@misc{_better_????-1,
	title = {Better {Know} a {Visualization}: {Small} {Multiples}},
	shorttitle = {Better {Know} a {Visualization}},
	url = {http://www.juiceanalytics.com/writing/better-know-visualization-small-multiples},
	abstract = {(With enough visualization methods to warrant a periodic table, it can be 
confusing to know what to use and when—and which visualizations are even 
worth considering at all. This series of posts is intended to introduce you 
to the visualization approaches that we find most useful, practical, and 
audience-friendly.)

What is a small multiple?

Small multiples are a visualization concept introduced by Edward Tufte. He 
described them as:

    "Illustrations of postage-stamp size are indexed by category or a
    label, sequenced over time like the frames of a movie, or ordered by a
    quantitative variable not used in the single image itself."

In other words, small multiples use the same basic graphic or chart to 
display difference slices of a data set. Small multiples can show rich, 
multi-dimensional data without trying to cram all that information into a 
single, overly-complex chart. Small multiples go by many names, including 
Trellis Chart, Lattice Chart, Grid Chart, and Panel Chart. I would even 
argue that sparklines are a simpler, smaller cousin of small multiples.

What problem does this solve?

Small multiples offer a few valuable features:

   1. They allow for the display of many variables with less risk of
      confusing your audience. Trying to display three or more variables in
      a single chart is a challenge Stephen Few calls overplotting (PDF).
   2. The reader can quickly learn to read an individual chart and apply
      this knowledge as they scan the rest of the charts. This shifts the
      reader’s effort from understanding how the chart works to what the
      data says. That’s a worthy goal in all data presentation.
   3. Small multiples enable comparison across variables and reveal the
      range of potential patterns in the charts.
What to watch out for when using small multiples?

Like any visualization, there are many ways to mess up and undermine the 
value of the data presentation:

    * Placement of the small multiples charts should reflect some logical
      order, e.g. dimensional matrix, geography, or time. This helps the
      user quickly find the charts that are interesting to them.
    * Small multiples should share the same measures, scales, size, and
      shape. Changing one of these factors undermines ability for people to
      re-use their understanding of the chart.
    * Simplicity of the chart is critical. Users should be able to process
      information across many of these charts. The following small chart
      from the New York Times works as an individual graphic; when shown 
      in "postage stamp" size across 20 cities, this chart is too
      data-dense.
Small multiples in the practice

Andrew Gelman’s analysis of public support for vouchers, broken down by 
religion/ethnicity, income, and state.

Trilogy Meter by Dan Meth shows movie enjoyment by sequel.

Jorge Camoes’ small multiple graphic for unemployment data

Do it yourself in Excel
    * Jon Peltier creates small multiples in Excel
    * Kelly O’Day offers numerous tutorials and example workbooks on how to
      create "Panel charts" in Excel
    * Juice provides a few Excel tricks for replicating a chart across a
      data set
Do it yours with other free tools
    * Tableau Public. Nobody has small multiples more baked into their DNA
      than the folks at Tableau.
    * Many Eyes for map-based small multiples.
More resources
    * BI Commons: The book on trellis charts, AKA small multiples
    * Junk Charts’ posts about small multiples},
	urldate = {2016-11-08},
	journal = {Juice Analytics},
	file = {Snapshot:/Users/baptiste/Library/Application Support/Zotero/Profiles/d9rq1atq.default/zotero/storage/38RUPHKQ/better-know-visualization-small-multiples.html:text/html}
}

