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!

    Quantified Awesome: Time and building mastery

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    There’s an often-repeated number in studies on expertise: it takes 10,000 hours of deliberate practice in order to build a skill to mastery.

    Last year, I logged 265 hours writing, or around 45 minutes a day. Don’t be scared off by that. If you’re repurposing something you’ve already done, blogging takes maybe 5-10 minutes. It takes an astonishingly long time to think through new things. I can type at 90-110 words per minute, but my brain chugs along at 16 words per minute when reflecting, and I haven’t quite gotten the hang of using speech recognition or dictation to get past that barrier. I suspect I won’t be significantly faster. Thinking takes time. That 265.5 hours is butt-in-seat time. Yes, that’s the professional term for it. I have surprisingly little of it, considering how much I perceive writing to be a part of my life. (Really? Just 45 minutes a day? What would happen if I doubled that?)

    Generously including quick blog posts as part of this practice, assuming that I’ve maintained a similar pace since around 2003, neglecting the fact that real writing involves a whole lot more rewriting (which I tend to do out of forgetfulness rather than deliberate improvement), and ignoring the assigned writing I slogged through in school (or the countless e-mails I dash off), the calculations show that I’ll probably be inching closer to awesomeness… oh… when I’m 57 or so. I know life doesn’t quite work out like that, but let’s pretend for the sake of calculation.

    It doesn’t actually look half-bad, you know. If I can get a decade or two of great writing out right around the time I should have tons of experiences to write about, that should be fine. Of course, with the unreliability of memory (both mine and the computer’s), I’ll just have to hope my blog will survive the years. And if I turn out to be a passably good writer who can package up what I’ve learned and turn it over to the next generation of young whippersnappers, then that’s great. Don’t need to win any awards.

    On a similar note, I logged 198 hours drawing last year. This does not include the times that I filed drawing under “Business – Plan” or “Personal – Plan” instead. Even less revision going on over here, but I’m working on ways to improve that, and other ways to increase the proportion of writing and drawing in my life. Malcolm Gladwell’s Outliers: The Story of Success broke down that 10,000 hours into around 10 years of daily 3-hour practices.

    I have spent a ton of time coding. I’ve been coding since I was six or so, and I worked on quite a few web development engagements at IBM. I’m still nowhere near “master” level. I can point to lots and lots and lots of people who are way better than I am. I have fun with it, though. I can make stuff happen.

    It’s good to remember that invisibly sunk time, the accumulation of experience over years. That way, I don’t get frustrated about drawing if it feels less natural than coding, and I can see all this writing as building a skill step by step.

    Thank goodness for visible progress. Hooray for a blog that lets me go back in time! Compared to a year ago, I draw so much more than I used to. I feel a little more organized and coherent as a writer – headings for my paragraphs, an index for my posts. I still don’t have the deft interweaving of personal story and insight I envy in Penelope Trunk’s blog or the lyrical geekiness of Mel Chua (no relation), but I’m growing into my own style.

    I think it would be fascinating to have 10+ years of time data. It’ll be interesting to see how I change things along the way. Not that quantity of time controls everything, but it’s fun to ask questions and realize that the composition of your time doesn’t always match up to what you think it is. Then you can tweak it.

    Other 2012 numbers to put this into perspective, because I have the data anyway:

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    • 3024 hours of sleep – 8.3 hours per night and I’m still fidgety when I go to bed; I wonder how one deliberately practises sleeping
    • 729 hours socializing (in person, answering e-mail, etc.) – not much deliberate practice going on there, but good to spend that time
    • 411 hours connecting with people for business (in person, answering e-mail, etc.) – a little bit of systematization and experimentation
    • 102 hours reading fiction, 88 hours reading nonfiction – funny, I thought it would be the other way around, although some of it did get classified as “Business – Drawing” instead
    • ~75 hours playing LEGO video games

    More time analysis looking at percentages

    So that’s where all the time went!

    Quantified Self Toronto: Where the Time Went

    Update 2013-6-6: Added a link to the video!

    Carlos Rizo convinced me to quickly throw together a presentation for today’s Quantified Self Toronto meetup. Here are my slides!

    Check out quantifiedawesome.com for my data, dashboard, and source code, and read through my Quantified Self blog posts for more geekery.

    Quantified Self time-tracking: Choosing your buckets

    This post missed its publishing schedule. I’m posting it today so that it doesn’t get lost.

    Kate asked me how I chose the categories I use for tracking my time, and if I had any tips for someone who’s starting out.

