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Figure 19.4: The rainbow colorscale is highly non-monotonic. This becomes clearly visible by converting the colors to gray values. From left to right, the scale goes from moderately dark to light to very dark and back to moderately dark. In addition, the changes in lightness are very non-uniform. The lightest part of the scale (corresponding to the colors yellow, light green, and cyan) takes up almost a third of the entire scale while the darkest part (corresponding to dark blue) is concentrated in a narrow region of the scale.
@@ -318,7 +318,7 @@19.3 Not designing for color-visi
As discussed in Chapter 4, there are three fundamental types of color scales used in data visualization: sequential scales, diverging scales, and qualitative scales. Of these three, sequential scales will generally not cause any problems for people with color-vision deficiency (cvd), since a properly designed sequential scale should present a continuous gradient from dark to light colors. Figure 19.6 shows the Heat scale from Figure 4.3 in simulated versions of deuteranomaly, protanomaly, and tritanomaly. While none of these cvd-simulated scales look like the original, they all present a clear gradient from dark to light and they all work well to convey the magnitude of a data value.
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Figure 19.6: Color-vision deficiency (cvd) simulation of the sequential color scale Heat, which runs from dark red to light yellow. From left to right and top to bottom, we see the original scale and the scale as seen under deuteranomaly, protanomaly, and tritanomaly simulations. Even though the specific colors look different under the three types of cvd, in each case we can see a clear gradient from dark to light. Therefore, this color scale is safe to use for cvd.
@@ -326,14 +326,14 @@19.3 Not designing for color-visi
Things become more complicated for diverging scales, because popular color contrasts can become indistinguishable under cvd. In particular, the colors red and green provide about the strongest contrast for people with normal color vision but become nearly indistinguishable for deutans (people with deuteranomaly) or protans (people with protanomaly) (Figure 19.7). Similarly, blue-green contrasts are visible for deutans and protans but become indistinguishable for tritans (people with tritanomaly) (Figure 19.8).
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Figure 19.7: A red–green contrast becomes indistinguishable under red–green cvd (deuteranomaly or protanomaly).
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Figure 19.8: A blue–green contrast becomes indistinguishable under blue–yellow cvd (tritanomaly).
@@ -341,7 +341,7 @@19.3 Not designing for color-visi
With these examples, it might seem that it is nearly impossible to find two contrasting colors that are safe under all forms of cvd. However, the situation is not that dire. It is often possible to make slight modifications to the colors such that they have the desired character while also being safe for cvd. For example, the ColorBrewer PiYG (pink to yellow-green) scale from Figure 4.5 looks red–green to people with normal color vision yet remains distinguishable for people with cvd (Figure 19.9).
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Figure 19.9: The ColorBrewer PiYG (pink to yellow-green) scale from Figure 4.5 looks like a red–green contrast to people with regular color vision but works for all forms of color-vision deficiency. It works because the reddish color is actually pink (a mix of red and blue) while the greenish color also contains yellow. The difference in the blue component between the two colors can be picked up even by deutans or protans, and the difference in the red component can be picked up by tritans.
@@ -436,7 +436,7 @@19.3 Not designing for color-visi
While there are several good, cvd-safe color scales readily available, we need to recognize that they are no magic bullets. It is very possible to use a cvd-safe scale and yet produce a figure a person with cvd cannot decipher. One critical parameter is the size of the colored graphical elements. Colors are much easier to distinguish when they are applied to large areas than to small ones or thin lines (Stone, Albers Szafir, and Setlur 2014). And this effect is exacerbated under cvd (Figure 19.11). In addition to the various color-design considerations discussed in this chapter and in Chapter 4, I recommend to view color figures under cvd simulations to get a sense of what they may look like for a person with cvd. There are several online services and desktop apps available that allow users to run arbitrary figures through a cvd simulation.