{"_id":"jKY9MAwHFmdKFa6oZ","bibbaseid":"anonymous-betterknowavisualizationsmallmultiples","downloads":0,"creationDate":"2016-12-25T11:57:00.246Z","title":"Better Know a Visualization: Small Multiples","author_short":null,"year":null,"bibtype":"misc","biburl":"http://plasmonics.github.io/zotero.bib","bibdata":{"bibtype":"misc","type":"misc","title":"Better Know a Visualization: Small Multiples","shorttitle":"Better Know a Visualization","url":"http://www.juiceanalytics.com/writing/better-know-visualization-small-multiples","abstract":"(With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.) What is a small multiple? Small multiples are a visualization concept introduced by Edward Tufte. He described them as: \"Illustrations of postage-stamp size are indexed by category or a label, sequenced over time like the frames of a movie, or ordered by a quantitative variable not used in the single image itself.\" In other words, small multiples use the same basic graphic or chart to display difference slices of a data set. Small multiples can show rich, multi-dimensional data without trying to cram all that information into a single, overly-complex chart. Small multiples go by many names, including Trellis Chart, Lattice Chart, Grid Chart, and Panel Chart. I would even argue that sparklines are a simpler, smaller cousin of small multiples. What problem does this solve? Small multiples offer a few valuable features: 1. They allow for the display of many variables with less risk of confusing your audience. Trying to display three or more variables in a single chart is a challenge Stephen Few calls overplotting (PDF). 2. The reader can quickly learn to read an individual chart and apply this knowledge as they scan the rest of the charts. This shifts the reader’s effort from understanding how the chart works to what the data says. That’s a worthy goal in all data presentation. 3. Small multiples enable comparison across variables and reveal the range of potential patterns in the charts. What to watch out for when using small multiples? Like any visualization, there are many ways to mess up and undermine the value of the data presentation: * Placement of the small multiples charts should reflect some logical order, e.g. dimensional matrix, geography, or time. This helps the user quickly find the charts that are interesting to them. * Small multiples should share the same measures, scales, size, and shape. Changing one of these factors undermines ability for people to re-use their understanding of the chart. * Simplicity of the chart is critical. Users should be able to process information across many of these charts. The following small chart from the New York Times works as an individual graphic; when shown in \"postage stamp\" size across 20 cities, this chart is too data-dense. Small multiples in the practice Andrew Gelman’s analysis of public support for vouchers, broken down by religion/ethnicity, income, and state. Trilogy Meter by Dan Meth shows movie enjoyment by sequel. Jorge Camoes’ small multiple graphic for unemployment data Do it yourself in Excel * Jon Peltier creates small multiples in Excel * Kelly O’Day offers numerous tutorials and example workbooks on how to create \"Panel charts\" in Excel * Juice provides a few Excel tricks for replicating a chart across a data set Do it yours with other free tools * Tableau Public. Nobody has small multiples more baked into their DNA than the folks at Tableau. * Many Eyes for map-based small multiples. More resources * BI Commons: The book on trellis charts, AKA small multiples * Junk Charts’ posts about small multiples","urldate":"2016-11-08","journal":"Juice Analytics","file":"Snapshot:/Users/baptiste/Library/Application Support/Zotero/Profiles/d9rq1atq.default/zotero/storage/38RUPHKQ/better-know-visualization-small-multiples.html:text/html","bibtex":"@misc{_better_????-1,\n\ttitle = {Better {Know} a {Visualization}: {Small} {Multiples}},\n\tshorttitle = {Better {Know} a {Visualization}},\n\turl = {http://www.juiceanalytics.com/writing/better-know-visualization-small-multiples},\n\tabstract = {(With enough visualization methods to warrant a periodic table, it can be \nconfusing to know what to use and when—and which visualizations are even \nworth considering at all. This series of posts is intended to introduce you \nto the visualization approaches that we find most useful, practical, and \naudience-friendly.)\n\nWhat is a small multiple?\n\nSmall multiples are a visualization concept introduced by Edward Tufte. He \ndescribed them as:\n\n \"Illustrations of postage-stamp size are indexed by category or a\n label, sequenced over time like the frames of a movie, or ordered by a\n quantitative variable not used in the single image itself.\"\n\nIn other words, small multiples use the same basic graphic or chart to \ndisplay difference slices of a data set. Small multiples can show rich, \nmulti-dimensional data without trying to cram all that information into a \nsingle, overly-complex chart. Small multiples go by many names, including \nTrellis Chart, Lattice Chart, Grid Chart, and Panel Chart. I would even \nargue that sparklines are a simpler, smaller cousin of small multiples.\n\nWhat problem does this solve?\n\nSmall multiples offer a few valuable features:\n\n 1. They allow for the display of many variables with less risk of\n confusing your audience. Trying to display three or more variables in\n a single chart is a challenge Stephen Few calls overplotting (PDF).\n 2. The reader can quickly learn to read an individual chart and apply\n this knowledge as they scan the rest of the charts. This shifts the\n reader’s effort from understanding how the chart works to what the\n data says. That’s a worthy goal in all data presentation.\n 3. Small multiples enable comparison across variables and reveal the\n range of potential patterns in the charts.\nWhat to watch out for when using small multiples?\n\nLike any visualization, there are many ways to mess up and undermine the \nvalue of the data presentation:\n\n * Placement of the small multiples charts should reflect some logical\n order, e.g. dimensional matrix, geography, or time. This helps the\n user quickly find the charts that are interesting to them.\n * Small multiples should share the same measures, scales, size, and\n shape. Changing one of these factors undermines ability for people to\n re-use their understanding of the chart.\n * Simplicity of the chart is critical. Users should be able to process\n information across many of these charts. The following small chart\n from the New York Times works as an individual graphic; when shown \n in \"postage stamp\" size across 20 cities, this chart is too\n data-dense.\nSmall multiples in the practice\n\nAndrew Gelman’s analysis of public support for vouchers, broken down by \nreligion/ethnicity, income, and state.\n\nTrilogy Meter by Dan Meth shows movie enjoyment by sequel.\n\nJorge Camoes’ small multiple graphic for unemployment data\n\nDo it yourself in Excel\n * Jon Peltier creates small multiples in Excel\n * Kelly O’Day offers numerous tutorials and example workbooks on how to\n create \"Panel charts\" in Excel\n * Juice provides a few Excel tricks for replicating a chart across a\n data set\nDo it yours with other free tools\n * Tableau Public. 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