Let’s Go To The Ex!

I went to The Ex (that’s the Canadian National Exhibition for those of you not ‘in the know’) on Saturday. I enjoy stepping out of the ordinary from time to time and carnivals / fairs / midways / exhibitions etc. are always a great way to do that.

As far as exhibitions go, I believe the CNE is one of the more venerable – it’s been around since 1879 and attracts over 1.3 million visitors every year.

Looking at the website before I went, I saw that they had a nice summary of all the ride height requirements and number of tickets required. I thought perhaps the data could stand to be presented in a more visual form.

First, how about the number of tickets required for the different midways? All of the rides on the ‘Kiddie’ Midway require four tickets, except for one (The Wacky Worm Coaster). The Adult Midway rides are split about 50/50 for five or six tickets, except for one (Sky Ride) which only requires four.

With tickets being $1.50 each, or $1 if you buy them in sets of 22 or 55, that makes the ride price range $6-9 or $4-6. Assuming you buy the $1 tickets, the average price of an adult ride is $5.42 and the average price of a child ride $4.04.

The rides also have height requirements. Note that I’ve simplified things by taking the max height for cases where shorter/younger kids can ride supervised with an adult. Here’s a breakdown of the percentage of the rides in each midway type children can ride, given their height:

Google Docs does not allow non-stacked stepped area charts, so line graph it is.

And here’s the same breakdown with percentage of the total rides (both midways combined), coloured by type. This is a better way to represent the information, as it shows the discrete nature of the height requirement:

Basically if your child is over 4′ they are good for about 80% of all the rides at the CNE.

Something else to consider – how to get your maximum value for your tickets with none left over, given that they are sold in packs of 22 and 55? I would say go with the $36 all-you-can-ride option. Also, how miniscule are your actual odds of winning those carnival games? Because I want a giant purple plush gorilla.

See you next year!

Tableau A-Go-Go: Signalized Intersection Traffic and Pedestrian Volume (Toronto Open Data)

First go at creating something usable with Tableau Public. There’s no search suggestions in the text box filter, but you can type the name of a street and just see those points (e.g. Yonge). Kind of cool.

You can find the original data set here.

Prior art here and here.

P.S. Tableau Maps are not the same as Google Maps. Hold shift and click and drag to pan.

Zzzzzz….. – Quantified Self Toronto #14

Sleep is another one of those things like diet, where I feel if you asked anyone if they wanted to improve that area of their life most would say yes.

I remember hearing a quote that sleep is like sex; no one is quite sure how much everyone else is getting, but they are pretty sure it is more than them. Or wait, I think that was salary. With sleep it is more like – no one is quite sure how much they should be getting, but they sure as hell wish they were getting a lot more.

A lot of research has been done on the topic and it seems like the key takeaway from it is always the same: we are not getting enough sleep and this is a problem.

I know that I am a busy guy, that I am young, and that I go out on the weekends, so I know for a fact that my sleep is ‘bad’. But I was curious as to how ‘bad’ it actually is. I started tracking my sleep in April to find out, and also to see if there were any interesting patterns in it of which I was not aware.
 
I spoke again at Quantified Self Toronto (#14) (I spoke previously at #12 on June 7th) about it on August 7th. I gave an overview of my sleep-tracking activities and my simple examination of the data I had gathered. Here is the gist of my talk, as I remember it.

Hi everyone, I’m Myles Harrison and this is my second time speaking at Quantified Self Toronto, and the title of my second presentation is ‘Zzzzzzzz….’. 

I started tracking how much I was sleeping per night starting in April of this year, to find out just how good or bad my sleep is, and also to see if there are any patterns in my sleep cycle.

Now I want to tell you that the first thing I thought of when I started to putting this slide deck together was Star Trek. I remember there was the episode of Star Trek called ‘Deja Q’. Q is an omnipotent being from another dimension that torments the crew of the Enterprise for his own amusement, and in this particular episode he becomes mortal. In one part of the episode he is captured and kept in a cell onboard the ship, and he describes a terrible physical experience he has:

Q
I have been entirely preoccupied by a most frightening experience of my own. A couple of hours ago, I started realizing this body was no longer functioning properly… I felt weak, the life oozing out of me… I could no longer stand… and then I lost consciousness…

PICARD
You fell asleep.

