Since grey can seem a bit cold, consider using it with a hint of color: Try a warm grey (grey+yellow/orange/red), or use another very light color as an alternative (e.g. Grey is also helpful for general context data, less important annotations, to show what’s unselected by the user, or to calm down the overall visual impression of your charts. Using grey for less important elements in your chart makes your highlight colors (which should be reserved for your most important data points) stick out even more. Consider the color grey as the most important color in Data Vis. There are many ways to create a color key. Make sure to explain to readers what your colors encode.Įvery visual mark that represents a value or variable should be explained: What does the height of your bar mean? What does the size of your circles on a symbol map represent? The same is true for colors. To not confuse readers and increase comparability, consider only using these colors again if you’re showing data about the same category/country/etc.:Ģ. However, if you use more than one color for your first chart, the colors in this chart will be “taken”. For example, it’s ok to show the unemployment rate in blue in the first chart and a few paragraphs later a GDP chart with the same color. If you just use one color for all your charts, using the same color is the best option to make your article not overly colorful. Consider using the same color for the same variables. Your readers will need to often consult the color key to understand what is shown in your chart. The more colors in a chart represent your data, the harder it becomes to read it quickly. If you need more than seven colors in a chart, consider using another chart type or to group categories together. Colors make it easy to let readers distinguish between categories in your data, but try to avoid using more than seven of them. Readers will be able to decipher your values faster: Consider showing your most important values with bars, position (like in a dot plot) or even areas, and use colors to only show categories. on a choropleth map, but it’s hard to decipher the actual values from them and to see differences between the values. But we’ll also take a general look at colors and what to consider when choosing them: When to use colors in data visualizationĬonsider if there is a better alternative to gradient colors when encoding your most important values. Gradient colors can be great to show a pattern, e.g. We’ll focus mostly on the latter in this article. Sometimes colors just make the shapes visible, sometimes they encode data or categories themselves. XData Visualisation can be defined as representing numbers with shapes – and no matter what these shapes look like (areas, lines, dots), they need to have a color.
What to consider when choosing colors for data visualization
0 kommentar(er)
