster" in the name "Stellar Cluster" come from? Well, we can try plotting up the position of each star on the sky - using the RA_ICRS and DE_ICRS fields - to find out.

The following specification should only contain one new feature - other than sneakily switching from Point to Circle type for the mark - and that is displaying the x axis (namely Right Ascension) in reverse (using PSort [ Descending ]. This is needed because Right Ascension is measured from right to left. I like to explain it by talking about oranges, and how we are at the center of an orange looking out at its skin, and so have the direction reversed to if you were outside, looking in. You can see that we also have one cluster that straddles the 0 and 360 degrees Right Ascension barrier, which will lead to some fun later.

Open this visualization in the Vega Editor

let enc = encoding
. position X (axOpts "RA_ICRS" "Right Ascension (deg)" ++ [ raScale, PSort [ Descending ] ])
. position Y (axOpts "DE_ICRS" "Declination (deg)" ++ [ decScale ])
. color [ MName "Cluster", MmType Nominal ]

axOpts field lbl = [ PName field, PmType Quantitative, PAxis [ AxTitle lbl ]]

scaleOpts minVal maxVal = [ SDomain (DNumbers [ minVal, maxVal ]), SNice (IsNice False) ]
raScale = PScale (scaleOpts 0 360)
decScale = PScale (scaleOpts (-90) 90)

, mark Circle []
, enc []
, width 400
, height 300
]


We can see that these clusters are indeed localised on the sky, with Hyades looking like it covers the largest area. However, we should be careful and not forget either Grover's hard work or Father Ted's explanation to Father Dougal, since these clusters are different distances from us, which makes size a tricky thing to measure from this plot.

There is also the fact that I have used possibly the worst way of displaying the Right Ascension and Declination data. Although the night sky is not the same as the Earth's surface, the issues when trying to display the Globe on a flat surface also apply to displaying up the sky. For this plot the distortions near the pole are huge, although fortunately we don't have any clusters too close to either pole.

Vega-Lite supports a large number of projections - via the Projection type - which we use below to create a similar visualization to posPlot. Here I use the Longitude and Latitude channels, along with a Mercator projection, to display the data.

The trick in this case is that longitude runs from -180 to 180 degrees, but the data has Right Ascension going from 0 to 360 degrees. We can take advantage of Vega Lite's data transformation capabilities and create a new column - which I call fakeLongitude - and is defined as "180 - Right Ascension". The "expression" support is essentially a sub-set of Javascript, and the datum object refers to the current row. The new data column can then be used with the Longitude channel.

Open this visualization in the Vega Editor

let enc = encoding
. position Longitude (axOpts "fakeLongitude")
. position Latitude (axOpts "DE_ICRS")
. color [ MName "plx"
, MmType Quantitative
, MScale [ SType ScLog
, SScheme "viridis" []
]
]
. tooltip [ TName "Cluster", TmType Nominal ]

axOpts field = [ PName field, PmType Quantitative ]

trans = transform
. calculateAs "180 - datum.RA_ICRS" "fakeLongitude"

in toVegaLite [ projection [ PrType Mercator ]
, trans []
, mark Circle []
, enc []
]


The other major change made to posPlot is that the stars are now color-encoded by the log of their parallax value rather than cluster membership, and the color scheme has been changed to use the "viridis" color scale. Since parallax is a numeric value, with ordering (i.e. Quantitative), the legend has changed from a list of symbols to a gradient bar. To account for this lost of information, I have added a tooltip encoding so that when the pointer is moved over a star its cluster name will be displayed (this replaces the default behavior when all the encoded channels, so in this case the position and parallax values, would be displayed, and it is only visible in "interactive" versions of the visualization).

From this visualization we can see that the apparent size of the cluster (if we approximate each cluster as a circle, then we can think of the radius of the circle as a measure of size) depends on parallax, with larger sizes having larger parallaxes. This is because the distance to a star is inversely-dependent on its parallax, so larger parallaxes mean the star is closer to us. However, there is no reason that the intrinsic size - that is its actual radius - of each cluster is the same. We can see that although the Hyades and Pleiades clusters overlap on the sky, they have significantly-different parallaxes (as can be seen in pointPlot for example), with Hyades being the closer of the two.

It is possible to add graticules - with the aptly-named graticule function - but this appears to need to be done as a layer, which we haven't covered yet. I may get to it below, but am having teensy problems with getting it working in the Vega Lite viewer I use (the one provided with Jupyter notebook, which defaults to an earlier version of Vega-Lite support, which doesn't seem to like the more-recent capabilities of Vega Lite).

# Layered and Multi-View Compositions

The Stacked-Histogram plot - created by gmagHistogramWithColor - showed the distribution of the "Gmag" field by cluster, but it was hard to compare them. A common approach in this situation is to split up the data into multiple plots - the small multiple approach (also known as trellis plots) - which we can easily achieve in Vega Lite. It also gets us back on track with the Elm walkthrough.

