From August 11: I’ve written myself into the next month already. Good thing the Share a Draft plugin lets me send people links to upcoming blog posts so that they don’t have to wait for answers. I leave Saturdays for weekly reviews and Sundays for other stories that come up, and all the rest have one blog post a day. I don’t know when I’m going to schedule this post. Maybe I’ll shuffle things around so that some posts are in September. Let’s see if I can fill September up.

There’s more to write. There always is. Ideas from my outline. Answers to comments and e-mailed questions. Things I’m learning.

The main trick is to remember which posts are time-related and which ones aren’t. Or, I suppose, to write things so that they aren’t time-sensitive: to refer to recent events as “recently” instead of “last Wednesday”.

I haven’t been coding as much. You can see it. Here’s my writing activity (yay Quantified Awesome):

Here’s my coding on Quantified Awesome:

Other coding:

Emacs:

At least I’ve been drawing (a little bit, not much):

Writing is just so much more squeezable into the spaces of my life. I can write anywhere. I just need a question, and off I go. Sometimes I write throughout the process of finding that question in the first place. And more people could possibly benefit from writing, while only a few people use my code. Although lots of people like my drawings (and I do too), so I should make more of those.

Writing is less frustrating than coding because I feel like I make immediate progress, and I don’t get error messages. Not that coding is frustrating. Coding is fun. But I’m picking writing more than I’m picking code, and that tells me that I should tweak the rewards so that I pick code more. Besides, there are a gazillion blogs out there, but not as many people working on Emacs, Org Mode, WordPress, Rails, or the other awesome tools that I use. I could make more of a difference with code.

Maybe I need to put a time limit on my writing so that I get forced to do something different. Except it doesn’t really take all that much time to write.

If I’m a month ahead, maybe I should hold off writing and focus on outlining instead. Except writing is fun and it clears my head… Maybe writing one blog post, maybe a maximum two blog posts every time I sit down to write, and checking off some other non-writing task (code, drawing, learning Latin) before I allow myself a writing session again? I’m allowed to write if I’m blogging in the process of learning something.

People think flow is awesome (as in Mihály Csíkszentmihályi’s research). It is, but it’s dangerous. Too much flow could mean neglecting other parts of life. So, time to revisit other interests…

August 13: Hmm. Writing really has a strong pull. I’ve learned that it’s easier (and often much better!) if I don’t fight my interest, so maybe I should just give myself permission to write and outline (and draw, on occasion) whenever I feel like it.

Related blog posts:

• How to manage a large blog archive
• What do I want to do right now? Understanding my algorithm for discretionary time
• Writing, drawing, and coding while tired

Update Aug 22 2013: See presentation at the end of this post.

I built library-book tracking into Quantified Awesome in October 2011, hard-coding the patterns used by the Toronto Public Library system. I regularly hit the 50-book checkout limit and sometimes have to check items out on my husband’s account, so it really helps to have a system that can tell me what’s due and when on all the library cards that we have.

Crunching the numbers

In the 668 days (or 95.5 weeks) between October 1, 2011 and the time I exported my data for number-crunching, we checked out 1,252 items, or an average of 13 items a week. That included 250 movies and 44 other videos (TV series, documentaries, and so on). I’m boggled to find out that I checked out only 8 science fiction books. There were 152 other fiction books, including graphic novels and manga.

… 250 movies borrowed from the library results in saving of $150+ a month assuming we snag DVDs at $15. Not that we would watch 2.6 movies a week if we had to pay for them. In November 2011, I tracked the retail prices and page count of the books I read: $1,075 and 10,671 pages in a month, boggle. I don’t read all of those pages thoroughly, mind you; I tend to skim books looking for just what I need. Still, there’s no denying that the Toronto Public Library saves me a heck of a lot of book and entertainment money.

I figured I’d probably want to take a look at my reading list at some point, so I had programmed the system to record titles and Dewey Decimal System classifications as well. Fiction books and feature movies tend to have generic codes, but nonfiction books show me interesting patterns in my reading habits.

