{"id":608,"date":"2018-09-12T12:05:04","date_gmt":"2018-09-12T19:05:04","guid":{"rendered":"http:\/\/www.lightsondata.com\/?p=608"},"modified":"2020-09-06T22:03:29","modified_gmt":"2020-09-07T05:03:29","slug":"3-principles-on-how-to-use-color-to-draw-attention-to-data-visualizations","status":"publish","type":"post","link":"https:\/\/www.lightsondata.com\/3-principles-on-how-to-use-color-to-draw-attention-to-data-visualizations\/","title":{"rendered":"3 principles on how to use color to draw attention to data visualizations"},"content":{"rendered":"
In all data visualizations you want to have the user quickly identify the most important information. You might need to draw their attention to an outlier, to a metric that is under-performing or over-performing. This can be done through color, though not only, by applying these principles:<\/p>\n
1. Use commonly understood colors<\/h2>\n
We get bombarded by colors every day. We see a multitude of colors with different tints, shades, and tones. Unknowingly, we associate these colors with different moods, feelings, messages – and advertisers know this, of course. For example, we might associate green with freshness, new, luck, safety, and so on.\u00a0The reason why I’m mentioning this is to be aware of these meanings and associations and apply\u00a0them when\u00a0choosing colors\u00a0for your data visualizations.\u00a0Let’s take another example, which is more universal: the traffic light colors. Red represents danger, yellow represents attention required or caution, and green means safety, or “go ahead”. It is very common to use primary colors (blue, red, yellow) and\/or secondary colors (green, orange, purple) in reports and dashboards, to draw awareness.<\/p>\n
In the bar graph below (Figure 1)\u00a0you can see that through the usage of color green, we can quickly identify those manufactures with sales over $18k, and in red, those under $9k.<\/p>\nFigure 1<\/figcaption><\/figure>\n
On the other hand, in Figure 2, when we graph the profit from each manufacturer, the same colors are now\u00a0deceiving us. They don’t convey a message of positive vs negative anymore, but they just take into account the large absolute values, ignoring the fact that they are positive or negative. Our brain can get a bit confused and tries to understand why a negative value is portrayed by a “positive” color, such as green. In this example, green was not properly used and you end up spending too much time on trying to understand the message this graph is sending.<\/p>\nFigure 2<\/figcaption><\/figure>\n
If you want to draw attention to those manufacturers with the highest profit and the lowest negative profit, in relation to sales, here’s a better way of doing so. Use red to denote a loss and green a profit. You can quickly identify in the bar graph below that Cubify has a plenty of sales, but also is losing money, whereas Ativa has a bit over the average number of sales, but the highest profit. This simple use of color draws the attention where it’s needed.<\/p>\nFigure 3<\/figcaption><\/figure>\n
My\u00a02 take away messages:<\/p>\n
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Regardless of the visualization, the user should always have the same color-meaning association. In this case green = positive and red = negative. So be consistent<\/li>\n
Be mindful that depending on the culture, the same color can convey different messages. So adjust the colors based on your audience.<\/li>\n<\/ol>\n
2. Reference the color wheel<\/h2>\n
The color wheel is a designer’s friend. I’m not going to go into details of design principles, but will offer an advice I often see people don’t take:
\n
<\/span><\/span><\/div>\n
<\/div>If you want to draw a user’s attention, don’t only use colors adjacent to each other on the color wheel as they tend to seem too similar to a lot of end users.\n
In the example below, you might not think there is much of a difference between each color. In fact, you might assume there are 3-4 colors which repeat themselves, but actually each state is represented by a different shades of blue and blue violets. Not very helpful. If one is looking for North Carolina and is familiar to the color assigned to it, it cannot be easily identified with the pallet below.<\/p>\nFigure 4<\/figcaption><\/figure>\n
On the other hand, if you knew North Carolina was associated with pink, it can be easily identified in the graph below because all these colors are not adjacent to each other. Ok, orange and yellow are, but they don’t have the same hue level.<\/p>\nFigure 5<\/figcaption><\/figure>\n\n
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