Our first attempt is with the column function, which tells Vega-Lite to create a plot for each Cluster field (and introduces us to the F family of FacetChannel constructors).

The legend has been turned off with MLegend [], since it doesn't add anything to this visulization (as the individual plots, labelled by the cluster name, provide the same information).

Open this visualization in the Vega Editor

let enc = encoding
. position X [ PName "Gmag", PmType Quantitative, PBin [] ]
. position Y yAxis
. color [ MName "Cluster", MmType Nominal, MLegend [] ]
. column [ FName "Cluster", FmType Nominal ]

yAxis = [ PAggregate Count
, PmType Quantitative
, PAxis [ AxTitle "Number of Stars" ]
]

in toVegaLite
, mark Bar []
, enc []
]


Since we have nine clusters in the sample, the overall visualization is too wide, unless you have a very-large monitor. Can we do better?

The number of columns used in small-multiple can be defined using the columns function. However, this requires us to:

• move the facet definition out from the encoding and into the top-level, with the facetFlow function;
• and define the plot as a separate specification, and apply it with specification and asSpec.

The actual syntactic changes to smallMultiples are actually fairly minor:

let enc = encoding
. position X [ PName "Gmag", PmType Quantitative, PBin [] ]
. position Y yAxis
. color [ MName "Cluster", MmType Nominal, MLegend [] ]

yAxis = [ PAggregate Count
, PmType Quantitative
, PAxis [ AxTitle "Number of Stars" ]
]

in toVegaLite
, columns 4
, facetFlow [ FName "Cluster", FmType Nominal ]
, specification (asSpec [ mark Bar [], enc [] ])
]


Open this visualization in the Vega Editor

Note that Vega Lite does support a "facet" field in its encodings, but hvega follows Elm VegaLite and requires you to use this wrapped facet approach.

## One plot, two plot, red plot, blue plot

There are four ways in which multiple views may be combined:

• The facet operator takes subsets of a dataset (facets) and separately applies the same view specification to each of those facets (as seen with the column function above). Available functions to create faceted views: column, row, facet, facetFlow, and specification.
• The layer operator creates different views of the data but each is layered (superposed) on the same same space; for example a trend line layered on top of a scatterplot. Available functions to create a layered view: layer and asSpec.
• The concatenation operator allows arbitrary views (potentially with different datasets) to be assembled in rows or columns. This allows 'dashboards' to be built. Available functions to create concatenated views: vConcat, hConcat, and asSpec.
• The repeat operator is a concise way of combining multiple views with only small data-driven differences in each view. Available functions for repeated views: repeat and specification.

We start with a "basic" plot for the dataset: the median value of the parallax of the stars in each cluster.

Open this visualization in the Vega Editor

let plx = position Y [ PName "plx", PmType Quantitative, PAggregate Median ]
cluster = position X [ PName "Cluster", PmType Nominal ]
enc = encoding . cluster . plx

in toVegaLite
, mark Bar []
, enc []
]


## Composing layers

We start our exploration by combining two visualizations, layering one on top of the other. The base plot shows the same data as basePlot, and then on top we will show a horizontal line that indicates the median parallax for all the stars in the sample.

Open this visualization in the Vega Editor

let plx = position Y [ PName "plx", PmType Quantitative, PAggregate Median ]
cluster = position X [ PName "Cluster", PmType Nominal ]

perCluster = [ mark Bar [], encoding (cluster []) ]
allClusters = [ mark Rule [] ]

in toVegaLite
, encoding (plx [])
, layer (map asSpec [perCluster, allClusters])
]


For this visualization, the specification starts with the data source and an encoding, but only for the y axis (which means that all layered plots use the same encoding for the axis). The layer function introduces the different visualizations that will be combined, each as there own "specification" (hence the need to apply asSpec to both perCluster and allClusters). Note that there is no x-axis encoding for the Rule, since the data applies to all clusters (i.e. it should span the whole visualization).

This example is similar to layeredPlot but includes an x-axis encoding for the second layer. We use this to show the range of the data - so the minimum to maximum parallax range of each cluster - with the Rule type. The difference to the previous plot is that an extra positional encoding is added (Y2) to define the end point of each line (Y is used as the start point).

Open this visualization in the Vega Editor

let plx op = position Y [ PName "plx", PmType Quantitative, PAggregate op ]
cluster = position X [ PName "Cluster", PmType Nominal ]

