with Ann K. Emery 1

Two-Day Workshop with the DC Child and Family Services Agency

Welcome & Introductions 9 – 9:15 am

Simple Spreadsheets 9:15 – 10:30 am

Morning Break 10:30 – 10:45 am

Simple Spreadsheets (continued) 10:45 am – 12 pm

Lunch 12 – 1 pm

Great Graphs 1 – 2:30 pm

Afternoon Break 2:30 – 2:45 pm

Great Graphs (continued) 2:45 – 4:45 pm

Wrap-Up 4:45 – 5 pm

Great Graphs (continued) 9 – 10:30 am

Morning Break 10:30 – 10:45 am

Great Graphs (continued) 10:45 am – 12 pm

Lunch 12 – 1 pm

Report Design 1 – 1:30 pm

Dashboard Design 1:30 – 3 pm

Afternoon Break 3 – 3:15 pm

Individual and Small Group Work Time 3:15 – 4:45 pm

Next Steps, Resources, & Course Evaluations 4:45 – 5 pm

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❶ Here’s your step-by-step guide to analyzing demographic data. We’ll figure out how many people are in our dataset, how many males and females are there, and so on.

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Ask yourself these questions: ✓ How many sheets of data live inside your file? ✓ How many rows and how many columns are in each sheet? ✓ Can you figure out what’s inside each column? You may need to re-name

variables. Find out if a Data Dictionary or Data Codebook exists. If not, create one! ✓ Can you figure out what’s inside each row? Does each row represent a survey

respondent? Program participant? Project? Country? Time period? ✓ Is there a column that contains ID numbers?

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We’re simply trying to summarize or describe this dataset. We need to know the basics, including:

✓ How many people are in our dataset? ✓ Treatment or Control Group: How many people are in each group? ✓ Gender: How many males and females are there? ✓ Birthdate: How old are these people? ✓ Household Income: What types of incomes do these people have? (e.g., lowest,

highest, average) ✓ Work Setting: Where do these people work? ✓ Years of Experience: How much experience do they have? ✓ City, State, and Zip Code: Where do these people live?

We’ll look at the data overall (everyone combined) and by subgroup (treatment vs. control group).

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❷ We're pretending that the important variables are spread across two different spreadsheets, currently called Sheet1 and Sheet2. We need to build a single master dataset so that we can get a comprehensive picture of what's going on.

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Combine the two datasets into a single master dataset.

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Spot the duplicates in a different color. Remove the duplicate entries (manually or with Remove Duplicates).

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Find the counts and blanks with counta and countblank. Spot the blank cells in a different color.

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Transform Male into Gender. Transform Birthdates into Ages. Categorize Incomes into Income Levels ($100,000 or more vs. Less than $100,000).

❹ Have some fun exploring your data! Use sparklines, data bars, heat tables, and/or additional color-coding to become an expert in your numbers and their quirks.

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Sparklines are most helpful when trying to visualize patterns across a row—so they’re not applicable for this dataset.

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Data bars are most helpful when we’re trying to visualize patterns down a column—so we’ll use them to explore numeric fields like household income.

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Heat tables are most helpful when we’re trying to visualize patterns between continuous numbers (rather than discrete categories)—so we’ll use them to explore numeric fields like household income.

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I often color-code by category. For example, you could make males a different color than females.

❺ We simply want to describe our dataset and look for interesting patterns. Here’s what we’re trying to learn:

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Use counta.

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Use countif.

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Use countif to find the overall number of males and females. Use countifs to find the number of males and females by group.

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For the ages, calculate descriptive statistics (e.g., mean, median, mode, min, max, range, and standard deviation).

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For the incomes, calculate descriptive statistics (e.g., mean, median, mode, min, max, range, and standard deviation). For the income levels ($100,000 or more vs. less than $100,000), tally up how many people fall into each category with countif or countifs.

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Use countif or countifs.

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Use countif or countifs.

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Use countif or countifs.

❺ I love using pivot tables for this type of dataset—where every variable is structured a little bit differently. In other words, we’ve got a mix of numbers, categories, dates, and so on, so using a different formula to summarize every column is time-consuming and error-prone.

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Every column must have a label. Contiguous cells (No completely empty rows or columns; Swiss cheese pattern okay).

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Click on the upper left corner of your table (usually—but not always—cell A1). Go to the Insert tab. Insert a new pivot table into a blank sheet.

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✓ Field List: The list of variables (column labels) from your spreadsheet. ✓ Rows: The variable(s) that will appear in the left-hand side of your table. ✓ Column: The variable(s) that will appear along the top of your table. ✓ Values: The variable that will get summarized (summed, tallied, averaged) and

placed inside the body of your table. Hint: ID numbers, as a “count,” always go here.

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❶ During the planning phase, discuss these questions with your teammates so that you can narrow down your audience and dissemination format before you begin visualizing your data. If your viewers are expecting a story, then use dark colors, titles, subtitles, and/or annotations to interpret the numbers.

Beginning in 2013, we set aside new funding to measure the effectiveness of our work.

