Category Archives: Online Tools / Apps / Data Sources

U.S. life expectancy down again

The U.S. Centers for Disease Control has released statistics on life expectancy and causes of death for 2016. Some interesting findings:

  • Overall average life expectancy fell by 0.1 year, from 78.7 to 78.6 years.
  • The average masks the finding that for women, life expectancy held steady at 81.1 years while for men, it decreased by 0.2 years from 76.3 to 76.1 years.
  • Deaths from disease were down in almost every category. The increases come from “unintentional injury” and suicide. Unintentional injury sounds like car accidents and falling off a ladder, and it does include those things. But dig a little bit and it includes “poisoning”, and poisoning in turn includes drug overdose.

The Guardian explains that life expectancy has fallen two years in a row and how unusual that is:

Drug overdoses killed 63,600 Americans in 2016, an increase of 21% over the previous year, researchers at the National Center for Health Statistics found.

Americans can now expect to live 78.6 years, a decrease of 0.1 years. The US last experienced two years’ decline in a row in 1963, during the height of the tobacco epidemic and amid a wave of flu.

“We do occasionally see a one-year dip, even that doesn’t happen that often, but two years in a row is quite striking,” said Robert Anderson, chief of the mortality statistics branch with the National Center for Health Statistics. “And the key driver of that is the increase in drug overdose mortality.”

The article goes on to explain that the last time we saw three years of decline was during the Spanish flu epidemic 100 years ago.

Comparing any two years could easily be a statistical blip, as any climate science denier could tell you. But it seems clear that over time the U.S. is losing ground to its peers in the developed world. The solution our elected politicians have identified, of course, is to take away health care and mental health coverage from the working class.

browser extensions to get cheaper stuff

This blog is not about how to get more stuff. It’s not about how to get cheaper stuff. For the most part, I am almost totally against stuff and the idea that life is about getting more of it.

But there is in fact some stuff I need and even some I want. So I might occasionally mention a story about browser extensions that help us get more stuff cheaper. But we have to be disciplined! Just because we can get cheaper stuff does not mean we should get more of it. Try to get used stuff if you can, and try to get rid of some when you get some new stuff. But if there is a thing you are absolutely going to buy new, no matter what, whether you have any of these browser extensions are not, then go ahead and see if they will help you get it cheaper.

SDG Index and Dashboards Report 2017

The UN has released an update on the Sustainable Development Goals. I find the number of indicators a bit bewildering. It is interesting to dig into some of the thresholds and methods behind the indicators, and to see how individual countries score. I wonder though if countries are really using these metrics to guide their planning and policy decisions. I wonder if something a bit simpler (not simpler to compile, but simpler to interpret) like a GDP adjustment or ecological footprint would work better. If every country were in the “good” range for all these metrics, do we really know that the world would be sustainable in an absolute sense, meaning not exceeding the limits of our planet? These indicators instead seem to rank countries against each other, taking the countries doing the best in each category as the model for all the others. I wonder if the best the world currently has to offer is really the best we can aspire to in every category. Well, this is an academic question when there is clearly such a gap between the best and the worst, or even the best and the average. And I wonder if we will be patting ourselves on the back in the future because some percentage of countries met some percentage of these goals.

alternatives to word clouds

I like this post on R bloggers proposing several alternatives to word clouds. I’ll list them below but really, you should look at the pictures because hey, this is about pictures.

  1. circle packing (basically this replaces the words with circles, dealing with the problem of bigger/longer words appearing to be more important in standard word clouds); there is a variation on this called the “horn of plenty” where the circles are arranged in order rather than randomly
  2. cartogram (in my ignorance, I have been calling this a “bubble map”. I have used these frequently to show engineering model results and find they work well for many people)
  3. chloropleth (these shade in geographic areas to convey data. I find these work well if the size of the geographic area is important information. If it is not, these tend to draw the viewer’s eye to larger areas, and in that case the bubbles are better. For example, per-person income of Luxembourg vs. China.)
  4. treemap (I’ve been calling these “packed rectangles” and I generally find them good for anything where conveying relative magnitudes of things to people is important)
  5. donuts (surpringly, the author concludes a donut is the best option for the data he is trying to show and I kind of agree, it gets the point across and leaves lots of room for labels)

The article has links to the specific packages and code used to create the graphics.

Will robots take my job?

If you want to know if robots will take your job, you can go to It turns out my job (“environmental engineer” is the closest match) is particularly hard to automate at just a 1.8% chance robots will take my job, so I’ve got that going for me. I typed in ten other other career choices to see what I would get, then ranked them from most to least at risk.

  • auto mechanic: 59%
  • electrician: 15%
  • electrical engineer: 10%
  • mathematician: 4.7%
  • biochemist/biophysicist: 2.7%
  • materials scientist: 2.1%
  • chemical engineer: 1.7%
  • computer scientist: 1.5%
  • mechanical engineer: 1.1%
  • nurse: 0.9%

I won’t bother typing in the obvious ones like taxi driver (89%) or court reporter (50%). Okay, I did and that last one surprised me a little. The ten I picked weren’t random, they were ones I thought would be safe, and it turns out I was right except for auto mechanic. I’m a little surprised at that. Vehicles are merging with computers and getting more complex all the time, which means they are going to require more troubleshooting, updating, and will become obsolete faster than the past. I would also think a car mechanic could cross-train as a robot mechanic pretty easily. So the mechanics of the future will have to be equal parts grease monkey and tech support. Maybe they won’t be called mechanics, but the complicated systems we are creating are going to break in unpredictable ways and skilled troubleshooters are going to be in demand.

Anyway, the bottom line is that most types of engineering, and research positions related to genetics and/or materials, are pretty safe. Nursing is a field where supply just never seems to catch up to demand, and medical technology (and spending) just keep marching forward as the population ages and lives longer. You can still make a living as an electrician or a plumber.

I also learned something about the Standard Occupational Classification system used by the U.S. Department of Labor.

The 2010 Standard Occupational Classification (SOC) system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of 840 detailed occupations according to their occupational definition. To facilitate classification, detailed occupations are combined to form 461 broad occupations, 97 minor groups, and 23 major groups. Detailed occupations in the SOC with similar job duties, and in some cases skills, education, and/or training, are grouped together.


R and differential equations

Here’s a new R package for solving differential equations. Sounds like something that might be of interest to only a few ivory tower mathematicians, right? But solving differential equations numerically is the critical core of almost any dynamic simulation model, whether it is simulating water, energy, money, ecology, social systems, or the intertwinings of all of these. So if we are going to understand our systems well enough to solve their problems, we have to have some people around who understand these things on a practical level.