median = [ mark Circle [ MSize 20 ]
, encoding (plx Median [])
]
range = [ mark Rule [ ]
, encoding
. plx Min
. position Y2 [ PName "plx", PAggregate Max ]
[] ] in toVegaLite [ gaiaData , encoding (cluster []) , layer (map asSpec [ median, range ]) , width 300 , height 300 ]  The MSize option is used to change the size of the circles so that they do not drown out the lines (the size value indicates the area of the mark, and so for circles the radius is proportional to the square root of this size value; in practical terms I adjusted the value until I got something that looked sensible). Note that the y axis is automatically labelled with the different operation types that were applied - median, minimum, and maximum - although there is no indication of what marks map to these operations. ## Concatenating views Instead of layering one view on top of another (superposition), we can place them side by side in a row or column (juxtaposition). In Vega-Lite this is referred to as concatenation: Open this visualization in the Vega Editor let enc field = encoding . position X [ PName "Cluster", PmType Nominal ] . position Y [ PName field, PmType Quantitative, PAggregate Median ] parallaxes = [ mark Bar [], enc "plx" [] ] magnitudes = [ mark Bar [], enc "Gmag" [] ] specs = map asSpec [ parallaxes, magnitudes ] in toVegaLite [ gaiaData , vConcat specs ]  The hConcat function would align the two plots horizontally, rather than vertically (and is used in combinedPlot). Note that as the axes are identical apart from the field for the y axis, the encoding has been moved into a function to enforce this constraint (this ensures the x axis is the same, which makes it easier to visually compare the two plots). However, there is no requirement that the two plots be "compatible" (they could use different data sources). The alignment of the plots can be adjusted with spacing, which we use here to remove the vertical gap between the two plots (the example is written so that we can see the only difference between the two plot specifications is the addition of PAxis [] to the parallax plot). Open this visualization in the Vega Editor let enc field flag = encoding . position X ([ PName "Cluster", PmType Nominal ] ++ if flag then [ PAxis [] ] else []) . position Y [ PName field, PmType Quantitative, PAggregate Median ] parallaxes = [ mark Bar [], enc "plx" True [] ] magnitudes = [ mark Bar [], enc "Gmag" False [] ] specs = map asSpec [ parallaxes, magnitudes ] in toVegaLite [ gaiaData , spacing 0 , vConcat specs ]  Even though we set spacing to 0 there is still a small gap between the plots: this can be removed by using bounds Flush, but we'll leave using that until the grand finale. ## Repeated views Creating the same plot but with a different field is common-enough that Vega-Lite provides the repeat operator. The concatenatedPlot example can be extended to view the distribution of several fields - in this case Right Ascension, Declination, parallax, and magnitude: Open this visualization in the Vega Editor let enc = encoding . position X [ PName "Cluster", PmType Nominal ] . position Y [ PRepeat Row, PmType Quantitative, PAggregate Median ] spec = asSpec [ gaiaData , mark Bar [] , enc [] ] rows = [ "RA_ICRS", "DE_ICRS", "plx", "Gmag" ] in toVegaLite [ repeat [ RowFields rows ] , specification spec ]  This more compact specification replaces the data field name (for example PName "plx") with a reference to the repeating field (PRepeat) either as a Row or Column depending on the desired layout. We then compose the specifications by providing a set of RowFields (or ColumnFields) containing a list of the fields to which we wish to apply the specification (identified with the function specification which should follow the repeat function provided to toVegaLite). We can combine repeated rows and columns to create a grid of views, such as a scatterplot matrix, adding in color encoding to separate out the clusters: Open this visualization in the Vega Editor let enc = encoding . position X [ PRepeat Column, PmType Quantitative ] . position Y [ PRepeat Row, PmType Quantitative ] . color [ MName "Cluster", MmType Nominal ] spec = asSpec [ gaiaData , mark Point [ MSize 5 ] , enc [] ] fields = [ "RA_ICRS", "DE_ICRS", "plx", "Gmag" ] in toVegaLite [ repeat [ RowFields fields, ColumnFields fields ] , specification spec ]  To be honest, this is not the best dataset to use here, as there is no direct correlation between location (the RA_ICRS and DE_ICRS fields) and the other columns, but it's the dataset I chose, so we are stuck with it. Once you have sub-plots as a specification, you can combine them horizontally and vertically to make a dashboard style visualization. Interested parties should check out the Building a Dashboard section of the Elm Vega-Lite Walkthrough for more details. # Interactivity Interaction is enabled by creating selections that may be combined with the kinds of specifications already described. Selections involve three components: • Events are those actions that trigger the interaction such as clicking at a location on screen or pressing a key. • Points of interest are the elements of the visualization with which the interaction occurs, such as the set of points selected on a scatterplot. • Predicates (i.e. Boolean functions) identify whether or not something is included in the selection. These need not be limited to only those parts of the visualization directly selected through interaction. Arguments  :: Text The selection name -> Text The title for the plot -> [PropertySpec] The next several plots show different types of selection - select a single point, a range of plots, or follow the mouse - and all have the same basic structure. To avoid repetition, and mistakes, I am going to introduce a helper function which creates the plot structure but without the selection definition, and then use that to build up the plots. The helper function, selectionProperties, takes two arguments, which are the selection name and the plot title. The selection name is used to identify the selection, as a visualization can support multiple selections, and the plot title has been added mainly to show some minor customization (the use of TOrient to move the title to the bottom). The definition of this helper function is: selectionProps selName label = let posOpts field = [ PName field , PmType Quantitative , PScale [ SZero False ] ] enc = encoding . position X (posOpts "Gmag") . position Y (posOpts "plx") . color [ MSelectionCondition (SelectionName selName) [ MName "Cluster", MmType Nominal ] [ MString "grey" ] ] . opacity [ MSelectionCondition (SelectionName selName) [ MNumber 1.0 ] [ MNumber 0.3 ] ] . size [ MSelectionCondition (SelectionName selName) [ MNumber 40 ] [ MNumber 5 ] ] trans = transform . filter (FExpr "datum.DE_ICRS < -20") in [ gaiaData , trans [] , mark Point [] , enc [] , title label [ TOrient SBottom ] ]  The three non-selection-related features added here are that SZero is used to tell Vega Lite that we do not need 0 displayed on either axis, which leads to a "tight" bounding box around the data, a filter is used to select a subset of rows, namely only those with a declination less than -20 (via FExpr), and the plot title is moved to the bottom with TOrient. The main change is that the selection is used in the encoding section, identified by name, using SelectionName and the supplied argument. It is used as a filter for the encoding section, where MSelectionCondition defines the properties to use when the selection occurs (the first list of properties) and when it does not (the second list). This is used for three different encodings: • color, where the selected star is labelled by its cluster color, and all the other are grey; • opacity, so that the selected star is fully opaque whereas un-selected stars are partially transparent; • and size, so that the selected star is much bigger than the others. When no selection has been made - such as when the visualization is first created - then all points are encoded with the "selected" case (so colorful, fully opaque, and large in this case). The actual plot just requires the selection information to be defined and then added to the plot properties: Open this visualization in the Vega Editor let selLabel = "picked" sel = selection . select selLabel Single [] in toVegaLite (sel [] : selectionProperties selLabel "Select a point")  The selection function is used to define the selection, via one or more applications of the select function. The form of select is that the selection is named, in this case we use "picked", and the type is given (a Single click), and then options, which in our case there aren't any, so an empty list is used. Note that hvega does not track the selection names, and will allow you to use a name that you have not defined. The only change here is to add a property to the selection - that is Nearest True - which means that the nearest point to the click will be highlighted. Open this visualization in the Vega Editor let selLabel = "picked" sel = selection . select selLabel Single [ Nearest True ] in toVegaLite (sel [] : selectionProperties selLabel "Select nearest point")  One consequence of this change is that once a point has been selected you can not remove this (i.e. un-select the point). This is in contrast to singleSelection, where clicking on an area with no stars would remove the previous selection. The Clear property can be added to the list to define a way to clear the selection. The selection can easily be changed to allow multiple stars to be selected, using shift-click, by swapping from Single to Multi. Open this visualization in the Vega Editor let selLabel = "this is just a label" sel = selection . select selLabel Multi [] in toVegaLite (sel [] : selectionProperties selLabel "Shift click to select points")  We can take advantage of browser event by using On to define which event to use, such as mouse movement over points: Open this visualization in the Vega Editor let selLabel = "picked" sel = selection . select selLabel Multi [ On "mouseover" ] in toVegaLite (sel [] : selectionProperties selLabel "Move the pointer to select a point")  The supported list of events is described in the Vega Event-Stream Selectors documentation. The addition of Nearest True to the list of properties sent to select would avoid the flickering, as the mouse moves between the stars. The final Selection value is Interval, which lets you drag a rectangle to select the interior points: Open this visualization in the Vega Editor let selLabel = "naming is hard" sel = selection . select selLabel Interval [ ] in toVegaLite (sel [] : selectionProperties selLabel "Drag a rectangle to select points")  The default interval option is to select a rectangle, but it can be restricted - such as to select all items within a range along a given axis using Encodings: Open this visualization in the Vega Editor let selLabel = "naming is still hard" sel = selection . select selLabel Interval [ Encodings [ ChY ] ] in toVegaLite (sel [] : selectionProperties selLabel "Drag to select points by parallax")  We'll come back to further things to do with interval selections when we get to interactive plots below (see bindScales). ## Selection Transformations Simple selections as described above create sets of selected data marks based directly on what was interacted with by the user. Selection transformations allow us to project that direct selection onto other parts of our dataset. For example, we can adjust the visualization to select all stars in the same cluster, which is useful in this case since the Blanco1 and IC2391 clusters occupy the same space in the magnitude-parallax plane. This is invoked simply by adding the Fields constructor to the select parameters naming the fields onto which we wish to project our selection. Additionally, we have set the default selection with Empty so that if nothing is selected, the selection is empty (as we have previously seen, without this the default selection is the entire encoded dataset). Open this visualization in the Vega Editor let sel = selection . select "pick" Single [ Fields [ "Cluster" ] , Empty , Nearest True ] in toVegaLite (sel [] : selectionProperties "pick" "Select a point, select a cluster")  ### Selection and bindings Selection need not be limited to direct interaction with the visualization marks. We can also bind the selection to other user-interface components. The Elm Vega-Lite walkthrough uses a dataset which has a column for which a range-slider makes sense. The dataset I'm using is less rich, and so I am going to use a HTML select widget - a drop-down list of values - instead. This lets the user select all stars from a given cluster, and is introduced with the Bind and ISelect constructors. The InOptions list is given the values of the Cluster column that can be selected: I start with a value not in the list (none) just to indicate that no values are selected, and then the list of clusters in this sub-sample (remembering that selectionProperties applies a declination cut off). Eagle-eyed readers will note that the cluster names in this list (the clusters variable) end in spaces: this is because the input data file has the cluster names stored in an eight-character field, even though it is a tab-separated file. This surprised me when I first tried