Shulou(Shulou.com)06/02 Report--

This article is about how R language to achieve data visualization analysis of Tmall Singles Day sales growth rate. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

(if you want to learn how to draw the above image, please see the previous blog: redraw Tmall's Singles Day sales chart with ggplot.)

At the same time, on Zhihu, this question also successfully appeared on the hot search list at that time, and all kinds of great gods gathered and expressed a lot of very interesting views:

How do you think some people question the fraud of Taobao's Singles Day data and successfully forecast sales of 268 billion yuan this year in April?

After reading a lot of articles by the Great God, I also explored this data to see if I could find something different and interesting.

Let's first take a look at the problem mentioned in the original text. This straight line fits too perfectly. In our general understanding, if an index changes with the year, such as sales, growth rate, etc., we use a very regular curve to return, it is almost impossible to show this kind of non-fluctuating situation, generally showing the results like the following figure.

But from the original picture, we can see that the red scattered dots are almost perfectly dotted on the fitted curve. This kind of situation is very rare and amazing in real life!

At first glance, I almost believed the content of the original text and such an exquisite fit. But if you think about it carefully, logically speaking, this assertion in the original text is very imprecise. The original author has a problem, that is, too arbitrary and too dependent on experience. Take chestnut, for example, some people have never seen gray swans, they say that all swans must not be gray, but as long as a gray swan appears, it can overturn the conclusion that swans must not be gray. This is falsification.

Similarly, we should not directly explain that there is a problem just because there are very few cases in which something happens. For example, for the lucky ones who win the first prize in every lottery, can we say that they won the lottery only when they opened and hung up?

In fact, in real life, many economic principles are linked to the stage of development or growth rate. So, could the perfect fit as in the original text be caused by some economic phenomena? If we can theoretically verify that it conforms to certain economic laws, we have evidence that such a very well-fitted sales curve is caused by such a coincidence because it obeys such a law, not because the data are falsified. On the other hand, if we find that it does not conform to the economic law through verification, then we can also prove that the original data are suspected of being falsified from a theoretical point of view.

So how does the growth rate change? Let's see!

Annual sales growth rate 20090.5-20109.361772.00201152455.562012191267.31201335083.25201457163.14201591259.722016120732.352017168239.352018213526.932019268425.71

We have plotted a decline in sales growth over the past decade:

In fact, from the perspective of sales growth rate, it is in line with some economic laws. After the specific quantitative analysis, we will conduct a more in-depth and rigorous analysis.

Finally, the drawings involved in this blog will be followed by a tutorial to teach you how to draw.

Redraw Tmall's Singles Day sales chart with ggplot

Ggplot adds the y-axis on the left and right sides (take Tmall's Nov 11 sales volume and growth rate as an example)

Thank you for reading! On "R language how to achieve data visualization analysis of Tmall double 11 sales growth rate" this article is shared here, I hope the above content can be of some help to you, so that you can learn more knowledge, if you think the article is good, you can share it out for more people to see!

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