10 Years of my Digital Life

 Today I'm revisiting a topic I've been fascinating (obsessed?) with for many years: my laptop battery (e.g. see past blog posts here, here, here, here...)

10 Years of Data

I started semi-regularly keeping track of my laptop battery's health using coconutBattery back in 2009, with my big 15" MacBook Pro. Once I figured out how to automate the process myself with cron, I began in late 2012 keeping a record of my battery status every minute I used my laptop. Here is the complete 10-year record of my laptop(s) battery charge capacity:

the "cost" of keeping battery data for every minute of use over many years is only a few hundred Mb 

Comparing Mac Batteries

For the hardware nerds (incl. me) who are curious how these batteries hold up over many years of daily use, here are all 4 computers' lives overlaid, including the 2016 MacBook Pro (w/ Touch Bar) I am using to write this:

Interesting stat: the standard deviation of the capacity data (about a ~14 day rolling mean) is a scant 0.6%!

My 2009 MacBook Pro really started to decay at the end of its life (I used it a couple more months past this data). The 2012 Air (red points) was a definite anomaly. In fact, after I blogged about this problem, Apple reached out and replaced the computer with a new model - my favorite computer of all time, the 2013 MacBook Air. Amazingly this workhorse is still under daily use, on loan to a student!

I've given the 2016 13" MacBook Pro with TouchBar a lot of grief, as I've had many problems with it. However, the verdict seems to be in: the 2016 MacBook Pro's battery appears to be the best I've had over the past 10 years! After 2.5 years of use, it's still claiming to hold 90% of it's original charge! I wonder...  if we start making batteries that substantially outlive the host computers, will they be up-cycled? Could we start to see removable Mac batteries again? Pure speculation.

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I have changed...

Despite all the advances in hardware over the past 10 years, I think the data suggests the biggest change is with myself... or at least how I use my laptops. Consider this slightly hard-to-read figure, showing the distribution of charge fraction (how full the battery is) for my past 3 computers. For all computers I tended to keep them charged most of the time (peaks near 1), but for the 2016 MBP I keep it fully charged (i.e. plugged in) almost always... which is not surprising since I have chargers at home and at the office. These are really more portable workstations, and less "laptops" for me now.

I make the conscious effort to not work in the evenings

Here is the now-classic digital fingerprint, first popularized by Stephen Wolfram, of my life: 1 dot for every data point, tracing the time of day versus years of use. This is a classic diagram for examining the "quantified self". Obvious big features are: I sleep at night, I take a break near dinner, etc. You can also see some seasonal variations, especially during the 2nd half of grad school (2013 MBA, purple), where I seem to work later in the winters (probably getting ready for the AAS conference).

There's some big data errors present in the 2016 MBP data (blue), which show up as stripes here (i.e. every minute was recorded). My laptop appears to have occasional insomnia? Strange... (note: I've tried to remove these spurious days from here on). I don't think this figure tells the right story, however.

A very real change seems to be in the overall use of my laptop. Here you can see the total use (in 1-week bins) of my past 3 computers, with a big running mean (orange line). This figure shows that in Aug/Sept of 2015 (i.e. ~3 on the x-axis here) my laptop use dropped by almost half! The reason: I finished grad school, became a postdoc, and ordered a big iMac for my desk.

My daily computer usage has changed too - this is the figure I'm most proud of. Here I show the time of day used for each computer. Despite using the iMac at work during the day, my evening laptop usage has dropped a TON. Reason: I make the conscious effort to not work in the evenings! This may seem silly to many, but for an early-career academic it's a serious choice.

My work day is now a bit shorter, with a slightly later start, and an earlier end. Reason: this is largely driven by my toddler's daycare schedule. Yay!

But, my workday is also more consistent. See how there's no dip for lunch? I'm in lunch meetings or  eating at my desk most days now. Boo.

Most interestingly (to me), when you fold this data over days of the week (0=Monday, etc), you can see a very big change as well. While I always was fairly good at working less on the weekends, now you can see I have almost NO computer usage on Saturday and Sunday. I also seem to work a LOT more on Friday mornings than I used to... but I think this is because my wife and I work from home some Fridays, and so my laptop usage would seem higher (since the iMac is at my office).

Conclusion
There are many metrics one could use to trace their digital lives - social media activity, emails, screen time (basically equivalent to my battery record). Given the explosion of use my iPhone has seen over the past decade, I'm very glad to see Apple (and others) are finally getting on board with providing this data to users, and (maybe) helping them use their devices more mindfully. As I've written before, the "cost" of keeping battery data for every minute of use over many years is only a few hundred Mb (and probably could be 5-10x smaller if I cared to compress it at all), and so I encourage Apple to make the usage archive part of every computer's "permanent record".

As always, all the Python code to do this analysis and make these figures (including the very clumsy parser I wrote for the ugly battery log data) is on my GitHub! Also, the simple cron script I use is also posted, including detailed install instructions, if you want to try this yourself.