this visualization, and using the value "Blanco1" did not select anything! Isn't working with data so much fun! Open this visualization in the Vega Editor let picked = "picked" clusters = [ "none", "Blanco1 ", "IC2391 ", "IC2602 ", "NGC2451 " ] sel = selection . select picked Single [ Fields [ "Cluster" ] , Bind [ ISelect "Cluster" [ InOptions clusters ] ] , Empty ] conf = configure . configuration (Background "beige") in toVegaLite (conf [] : sel [] : selectionProperties picked "Please select a cluster")  Note that the selection works both ways: the HTML widget can be used to select a cluster, and clicking on a point on the visualization will update the HTML widget. Unlike the other plots shown in the tutorial, this is a screen grab rather than a PNG file created by Vega Embed. The background color was changed - following the approach used in stripPlotWithBackground - to show where the visualization "ends" and the HTML select element starts. It also shows the Vega Embed "drop-down" menu in the top-right corner, namely the three dots in a circle. The selection can also be bound to an axis (or both axes, as in this case), using BindScales (applying it to the intervalSelectionY plot). Open this visualization in the Vega Editor let picked = "picked" sel = selection . select picked Interval [ Encodings [ ChX, ChY ], BindScales ] in toVegaLite (sel [] : selectionProperties picked "Drag or zoom the axes")  The image here was created after panning and zooming into the data. ### Multiple Coordinated Views One of the more powerful aspects of selection-based interaction is in coordinating different views – a selection of a data subset is projected onto all other views of the same data. The following plot doesn't contain anything new, but allows us to select a rectangular-range on one plot, and see the same selection automatically applied to the other plots. This is achieved by combining the repeat fuction with the selection; this causes the selection to be projected across all views as it is duplicated. For this example we use all the clusters, rather than the subset of Southern ones. One trick I use is to convert the Right Ascension values (which have a domain of 0 to 360 degrees, and wrap around at the 0/360 mark), into their cosine values (remembering to convert to radians first), and display that instead. This ensures the "Blanco1" cluster members are spatially co-located on this axis - with values close to 1 - rather than appearing near 0 and 360. Open this visualization in the Vega Editor let enc = encoding . position X [ PRepeat Column, PmType Quantitative ] . position Y [ PRepeat Row, PmType Quantitative ] . color [ MSelectionCondition (SelectionName "picked") [ MName "Cluster", MmType Nominal ] [ MString "grey" ] ] sel = selection . select "picked" Interval [ ] trans = transform . calculateAs "cos(datum.RA_ICRS * PI / 180)" "cosRA" spec = asSpec [ gaiaData , trans [] , mark Circle [] , enc [] , sel [] ] in toVegaLite [ repeat [ RowFields [ "cosRA", "DE_ICRS" ] , ColumnFields [ "plx", "Gmag" ] ] , specification spec ]  If the interval selection is bound the the axes with BindScales then we can zoom and pan the related plots - i.e. changing the range displayed in one plot will also change the two plots that it shares an axis with in this two by two arrangement. The conditional encoding of the color channel has also been removed. Open this visualization in the Vega Editor The image was captured after panning and zooming in the "parallax-RA_ICRS" plot. let enc = encoding . position X [ PRepeat Column, PmType Quantitative ] . position Y [ PRepeat Row, PmType Quantitative ] . color [ MName "Cluster", MmType Nominal ] sel = selection . select "picked" Interval [ BindScales ] spec = asSpec [ gaiaData , mark Circle [] , enc [] , sel [] ] in toVegaLite [ repeat [ RowFields [ "RA_ICRS", "DE_ICRS" ] , ColumnFields [ "plx", "Gmag" ] ] , specification spec ]  The "cosine" transformation has been removed in comparison to coordinatedViews. The ability to determine the scale of a chart based on a selection is useful in implementing a common visualization design pattern, that of 'context and focus' (or sometimes referred to as 'overview and detail on demand'). We can achieve this by setting the scale of one view based on the selection in another. The detail view is updated whenever the selected region is changed through interaction: Open this visualization in the Vega Editor let sel = selection . select "brush" Interval [ Encodings [ ChY ] ] encContext = encoding . position X [ PName "Gmag", PmType Quantitative, PScale [ SZero False ] ] . position Y [ PName "plx", PmType Quantitative ] specContext = asSpec [ width 400 , height 80 , sel [] , mark Point [] , encContext [] , title "Select a Y range to zoom in below" [] ] encDetail = encoding . position X [ PName "Gmag" , PmType Quantitative , PScale [ SZero False ] , PAxis [ AxNoTitle ] ] . position Y [ PName "plx" , PmType Quantitative , PScale [ SDomain (DSelection "brush") ] ] . color [ MName "Cluster", MmType Nominal ] specDetail = asSpec [ width 400, mark Point [], encDetail [] ] in toVegaLite [ gaiaData , vConcat [ specContext, specDetail ] ]  Not shown here, but selecting a range of y-values in the top plot (specContext) will cause the second plot (specDetail) to zoom in on that range, as the selection is bound to the y axis of this plot via DSelection. ### Cross-filtering The final example in this section brings together ideas of view composition and interactive selection with data filtering by implementing cross-filtering: the selection of a subset of the data in one view then only displaying that data in the other views. Here we show distributions of the four main numeric quantities in the dataset - position, magnitude, and prallax - using the totalEnc encoding, and add a second layer which repeats this data but with a different color (selectedEnc), and that is tied to the interval-selection along the x axis (ChX). Open this visualization in the Vega Editor Selecting a small range of parallax values in the fourth plot highlights the associated data in the other three plots. let sel = selection . select "brush" Interval [ Encodings [ ChX ] ] filterTrans = transform . filter (FSelection "brush") -- borrow a function from Elm pQuant = PmType Quantitative totalEnc = encoding . position X [ PRepeat Column, pQuant ] . position Y [ PAggregate Count, pQuant ] selectedEnc = totalEnc . color [ MString "goldenrod" ] in toVegaLite [ repeat [ ColumnFields [ "RA_ICRS", "DE_ICRS", "Gmag", "plx" ] ] , specification
asSpec
, layer
[ asSpec [ mark Bar [], totalEnc [] ]
, asSpec [ sel [], filterTrans [], mark Bar [], selectedEnc [] ]
]
]
]