    I track my time at a medium level of detail – not so high-level that I can’t ask interesting questions, but not so low-level that it’s hard to summarize. To select an activity category (the non-bolded text in the table below), I type in parts of it. For example, “un subway” becomes “Unpaid work – Subway” and “quantified” becomes “Business – Quantified Awesome.” If something is ambiguous, the system shows me all the matches and lets me pick one. I can make some activity categories inactive so that they don’t get matched by the search. If the text doesn’t match anything, I’m shown the category creation screen, and the timestamped record is automatically created once I create the category for it. For “Other”-type activities and other activities that I’ve added a note field to, I can add a pipe character followed by a note (ex: “disc other | Yada yada yada”) for more details.

    Here’s the general structure. I based the top-level categories on the OECD time studies so that I can compare my numbers with averages from other developed countries. The top-level categories I use are:

    • Business: Anything related to entrepreneurship or professional development
    • Discretionary: Hobbies, socializing, and other ways I choose to spend time
    • Personal: Personal care, daily routines, exercise, and things you can’t outsource because the point of the activity is personal benefit
    • Unpaid work: Chores, commuting, and other things you could theoretically outsource or eliminate
    • Work: Working as an employee; also, the occasional work lunch
    • Sleep: Sleep and naps!

    The second-level group (Business – Build, Business – Connect, etc.) are the ones I recently created for reporting purposes. They look useful, so I might figure out how to build them into my database for more reporting goodness.

    Within those major groups, I have one or two levels of record categories that I really use to track time. The higher groups are just for reporting. I create more activity types as needed.

    Business
    Business – Build
    Business – Android
    Business – Book review
    Business – Business development
    Business – Coding
    Business – Delegation
    Business – Drawing
    Business – Learn
    Business – Marketing
    Business – Other
    Business – Paperwork
    Business – Plan
    Business – Quantified Awesome
    Business – Research
    Business – Sales
    Business – Connect
    Business – Connect
    Business – Correspondence
    Business – Presentation
    Business – Pro bono
    Business – Earn
    Business – Consulting – E1 – Conf
    Business – Consulting – E1 – General
    Business – Consulting – R1
    Business – E-book
    Business – Illustration – I1
    Business – Illustration – I2 – UPV
    Business – Illustration – I3 – M
    Business – Illustration – I4 – SR
    Business – Illustration – I5 – MT / G
    Business – Sketchnoting
    Discretionary
    Discretionary – Other
    Discretionary – Other
    Discretionary – Play
    Discretionary – Harry Potter (… because I forgot I already had “Discretionary – Play – LEGO Harry Potter”)
    Discretionary – Play – Final Fantasy
    Discretionary – Play – Katamari Forever
    Discretionary – Play – LEGO Batman
    Discretionary – Play – LEGO Harry Potter
    Discretionary – Play – LEGO Heroica
    Discretionary – Play – LEGO Indiana Jones
    Discretionary – Play – LEGO Lord of the Rings
    Discretionary – Play – LEGO Pirates
    Discretionary – Play – LEGO Star Wars
    Discretionary – Play – Nethack
    Discretionary – Play – Other
    Discretionary – Read – Nonfiction
    Discretionary – Relax
    Discretionary – Productive
    Discretionary – Emacs
    Discretionary – Gardening
    Discretionary – Latin
    Discretionary – Read – Blogs
    Discretionary – Read – Fiction
    Discretionary – Sewing
    Discretionary – Tracking
    Discretionary – Travel
    Discretionary – Writing
    Discretionary – Social
    Discretionary – Family
    Discretionary – Social
    Personal
    Personal – Exercise
    Personal – Bike
    Personal – Exercise
    Personal – Scoot
    Personal – Walk – Home
    Personal – Walk – Other
    Personal – Walk – Subway
    Personal – Walk – Work
    Personal – Life
    Personal – Eat – Breakfast (… sometimes I track meals separately, but usually they’re just part of Personal – Routines)
    Personal – Eat – Dinner
    Personal – Eat – Lunch
    Personal – Plan
    Personal – Planning (… because I forgot I already have Personal – Plan)
    Personal – Routines
    Sleep
    Sleep
    Sleep
    Unpaid work
    Unpaid work – Commute
    Unpaid work – Subway
    Unpaid work – Wait
    Unpaid work – Errands
    Unpaid work – Errands
    Unpaid work – Groceries
    Unpaid work – Home
    Unpaid work – Clean the kitchen
    Unpaid work – Cook
    Unpaid work – Laundry
    Unpaid work – Tidy up
    Unpaid work – Other
    Unpaid work – Other
    Unpaid work – Other travel
    Work
    Work
    Work – C
    Work – Lunch
    Work – O
    Work – Other
    Work – T

    I track business projects as their own categories so that I can bill for my time or figure out if something was worth doing. I track games separately so that I can figure out what I spend more time on.