Q
It’s terrifying…. how can you stand it day after day?

PICARD
One gets used to it…


And this is kind of how I have always felt about sleep: I may not like it, there are many other things I’d rather be doing during all those hours, however it’s a necessary evil, and you get used to it. If I could be like Kramer on Seinfeld and try to get by on ‘Da Vinci Sleep’, I probably would. However for me, and for most of the rest of us, that is not a reasonable possibility.

So now we come to the question of ‘how much sleep do we really need?’. Obviously there is a hell of a lot of research which has been done on sleep, and if you ask most people how much sleep they need to get every night, they will tell you something like ‘6-8 hours’. I believe that number comes from this chart which is from the National Sleep Foundation in the States. Here they give the figure of 7-9 hours of sleep for an adult, however this is an average. If you read some of the literature you will find, unsurprisingly, that the amount of sleep needed depends on a lot physiological factors and so varies from person to person. Some lucky people are perfectly capable of functioning normally during the day on only 3 or 4 hours of sleep a night, whereas some other unlucky people really need about 10 to 12 hours of sleep a night to feel fully rested. I highly doubt these unlucky folks regularly get that much sleep a night, as most of us have to get up in the morning for this thing called ‘work’. So yes, these are the extremes but they serve to illustrate the fact that this 6-8 (or 7-9) hours per night figure is an average and is not for everyone.

Also I found a report compiled by Statistics Canada in 2005 which says that the average Canadian sleeps about 8 and a half hours a night, usually starting at about 11 PM. Additionally, most Canadians get about 20 extra minutes of sleep on weekend nights as they don’t have to go to work in the morning and so can hit the snooze button.

So knowing this, now I can look at my own sleep and say, how am I doing and where do I fit in?


So as I said, I have been recording my sleep since early April up until today. In terms of data collection, I simply made note of the approximate time I went to bed and the approximate time at which I woke up the following morning, and recorded these values in a spreadsheet. Note that I counted only continuous night-time sleep and so the data do not include sleep during the day or things napping [Note: this is the same as the data collected by StatsCan for the 2005 report]. Also as a side interest I kept a simple yes/no record of whether or not I had consumed any alcohol that evening, counting as a yes any evening on which I had a drink after 5 PM.

O
n to the data. Now we can answer the question ‘What does my sleep look like?’ and the answer is this:

There does not appear to be any particular rhyme or reason to my sleep pattern. Looking at the graph we can conclude that I am still living like a University student. There are some nights where I got a lot of sleep (sometimes in excess of 11 or 12 hours) and there are other nights where I got very, very little sleep (such as this one particular night in June where I got no sleep at all, but that is another story). The only thing I can really pick out of this graph of note is that following nights or sequences of nights where I got very little sleep or went to bed very late, there is usually a night where I got a very large amount of sleep. Interestingly this night is sometimes not until several days later but this may be due to the constraints of the work week.

So despite the large amount of variability in my sleep we can still look at it and do some simple descriptive statistics and see if we can pull any meaningful patterns out of it. This is a histogram of the number of hours of sleep I got each night.

Despite all the variability in the data from what we saw earlier, it looks like the amount of sleep I get is still somewhat normally distributed. It looks like I am still getting about 7 hours of sleep on average, which actually really surprised me and in my opinion is quite good, all things considered and given the chaotic nature of my personal life. [Note: the actual value is 6.943 hrs for the mean, 7 for the median with a standard deviation of 1.82 hours]. 

So we can ask the question, ‘Is my amount of nightly sleep normally distributed?’. Well, at first glance it sure appears like it might be. So we can compare to what the theoretical values should be, and this certainly seems to be the case, though using a histogram is maybe not the best way as it will depend on how you choose your bin sizes.