# Errors: lines, bars, bands, and boxes

Here we dive into some of the ways for representing the spread of a value, focussing on the "error" of a variable.

We have already seen "error bars" in the layeredDiversion plot, where the Rule type was used to draw a line between the Y and Y2 encodings. In that example the two positions were calculated "on the fly" by Vega-Lite (using the Min and Max aggregation operations). In this example I use the data to calculate the display range, namely plx - e_plx to plx + e_plx. These are mapped to the X and X2 channels (not because it makes a better visualization, but just to show you can create lines along the x axis), and a small-multiples approach is used to separate out the clusters, but only after a filter designed to select the two clusters - with the "most interesting" data for this plot - has been applied.

Open this visualization in the Vega Editor

let trans = transform
. filter (FExpr "datum.Cluster[0] == 'C' || datum.Cluster[0] == 'H'")
. calculateAs "datum.plx - datum.e_plx" "plx_lo"
. calculateAs "datum.plx + datum.e_plx" "plx_hi"

errorEnc = encoding
. position X [ PName "plx_lo"
, PmType Quantitative
, PScale [SZero False]
, PAxis [ AxTitle "parallax (mas)" ]
]
. position X2 [ PName "plx_hi" ]
. position Y [ PName "Gmag", PmType Quantitative ]
. column [ FName "Cluster", FmType Nominal ]

sel = selection
. select "picked" Interval [ BindScales ]

, trans []
, errorEnc []
, mark Rule []
, sel []
]


For the interested reader, it was the calculation of the "plx_hi" column that lead me to the discovery that the columns were being read in as a string, and the introduction of the Parse option to gaiaData.

As can be seen, the e_plx terms are generally very small. This is good for anyone using the data, as we want precise measurements, but makes it harder for me to come up with meaningful visualizations! I have taken advantage of the BindScales interaction to zoom in on a subset of stars which show larger parallax errors:

Alternatively, I could have made life simpler for myself and used the ErrorBar (or ErrorBand) mark type, together with XError (or YError) to indicate that the channel gives the offset from the central value. For this visualization I restrict to a single cluster (since I now know there's only one in this sample which begins with C), but retain the column encoding as a means to getting a useful title. I've also switched things so that the errors are back along the y axis.

Open this visualization in the Vega Editor

let trans = transform
. filter (FExpr "datum.Cluster[0] == 'C'")

errorEnc = encoding
. position Y [ PName "plx"
, PmType Quantitative
, PScale [SZero False]
, PAxis [ AxTitle "parallax (mas)" ]
]
. position YError [ PName "e_plx" ]
. position X [ PName "Gmag", PmType Quantitative ]
. column [ FName "Cluster", FmType Nominal ]

, trans []
, errorEnc []
, mark ErrorBar []
]


In this plot the error range is calculated by Vega-Lite, and is taken from the standard deviation of the Gmag field (StdDev). The MTicks and MRule constructors are used to color the different parts of the error bars. Since the error bar does not reference the central value, a separate layer is used to add a square symbol (SymSquare) at the average (Mean) value of the distribution.