    I usually create a tracking record at the beginning of the activity so that quantifiedawesome.com can timestamp it. If I forget, I can say things like “-15m relax” to note that I started relaxing 15 minutes ago, or say things like “13:30 writ” to note that I started writing at 1:30 PM. If I’m seriously late, I can specify the date like this: “3/24 19:05 social”, or use the batch entry form. When I record an entry, the system shows me the edit form, so if I was wrong (I thought I was going to start Personal – Routines, but really, I went back to sleep), I can change the category using a dropdown and save it. I can also adjust start and end times, and the previous or next record is automatically adjusted too.

    I track my time based on the primary activity so that I don’t double-count the time. For example, if I’m taking the subway, I file it as “Unpaid work – Subway” instead of “Discretionary – Read – Nonfiction” even if I read a book during the trip.

    I have to build some kind of split/merge/refactor activity category tool someday, but so far, this is fine. And more reports! Reports are fun.

    Tips and lessons learned:

    If you’re starting out, a simple thing that lets you capture some text with a timestamp will work just fine. Jot down a few keywords that explain what you’re doing – enough to remember. Do this for a few days to a week in order to get a sense of what categories you may want to file things under.

    Once you’ve figured out what general categories you want, use a button-based tracker like Tap Log or a list-based tracker like Time Recording (both Android). They’re great for selecting something from a defined list or structure. The downside is that it takes a liiittle more time to add a new category.

    When you have lots of categories, going back to text input makes a lot more sense. No scrolling, no clicking around, and you can add new things fairly quickly. The substring search I put into quantifiedawesome.com works really well for me because I know which shortcuts map to which categories, and the structure is better than freeform text because reporting is easier.

    Reporting is a lot more fun if you’re comfortable with spreadsheet pivot tables and other nifty features. I should do a screencast of how I use Excel to slice and dice my data. =)

    Next step for me: Time estimates

    I’ve started recording time estimates for more detailed tasks/activities so that I can a) figure out if I routinely overestimate or underestimate certain things, and b) get finer-grained time data. I know that it takes me roughly an hour from the time I get up to the time I get out of the house with my usual morning routine and maybe half an hour for the rush version, but it would be great to break that down into components and perhaps experiment with it. I’m also curious about how much time it takes me to get to places so that I can adjust Google Maps estimates for walking, biking, or public transit.

    I write predictions down in Evernote (“Predict home by 7:05”). Evernote automatically timestamps the creation date, and I update the note with the actual time and any other notes I want to include (“home at 7:03; bike”). When I have several estimates and measurements, I’ll make a spreadsheet. When the spreadsheet structure settles down, I might build the functionality into Quantified Awesome. Successive prototyping helps me figure out how the data feels before I spend time building a structure for it. =)

    So that’s how I track my time! See Where the Time Went for a recent presentation sharing my results.

    Unstructured time, shaping your wants, and giving yourself permission

    I was talking to a couple of other Quantified Self Toronto members about the management of unstructured time, since one of them was taking a gap year from school and the other one had just wrapped up regular employment. “How do I make sure I don’t waste my time?” they asked.

    Here’s what I’ve been learning from semi-retirement: it can be easy to make good use of your discretionary time. (And to feel like you’re making good use of it!)

    When I was planning for this experiment, I worried that I would end up frittering away the time on frivolities that people frown on: vegetating on the couch, playing games, getting sucked into the blackholes of social media and random Internet browsing.

    It turns out that when you fill your life with so many more interesting possibilities, it’s easy to choose those instead. It reminds me of something I’ve learned about finances, too. Many activities make me just as happy as other activities do, so I might as well pick activities that are free or inexpensive and that align with my values. Likewise, I might as well pick activities that give me multiple benefits or that align with how I want to spend my time. A movie is diverting and it’s also good for learning about emotions and storytelling, but watching a movie while folding laundry is more useful than watching a movie in the theatre. I enjoy cooking more than I enjoy eating out. I enjoy writing, drawing, or spending time with W- more than I enjoy playing games.

    So I don’t fill my days with plans or box myself in with calendared intentions. I look at the week ahead and list tasks that I need to remember, promises and appointments I’ve made, and maybe make space for one or two personal projects or ideas that I don’t want to forget about. I have a regular client engagement on Tuesdays and Thursdays, which I do because I like the client and what I get to help them with. Sometimes I take a week or an entire month off, to re-set my sense of time. Even during my regular weeks, I try to leave plenty of space.