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❷ Will you use a bar chart, a pie chart, or something else altogether? Sketch a couple options and discuss the pros and cons with a colleague. See more at www.annkemery.com/essentials.

Thank me later Color-coding Within-cell bars Mini trendlines Mini columns

2-3 slices max! Pie alternative Focus on poles aka Square pies aka Pictographs

The trusty steed Minimalist bar Two pairs, max! Minimalist cluster

Disaggregated

Comparisons based on area

aka Tree When every bar has a different

goal

When every bar has the same

goal

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aka Heat Map Equal-sized

shapes Patterns over

time Connections

between people

Ordinal variables

Ordinal & nominal

Correlations (x & y)

Correlations (x, y, & z)

Min, Q1, median, Q3, & max

Focus on the slope of the line

Focus on distance

between the dots

Part-to-whole How parts add up to the whole

Just display the # or %

differences

The classic choice

Separate the spaghetti

Part-to-whole Part-to-whole

Goal is light Guess is dotted Guess is shaded Milestones Try words & icons

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❸ Declutter your visualization so that viewers can focus on what matters—the data.

…from the graph as a whole. …from segments within the graph (e.g., stacked bar charts get outlined in white). …from tables.

Bonus: Use icons to aid interpretation.

Delete the legend and place labels directly beside or on top of the data.

Label the axis or the individual data points, but not both.

Say what you need to say—once.

I intentionally removed the border, the vertical grid lines, and the horizontal grid lines. The x- and y-axes are still there, but I lightened the remaining lines (from black to gray ink).

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❹Color can enhance branding and guide your viewers to the most important pieces of the graph.

Consult a style guide or find RGB codes with eyedroppers (e.g., instant-eyedropper.com). In Excel, select Shape Fill (the paint can) → More Fill Colors → Custom.

Preview a draft in grayscale. Is there enough contrast between the dark gray and light gray?

Avoid red/green combinations. Instead, use orange/green or red/blue. Test drafts at color-blindness.com.

Your best bet is black text on a white background (or vice versa). Colored text on a colored background can get complicated. Test drafts at webaim.org/resources/contrastchecker.

Apply your branded colors using one of the following techniques.

Select one color. “Presence” is darker.

Select one color. “More” is darker.

Select two colors. Poles are dark.

Select one color per category.

Draw attention selectively.

Highlight a negative message.

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❺Your graph’s title, subtitle, and annotations can strengthen your message.

Consult a style guide or identify fonts with www.whatthefont.com.

6 to 12 words.

1 or 2 sentences.

Use free tools like www.readable.io.

Vertical and diagonal words take longer to read than plain ol’ horizontal words.

The most important information should be large and the least important information should be small.

Twisted text takes longer to read than horizontal text, so rotate your graphs as needed.

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Maryland

New York

New Jersey

South Dakota

Montana

Oregon

Minnesota

Ohio

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Let’s put it all together! Here’s how small adjustments to clutter, color, and text add up to completely transform a graph.

Before, the years along the x-axis were tiny and tilted, and the title was too lengthy and needed to be translated for a lay audience. After, labels are used sparingly and highlight key milestones, and the revised title is short and understandable.

There’s nothing more boring than a slideshow full of bullet points! Bullet points scream, “I cared so little about my audience that I made my slides on the airplane last night.” In this makeover, we created one visual per bullet point, increased the font sizes, and color-coded by category.

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Congrats! You’ve got great graphs. Now, take your visualization skills to the next level by designing great reports and slideshows.

We transformed this sea of paragraphs into a few skimmable sections. The makeover includes a report cover; color-coding by section; a text hierarchy; and intentional page breaks.

We transformed this dense table into three visuals. Then, we applied storyboarding to guide our viewers’ eyes during the presentation.

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Excel Google Sheets

Numbers

Tableau

R

etc.

Excel with VBA code or

drop-down menus

Tableau

R

etc.

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Here’s your Dashboard Cheat Sheet for easy-to-use dashboard elements.

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You can combine these dashboard elements in myriad ways to fit your audience’s needs. Here are a few ways to make comparisons, monitor changes over time, and track progress towards goals.

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You’ve learned a lot. Where do you go from here?

What was your favorite technique that you learned today? Maybe you loved waffle charts, tile grid maps, or Sankey diagrams. Or maybe the information about colorblindness spoke to you. Choose just one technique to prioritize for your next project. Don’t overwhelm yourself by applying all the techniques at once.

Assess your existing color palette. Which color combinations are colorblind-friendly (and which aren’t)?

Add a few pages to your existing style guide. You might add a chart-choosing section with guidance on when to use pie charts (and when to avoid them). Or, you might add sample makeovers with your before and after graphs beside each other. My favorite style guide comes from the Urban Institute: http://urbaninstitute.github.io/graphics-styleguide/

An internet search for “data visualization” will pull up thousands of examples of what not to do, like 3D exploding pie charts. I’ve combed the internet for the best data visualization resources. Start here.

by Ann K. Emery

by Jorge Camoes

by Jon Schwabish

by Stephanie Evergreen

by Cole Nussbaumer Knaflic

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What did you think of today’s session? I’m listening.

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