Open this visualization in the Vega Editor

let cluster = position X [ PName "Cluster", PmType Nominal ]

barOpts = [ MExtent StdDev
, MTicks [ MColor "purple" ]
, MRule [ MColor "teal" ]
]
range = [ mark ErrorBar barOpts
, encoding
. position Y [ PName "Gmag"
, PmType Quantitative
, PScale [ SZero False ]
]
$[] ] center = [ mark Point [ MShape SymSquare, MSize 20 ] , encoding . position Y [ PName "Gmag" , PmType Quantitative , PmType Mean ]$ []
]

in toVegaLite
, encoding (cluster [])
, layer (map asSpec [ range, center ])
, width 300
, height 300
]


The next plot shows the ErrorBand mark, which fills the area between the chosen range with a color, and optional borders. Here the blue band shows the calculated standard deviation - as used in errorBars - and the gray band with borders shows the inter-quartile range. On top of these are drawn the median (blue) and median (green dashed) lines.

Open this visualization in the Vega Editor

let posY extra = position Y ([ PName "Gmag"
, PmType Quantitative
, PScale [ SZero False ]
] ++ extra) []

bands = [ [ encoding (posY [])
, mark ErrorBand [ MExtent StdDev ]
]
, [ encoding (posY [])
, mark ErrorBand [ MExtent Iqr
, MBorders [ MStrokeDash [ 6, 2 ] ]
, MColor "gray"
]
]
, [ encoding (posY [ PAggregate Median ])
, mark Line []
]
, [ encoding (posY [ PAggregate Mean ])
, mark Line [ MColor "green"
, MStrokeDash [ 6, 2, 4, 2 ]
]
]
]

in toVegaLite
, encoding (position X [ PName "Cluster", PmType Nominal ] [])
, layer (map asSpec bands)
, width 300
, height 300
, title "Comparing ranges" []
]


Note that I don't think this is a good visualization for this particular dataset, since it implies there's a connection or correlation between clusters, as given by the x-axis ordering, but the aim here is to show how to use hvega rather than creating sensible plots!

An alternative visualization of a distribution is the "box and whiskers" plot, which can be achieved in hvega with the Boxplot mark. The example below shows the default settings, but the various components can be controlled with MBox, MMedian, MOutliers, and MTicks.

