EDS 240: Lecture 10.3

A walk down memory lane

Week 10 | March 10th, 2025

Week 1



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We started off with some definitions and data viz history

“any graphical representation of information and data” (Cédric Scherer) & “part art and part science” (Claus O. Wilke)


There’s a long history of people (across time, regions, cultures) using visual representations of data to communicate information.

Lascaux Cave Paintings. Image source: Rock Art Blog

Florence Nightingale. Image source: Cool Infographics

W.E.B. DuBois. Image source: Smithsonian Magazine

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And discussed the many reasons to visualize data


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We (re)familiarized ourselves with {ggplot2}

a layered “grammar of graphics” i.e. compose graphs by combining independent components


Graphic layers: data (tidy format), geoms, statistical transformations, position adjustments, coordinate system, facets


Communication layers: labels, annotations, scales, themes, layout


lots of extensions + an active / helpful online community

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Weeks 2 & 4



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We explored guidelines for choosing a graphic form . . .




“If I had the answer to that, I’d be rich by now…I have no idea, but I can give you some clues to make your own choices based on what we know about why and how visualization works”

-Albert Cairo

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. . . and learned that there are caveats!


Source: New York Times



Source: The Medium

(Plus, sticking with just the graphic forms from the hierarchy would get pretty boring!)

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We discussed some additional tools and approaches for helping us on our chart-choosing journeys



  1. think about the task(s) you want to enable (e.g. compare groups, visualize flow) and / or the message you want to convey
  2. consider the types and number of variables and data points – decision trees, like From Data to Viz can help!
  3. pair multiple graphs together
  4. arrange components of your graphic
  5. test the outcomes of your graphic on others

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We explored fundamental chart types. . .

Distributions:

histograms, density plots, ridgeline plots, box plots, violin plots


Evolution:

line charts, area charts (including stacked & proportional stacked area charts)


Amounts:

bar plots, lollipop plots (and dumbbell variant), dotplots, heatmaps


Numerical relationships:

scatter plots, bubble plots, 2d density plots

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. . . and were introduced to some helpful approaches for improving interpretation

highlighting groups of interest (e.g. {gghighlight})

formatting axis labels using {scales}

direct labeling & arranging components

marginal density plots using {ggExtra}

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We learned how to customize plot themes, which is an important part of building aesthetically-pleasing viz. . .


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. . . and got some early practice by recreating this iconic visualization


Source: US Drought Monitor

Our recreation!


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Week 3


A formula for writing alternative text, which says you should always include (a) the chart type, (b) the type of data being presented, and (c) the reason for including the chart. You should also link to the data source somewhere in the text.

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We crafted effective alt text to improve data viz accessibility


Colored stripes of chronologically ordered temperatures where they increase in red to show the warming global temperature


alt=“Colored stripes of chronologically ordered temperatures where they increase in red to show the warming global temperature”

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And learned how to make sure it was added successfully

Colored stripes of chronologically ordered temperatures where they increase in red to show the warming global temperature


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Weeks 5 & 6


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We explored some things that make a good data viz . . .


Maximizing the data-ink ratio (without sacrificing readability and aesthetics) and reducing eye movement (removing redundant info, moving legends, using direct labels, avoiding rotated text).


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. . . and things to generally avoid

Information overload

3D plots

Pie charts

Dual axes

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We spent a lot of time discussing the importance of color

From encoding information, to eliciting emotion, it’s critical that we choose our colors purposefully. Some topics we covered:

  • scale types & when to use which (quantitative vs qualitative; sequential vs. diverging, classed vs. unclassed)

  • colorblind-friendly palette choices (plus tools for checking)

  • redundant mapping (e.g. colors + shapes / patterns)

  • widely-used and recognizable climate science palettes (which you should totally use, when applicable!)

  • a little bit of color theory to help guide us in designing our own palettes

  • how colors can (and do) elicit emotions (which can differ across cultures)

  • general guidelines

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. . . and explored different tools for building and applying color palettes

Tools:

  • {viridis} & {RColorBrewer} (great options for colorblind-friendly palettes)

  • {paletteer} for accessing lots of different palettes

  • Chrome extensions for simulating vision deficiencies & gray scale

  • Online palette generators

  • {ggplot2} color scaling functions

Tips:

  • save palettes as vectors outside of your plot

  • set color names


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We applied what we learned about colors as we built our first choropleth maps


Unclassed map

Classed map:


We also touched on simple features and accessing shapefiles using {tigris}.

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We learned how our typographic choices can be just as important and impactful as our color choices . . .




  • difference between typeface & font
  • how typeface can affect emotions & perceptions
  • how to choose a typeface(s)
  • using typeface & font style to guide a reader through your visualization
  • finding typefaces and font styles on Google Fonts


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. . . and practiced applying Google fonts / styling text in our dumbbell plot



  • {showtext} for importing Google Fonts (and others, incl. Font Awesome icons)
  • apply fonts using the family argument
  • {ggtext} to render text using HTML and / or Markdown

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Week 7


Image source: istockphoto


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Week 8

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We saw that Observable Plot isn’t so different from {ggplot2}


Building inputs that update data viz outputs in an Observable Notebook is pretty sleek

D3 is complex, but opens up limitless data viz possibilities!

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Week 9

Image source: Allison Horst

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We practiced telling and understanding stories . . .





  • adding context, narrative, visuals
  • storytelling earns trust and evokes emotion
  • know your audience
  • no matter the format, audience, or setting we have the opportunity to tell a story

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. . . and considered our role in how viewers perceive and interpret information

We discussed incorporating equity awareness as we work with data, how connecting readers with content requires empathy, considering how we (dis)aggregate data, awareness of “othering,” and data humanism.



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Week 10





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But that’s not all!



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Along the way, we also did a lot of data wrangling

Never not a precursor to building awesome viz – the {tidyverse} became a familiar friend in helping us clean and tidy data sets, including:


fracking (FracFocus)

ocean temperatures (SBC LTER)

CA droughts (US Drought Monitor)

Lyme disease (CDC)

Census data ({tidycensus})

gender pay gap (Bureau of Labor Statistics & Census Bureau, courtesy of TidyTuesday)

stream chemistry (Hubbard Brook Experimental Forest, via DataOne’s {metajam})

precipitation data from NOAA National Centers for Environmental Information

{tigris} geometries

Your own data!

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And practiced so many other super valuable skills!


Goal setting (self-reflections)

Writing and communication (assignments & reflections)

Repo organization

Writing / rendering / deploying Quarto docs

git / GitHub for version control

Collaborating with peers (in class, on HWs, giving / receiving feedback)

Asking for help (learning partners, student hours)

Looking up documentation

Resourcefulness (Googling, adapting code, looking for inspiration, troubleshooting)

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I hope that you all walk from our time together away feeling a bit like this


A cartoon monster student in a backpack looks on at stunning gems labeled 'Touchstones of Intuition', which have the text 'You know some stuff' written on sequential crystals.

Artwork by Allison Horst

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