    It’s important to have space to follow where your interests and energy take you. I try to minimize the number of things I’ve promised to other people so that I have the flexibility to follow opportunities when they come up. That way, if I don’t feel like writing, I don’t. Maybe I’ll draw. Maybe I’ll code. Maybe I’ll work in the garden. Maybe I’ll tidy the house. Maybe I’ll read. Maybe I’ll plan.

    I make exceptions for conversations. It’s hard to not schedule those if I want to make sure they happen at some point. Left to my own devices, I might never get around to talking to people. So I pay someone to handle my scheduling, and I ask her to space some of the optional ones apart (maybe one a week?) so that I have room for focusing on my things. It’s a little weird scheduling three or more weeks in advance, but space is important.

    The rest of the time goes to whatever I feel like doing the most at that moment. It helps that I feel good about the things that I want to do, like writing, coding, and drawing, and that many of the things I do are also valued by others. I remember coming across in some book (was it Early Retirement Extreme? I should dig that up again) the idea that you can raise your skill in some activities or hobbies to the point that people are willing to pay you for it (now or in the future). Other things like exercise or cleaning the kitchen have their own rewards.

    Did I luck into wanting these things by nature, or did I shape my wants to fit what I wanted to do? It’s hard to say. Most of it feels natural, but I do consciously tweak my motivations. Here’s an example of where I’m deliberately working on hacking my wants: exercise. W-‘s been helping me build a strength training habit through lots of encouragement and positive reinforcement. I also remind myself that the time I spend exercising will pay off both short-term and long-term, and that helps me get better at picking it over other alternatives (ex: bike to work and get some exercise versus work from home). It’s like what Mel wrote about digging out a path of least resistance so that it goes where you want to go. The other day, I was on my bike for almost 4 hours: 6 short trips, back and forth, covering mostly the same ground. I might not add as much to my “Done” list, but it’s good for me.

    One of the benefits of choosing to spend my time this way is that it’s easy to say no to the common time-wasters that people often beat themselves up about. You don’t feel that need to escape because you haven’t been trying to keep yourself disciplined all day long. This also means that you aren’t wasting the emotional energy you’d otherwise use to beat yourself up about bad decisions. =) There are tasks that I postpone or don’t get around to, but it’s not because I suck. it’s just that I wanted to do other things instead, and I may get around to those tasks someday.

    Even leisurely activities become experiments. I spent one Monday watching animé practically the whole day. I’m studying Japanese, so I watched the episodes with the original soundtrack and English subtitles. It was fun hearing the sounds start resolving themselves into intelligible words… and it was interesting feeling that barrier of “Oh, I should be doing productive things because it’s a weekday morning!” start to erode as I learned more about giving myself permission to follow my interests. (It turned out that watching those animé episodes was great for helping me follow along with the audio and the script. I often listen to just the audio as a way to immerse myself in the language and enjoy commuting or working… Bonus!)

    Maybe the trick to managing an unstructured schedule isn’t to get better at discipline, but to get better at wanting good things, to get better at seeing the value in different activities. Then you can trust in yourself, with a little review and feedback so that you can tweak your course and make better decisions. At least that’s what seems to be working for me, and it might be something that would work for you too. =)

    Quantified Awesome: Analyzing time data–the questions I ask and how I answer them

    I track my time using QuantifiedAwesome.com because I’ve built an interface that fits the way I work (mobile/web, lets me backdate entries, lets me disambiguate categories with text), but you can use whatever works for you – even a paper notebook where you write down the time and the category.

    Here are some of the questions I ask about time and how I slice the data to answer them.

    First level of analysis

    There are a lot of things you can quickly analyze based on time, particularly if you have durations already pre-calculated. Here’s what I often look at:

    How much sleep am I getting? To answer this question, I split my time records by midnight so that I can easily get the sum of sleep durations per day. This accounts for naps and late nights much better than just looking at the starting timestamp does. I can quickly check my sleep length by looking at my dashboard, which shows me how much sleep I got the previous day. I also have some Emacs Lisp code that gets the data from QuantifiedAwesome using an API and calculates my average sleep for a week, which I include in my weekly reviews.

    Am I working too much? I want to keep my “Business – Earn” total to less than or equal to 20 hours per week (50%), and my total business-related hours to be less than 44 hours per week. I do this because otherwise work can be tempting to focus on, and I want to remind myself to do other things as well. This is reported on my dashboard, and I also see it in my weekly reviews.

