Recent case incidence is useful for estimating risk.
In these days and months of COVID-19, we wonder about risk. When I go out in public, what is my risk? The answer in part depends on prevalence: How many people in my area are infectious?
In the United States, we don’t have an adequate testing program, and we don’t know how high the prevalence is. What we do know is case incidence–the number of cases found today and in recent days. These counts are often listed in news sources or displayed like this:
The graph is a bit cluttered, and it’s not obvious what to do with the information. The daily counts fluctuate with day of the week reporting and other factors that carry little information. Most of these graphs include a curve that is a moving average of the daily counts to smooth out these fluctuations.
The case counts go up and down, but what does that tell us about current risk? The experts at Harvard Global Health Institute have developed a framework that adds context. It categorizes COVID-19 risk level as Green, Yellow, Orange, or Red based on daily case count per 100, 000 people.
For our use, it is valuable to combine the smoothed daily case count with this risk scale. COVID Action Network provides graphs that look like this:
The stylish color change in the curve would delight data visualizers. Unfortunately it is likely to take too much cognitive effort for other viewers. Something simple and bold is probably better.
According to The Economist, India is held back economically because it has a weak middle class.
The article is 3000 words and has three graphs. How to connect the parts of the text to the appropriate graphs? Numbered captions are one standard technique, but keeping the figure together with a legible caption can be a challenge.
Running the figures inline and referring to them as the next figure or the figure below can work but has issues for multiple columns and page breaks.
A convenient solution is to place a figure number in the chart and use text like (see chart 2).
This works especially well with charts that are largely self documenting.
Bitcoin is not the only digital currency. An article in The Economist discusses the most popular crypto-currencies. There are now approximately 1492!
The growth of these increasing number of currencies has reduced the market share of bitcoin.
As is usual with area charts, it is easy to follow the base area and the top area, but more challenging to understand the others. Bitcoin is dropping rapidly and now well under 40%. The other basket of currencies is increasing and now amounts to approximately 30%
The data source provides an alternate presentation, an overlapping area chart.
With this display, it is easier to follow the individual coins, but the chart includes too many of them. More important, almost all area charts are the stacked variety, so this one might be misinterpreted. It would probably be better to use lines.
It can be a bit easier to see which coin is most eating away at Bitcoin be reversing the stacking order.
In October 2017, the Council of Economic Advisers, an agency within the office of the President, released a short report on the effect of a large proposed corporate tax cut on wage growth. The only graphical evidence included is
The presentation is clear, but a graph like this raises the obvious questions: Are these countries similar to the United States? What is significant about these four years?
Presumably the CEA has access to more clearly relevant data.
An analysis by the Economic Policy Institute comes to a very different conclusion. It includes this graph
The evidence is less compelling. You could argue that the 1986 corporate tax cut led to a modest increase in compensation, perhaps through productivity growth. Or perhaps the tax cut interrupted the decades long decline of compensation growth. In any case the United States has not experienced a compensation growth of 2% or more for at least 40 years.
Bitcoin is a major phenomenon of out time. There is nothing like very rapid growth to generate excitement.
As is often the case, this linear scale plot obscures rate of change and the small early values. It is worth also looking at a log scale plot.
The early adopters had the best earning potential.
Some people talk about the promise of bitcoin as the new money. Of course this price history shows that it would have been amazingly deflationary. Hardly a good thing.
The price of bitcoin is financial, but its cost is environmental. “Mining” bitcoin takes a huge amount of electricity, and the major mining countries rely heavily on coal fired power plants.
The scale of this electricity consumption can be compared to countries.
The choice of a bar chart is appropriate, as is ordering the countries so that the longest bar is at the bottom, nearest to the scale. The small drop shadows, however, only add blur.
The Nikkei 225 recently reached a 21 year high according to The Economist in the October 12, 2017 issue.
The graph is a bit misleading. Because the vertical axis starts at 5000 rather than 0, the index may appear to have tripled since its low rather than doubled.
It is certainly okay to start an axis at a value other than zero for a line graph. But in this case, there is no downside in starting at zero. And our initial impression would be more accurate.
There is another concern: What is special about 1996? Was it a high point?
Including the high point in 1989 would have made a more complete, if longer story.