We can also look at what is called a Q-Q plot which plots the values against the theoretical values, and if the two distributions are the same then the values should lie along that straight line. They do lie along it well, with maybe a few up near the top there straying away… so perhaps it is a skew-normal distribution or something like that, but we can still safely say that the amount of sleep I get at night is approximately normally distributed.


Okay, so that is looking at all the data, but now we can also look at the data over the course of the week, as things like the work week and weekend may have an affect on how many hours of sleep I get.

So here is a boxplot of the number of hours of sleep I got for each day of the week and we can see some interesting things here.

Most notably, Wednesday and Saturday appear to be the ‘worst’ nights of the week for me for sleep. Saturday is understandable, as I tend to go out on Saturday nights, and so the large amount of variability in the number of hours and low median value is to be expected; however, I am unsure as to why Wednesday has less hours than the other days (although I have do go out some Wednesday nights). Tuesdays and Thursdays appears to be best both in terms of variability and the median amount, these days being mid-week where presumably my sleep cycle is becoming regular during the work week (despite the occasional bad Wednesday?).

We can also examine when I feel asleep over the course of the week. Wait, that sounds bad, like I am sleeping at my desk at work. What I mean is we can also examine what time I went to bed each night over the course of the week:

Again we can see some interesting things. First of all, it is easy to note that on average I am not asleep before 1 AM! Secondly we can see that I get to sleep latest on Saturday nights (as this is the weekend) and that there is a large amount of variability in the hour I fall asleep on Fridays. But again we see that in terms of getting to bed earliest, Wednesday and Saturday are my ‘worst’ days, in addition to being the days when I get the least amount of sleep on average. Hmmmmmm….! Could there be some sort of relationship here?

So we can create a scatterplot and see if there exists a relationship between the hour at which I get to bed and the number of hours of sleep I get. And when we do this we can see that there is appears to be [surprise, surprise!] a negative correlation between the hour at which I get to sleep and the number of hours of sleep I get.

And we can hack a trend line through there to verify this:

> tl1 <- lm(sleep$hours ~ starthrs)
> summary(tl1)

Call:
lm(formula = sleep$hours ~ starthrs)

Residuals:
    Min      1Q  Median      3Q     Max
-7.9234 -0.6745 -0.0081  0.5569  4.8669

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  9.78363    0.43696  22.390  < 2e-16 ***
starthrs    -0.62007    0.09009  -6.883 3.56e-10 ***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.533 on 112 degrees of freedom
Multiple R-squared: 0.2973,    Adjusted R-squared: 0.291
F-statistic: 47.38 on 1 and 112 DF,  p-value: 3.563e-10

So there is a highly statistically significant relationship between how late I get to sleep and the number of hours of sleep I get. For those of you that are interested, the p-value is very small (on the order of e-10). However you can see that the goodness of fit is not that great, as the R-squared about 0.3. This means that perhaps there are other explanations as to why getting to sleep later results in me getting less sleep, however I could not immediately think of anything. I am open to other suggestions and interpretations if you have any.

Also I got to thinking that this is the relationship between how late I get to sleep and how much sleep I get for all the data. Like a lot of people, I have a 9 to 5, and so I do not have the much choice about when I can get up in the morning. Therefore I would expect that this trend is largely dependent upon the data from the days during the work week.

So I thought I would do the same examination only for the days of the week where the following day I do not have to be up by a certain hour, that is, Friday and Saturday nights. And we can create the same plot, and:

We can see that, despite there being less data, there still exists the negative relationship.

> tl2 <- lm(wkend$hours ~ hrs)
> summary(tl2)

Call:
lm(formula = wkend$hours ~ hrs)

Residuals:
    Min      1Q  Median      3Q     Max
-5.4288 -0.4578  0.0871  0.5536  4.4300

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  12.1081     0.9665  12.528 1.89e-13 ***
hrs          -0.8718     0.1669  -5.224 1.24e-05 ***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.737 on 30 degrees of freedom
Multiple R-squared: 0.4764,    Adjusted R-squared: 0.4589
F-statistic: 27.29 on 1 and 30 DF,  p-value: 1.236e-05

So it appears that on the days on which I could sleep in and make up the hours of sleep I am losing by going to bed later I am not necessarily doing so. Just because I can sleep in until a ridiculously late hour doesn’t necessarily mean that my body is letting me do so. This came as a bit of a surprise to me, as I thought that if I didn’t have to be up at a particular hour in the morning to do something, I would just sleep more to make up for the sleep I lost. An interesting insight – even though I can sleep in and make up for hours lost doesn’t necessarily mean that I will. 

So basically I just need to get to sleep earlier. Also, I am reminded of what my Dad always used to say to me when I was a kid, ‘An hour of sleep before midnight is worth two afterwards.’

Lastly, as I said, I did keep track of which nights I had consumed any alcohol in the evening to see what impact, if any, this was having on the quality and duration of my sleep. For this I just did a simple box plot of all the data and we can see that having a drink does mean I get less sleep overall.


Though this is a very simple overview it is consistent with what you can read in the research done on alcohol consumption and sleep. The belief that having a drink before bed will help you sleep better is a myth, as alcohol changes physiological processes in the body which are necessary for a good night’s sleep, and disrupts it.


So those were the conclusions I drew from tracking my sleep and doing this simple analysis of it. In terms of future directions, I could also further quantify my tracking of my sleep. I have simply measured the amount of sleep I have been getting, going with the assumption that getting close to the recommended amount of time is better. I could further quantify things by rating how rested I feel when I awake (or during the day) or rating how I felt the quality of rest I got was, on a scale of 1-10.

I could also measure other factors, such eating and exercise, and the time these things occur and how this play in to the amount and quality of the sleep I get.

Lastly, though I did have a simple yes/no measurement for whether or not I had consumed alcohol each evening, I did not quantify this. In the future I could measure caffeine consumption as well, as this known to be another important factor affecting sleep and restfulness.

That concludes my presentation, I hope I kept you awake. I thank you for your time, and for listening. If you have any questions I would be happy to answer them.

References & Resources 

National Sleep Foundation
http://www.sleepfoundation.org/

Who gets any sleep these days? Sleep patterns of Canadians (Statistics Canada)
http://www.statcan.gc.ca/pub/11-008-x/2008001/article/10553-eng.htm

The Harvard Medical School’s Guide to A Good Night’s Sleep
http://books.google.ca/books?id=VsOWD6J5JQ0C&lpg=PP1&pg=PP1#v=onepage&q&f=false

Quantified Self Toronto
http://quantifiedself.ca/

Facebook Friends (in a graph)

I saw this post on FlowingData and thought, “Hey, I can do that, let’s give this Gephi thing a go.”

I don’t have that many Facebook friends, as I try to keep my network well maintained, and I’m also not a heavy user of the service. Also I’ve always kind of wondered – if you are one of those people who has 2000 Facebook friends, are they really all your ‘friends’? If I put you (you silly teenage girl) in a room with those 2000 people, would you be able to call all of them by name? Remember where you met them? What their favorite color is? I digress.

The steps to producing the graph are simple:
1. Install the netvizz Facebook application
2. Run it
3. Import gdf file into Gephi
4. Wow! A graph!

As I said, I don’t have that many Facebook friends but I still found the results pretty interesting:

Red is immediate family, green my Mom’s side and blue my Dad’s. The orange are university friends, and purple High School. Yellow are randoms and friends of friends. Teal is friends of my Mom’s relatives, and pink friends of one of the immediate family. Light blue is a group of friends made while travelling.

The nodes are sized by degree.

Interesting point to note:
High school friend (purple, outlying from others) and friend of immediate family (pink, bottom right node) are both connected to friend of Mom’s family (teal node, bottom) through events totally unrelated to the rest of the network. Small world.

This is that case when you add a new friend on Facebook and it says you already have a mutual friend, and you stop and think, ‘Wait, we do? Sarah knows Thomas? But how did…. through who… when did…..? Huh.’

50 Shades of Grey Wordcloud

Sometimes you just want to see what all the fuss is about. File this under the ‘because I can’ category: I proudly (?) present – a wordcloud produced from the text of E. L. James’ “50 Shades of Grey”.

For a book which is getting all this press about being full of explicit sexuality, the data are not what you expect. Obviously the main characters’ names feature prominently, but if you ask me this visualization shows that this is another romance novel much like any other.

Sure, you probably wouldn’t expect to see the words ‘dominant’ (left, next to grey) and ‘submissive’ (right, next to don’t) in some other books of this type. But look at some of the other words which are largest besides the names of the main characters – eyes, head, hands, hair, voice, smile. Obviously, it’s not just about the sex.

Produced in R using the excellent tm and wordcloud packages.

How much do I weigh? – Quantified Self Toronto #12

Recently I spoke at the Quantified Self Toronto group (you can find the article on other talk here).

It was in late November of last year that I decided I wanted to lose a few pounds. I read most of The Hacker’s Diet, then began tracking my weight using the excellent Libra Android application. Though my drastic reductions of my caloric intake are no more (and so my weight is now fairly steady) I continue to track my weight day-to-day and build the dataset. Perhaps later I can do an analysis of the patterns in fluctuations in my weight separate from the goal of weight loss.

What follows is a rough transcription of the talk I gave, illustrated by the accompanying slides.

Hello Everyone, I’m Myles Harrison and today I’d like to present my first experiment in quantified self and self-tracking. And the name of that experiment is “How Much Do I Weigh?”

So I want to say two things. First of all, at this point you are probably saying to yourself, “How much do I weigh? Well, geez, that’s kind of a stupid question… why don’t you just step on a scale and find out?” And that’s one of the things I discovered as a result of doing this, is that sometimes it’s not necessarily that simple. But I’ll get to that later in the presentation.

The second thing I want to say is that I am not fat.

However, there are not many people whom I know where if you ask them, “Hey, would you like to lose 5 or 10 pounds?” the answer would be no. The same is true for myself. So late last November I decided that I wanted to lose some weight and perhaps get into slightly better shape. Being the sort of person I am, I didn’t go to the gym, I didn’t go a personal trainer, and I didn’t meet with my doctor to discuss my diet. I just Googled stuff. And that’s what lead me to this

The Hacker’s Diet, by John Walker. Walker was one of the co-founders of the company Autodesk which created the popular Autocad software and later went on to become a giant multinational company. Mr. Walker woke up one day and had a realization. He realized that he was very successful, very wealthy, and had a very attractive wife, but he was fat. Really fat. And so John Walker though, “I’ve used my intelligence and analytical thinking to get all these other great things in my life, why can’t I apply my intelligence to the problem of weight, and solve it the same way?” So that’s exactly what he did. And he lost 70 pounds.

Walker’s method was this. He said, let’s forget all about making this too complicated. Let’s look at the problem of health and weight loss as an engineering problem. So there’s just you:

and your body is the entire system, and all this system has, the only things we’re going to think about are inputs and outputs. I don’t care if you’re eating McDonald’s, or Subway, or spaghetti 3 times a day. We’re just talking about the amount of input – how much? Therefore, from this incredibly simplified model of the human body, the way to lose weight is just to ensure that the inputs are less than the outputs.

IN < OUT

Walker realized that this ‘advice’ is so simple and obvious that it is nearly useless in itself. He compared it to the wise financial guru, on being asked how to make money on the stock market by an apprentice, giving the advice: “It’s simple, buy low and sell high.” Still, this is the framework we have as a starting point, so we proceed from here.

So now this raises the question, “Okay well how do we do that?” Well, this is a Quantified Self meet up, so as you’ve probably guessed, we do it by measuring.

We can measure our inputs by counting calories and keeping track of how much we eat. Measuring output is a little more difficult. It is possible to approximate the number of calories burned when exercising, but actually measuring how much energy you are using on a day-to-day basis, just walking around, sitting, going to work, sleeping, etc. is more complicated, and likely not practically possible. So instead, we measure weight as a proxy for output, since this is what we are really concerned with in the first place anyhow. i.e. Are we losing weight or not?

Okay, so we know now what we’ve got to do. How are we going to keep track of all this? Walker, being a technical guy, suggests entering all the information into a piece of computer software, oh, say, I don’t know, like a certain spreadsheet application. This way we can make all kinds of graphs and find the weighted moving average, and do all kinds of other analysis. But I didn’t do that. Now don’t get me wrong, I love data and I love analyzing it, and so I would love doing all those different types of things. However, why would I use a piece of software that I hate (and am forced to on a regular basis) any more than I already have to? Especially when this is the 21st century and I have a perfectly good smartphone and somebody already wrote the software to do it for me!

So, I’m good! Starting in late November of last year I followed the Hacker’s Diet directions and weighed myself every day (or nearly every day, as often as I could) at approximately the same time of day. And along the way, I discovered some things.

One day I was at work and I got a text from my roommate, and it said “Myles, did you draw a square on the bathroom floor in black permanent marker?” To which I responded, “Why yes I did.” To which the response was “Okay, good.” And the reason I that I drew a square on the tiles of the bathroom floor in black permanent marker was because of observational error. More specifically, measurement error. 

If you know anything about your typical drugstore bathroom scale you probably know that they are not really that accurate. If you put the same scale on an uneven surface (say, like tiles on a bathroom floor) you can make the same measurement back-to-back and get wildly different values. That is to say the scales have a lot of random error in their measurement. And that’s why I drew that square on the bathroom floor. That was my attempt to control measurement error, by placing the scale in as close to the same position I could every morning when I weighed myself. Otherwise you get into this sort of bizarre situation where you start thinking, “Okay, so is the scale measuring me or am I measuring the scale?” And if we are attempting to collect some meaningful data and do a quantified self experiment, that is not the sort of situation we want to be in.

So I continued to collect data from last November up until today. And this is what it looks like.

As you can see like most dieters, I was very ambitious at the start and lost approximately 5 pounds between late November and and the tail end of December. That data gap, followed by a large upswing corresponds to the Christmas holidays, when I went off my diet. After that I continued to lose weight, albeit somewhat more gradually up until about mid-March, and since then I have ever-so-slowly been gaining it back, mostly due to the fact that I have not been watching my input as much as I was before.

So, what can we take away from this graph? Well, from my simple ‘1-D’ analysis, we can see a couple of things. The first thing, which should be a surprise to no one, is that it is a lot easier to gain weight than it is to lose it. I think most everyone here (and all past dieters) already knew that. 

Secondly, my diet aside, it is remarkable to see how much variability there is in the daily measurements. True, some of this may be due to the aforementioned measurement error, however in my readings online I also found that a person’s weight can vary by as much as 1 to 3 pounds on a day-to-day basis, due to various biological factors and processes.

Walker comments on this variability in the Hacker’s Diet. It is one of his reasons as to why looking at the moving average and weighing oneself every day is important, if you want to be able to really track whether or not a diet is working. And that’s why doing things like Quantified Self are important, and also what I was alluding to earlier when I said that the question of “How much do I weigh?” is not so simple. It’s not simply a matter of stepping on the scale and looking at a number to see how much you weigh. Because that number you see varies on a daily basis and isn’t a truly accurate measurement of how much you ‘really’ weigh.

!

This ties into the third point that I wanted to draw from the data. That point is that the human body is not like a light switch, it’s more like a thermostat. I remember reading about a study which psychologists did to measure people’s understanding of delayed feedback. They gave people a room with a thermostat, but there was a delay in the thermostat, and it was set to something very very high, on the order of several hours. The participants were tasked with getting to room to stay at a set temperature, however none of them could. Because people (or most people, anyhow) do not intuitively understand things like delayed feedback. The participants in the study kept fiddling with the thermostat and setting it higher and lower because they thought it wasn’t working, and so the temperature in the room always ended up fluctuating wildly. The participants in the study were responding to what they saw the temperature to be when they should have been responding to what the temperature was going to be.

And I think this is a good analogy for the problem with dieting and why it can be so hard. This is why it can be easy to become frustrated and difficult to tell if a diet is working or not. Because if you just step on the scale every day and look at that one number, you don’t see the overall picture, and it can be hard to tell whether you’re losing weight or not. And if you just see that one number you’d never realize that though I can eat a pizza today and I will weight the same tomorrow, it’s not until 3 days later that I have gained 2 pounds. It’s a problem of delayed feedback. And that’s one of the really interesting conclusions I came to ask a result of performing this experiment.

So where does this leave us for the future?

Well, I think I did a pretty good job of measuring my weight almost every day and was able to make some interesting conclusions from my simple ‘1-D’ analysis. However, though I did very well tracking all the output, and did not track any of my inputs whatsoever. In the future if I kept track of this as well (for instance by counting calories) I would have more data and be able to draw some more meaningful conclusions about how my diet is impacting my weight.

Secondly, I did not do one other thing at all. I didn’t exercise. This is something Walker gets to later in his book too (like most diet/health books) however I did not implement any kind of exercise routine or measurement thereof.

In the future I think if I implement these two things, as well as continuing with my consistent measurement of my weight, then perhaps I could ‘get all the way there’

 

|—————| 100%

 
That was my presentation, thank you for listening. If you have any questions I will be happy to answer them.

References / Resources

Libra Weight Manager for Android
https://play.google.com/store/apps/details?id=net.cachapa.libra 

The Hacker’s Diet
http://www.fourmilab.ch/hackdiet/www/hackdiet.html 

Quantified Self Toronto
http://quantifiedself.ca/ 

Google Domestic Trends

Google’s mission is to organize all the world’s information and make it universally accessible and useful. In following their mission, the company has produced some amazing tools which allow any internet user to do some data visualization without so much as having to open a spreadsheet.

One of these tools which I stumbled across the other day (which apparently has has existed for some time) is Google Domestic Trends.

I was previously aware of Google Trends, which allows a user to compare the popularity of different search terms, whether if be for serious reasons (e.g. Android vs. iPhone) or say, for something less serious. In Domestic Trends, Google has aggregated relevant search terms across different sectors of the economy, with the results presumably providing insight into market trends by sector (or at least the popularity of those market sectors with respect to time).

I am not an economist, but data are data, so here goes with the pithy commentary and observations.

Air Travel
It’s seasonal, unsurprisingly. Looks like there might be some deals over the holidays I was unaware of. Or that might be a really bad time to buy tickets.

Link

Auto Buyers
As Google notes on the Domestic Trends frontpage, July 2009 was when the U.S. Government instituted its “Cash for Clunkers” program. However, it was also when Toyota recalled almost half a million vehicles due to defective airbags. Oh yeah, and that spike in 2005 is related to the outrageous change in the gas prices of the time.

Link

Bankruptcy
New record. I’m glad I rent.

Link

Computers and Electronics
Seriously, who buys desktops anymore?

Link

Credit Cards
A poignant portrait of the changing state of the American economy and personal debt.

Link

Durable Goods
Merry Christmas honey, I got you a Rhoomba.

Link

Education
School’s out for summer.

Link

Jobs
I want to say that the little spike later in 2011 has nothing to due with employment and is due to Mr. Jobs retiring, however then I would expect a much larger one to be in October.

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Mobile and Wireless
The iPhone was revealed to the public on January 9th, 2007 and went on sale in June of the same year. The iPhone 3G and 3GS came out in June and July of 2008 and 2009 respectively. The 4S was released in October 2011. Not sure about mid-2010. The Blackberry Torch came out in June but that would hardly warrant what we see here.

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Rental & Real Estate
Apparently it is quite seasonal. Peaks drop off around late July and early August. Students, I would guess.

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Shopping
We’ve seen this before. No surprises here.

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Unemployment
I know the word you’re thinking of. It’s on the tip of your tongue and it starts with ‘R’.

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See also: Google NGram Experiments.