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Figure 19.11: Colored elements become difficult to distinguish at small sizes. The top left panel (labeled “original”) shows four rectangles, four thick lines, four thin lines, and four groups of points, all colored in the same four colors. We can see that the colors become more difficult to distinguish the smaller or thinner the visual elements are. This problem becomes exacerbated in the cvd simulations, where the colors are already more difficult to distinguish even for the large graphical elements.
diff --git a/_book_production/pitfalls_of_color_use.md b/_book_production/pitfalls_of_color_use.md index 7f31bbd9..41d75839 100644 --- a/_book_production/pitfalls_of_color_use.md +++ b/_book_production/pitfalls_of_color_use.md @@ -52,7 +52,7 @@ In Chapter \@ref(color-basics), I listed two critical conditions for designing s (ref:rainbow-desaturated) The rainbow colorscale is highly non-monotonic. This becomes clearly visible by converting the colors to gray values. From left to right, the scale goes from moderately dark to light to very dark and back to moderately dark. In addition, the changes in lightness are very non-uniform. The lightest part of the scale (corresponding to the colors yellow, light green, and cyan) takes up almost a third of the entire scale while the darkest part (corresponding to dark blue) is concentrated in a narrow region of the scale.
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(\#fig:rainbow-desaturated)(ref:rainbow-desaturated)
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(\#fig:heat-cvd-sim)(ref:heat-cvd-sim)
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(\#fig:red-green-cvd-sim)(ref:red-green-cvd-sim)
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(\#fig:blue-green-cvd-sim)(ref:blue-green-cvd-sim)
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(\#fig:PiYG-cvd-sim)(ref:PiYG-cvd-sim)
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(\#fig:colors-thin-lines)(ref:colors-thin-lines)
20.1 Designing legends with redun
Surprisingly, the green and blue points look more distinct for people with red–green color-vision-deficiency (deuteranomaly or protanomaly) than for people with normal color vision (compare Figure 20.2, top row, to Figure 20.1). On the other hand, for people with blue–yellow deficiency (tritanomaly) the blue and green points look very similar (Figure 20.2, bottom left). And if we print out the figure in gray-scale (i.e., we desaturate the figure), we cannot distinguish any of the iris species (Figure 20.2, bottom right).
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Figure 20.2: Color-vision-deficiency simulation of Figure 20.1.
@@ -283,7 +283,7 @@20.1 Designing legends with redun
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Figure 20.4: Color-vision-deficiency simulation of Figure 20.3. Because of the use of different point shapes, even the fully desaturated gray-scale version of the figure is legible.
@@ -313,7 +313,7 @@20.1 Designing legends with redun
Matching the legend order to the data order is always helpful, but the benefits are particularly obvious under color-vision deficiency simulation (Figure 20.7). For example, it helps in the tritanomaly version of the figure, where the blue and the green become difficult to distinguish (Figure 20.7, bottom left). It also helps in the grayscale version (Figure 20.7, bottom right). Even though the two colors for Facebook and Alphabet have virtually the same gray value, we can see that Microsoft and Apple are represented by darker colors and take the bottom two spots. Therefore, we correctly assume that the highest line corresponds to Facebook and the second-highest line to Alphabet.
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Figure 20.7: Color-vision-deficiency simulation of Figure 20.6.
@@ -354,7 +354,7 @@20.2 Designing figures without le
We can also use density plots such as the one in Figure 20.10 as a legend replacement, by placing the density plots into the margins of a scatter plot (Figure 20.11). This allows us to direct-label the marginal density plots rather than the central scatter plot and hence results in a figure that is somewhat less cluttered than Figure 20.9 with directly-labeled ellipses.
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Figure 20.11: Sepal width versus sepal length for three different iris species, with marginal density estimates of each variable for each species.
diff --git a/_book_production/redundant_coding.md b/_book_production/redundant_coding.md index 0a5340e6..9c26e34d 100644 --- a/_book_production/redundant_coding.md +++ b/_book_production/redundant_coding.md @@ -21,7 +21,7 @@ Surprisingly, the green and blue points look more distinct for people with red-- (ref:iris-scatter-one-shape-cvd) Color-vision-deficiency simulation of Figure \@ref(fig:iris-scatter-one-shape).
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(\#fig:iris-scatter-one-shape-cvd)(ref:iris-scatter-one-shape-cvd)
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(\#fig:iris-scatter-three-shapes-cvd)(ref:iris-scatter-three-shapes-cvd)
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(\#fig:tech-stocks-good-legend-cvd)(ref:tech-stocks-good-legend-cvd)
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(\#fig:iris-scatter-dens)(ref:iris-scatter-dens)