ae制作數據可視化

by Krist Wongsuphasawat

克里斯特·旺蘇帕薩瓦(Krist Wongsuphasawat)

我如何精心制作真正可怕的數據可視化 (How I carefully crafted a truly terrible data visualization)

Yes, you read that right. I am going to explain how I put together a really bad visualization, intentionally.

是的，你看的沒錯。 我將解釋如何有意地將一個非常糟糕的可視化組合在一起。

Andy Kirk of visualisingdata.com posted an interesting contest challenging everyone to come up with the “best worst viz.” Of course, one of the motivations for me doing this is to win a copy of his book. But the contest itself is also a thoughtful exercise.

visualisingdata.com的安迪·科克(Andy Kirk)發布了一個有趣的競賽，要求所有人提出“最好的最糟糕的結果”。 當然，我這樣做的動機之一就是贏得他的書的副本。 但是比賽本身也是一項深思熟慮的活動。

When talking about extremely bad visualizations, the stereotypical ones often involve 3D pie charts, rainbow color palettes and terrible choices of fonts, layouts, and colors.

當談到極差的可視化時，陳規定型的通常涉及3D餅圖， 彩虹色調色板和 字體，布局和顏色的糟糕選擇。

In my opinion, bad visualizations don’t have to be just that. The goal I had in mind was to create a piece that looks totally harmless, but will torture your brain until you realize how absurdly ridiculous the whole thing is.

在我看來，糟糕的可視化并不僅限于此。 我想到的目標是創建一塊看上去完全無害的作品，但是會折磨您的大腦，直到您意識到整件事情多么荒謬可笑。

I collected data from visualizations featured on viz.wtf and drew each mark to represent one of the visualizations and its properties. Example questions to exercise your WTF gland are:

我從viz.wtf上的可視化文件中收集了數據，并繪制了每個標記以表示可視化文件及其屬性之一。 行使WTF腺的示例問題包括：

• What’s the most common color for these visualizations?
  這些可視化最常用的顏色是什么？
• Where are the pie charts?
  餅圖在哪里？
• Can you point out the least popular piece?
  您能指出最受歡迎的作品嗎？
• How often is 3D used?
  3D多久使用一次？
• Is there any pattern at all?
  是否有任何模式？

Before reading the next section, try to figure out everything that is wrong with this chart by yourself.

在閱讀下一部分之前，請嘗試自行找出該圖表的所有錯誤。

概念 (Concept)

The main idea was to create conflicts in perception and mess with viewers’ cognitive thinking.

主要思想是在感知上產生沖突，并與觀看者的認知思維陷入混亂。

Bad visualizations usually have mismatches between visual encodings and data, such as encoding incomparable areas (3D pie) for numerical values. These mismatches leave viewers with little to do but scratch their heads, then abandon the visualization because it takes too much effort to make sense of it.

不良的可視化效果通常在視覺編碼和數據之間不匹配，例如對數值進行無可比擬的區域編碼(3D餅圖)。 這些不匹配使得觀看者幾乎無所事事，但會撓頭，然后放棄可視化，因為要花費太多精力才能理解它。

I wanted to take bad to the next level, and was inspired by one of my favorite responses from the Stack Overflow questions, “What is the best comment in source code you have ever encountered?”

我想把自己提升到一個新的水平，并受到Stack Overflow問題中我最喜歡的回答之一的啟發，“ 您遇到過的源代碼中最好的注釋是什么？ ”

#define TRUE FALSE

＃定義真假

My goal was to make something that seems like it can be interpreted, but creates very strong conflicts with our prior knowledge that are almost impossible to overcome. To do this, I chose very direct choices of encoding, such as using color to represent color, and position to represent position, then set the mappings counterintuitively so I could wreak complete havoc with viewers’ minds.

我的目標是做出看起來似乎可以解釋的內容，但與我們現有的知識產生非常強烈的沖突，而這幾乎是無法克服的。 為此，我選擇了非常直接的編碼選擇，例如使用顏色表示顏色，使用位置表示位置，然后反直覺地設置映射，這樣我就可以完全破壞觀看者的思想。

數據 (Data)

I was looking for a good dataset to try the idea on but could not find one I really liked. Then I got the idea that it would be recursively bad to create a bad visualization, of bad visualizations, so I manually collected some data from viz.wtf

我一直在尋找一個很好的數據集來嘗試這個想法，但是找不到我真正喜歡的數據集。 然后我想到了創建不良的可視化效果和遞歸的可視化效果將是遞歸的，所以我手動從viz.wtf收集了一些數據

以下是我對這張圖表犯下的所有罪行： (Here are all the crimes I have committed to this chart:)

1. I used color to represent color, but didn’t guarantee that they would be the same color. As a result, green is the new black.

   我用顏色表示顏色，但不保證它們會是相同的顏色。 結果， 綠色就是新的黑色 。

2. I also didn’t add enough unique colors, so there are duplicates. For instance, both red and blue are represented by green. (This was not intentional at first, but then it made things look worse so I kept it.)

   我也沒有添加足夠的獨特顏色，因此存在重復項。 例如， 紅色和藍色都由綠色表示。 (起初這不是故意的，但隨后使情況看起來更糟，因此我保留了它。)

3. There was a special case for “mixed” color, as I couldn’t decide what color to encode it with. As a result, each of these “mixed” visualizations received a randomly selected color.
   “混合”顏色有一個特例，因為我無法決定用哪種顏色編碼。 結果，這些“混合”可視化中的每一個都收到了隨機選擇的顏色。
4. I used position to represent position, but ensured that these never matched up. With that, right is on the left.
   我使用位置來表示位置，但確保這些位置永遠不會匹配。 這樣，右邊在左邊。
5. I used shapes to represent chart types, but ensured that they never matched. With that, a bar chart is a circle, while a pie chart looks like a bar.
   我使用形狀表示圖表類型，但確保它們從未匹配。 這樣，條形圖就是一個圓圈，而扇形圖看起來就像條形圖。
6. I used size to encode popularity, but used an inverse scale with the biggest size meaning zero.
   我使用大小來編碼受歡迎程度，但是使用了最大大小為零的反比例。
7. I made axis labels more complicated than they needed to be. No 3D? True or false?
   我使軸標簽變得比原來復雜得多。 沒有3D嗎？ 對或錯？
8. I rotate each giant number by its value in degrees. This one is pointless encoding.
   我按其值旋轉每個巨型數字。 這是無意義的編碼。
9. The circles around the giant numbers don’t mean anything. They do not indicate boundaries.
   巨大數字周圍的圓圈沒有任何意義。 它們不表示邊界。
10. If you sum all the numbers, there are actually 102 visualizations, not 100.
    如果將所有數字相加，則實際上有102個可視化，而不是100個。
11. I added a dinosaur. Because I could.
    我加了一只恐龍。 因為我可以。
12. Lastly, there was a link to the raw data, proudly shared in PDF format.
    最后，有一個原始數據的鏈接，以PDF格式自豪地共享。

If you spot other terrible aspects of this visualization that I’ve overlooked, please feel free to leave a response below, or tweet at me.

如果您發現我忽略了此可視化的其他可怕方面，請隨時在下面留下您的回復，或者在推特上發消息給我。

翻譯自: https://www.freecodecamp.org/news/how-i-carefully-crafted-a-terrible-visualization-2c8e06d50ebb/

ae制作數據可視化