## A few rules of thumb for designing with charts and graphs

You’ve got a credible statistic or two, and you’re ready to share that information with your audience. Do you write it out? Draw a picture? Use a chart? To make sure your audience understands and retains the information, it needs to be compelling and accurate.

But the choice of what type of visualization to use isn’t purely aesthetic, nor is it entirely personal. The wrong choice can lead your viewer to boredom, confusion, or both. Even worse, visualizing data inaccurately can constitute a breach of trust between you and your audience.

So let’s take a look at how to choose the most accurate and engaging way to visualize your data.

For data sets that evolve over time or are grouped by multiple categories—like different industries or foods—or both, a bar graph is a solid choice. A few tips will help ensure your bar graph is easy to read:

- Order your bars chronologically.
- Use one axis to label the time frames, and use the other to label the quantities.
- Never order the data from most to least or least to most—chronology is the better measurement for your viewer.

For bar graphs that involve multiple categories, you can either make individual graphs for every category or keep it as one by including multiple bars (one for each category) at each time label. These bars can be side-by-side or stacked on top of each other, as in this graph from an interactive annual report for Bluetooth:

If your data set is grouped into multiple categories and* *is NOT bound by time, you should organize the bars from most to least, or least to most. This type of organization helps viewers to draw conclusions quickly. However, if it adds up to a whole—such as total revenue by category—that won’t be apparent in a bar graph. For this type of information, you should use a pie chart instead. I’ll get to those shortly.

Much like bar graphs, line graphs are useful for showing data over time or grouped by category. But a line graph allows for nuance. It’s a great choice for showing information over very long periods or a wealth of incrementally changing data. That’s because the organic nature of a line allows it to bend and change with more detail.

A beautiful chart that no one can read is just abstract art.

In fact, you should be careful when using line graphs to show only a few points in time. Without knowing how to accurately fill in the data in between the time periods for which you have data, you’ll presumably draw a straight line. However, the rate of growth or decline between those times may not have been so linear. For this reason, line graphs should be used carefully and with complete data sets to avoid distorting data.

Allen Downey offers up a great example of when to use a line graph in his article about whether first-born babies are more likely to be born late. He uses a line chart to map the likelihood of birth over a nine-week window:

Given that this chart is based on over 30,000 data points — each a single recorded live birth — there’s more than enough data to account for all the incremental changes over time and to arrive at an average distribution.

If you aren’t showing data over time or by category, a line graph is not for you. Categorical data has many helpful graph applications, though. The following is another option that works well for showing portions of a whole.

The circle chart is one of the most commonly used forms of data visualization. There are pie charts (filled in) and donut charts (hollow, with a circular bar containing the data).

This type of chart is so popular that, unfortunately, it’s also one of the most misused types of data visualization.

A circle chart can only be used when you are showing portions that add up to a whole. For example, “75% of all caterpillars like apples” could be shown with a pie chart because it’s referring to 75% out of a total 100% of all caterpillars.

You can also convert proportions to percentages for this goal. If your data point is three out of four caterpillars, that’s equal to 75% of caterpillars.

Visualizing data inaccurately can constitute a breach of trust between you and your audience.

Unlike bar and line graphs, circle charts cannot be used on their own to show an increase or decrease. To see an example of what I mean, let’s take a look at a stat about video marketing from Tubular Insights.

Between 2016 and 2017, there was a 99% increase in branded video content views on YouTube. A circle chart showing 99% would be incorrect. That would make it appear that 99% of video views were of branded video, which is wrong. Instead, you need a bar chart with two bars, one representing our baseline number of views from 2016, and one representing a 99% increase over that baseline:

This may not feel intuitive. Percentage changes can be tricky if you don’t work with them all the time. This cheat sheet from Investopedia can help you work with these kinds of numbers.

If you want to use pie charts to show changing data over time, you’d create a new chart for every time period you’re measuring and display them together for comparison.

A quantagram is a repeated pictogram or icon used to show quantity. A common example is using multiple characters to show a number of people. You’ve probably seen this technique using the classic male and female icons from bathroom doors.

Quantagrams are great for small numbers (like “12 new restaurants opened on our street”). They also work well for small percentages or proportions where a pie chart could work. An example would be “three in four restaurants [75%] on our street serve pizza.”

If you need a key to explain it, a quantagram isn’t the right choice.

Quantagrams generally don’t work well for larger numbers. Imagine your stat was “11,214 items sold in 2018.” You don’t have space for 11,214 icons in your design — and if you think you do, I recommend you think again! That’s a massive number to count out one by one. So, it’s tempting to add a key — “1 shopping bag = 1,000 items” — and just show 11 shopping bags. Right?

You’re probably trying to show that this is a big, impressive number. But when you reduce it like this, this visualization now has the opposite effect. Eleven shopping bags don’t look or feel that large, even with a key. The number “11,214” is more powerful on its own. (I’ll talk about why typography is the better fit for stats like these in a minute.)

The same thing happens with ratios. For example, imagine visualizing the stat “8,370 of the 11,214 items sold in 2018 were mugs” using quantagrams. No thanks! So if you need a key to explain it, a quantagram isn’t the right choice.

If your stat fits the bill for a quantagram so far, think about what pictogram you should use. Be careful: Because they’re so simple, pictograms can feel too reductive for serious topics. You don’t want to appear to be trivializing a serious topic by using simple icons.

If your stat is too large or not suited to pictograms, there’s an easy fix: typography. Here’s how and when to incorporate it into your design.

I bet you didn’t expect to see a section on typography in an article about data visualization. But when used correctly, typography does have the capacity to bring information to life.

The truth is, there are limited cases in which typography really is the best solution. To be clear, it should never be used just because you don’t want to create visuals. Don’t go back to the old text-only solutions of the past! Instead, use typography intelligently to achieve a successful and effective piece of content.

Your data point or number is probably a good candidate for typography if:

- It’s large (greater than 100)
- It isn’t a percentage of a whole or a percentage increase/decrease
- It’s standalone — that is, it’s not being compared to another number

Before settling on typography, check your data against each of the points above and consider the other types of data visualization I’ve already discussed. You should eliminate all other possibilities before using type. That’s because visuals are simply more compelling and more effective at engaging your audience. Yet, visuals are only effective insofar as they’re accurate. If you face confusion or inaccuracy by visualizing your number, just go with text.

One way to enhance your typography is to combine it with a pictogram (like you would use in a quantagram, but just a single one), an icon, or an illustration. This will help provide the viewer with visual context as to the subject matter of the stat, while letting the number speak for itself.

Here’s an example of intentional choices for different types of data visualization, including typography:

In this example, it makes sense to visualize the number 16 using quantagrams — it’s a small number and therefore easy to add up visually. But the 1.8 million stat would be incomprehensible using a quantagram with a 1-to-1 ratio. And we’ve already learned that if you feel the need to use a key, such as equating every icon to quantities of 100 or 1,000, then a quantagram just isn’t the right choice. That’s why very large numbers are generally best left to typography.

No matter what solution is best for your data, aesthetic considerations span all forms of data visualization. Beyond simply choosing the right data visualization technique to use, you must pick the right aesthetic to represent your information and reach your audience. A fun neon line graph with modern type might not work for a report to investors and the C-suite. A flat, grayscale pie chart is probably the wrong choice for a summer camp pamphlet.

So always ensure that form and function are equally considered — because a beautiful chart that no one can read is just abstract art.