Charts and graphs are a great way to visualize data and understand what it means. These visualizations help you identify trends and make critical strategic decisions accordingly. For example, you can use a React bar chart to visualize categorical data and compare different categories. Similarly, a line chart can depict continuous data over time. You can even use charts to enhance productivity. For instance, you can use a flow chart for task distribution, an organizational chart to clearly define each employee’s role, and so on. However, visualizing data isn’t enough – you must ensure your visualization is easy to understand. One way to do that is to use the right colors for charts.
In this article, we’ll discuss why it’s crucial to use the right colors for charts, and how to use different chart color schemes to make your visualizations more effective.
Why Is It Important To Choose Proper Colors For Charts?
Data visualizations should be visually appealing. However, the primary purpose of a chart or graph is to communicate key results. For this reason, it’s crucial to use the right graph color schemes. For instance, if you use colors for charts that aren’t visually distinct, it will be difficult for viewers to tell them apart and interpret the chart. You should use colors that make it easy to communicate the key information. Using the right and consistent colors for charts automatically makes them beautiful.
Also, it’s not necessary to always use colors for every chart element. Sometimes it’s best to use a neutral color like grey for most elements, while you should use bright colors for the most significant data points.
You can use a color scheme creator to generate a visually distinct color palette for your charts. You can then use this palette for most of your charts, such as bar charts, pie charts, or maps.
What Are Different Types Of Color Pallete For Charts?
When you’re working with data visualizations, there are three main types of color palettes that you can choose from.
You can use a qualitative palette when you have categorical data or variables with distinct names, but there is no specific ordering. Some examples of categorical variables are countries, states, or race. When using the qualitative palette, every variable is assigned one color from the palette.
Typically, a categorical palette should have a maximum of 10 unique colors. If you add too many colors to the palette, it’ll be difficult to comprehend the chart.
This color set for a qualitative palette consists of eye-catching colors like red, purple, multiple shades of yellow and green.
This color set consists of vibrant colors for charts, such as bright pink, blue, and orange.
This color set consists of multiple shades of blue that are easily distinguishable, as well as some other bright and dark shades of different colors.
This palette consists of pastel colors for charts, such as green, soft pink, and warm yellow.
Sequential palettes are best for when you’re dealing with variables that have numeric values or a specific sequence or order. When you’re using a sequential palette, colors are assigned to variables or values based on lightness or hue. Generally, higher values are assigned darker colors, while lower values have lighter colors, especially when the background of the visualization is white or some very light color.
Additionally, if your dashboard’s theme is light, it’s best to use light-to-dark sequential palettes. However, for dark-themed dashboards, dark-to-light palettes are a better choice.
Blue to Yellow
This color set consists of several different shades of blue and yellow. However, all the shades are easily distinguishable.
Grey to Red
This palette consists of neutral colors like different shades of grey as well as multiple shades of red.
Black to Pink
This palette is a combination of bright and dark shades.
This is an all-blue color palette consisting of several shades of blue.
When your numeric data has a central value like zero, it’s best to use a diverging palette. This palette is made by combining two sequential palettes, but there is a shared endpoint at the central value. The colors on one side of the center are assigned to values that are smaller than the center, and the colors on the other side are assigned to values larger than the center.
Blue to Red
This palette consists of a neutral color at the center and different shades of blue on one side, and different shades of red on the other side.
Orange to Purple
This palette has a dark shade at the center, and shades of orange on one side and purple on the other side.
This palette has a neutral color at the center and shades of pink on one side and blue on the other side.
Salmon to Aqua
This palette has a dark shade at the center.
Discrete And Continuous Palette
We can associate sequential and diverging palettes with data values as a discrete set of colors, where each color is assigned to a numeric range or continuous function. A palette generator typically lets you create discrete palettes. However, data visualization tools are also capable of building a continuous association.
How To Choose Colors For Charts?
Don’t Use Distracting Colors
It’s important to choose colors that make it easier to understand and interpret the chart. For example, you should avoid using colors that are too bright or too dark.
Avoid Opposite Colors
Using colors that are totally on the opposite side of the color wheel chart can confuse the viewers and divert their attention away from the data.
Use Data Theme Friendly Colors
You should try to use colors that reflect the theme of your data to make the visualizations easier to understand.
Same Color Shades For Different Values
Using different shades of the same color on the chart helps create a uniform appearance. As a result, viewers can focus more on data, instead of getting distracted by the colors. However, you should ensure the colors are easily distinguishable.
Another critical point to remember is to keep the colors consistent. For instance, it’s not recommended to use red on one chart and green on another chart in the same report.
Highlight Important Sections With Light Colors
While you should use colors in a way that doesn’t distract viewers from the data, sometimes you need to highlight important sections to make the readers focus on a particular data point. A dark shade of the same color can also be used if you don’t want to use an entirely different color.
Avoid Reliance On Just Colors
It’s crucial to consider this point to ensure that your charts are also easily readable for the color-blind. Additionally, if you want to attend to color blindness, avoid using colors like shades of red and green that can cause confusion for color-blind.
Black Text Is King
Black text is usually the easiest to read for everyone. However, if your chart’s background is black or some other dark color, you should use white text.
Color Differentiation Requires A Grayscale
An easy but effective way to check if the shades of your palette are different enough for anyone to distinguish is to make your image grayscale.
Avoid Over-Using Colors
Using proper colors for data visualization is important. However, you should only use them where appropriate. Not every chart requires multiple colors.
What Tools Can You Use For Chart/Graph Colors?
Below are some of the best tools that you can use to select and test colors for your charts:
ColorBrewer is a free online tool that allows you to select the right color palette for charts. It offers a range of color schemes for qualitative, diverging, and sequential palettes.
Data Color Picker
Data color picker is another easy-to-use tool for creating diverging and sequential palettes quickly. You can use the default Palette tab to create multi-hue sequential palettes.
Chroma.js Color Palette Helper
Chroma.js Color Palette Helper is a great tool for creating diverging and sequential palettes for your visualizations. It provides several useful options, such as correct lightness and bezier interpolation. It also tells you whether the palette is colorblind-safe.
This is a unique tool that lets you create a qualitative palette by extracting the palette from uploaded images. However, you may sometimes need to modify the palette to ensure it works well with your visualizations.
This tool allows you to check your data visualization colors before you implement them on your visualizations.
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