Colors direct our eye movements, and therefore our brains and attention.

It’s up to you: will you help or hinder your reader’s understanding?

Step 1: Start with Your Brand Colors

Otherwise, your graphs, slides, and dashboards will be Frankensteined.

I’ve written about brand colors and brand presents in other posts.

Some of those resources include:

• Examples of organizations’ brand colors used in graphs
• How to read color codes in style guides
• How to enter your custom color codes in Excel

Step 2: Do Your Accessibility Testing

I’ve written about colorblindness, color contrast, grayscale printing in other posts.

Some of those resources include:

• An official color contrast test
• An official colorblindness and grayscale printing test

Then, your accessibility testing “results” should go inside your organization’s Dataviz Style Guide.

Step 3: Apply Those Brand Colors According to the Data & Variables

Now, it’s time to apply those branding colors to ensure that your graph is intuitive.

Look at your graph: Is your variable binary, sequential, diverging, or categorical?

Or, do you want to tell a story with a dark-light contrast?

Binary Variables Get Binary Color Schemes

Binary variables include yes/no data, such as:

• yes/no survey questions
• people who speak Portuguese as their primary language vs. people who don’t
• people who own a home vs. people who don’t
• people who graduated from program on time vs. people who didn’t
• people diagnosed with an illness vs. people who don’t have it

For binary variables, choose one brand color. The “presence” of the attribute gets the darker color, and the “absence” of the attribute gets the lighter color.

Here’s an example:

Sequential Variables Get Sequential Color Schemes

a.k.a. ordinal

Sequential variables have a natural order.

Examples include:

• age ranges (5-9 year olds, 10-14 year olds, and 15-19 year olds)
• income levels
• highest educational level completed (some high school, high school diploma, some college, etc.)
• years (Year 1, Year 2, and Year 3 of a project)
• semesters (fall, spring, fall, spring…)
• cohorts (first cohort of participants, second cohort, etc.)

For sequential variables, choose one brand color, and use a light-dark gradation of that color.

Here’s an example:

Categorical Variables Get Categorical Color Schemes

a.k.a. nominal

Categorical variables include:

• race/ethnicity (African American, Asian, Hispanic/Latin@, White, etc.)
• gender (male, female, nonbinary, genderfluid, etc.)
• chapters of a report
• sections of a presentation
• categories of a dashboard

For categorical variables, use a different brand color for each category.

Here’s an example:

Diverging Variables Get Diverging Color Schemes

Diverging variables are opposites.

Examples include:

• agree/disagree scales on surveys
• changes over time (e.g., “50 percent decrease” or “70 percent increase”)

For diverging variables, choose two brand colors, and place the darkest shades on the poles.

Here’s an example:

Combining these Techniques

In most real-life projects, we need to combine these color techniques.

In this map makeover, for example, we needed to:

• use brand colors, not software defaults;
• use two brand colors, one for each category; and
• apply a dark-light gradation to each map, because these are ordinal variables.

In this population pyramid makeover, we needed to:

• use two brand colors, one for each timeframe, and
• apply a dark-light storytelling emphasis to each pyramid.

Your Turn

What types of color questions do you have? Comment below..

The post How to Apply Your Brand Colors in Dataviz (Ordinal, Diverging, Categorical, and More) appeared first on Depict Data Studio.

Use Horizontal Bar Charts When You’re Graphing Nominal Variables

Nominal variables—favorite ice cream flavors, types of organizations where conference attendees are employed—can be arranged in any order. I arrange nominal variables in a list from top to bottom, which means their bars are horizontal.  You can sort nominal variables from greatest to least or from least to greatest. Both are correct. My advice: Sort the data so that the item that warrants attention is displayed first—a high number deserving a celebration, or a lower-than-hoped-for number that needs to be turned around.

Use horizontal bar charts to display nominal variables like favorite ice cream flavors or employment settings.

Use Vertical Column Charts When You’re Graphing Ordinal Variables

Ordinal variables follow a natural progression—an order. Another name for ordinal variables is sequential variables because the subcategories have a natural sequence. I arrange ordinal categories from left to right so my viewers can view the sequence across the page, which means their bars are vertical.

Use vertical column charts to display ordinal variables like age ranges, salary ranges, and even cohorts or graduating classes (e.g., the percentage of students from each graduating class who achieved x outcome).

Real-Life Examples

Okay, you’ve seen some fictional, generic examples. Let’s look at a few examples of horizontal bar charts and vertical column charts from real projects.

Compare Age Ranges with Vertical Histograms

While working with a museum, we wanted to display some key demographic characteristics about people who had responded to the museum’s survey. Age ranges are ordinal, so we used vertical column charts to visualize how many people fell into each age bracket.

While working with public health researchers, we needed to visualize how many males and females in each age range were diagnosed with x disease. We built stacked column charts with vertical columns. We also used a population pyramid to compare the distributions of males and females.

Compare Cohorts of Survey Respondents with Vertical Column Charts

In the State of Grantseeking project, an organization called GrantStation sends out surveys a couple times a year, in the spring and fall. We wanted to compare how different iterations or cohorts of survey takers responded. In other words, we wanted to see whether people who responded to the spring 2016 survey were any different from people who responded to the fall 2016 survey or the spring 2017 survey. Cohorts are ordinal, so we used vertical stacked column charts to depict the proportion of survey respondents who are employed in nonprofits.

Compare Patterns Over Time with Vertical Charts

While working with a hospital’s analytics team, we needed to display the proportion of hospital procedures (“Product A” and “Product B”) that were performed by this hospital (“the ABC Org”). We wanted to compare two points in time, 2012 and 2016. Time is ordinal, so we used vertical columns.

Visualize Agree-Disagree Scales with Horizontal Stacked Bars

While working with a museum, we needed to display how many people agreed or disagreed with each statement on a survey. Agree/disagree scales are ordinal. (Well, technically, agree/disagree scales are a special type of ordinal variable called a diverging variable.) We wanted our readers to see the natural progression from agree to disagree, so we used horizontal bars to show that progression from left to right across the page.

Compare Before-After Survey Responses with Vertical Column Charts

In this example, we needed to make a one-page handout that summarized whether program participants were more knowledgeable about New England history, Martha’s Vineyard, or whaling after completing an educational program at a museum. The museum administered the survey twice, at the beginning of the program (pre) and at the end of the program (post). Pre-post comparisons are comparisons over time… and time is an ordinal variable… so we selected vertical columns. But wait! There’s more! We were graphing two sets of ordinal variables: 1) the timeframe (pre or post survey administration) and 2) the scaled survey responses (very, moderate, somewhat, slightly, or not at all). We chose to give the timeframe more weight than the scaled survey responses. In other words, we decided that the pre-post timeframe was the most important, and used vertical columns first and foremost. Then, within those columns, we displayed the ordinal survey responses.

These are guidelines, not rules. If you can explain the logic for going against this guidance, then your graph is probably going to be alright. My goal is to develop critical thinking masterminds, not robots.

Bonus: Download the Materials

Download the Excel spreadsheet that I used to make the generic bar and column charts at the top of this article.

Download the template.

The post When to Use Horizontal Bar Charts vs. Vertical Column Charts appeared first on Depict Data Studio.