Open this visualization in the Vega Editor

toVegaLite
, encoding
. position X [ PName "Cluster", PmType Nominal ]
. position Y [ PName "Gmag", PmType Quantitative ]
[] , mark Boxplot [] , width 300 , height 300 ]  The Boxplot option supports two different "ranges": • the default is the Tukey Box plot, where the whiskers span a range Q1 - k * IQR to Q3 + k * IQR, IQR = Q3 - Q1, Q1 and Q3 are the lower and upper inter-quartile values (so 25 and 75 per cent of the distribution), and k defaults to 1.5 but can be changed with IqrScale; • or ExRange, which uses the full range of the data (i.e. minimum to maximum values). Here I combine errorBox with smallMultiples2 so we can compare the distribution (from the histogram) with that from the box plot. Open this visualization in the Vega Editor let histEnc = encoding . position X [ PName "Gmag", PmType Quantitative, PBin [] ] . position Y yAxis . color [ MName "Cluster", MmType Nominal, MLegend [] ] errEnc = encoding . position X [ PName "Gmag", PmType Quantitative ] . position Y [ PNumber 80 ] . color [ MName "Cluster", MmType Nominal, MLegend [] ] yAxis = [ PAggregate Count , PmType Quantitative , PAxis [ AxTitle "Number of Stars" ] ] boxOpts = [ MMedian [ MColor "black" ] , MBox [ MStroke "white" ] , MNoOutliers ] histSpec = asSpec [ mark Bar [], histEnc [] ] errSpec = asSpec [ mark Boxplot boxOpts, errEnc [] ] combinedSpec = asSpec [ layer [ histSpec, errSpec ] ] in toVegaLite [ gaiaData , columns 4 , facetFlow [ FName "Cluster", FmType Nominal ] , specification combinedSpec ]  The main additions here are the configuration of the box plot - with MMedian, MBox (used to ensure the box is visually distinct from the bar for the Pleiades cluster, where they overlap), and MNoOutliers (to turn off the display of the outliers) - and the use of PNumber to define the location on the y axis of the boxplot visualization. Note that PNumber is defined in pixel units, with 0 being the top of the visualization and 80 was found by trial and error. # Dashboard-esque In the following visualization I try to combine as many of the concepts we have explored in this tutorial into one. There are layers, combined visualizations, and a selection that ties the different plots together. This is based on the Marginal Histogram example from the Vega-Lite Example Gallery. There is very-little new in this plot, in that pretty-much everything has been shown before. However, there are some interesting wrinkles, such as • combining multiple plots, in this case the "top" area - which is a histogram on top of a plot which is itself a "map" and a histogram - and "bottom" area - which is just a point plot - requires judicious use of asSpec; • selection works in both the main plots - the "map" and "point" plots - to highlight all stars in the same cluster, and I was pleasantly surprised to find out I could just use the same selection specification (selCluster) in both (hopefully I am not just ejnoying a hole in the Vega Lite specification); • I have been perhaps too defensive in defining the Right Ascension and Declination axes in the relevant plots, as I want to make sure the histogram bins and plot axes are well aligned (that is the Nice False statements may not be needed when defining the histogram axes); • I am not 100% sure I understand what is going on with the grid labels on the Declination axis, as I had thought I was asking for marks every 15 degrees, but the plot shows them every 30 degrees (however, if I change the deTicks array then the marks change in ways I currently do not understand); • and I have decided to display Right Ascension in hours, rather than degrees, because why have one way to measure a value when you can have many! Open this visualization in the Vega Editor (although the link is long, and may not work with Internet Explorer) let trans = transform . calculateAs "datum.RA_ICRS / 15" "RA" quant n = [ PName n, PmType Quantitative ] big = 400 small = 100 wmain = width big hmain = height big wsub = width small hsub = height small noTitle = PAxis [ AxNoTitle ] raAxis = [ PScale [ SDomain (DNumbers [ 0, 24 ]) , SNice (IsNice False) ] , PSort [ Descending ] , PAxis [ AxTitle "Right Ascension (hours)" ] ] deMin = -90 deMax = 90 deStep = 15 -- we do not get ticks/grids at all these values, but it does -- something (e.g. if do not specify the axis ticks are different) -- deTicks = Numbers [ deMin, deMin + deStep .. deMax ] deAxis = [ PScale [ SDomain (DNumbers [ deMin, deMax ]) , SNice (IsNice False) ] , PAxis [ AxTitle "Declination (degrees)" , AxValues deTicks ] ] colorEnc = color [ MSelectionCondition (SelectionName "pick") [ MName "Cluster", MmType Nominal ] [ MString "grey" ] ] mapEnc = encoding . position X (quant "RA" ++ raAxis) . position Y (quant "DE_ICRS" ++ deAxis) . colorEnc circleMark = mark Circle [ MOpacity 0.5 ] mapSpec = asSpec [ mapEnc [] , circleMark , wmain , hmain , selCluster [] ] -- histogram of the RA values -- raBinning = [ PBin [ Extent 0 24 , Step 2 , Nice False ] , PSort [ Descending ] , PAxis [] ] -- histogram of the Declination values -- deBinning = [ PBin [ Extent deMin deMax , Step deStep , Nice False ] , PAxis [] ] histAxis = [ PAggregate Count , PmType Quantitative , noTitle , PScale [ SDomain (DNumbers [ 0, 3000 ]) ] ] raEnc = encoding . position X (quant "RA" ++ raBinning) . position Y histAxis deEnc = encoding . position Y (quant "DE_ICRS" ++ deBinning) . position X histAxis allRA = [ raEnc [] , mark Bar [ MColor "gray" ] ] filtRA = [ filterCluster [] , raEnc . colorEnc []
, mark Bar []
]

allDE = [ deEnc []
, mark Bar [ MColor "gray" ]
]
filtDE = [ filterCluster []
, deEnc
. colorEnc
$[] , mark Bar [] ] raSpec = asSpec [ wmain, hsub, layer [ asSpec allRA, asSpec filtRA ] ] deSpec = asSpec [ hmain, wsub, layer [ asSpec allDE, asSpec filtDE ] ] borderSpacing = 20 mapAndDecSpec = asSpec [ spacing borderSpacing , bounds Flush , hConcat [ mapSpec, deSpec ] ] histSpecs = [ raSpec, mapAndDecSpec ] -- select the cluster which the star belongs to; do not use -- "nearest click" as that means a user can not cancel the -- selection. -- pick = "pick" selCluster = selection . select pick Single [ Fields [ "Cluster" ] ] filterCluster = transform . filter (FSelection pick) plxOpts = [ PScale [ SType ScLog, SNice (IsNice False) ] , PAxis [ AxTitle "parallax (milli-arcsecond)" ] ] gmagOpts = [ PAxis [ AxTitle "G magnitude" ] ] encData = encoding . position X (quant "plx" ++ plxOpts) . position Y (quant "Gmag" ++ gmagOpts) parallaxSpec = asSpec [ width (big + borderSpacing + small) , encData . colorEnc$ []
, circleMark
, selCluster []
]

allSpecs = [ asSpec [ spacing borderSpacing
, bounds Flush
, vConcat histSpecs
]
, parallaxSpec ]

in toVegaLite
, trans []
, vConcat allSpecs
-- remove the "other" axis (e.g. top of Y, right for X)
, configure
. configuration (View [ ViewStroke (Just "transparent") ])
\$ []
, title "Gaia data from arXiv:1804.09378" [ TAnchor AMiddle ]
]


Here is the visualization after selecting a star: