Category Archives: quantified

Blog analysis for 2012: ~133,000 words so far

I reviewed my blog posts in 2012 and rated them on a scale of 1 to 5, where 5 indicates my favourite posts – the ones I want to keep around and refer back to, or the ones that represent key moments in my life.

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I’ve been pretty good at keeping to ~1 post a day, although travel and family time meant I was away from my computer during much of September.

Year-by-year comparison:

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Observation: I write fewer posts, but I feel better about them.

Number of words in 2012: ~133,000 (~ 380 words per post)

Observation: Fewer words, but bigger ones.

Looking at the three-word phrases I used the most, I can see that I continue to use my blog for a lot of planning. They’re mostly the same as last year’s words, except that “a lot of” has dropped down to fourth place from second.

  Times  
  This year Last year
I want to 238 158
so that I 115 94
that I can 113 76
a lot of 89 126
be able to 86 86
in order to 67 55

For comparison, here’s the analysis from last year, and here’s the post spreadsheet with ratings.

Quantified Self: Learning from a year of time data and planning what to tweak in 2013

Last year, I decided to move from tracking my time using off-the-shelf applications (Time Recording, then Tap Log Record) to building my own system using Ruby on Rails so that I could tailor it to my quirks. Quantified Awesome now has more than a year of time data, and I wanted to see the patterns in how I use my time.

How I categorize my time

For ease in comparison with OECD time studies, I use the following high-level categories:

  • Sleep: What it says it is. Important!
  • Discretionary: Hobbies, socializing; anything optional or chosen
  • Personal: Morning and evening routines, personal care, exercise
  • Work: Working on IBM projects
  • Business: I split this out from work because I wanted to see how much time I was spending on building my business or improving my skills
  • Unpaid work: Commuting and other unpaid work/business-related activities; also, tidying up, getting groceries, cooking, doing laundry, and any household tasks that I could theoretically outsource
  • Within the categories, I have one or two levels of detail, which I’ll discuss later.
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    This graph shows the major changes in how I used time this year. To account for the varying numbers of days in a month, I’ve expressed each category as a percentage of the time available for the month. The major change was the swap between working with IBM and experimenting with running my own business, but all my other categories are surprisingly stable.

    Here are some basic statistics looking at the monthly and weekly variation. There’s a bit more variation on the weekly level, but it smoothens out a lot when it gets to the monthly level. Also, the overall numbers tell me I should probably work less and spend more time on discretionary activities.

    OECD 2011 – Canada   Mean ~ total hours / week Monthly STDEV Weekly STDEV
      Sleep 35% 58 2% 3%
    22% Business + work 28% 47 5% 6%
    21% Discretionary 16% 28 5% 7%
      Personal 14% 23 2% 4%
    14% Unpaid work 7% 12 2% 3%

    Sleep + personal for me = 49%; OECD 2011 stats for Canada: 42%

    Sleep

    I get an average of 8.3 hours of sleep per day, which is a familiar and fairly stable number, and right in line with the OECD 2011 leisure time study’s findings. Looking at the inter-day statistics for sleep, I see a standard deviation of 1.63 hours, which means my sleep pattern is a little jagged. Here’s a daily chart that shows the variation.

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    It doesn’t look so irregular on a weekly scale, though. I tend to be pretty good at taking it easy after I catch myself getting tired due to lack of sleep.

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    Business

    Business-wise, I was thrilled to have a running start. Here’s billable time as a percentage of total time (out of 7 days a week). May was a little crazy because I was helping out two clients at the same time. I took time off in September and December to focus on other interests, and I’m generally scaling back consulting because I need to make myself learn how to do other kinds of business too.

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    Here’s some more information in a table, showing that while I don’t reach the utilization ratios I remember from my performance review days, I still do okay.

      Billable time  
      % of total time % of business time
    Mar 2012 17% 61%
    Apr 2012 16% 71%
    May 2012 24% 76%
    Jun 2012 17% 60%
    Jul 2012 21% 66%
    Aug 2012 18% 66%
    Sep 2012 1% 5%
    Oct 2012 11% 37%
    Nov 2012 14% 42%
    Dec 2012 2% 10%

    Going forward, I should probably plan for a 25% billable : 75% marketing/overhead mix (or even more weighted towards marketing).

    On average, I spend about 10 hours a week connecting with people for business, which is a surprisingly large chunk of time. It’s good, though. I’m learning a ton and helping lots of people along the way. The weekly standard deviation for this is 7.8 hours, which probably points to “introvert overload” kicking in – after an intensely social week, I’ll hibernate for a while in order to recharge.

    Discretionary

    All work and no play makes for a boring sort of life, so this is where discretionary activities come in. Discretionary – Social is by far the juggernaut of this category, with 46% of all discretionary time use (average per week: 13.7 hours, stdev 11.9 hours – same introvert overload kicking in). Business networking + discretionary socializing works out to an average of 20.8 hours per week, with a standard deviation of 15.1 hours. Here’s the sparkline, with a spike around the September trip where I went to a conference and hung out with family.

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    The graph below shows that I’m not necessarily substituting business connecting for discretionary socializing. There’s actually a very slight positive correlation between them. I do need my breaks afterwards, though.

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    On to other things I do with my discretionary time. Because the Social sub-category is so much bigger than the other categories, these sparklines all use different vertical axes instead of using a shared axis for inter-category comparison. They show percentage of discretionary time, with the peak time highlighted. (Remember, we can’t compare heights across categories!) The third column shows the total percentage of discretionary time spent doing that activity.

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    The sparklines show that my interests tend to shift. They also show some categories that I’ve forgotten to use, such as Discretionary – Family which tends to get lumped under Discretionary – Social, and Discretionary – Read – Blogs, which has become more of either Personal – Routines or Unpaid work – Travel. Looking at this, I can see that LEGO games tend to give us about three months of obsession time, which may not be a good thing. Winking smile Fortunately, W- plays them too, so it’s actually “sit on the couch and chat” time, with bonus scritching of kitties who like sitting in our laps.

    Unpaid Work

    Duty comes before pleasure, though, so I need to make sure chores are taken care of before I settle in for some writing. Here’s how the chores worked out.

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    For scale: I spend about 3.3 hours a week cooking, which is really spending maybe 6-7 hours every other week or so cooking a whole batch of things. Or at least that’s what I think it works out to. The weekly data shows me that I tend to cook in cycles (mean = 3.5 hours, STDEV = 2.3 hours):

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    Other interesting things: Why, yes, biking and subway time are negatively correlated (coeff = -0.53). Yay biking! The weather’s been decent, actually, so I should totally break out the bicycle and bike some more. (Biking: 209 hours this year, average of 7.2 hours per week during biking season)

    % of total time Personal – Bike Unpaid work – Subway
    Nov 2011   2.4%
    Dec 2011   0.6%
    Jan 2012   0.9%
    Feb 2012 0.1% 1.6%
    Mar 2012 0.1% 4.5%
    Apr 2012 7.3% 0.3%
    May 2012 5.9% 0.2%
    Jun 2012 4.6% 0.3%
    Jul 2012 3.6% 0.7%
    Aug 2012 2.5% 1.2%
    Sep 2012 0.8%  
    Oct 2012 3.7% 1.1%
    Nov 2012   6.9%
    Dec 2012   3.6%

    Personal

    The personal category includes all the little things that keep life running, like having breakfast and brushing my teeth. On average, I spend 2.1 hours a day dressing up, eating, brushing my teeth, and so on. That’s 815 hours over the last 386 days! Biking, walking, and exercising account for 408 hours over that time span, which works out to be an hour a day. Not bad.

    So, what does this mean for 2013?

    I’m planning to:

    • Spend less time commuting; spend more time biking and exercising – extend biking season earlier and later (November was totally bikeable, but I chickened out and got a Metropass!), and ramp up personal exercise to ~4 hours a week.
    • Spend less time working as a whole (and yes, trying to not panic about this shift either); spend more time writing and doing other discretionary activities – keep business-related time to ~40 hours a week
    • Spend less time working on billable projects; spend more time marketing/selling/learning (and trying not to panic about this shift) – shift to 25-30% billing as a percentage of total business time

    Glad to have the numbers! You can actually see my time data on Quantified Awesome.  I’ve just added a “Split by midnight” option that makes analysis a little easier for me and other people who use the system to track their own data.

    Onward!

    New: Quantified Self Meetup Map!

    At our last Quantified Self Toronto meetup, we heard from Jason Brown, who had driven all the way up from Detroit – a 5-hour drive on the best of days, plus border crossing and Friday rush hour traffic – because there were no other meetups closer to him. How did he find us? Well, quantifiedself.com has a list, and Meetup.com also has a visualization of meetup groups.

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    The map there isn’t particularly useful – you can zoom in, but you can’t pan, and clicking on the markers doesn’t do anything. Searching for your postal code does get you a nice list of meetups, though.

    I wanted to do something about that map, and I also wanted to explore the Meetup API so that I could start tracking meta information about Quantified Self meetup. Which meetups were growing the fastest? What were meetups’ typical frequencies of events? Could we eventually plot those with active discussions or mailing lists, and figure out what’s going on? In addition to being curious about these questions, I also wanted to experiment with delegating data scraping and Rails development.

    Here’s the result so far:

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    I like the fact that this uses Google Maps, so it’s much easier for me to explore the meetups in the region. I’m looking forward to the other improvements we’ll be making, such as visualizing the member growth history per week.

    You can find this meetup map at http://quantifiedself.ca/map . Enjoy!

    Quantified Awesome: Grocery update – Oct 2012

    I’d gotten into the habit of scanning my receipts and sending them over to one of my virtual assistants, but I hadn’t analyzed the numbers in a while. I finally sat down and spent 23 minutes categorizing the new items from the line-item breakdown of our receipts. With the categories in place, I could update my reports (another half-hour or so). So here we are – almost a half-year update.

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    That works out to an average of $395.36 per month (looking at May-September) for 2.7 people (W-, me, and J-, who spends weekends at her mom’s), or $146 per person. I think we eat quite well. Yummy lunches all ready to go, dinners at home, various favourites making their appearance, and even a party or two somewhere in there…

    September is interesting – I was away for two weeks, and W- bought a lot of vegetables. Maybe if I let him take the lead in planning recipes and making a list, we’ll ramp up vegetable-eating again. =) It’s also interesting to see the regularity of our egg-buying patterns.

    Here are the top 10 individual items in terms of money spent:

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    I’ve been curious about sale patterns and comparing prices between places. We get our lamb shanks from a butcher and they’re priced by weight, so I haven’t been tracking fluctuations. Organic milk is $9.99 except for that one time we tried a different brand that was on sale for $6.99. Shrimp prices vary a bit depending on the kind of shrimp; we now often get peeled shrimp for convenience. We switched from buying small $7.49 bags of brown rice to the bigger $14.99 bags (way more than twice the rice, although we have to get this from an Asian market). We’ve also upgraded from the small bag of sushi rice ($12.98) to the huge bag of sushi rice ($22.99). We used to be able to get chicken leg quarters for around $2.50 in May, but now they’re more like $4.5-$5.0 per package (can’t find the halal chicken leg quarters). Cherries started off at 4.34 a kilo, dipped to 3.24, went up to 4.34, and were at 2.14 in August. Extra large eggs are between $4.27 and $4.47. Mozzarella is $2.99 normally and $2.29 on sale.

    In total, I’ve spent $10.98 on delegation (including bonuses) in order to get this data, and an hour here and there every time I want to crunch the numbers. =) Not bad. I like getting a sense of how we’re doing, and it will be interesting to see personal-scale inflation as the years go by.

    Sketchnotes: Quantified Self Conference 2012

    Realized I didn’t post a copy of my Quantified Self conference sketchnotes on my own blog, just the quantifiedself.com blog. So here they are!

    Quantified Self 2012 Opening Plenary

    20120915 QS2012 Opening Plenary

    Nancy Dougherty’s talk

    20120915 Nancy Dougherty

    Quantified Self 2012 – Ignite Talks for Day 1

    20120915 quantified self ignite day 1

    Notes from our session

    20120915 quantified self show-and-tell session 1

    Day 2 lunchtime ignite talks

    20120916 qs2012 lunchtime ignites day 2

    Opening plenary, day 2

    20120916 qs2012 opening plenary

    Day 2

    20120915 Quantified Self Plenary

    Kevin Kelly – closing plenary

    20120916 closing plenary - Kevin Kelly

    See my conference recap for more text notes.

    Feel free to share these! (Creative Commons Attribution License)

    Answering questions about the Quantified Self

    James Hennessey sent in these questions, and I decided to blog my answers instead of keeping them in e-mail. =)

    What motivates you to quantify parts of your life? Curiosity. How much time do I actually spend sleeping? Do I use the things I buy? Which clothes can I donate to simplify my wardrobe, and which work well for me? It’s easy to collect data to answer questions instead of relying on faulty human memory. (See more reasons)

    What have you done with your results? I often blog about what I’m learning, and I share them at Quantified Self Toronto meetups too.

    What data do you measure and how? See my profile at http://sachachua.com/blog/2012/09/thinking-about-a-quantified-self-directory/.

    Do you have methodology for collecting data? I sometimes set up 1-month “experiments” where I try tracking something new.

    What tools do you currently use? I track most things through a web-based system I built myself, and I track a few more things using a smartphone, spreadsheets, or paper.

    How much do you pay for these tools? Nothing! (Well, Microsoft Office, but I use that for other things.)

    Do you have any problems with these tools? Oh, there’s always more I want to track, and I’d love to consolidate more streams of information.

    When was the last time you had this problem? Could you walk me through it? All the time, but it’s not urgent. =)

    How do you take meaning from your data? I do a weekly review of my time data, an occasional review of my clothing data, and I set other times to review my data streams and answer questions.

    Do you share your data? Most of it! http://quantifiedawesome.com and http://sachachua.com/blog/category/quantified

    What things do you think needs to happen for QS to be adopted by the wider public? Many people are already using QS tools, they just don’t know what it’s called. =)

    What are your favourite stats to measure? Time – it’s surprising how much of it you have.

    What is the favourite thing you have learnt about yourself? It’s actually pretty easy to ask questions and answer them with data. My next step: plan more experiments!