    How much discretionary time do I have? How much time do I have for hobbies, socializing, and other activities outside work, chores, personal routines, and sleep? This helps me appreciate the freedom I have in each day and to focus on making the most of it. I can quickly see this by looking at the “Discretionary” row in my time review, or by adding categories and summing up the times in my spreadsheets. This is particularly mind-boggling to look at over a year. I had 1563 hours of discretionary time in 2012 – that’s a decent-sized time for developing skills or building relationships.

    What do I spend my discretionary time on? How much am I using for socializing, productive interests, and relaxation? I answer this question by looking at my time review or by creating pivot tables in my spreadsheet. It turns out that I spend more time on social things than I expected, so I don’t feel as guilty about blocking off time to work on my own things. I also have a lot of productive hobbies, so I can give myself more permission to play with less productive things or give myself downtime.

    Second level of analysis

    This might involve throwing your data into a spreadsheet and playing around with it. Here’s where I start digging into patterns and correlations.

    If I spend more time on some activities, where do I spend less time? I looked at correlations for time spent on various activities per day.

    How consistently do I do things? I’m curious about whether my sleep times, bedtimes, workload, etc. vary wildly from week to week or if they’re fairly stable and predictable. It’s easy to get a sense of this by looking at graphs and calculating standard deviations.

    How does my bedtime affect my wake-up time? I compared starting timestamps with ending timestamps, discarding naps and differentiating between weekdays and weekends. (I should rerun this analysis now that I have more control over my wake-up times…)

    How frequently do I write? I extracted the date from each timestamp and visualized it using a heatmap.

    How consistently do I bike, and when’s the earliest I started biking this year? How much have I saved by biking? I review my bike time or visualize it as a pivot table, a bar graph, or a heatmap in order to see patterns. (I biked in January! =D) Since most bike trips replace public transit trips (currently $2.60 per trip), I can also use my time data to estimate how much I’m saving. (259 trips in 2012 = ~$670+, 122 trips so far in 2013.)

    Do I need longer chunks of time to concentrate? I looked at discretionary activities and counted how many took 4+ hours, 3 hours, 2 hours, 1 hour, or less than an hour. I look at the character of the activities, too, so that I can figure out what I might not be working on if I only have short periods of time. It turns out that I get a lot of things done even in 1-2 hour chunks, and I tend to not take advantage of longer chunks even if I have them. (Hmm, I should do an analysis to see the longest chunks of straight discretionary time I have…)

    Does waking up early give me more discretionary time or longer chunks of time to work with? It turns out that I actually have a lot of “me” time even if I wake up at ~8 AM. Since this meshes well with my sleep needs if I stay up to 11 PM or 12 midnight, I can guiltlessly sleep in knowing that I’m not missing out on a lot of productivity.

    How does bulk cooking affect our time? How often do we do it? I look at sparklines and pivot tables to get a sense of when we’re doing lots of bulk cooking. (I should analyze this to find out how much time we actually save and where it goes to…)

    How do my patterns shift over time? What hobbies did I pick up or let go? How does my life adapt to external events and other commitments? Pivot tables, sparklines and line graphs are great for looking at the patterns in my data.

    How much time does it take me to get somewhere compared to, say, the estimates from Google Maps? I can figure this out by looking at the estimate before the trip and then comparing it with the time I’ve logged (door to door ). For example, a recent bike trip that was 32 minutes in Google was actually more like 43 minutes (including waiting for downtown traffic lights and finding someplace to lock up my bike). My morning commute is 36 minutes according to Google and around 48 minutes by my clock.

    Third level of analysis: What do I want to do with my life?

    This is where I take a step back and check: Am I happy? Is W- happy? Do I need to shift the way that I spend my time? What would I like to move my time towards? Am I getting the kind of value that I want to get out of my time? Then I can experiment. For example, I’m currently experimenting with increasing the time I spend exercising, and I’m curious about how that affects other parts of my life.

    Wrapping up…

    You can get a ton of information out of simple time tracking, even without anything else to correlate it with. Durations, start and end times, frequencies… There’s a lot you can do with a spreadsheet, some charts, or your own tools.

    It gets even more interesting when you start matching it up with other data. One day I should try comparing my bike data with temperature and wind, or time spent cooking with how many portions we produce and at what cost per portion. =)

    I get a lot of value out of my time-tracking. It helps me stay focused and be aware of the moment. I like reviewing and analyzing my data, too. I’m experimenting with ways to capture short sprints of more detail so that I can ask even more questions, and I love comparing notes with other people who track their time.

    Check out more posts about my Quantified Self tracking!