Choosing Beautiful Colors for Data Visualizations Made Easy

Основные понятия

The author aims to simplify the process of choosing beautiful and effective colors for data visualizations by providing practical tips and insights on color selection.

Аннотация

Selecting colors for data visualizations can be challenging, but this article offers valuable advice to make the process easier. The content covers topics such as broadening color understanding, avoiding common mistakes, utilizing saturation and lightness effectively, and choosing complementary colors. Practical examples and tools are provided to enhance color selection skills.

Статистика

Saturation ranges from 0% (gray) to 100% (super duper colorful!!): ⬤ ⬤ ⬤
Brightness/Value ranges from 0% (black) to 100% (the actual color): ⬤ ⬤ ⬤
Hue value of bright blue is 180°, Saturation value is 67%, and Lightness value is 91%
A pure green in combination with red, orange, or brown is hard for colorblind people to distinguish.
Neon colors with high saturation and brightness may not communicate trust or calm effectively.

Цитаты

"Colors stand out because they’re way darker or lighter or more saturated or more “pure”."
"If you do want to build a better intuitive understanding of which colors fit well together, try this: Analyze them."
"Pick colors from beautiful data visualizations. Change a few colors. Do they still work well together?"

Ключевые выводы из

How to pick more beautiful colors for your data visualizations - Datawrapper Blog

by Lisa Charlot... в blog.datawrapper.de 02-26-2024

https://blog.datawrapper.de/beautifulcolors/

Дополнительные вопросы

How can cultural differences influence color choices in data visualizations?

Cultural differences can significantly impact color choices in data visualizations. Different cultures associate varying meanings and emotions with colors, leading to diverse preferences and interpretations. For example, while red may symbolize luck and prosperity in Chinese culture, it can represent danger or passion in Western societies. Therefore, when creating data visualizations for a global audience, it is crucial to consider the cultural connotations of colors to ensure that the chosen palette resonates positively with all viewers.

Is there a risk of oversimplification when limiting the color palette based on complementary colors?

Limiting the color palette solely based on complementary colors does pose a risk of oversimplification in data visualizations. While complementary colors create contrast and harmony, relying exclusively on them may result in a lack of nuance and differentiation between different categories or elements within the visualization. This could lead to confusion for viewers trying to distinguish between various data points or trends. To avoid this issue, it is essential to incorporate a diverse range of hues, saturations, and lightness levels within the color palette while still leveraging complementary relationships strategically.

How does emotional response vary based on different color combinations used in visualizations?

Emotional responses can vary significantly based on the specific color combinations utilized in data visualizations. Colors have psychological effects that evoke certain feelings or moods in individuals. For instance:

Warm colors like reds and oranges tend to elicit emotions such as excitement, energy, or urgency.
Cool tones like blues and greens are often associated with calmness, trustworthiness, or professionalism.
High-saturation bright colors might grab attention but could also induce stress or overwhelm viewers.
By carefully selecting appropriate color combinations that align with the intended message of the visualization, designers can effectively influence how audiences perceive and engage with the presented information.