Category: graphics

Stats Chat is an interesting kiwi site—managed by the Department of Statistics of the University of Auckland—that centers around the use and presentation of statistics in the media. This week there was an interesting discussion on one of those infographics that make you cringe:

I understand the newspaper’s need to grab our attention, as well as the designer’s esthetical considerations, but they have to follow avoiding misleading the reader and providing at least a ball-park idea of the importance of whatever issue is under discussion. Clearly, as pointed out in the discussion, a line chart would convey the message with a minimum of ink; however, the designer may still want to use circles, and here we could go back to a faceted version of the always maligned pie chart.

Faceted pie charts using ggplot2

The code reads the data, reshapes it and plots it using pretty much the explanation for pie charts in the ggplot2 documentation.

Again, please remember my typical disclaimer about lack of design and color flair. Colors and scales need work, but I think it is an improvement over the original.

A pint of R plotted an interesting dataset: intentional homicides in South America. I thought the graphs were pretty but I was unhappy about the way information was conveyed in the plots; relative risk should be very important but number of homicides is very misleading as it also relates to country population (this problem often comes up in our discussions in Stats Chat).

Instead of just complaining I decided to try a few alternatives (disclaimer: I do not have a good eye for colors or design but I am only looking at ways that could better show relative risk). I therefore downloaded the MS Excel file, which contained a lot of information from other countries and extracted only the information relevant to these plots, which you can obtain here: homicides.csv (4 KB). Some quick code could display the following graph:

Tile graph for homicides.

It is also possible to use a line graph, but it quickly gets very messy, so I created totally arbitrary violence categories:

Another view, which still requires labeling countries. Venezuela, what did happen to you? You look like the Wild West...

I hope others will download the data and provide much better alternatives to display violence. If you do, please add a link in the comments.

Since 4^th September 2010 we have had over 2, 800 quakes (considering only magnitude 3+) in Christchurch. Quakes come in swarms, with one or few strong shocks, followed by numerous smaller ones and then the ocasional shock, creating an interesting data visualization problem. In our case, we have had swarms in September 2010, December 2010, February 2011, June 2011 and December 2011.

Geonet provides the basic information and there have been several attempts at displaying the full set of shocks. For example, Christchurch Quake Map uses animation, while Canterbury Quake Live uses four panels showing quakes for last 24 hours, last week, last month and since September 2010. While both alternatives are informative, it is hard to see long-term trends due to overplotting, particularly when we move beyond one week during a swarm.

Geonet allows data extraction through forms and queries. Rough limits for the Christchurch earthquakes are: Southern Latitude (-43.90), Northern Latitude (-43.15), Western Longitude (171.75) and Eastern Longitude (173.35). We can limit the lower magnitude to 3, as shocks are hard to feel below that value.

Graph presented by Canterbury Quake Live, notice how difficult is to read the bottom graph.

The file earthquakes.csv contains 2,802 shocks, starting on 2010-09-04. The file can be read using the following code:

The following code produces a plot that, in my opinion, presents a clearer idea of the swarms but that I still feel does not make justice to the problem.

Graph displaying quake swarms, but still far from perfect. Looking forward to your ideas.

Please let me know if you have a better idea for the plot.

P.S.1 If you want to download data from Geonet there will be problems reading in R the resulting earthquakes.csv file, because the file is badly formed. All lines end with a comma except for the first one, tripping R into believing that the first column contains row names. The easiest way to fix the file is to add a comma at the end of the first line, which will create an extra empty variable called X that is not used in the plots.

P.S.2 We had some discussions by email with Michael (who commented below), where he suggested dropping the grey lines and using alpha less than one to reduce plotting density. I opted for also using an alpha scale, so stronger quakes are darker to mimic the psychology of ‘quake suffering’: both frequent smaller quakes and the odd stronger quakes can be distressing to people. In addition, now the plot uses a 1:6 ratio.

New version, click to expand.

I ran out of time, but the background needs more work, as well as finding the right level of alpha to best tell the story.