This is the third article in my data viewing series. See Section 1: “Data data display described: What is it and why is it important” and Part 2: “Data Search has been described: Introduction to material. “
Do you see in the picture below?
Most people see four: White, green, and two separate pinkish-red shades. In fact, those two letters are the same; There are only three colors in the picture.
This famous optical imposer reflects an important fact that you should process when planning the information to: The impartial color combination can fool the person's eye. For more color therapy, I will have to use natural senses and learn how to actually have to “see” color.
However, seeing as this is not an optometry article, I will focus you on the Fundamentals for the color use you need to create a clear data.
The difference between the color hue and the number of colors
When I presented visual channels to the previous article, I present two different channels related to color: Hue and the number. Let us consider these officially.
Color Hue That's what you think often when you hear the word “color.” Red, green, blue, pink, yellow, etc. Everything is different hues. PriceOn the other hand, it means “light” of individual HUE. The image below shows different amounts of rain colors, showing that the same hue may vary greatly in light / saill:
While both of these practical documents (see my previous article in this detailed chain chat on the visible encodings), the number of colors have one significant benefit of the Hue: It can still be seen if you see the eye published in Grayscale.
The types of color scale
If you want to use color as a visual source, you need to start by selecting the color rating. In doing so, there are a few features you need to consider:
- If your data is basic, you can use the color rating, which only depends on color hue.
- For more information, you will need to make two additional decisions: 1) Even if your scale will be in order or contrary to one or 2 hys) whether your scale will continue or divided into classes or separated.
Therefore, there are five color scales where we will discuss below: 1) respectively and 2) consecutive and written, 4) [1].
Subsequent scales (one of the HUE) are useful in identifying the categories of sections that travel from low to the top. The divergent scale can seem to help when the prices ranged from bad to Music or where the Designer wishes to emphasize the difference between two colors at the end of two ends.
Of course, these are normal rules. Various types of scale is the best in terms of what you see something, and sometimes it can work.
In order and unregistered
The following map uses a consecutive, incompetent color to show a fraction of the Australians identified as Anglican during the 2011 Centeration. We see that one Hue, green, rising from the amount from the light to darkness. With only one color, no turns, and as the scale is going, there are no classes.
Respectively and divided
Unlike the answer to the answer above, we see that the Map of the United States below has discrete categories that separated from the number of colors. It is still available in a row, as the Pink Hue is used. The number of colors increased as percentage of old people in the early 20s within the County County.
Diplent, class and unchanged
Divegent scale is a little understanding of understanding, so let's consider both types together in the comparative example. By doing so, we will see different benefits of a higher and unchanged scale.
The two charts below are produced in Python using funny data. Details contains the following visual representations (ie, visual channels):
- Ix-axis contains a number that represents the storage area.
- Iy-axis represents months.
- The color represents the “customer score” collected by fictitious mornings.
A fixed feature of vs. On the left (unchanged) scale, the total number must be full of prices, and on the right (classroom), colors represent the prices for prices. Left views provide more accuracy, but what is right is easy to interpret and apply.
Divigent feature of these scales is more intense. Let's break:
- Divegent average here we use two colors: red and vegetation (not the most common colors in the world, as we will see later in the article).
- A neutral, white color (or two light colors on a high-quality scale) represents the “intermediate point” in the data, in this situation 0.
- This middle point is locked, as does the situation where the divergent scale is naturally borrowed. It is just reasonable to use more than one colors when prices are just in one place without a meaningful center.
Sincere
Finally, and direct specific, the type of colors is one phase option. The chart below, which shows government debit in various lands, provides a clear example.
If you have been dealing with the principles discussed in this chapter so far, you will probably notice that this is not a Data Visualization well. It gets a common point across, but there are few of the most different colors, leading to the final finalization.
That means, it is the effective use of the phase rate, using this kind of scales in deceased information (data with different, unacceptable sections). A common error in the data-and-one should take care of avoiding – to use a range of different hyes where your data shows the increase in clear price or decrease. In those cases, refer to one of the color scales discussed above, according to your specific data.
That summarizes the foundations of the color scale to know to engage in the data sensory. To conclude, let us look at many advice using color well.
(Do not use) the color of POOR
It can be tempted to use color in the eyes when not required. For example, it is quite often to see the garralms of the bar with clear X-axis labels to separate bars with different colors.
This is not the case badBut it may not be necessary. If there are only a few paragraphs and are linked to other eye sights, in every way to use the color to provide visualization. However, if the eye contact works well without it, then don't force you.
Often, any refreshing (representations) should be avoided unless they provide additional experiences. It is overflowing, as that channel that shows the combination can be used in different, or confusing, as the viewer is abandoned to determine something that shows something that shows something that reflects something that reflects something that reflects something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows something that shows you something that shows something that shows you something that shows something that shows you something that shows something that shows you something that shows something that shows something that shows you something that shows something that shows something that shows you.
Make a palette color accessible
This endses the short point, but it is very important. Don't think that just because you can distinguish between the colors in the eyes, so everyone can. Data Vialivizions should be accessible for everyone, including people with different types of writing [2].
For example, think about Python's observation in the divergent color rate above. Do you think that a red red color will be able to translate it well? It is impossible.
Fortunately, we do not have to do more work to ensure our recognition is available. There are countless online tools [3, 4, 5] Automatic checking for Palettes your selected color. Some will also help you. Take advantage of them to make your view accessible as possible.
The last thoughts
Congratulations! Through the third text in this series, you have learned important goals you will need to design for compelling data. In the coming topics, we will eventually begin to design our viewpoint! Until then.
Progress
[1]
[2] https: /www.ne.nih.gov/learn-aeal-Connchetimes-chisings-chiseseas/clingness/tologories-cector/tologories
[3] https://coolors.co/contrapter-checker/11222466-ArcC8E5
[4] https://sandaim.org/resources/contrastChecker/
[5]