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How much are professors paid?

The Washington State employee salary archive that I featured in my last post is a fascinating dataset to explore... including 432k entries for more than 244k people (actually unique names, so likely many more!). There is much you could do with this data, e.g. exploring gender distributions, infer the age distribution by matching the names to the US "Baby Names" data, comparing salaries for similar jobs around the state...

Last time we were looking at salaries for people with the job I have, and comparing them to expectations one might reasonably infer from reading the University's HR website.

Today I'm going to look at the job (notionally) I want: University Professor.

There are 5 publicly funded universities in Washington State. Here is how the 2017 salaries for people whose job contains the word "Professor" compare:

There's a lot to unpack in this graph, even with just 5 curves...

1. UW is the clear "winner", with a median salary (vertical bar) far above any of its "peers"
2. UW has a TON of faculty (and/or their faculty have more line-items in the budget - i.e. a single Prof having multiple entries)
3. The 4 other schools are quite closely clustered around $80k-ish
4. The 3 schools located east of the Cascade mountains (i.e Eastern, Central, and Washington State) all have very similar primary colors listed on their websites. Coincidentally, these schools all reside in "red" districts. This color proximity drove several design choices for the viz.

But comparing average salaries does not tell the whole story...

These schools are located in vastly different regions of our state: a major city, a rural farming community, a ground transportation hub, an international boarder... and life in each of these cities/towns is equally unique. So for your consideration, here is the same data as above, but normalized by the median home price for each city:

Wow

1. Given the (ridiculous) cost of living in Seattle, it is no wonder so many faculty now have to live outside the city. For example, I live ~12 miles north of Seattle where home prices are ~27% cheaper.
2. Bellingham has gotten expensive!
3. Faculty in eastern Washington are doing substantially better than their western counterparts...  probably much closer to what being a Professor in most cities used to be like (i.e. buying a reasonable house near the University on a faculty salary)

This is an age-old debate when considering the job market for faculty... should one chase the cosmopolitan lifestyle of a big city, or be in the upper echelon of a small town? Clearly you shouldn't just look at top-line salary when considering which university to work at. While there's no "right" answer, I for one find the high earning power of rural faculty quite promising. Small towns can be wonderful places to live, and competitive salaries can bring top talent to these schools. 

One more thing...

Washington state has 30 public colleges (mostly community colleges). Here is the data for jobs listed as "Professor" or "Faculty", with medians shown as heavy circles....

Most of these curves are very skewed towards low salaries, endemic of the state of college faculty hiring and the reliance on part-time labor...

Of course, all the Python code to do this analysis (mostly just bread/butter Pandas) and make these figures (matplotlib) is available on my GitHub profile.

How much should you be paid?

This is a basic question that many people struggle with, from academics to freelancers, office workers to people in the "trades".

Openly discussing pay, and transparency about how pay rates are set, are never in the best interest of your employer. This is just as true in academia as in the private sector, which is ironic since public employee salaries (e.g. university researchers) are a matter of public record!

So a tension naturally is present: faculty/administrators who are hiring staff or researchers have an incentive to pay them as low as possible (often for sensible reasons like stretching grant dollars), and prospective employees... need to pay their bills.

As a Research Scientist, I have struggled both to know what an appropriate pay for my job is, and to receive compensation at such a level.
(At UW, Research Scientist serves as a stop-gap position, bridging the postdoc and faculty academic jobs with a "staff" position, which is not protected by any unions, and has a decidedly ambiguous role within departments.)

My University claims that pay scales are based on "market rates", and that on the whole these are within acceptable ranges. They also make (though a bit tedious to find) the pay scales openly available, though the "market rate" data is not so far as I can tell. For transparency: I am a Research Scientist level 3 (though I requested to be level 4...), Pay Grade 8.

Here is my beef...

The University suggests  that people should, on average, be paid in the middle of these market pay grades.

However, this is not true.

This figure shows the distribution of salaries for Research Scientists, based on publicly available data.   Level 1 is bottom, Level 4 is top. The grey bars show the 2018 published UW pay grade min/max limits, red dots are the median (50th %) for each grade in the data, blue dots are simply the range middle – i.e. the average you might reasonably expect if pay reflected the supposed market rate. Note: this data is annual salaries, including people who didn't work an entire year (i.e. can be reported below the minimum pay)

My Takeaways

1. Research Scientists at all levels are being paid systematically near the bottom of their pay scales. In other words: Managers are paying people as little as possible
   
2. It much better to be a Level 3 than a Level 2, the mean pay goes from 43% below the range average to 34%.
   
3. The lowest paid Research Scientists (as always) are getting the worst deal. The median pay is basically the  minimum allowed for Level 1 researchers, and is highly skewed towards that limit. The distributions broaden for higher Levels.
   

For "fun", here is one other plot I quickly made:

Take from that what you will...

The code for all this analysis can, of course, be found openly on my GitHub page

Video: YOU SHOULD TAKE THE SUMMER OFF!

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