Here are my top categories:

Dewey Decimal Classification	Number of items
650 – Management & auxiliary services	328
330 – Economics	82
150 – Psychology	59
740 – Drawing & decorative arts	45
800 – Literature, rhetoric & criticism	38
000 – Computer science, knowledge & systems	33
640 – Home economics & family living	30
610 – Medical sciences; Medicine	22
300 – Social sciences, sociology & anthropology	18
340 – Law	14

Here are my top sub categories:

Dewey Decimal Classification	Number of items
658 – General management	213
650 – Management & auxiliary services	92
332 – Financial economics	45
808 – Rhetoric & collections of literature	37
741 – Drawing & drawings	28
158 – Applied psychology	24
153 – Mental processes and intelligence	22
641 – Food & drink	19
005 – Computer programming, programs & data	18
613 – Personal health & safety	15

Here they are, broken down by Dewey decimal group:

Dewey Decimal Classification	Number of items
650 – Management & auxiliary services	328
658 – General management	213
650 – Management & auxiliary services	92
657 – Accounting	9
659 – Advertising & public relations	5
651 – Office services	4
652 – Processes of written communication	3
653 – Shorthand	2
330 – Economics	82
332 – Financial economics	45
338 – Production	15
331 – Labor economics	10
330 – Economics	9
339 – Macroeconomics & related topics	2
333 – Land economics	1
150 – Psychology	59
158 – Applied psychology	24
153 – Mental processes and intelligence	22
155 – Differential and developmental psychology	11
152 – Perception, movement, emotions, and drives	1
150 – Psychology	1
740 – Drawing & decorative arts	45
741 – Drawing & drawings	28
743 – Drawing & drawings by subject	10
745 – Decorative arts	7
800 – Literature, rhetoric & criticism	38
808 – Rhetoric & collections of literature	37
809 – Literary history & criticism	1
000 – Computer science, knowledge & systems	33
005 – Computer programming, programs & data	18
006 – Special computer methods	12
001 – Knowledge	2
003 – Systems	1
640 – Home economics & family living	30
641 – Food & drink	19
646 – Sewing, clothing, personal living	5
640 – Home economics & family living	3
647 – Management of public households	1
644 – Household utilities	1
643 – Housing & household equipment	1
610 – Medical sciences; Medicine	22
613 – Personal health & safety	15
616 – Diseases	4
612 – Human physiology	3
300 – Social sciences, sociology & anthropology	18
306 – Culture & institutions	7
303 – Social processes	4
305 – Social groups	3
302 – Social interaction	3
304 – Factors affecting social behavior	1
340 – Law	14
346 – Private law	8
343 – Military, tax, trade, industrial law	4
349 – Law of specific jurisdictions & areas	2
490 – Other languages	13
495 – Languages of East & Southeast Asia	13
690 – Buildings	12
690 – Buildings	8
695 – Roof covering	2
696 – Utilities	1
692 – Auxiliary construction practices	1
170 – Ethics (Moral philosophy)	9
170 – Ethics (Moral philosophy)	5
174 – Occupational ethics	2
171 – Ethical systems	2
370 – Education	6
371 – School management; special education; alternative education	5
372 – Elementary education	1
720 – Architecture	5
729 – Design & decoration	2
728 – Residential & related buildings	2
720 – Architecture	1
810 – American literature in English	5
813 – Fiction	2
818 – Miscellaneous writings	1
819 – Puzzle activities	1
814 – Essays	1
680 – Manufacture for specific uses	4
684 – Furnishings & home workshops	2
688 – Other final products & packaging	1
686 – Printing & related activities	1
770 – Photography & photographs	4
775 – Digital photography	2
779 – Photographs	1
778 – Fields & kinds of photography	1
470 – Italic languages; Latin	4
478 – Classical Latin usage	4
910 – Geography & travel	4
915 – Asia	4
420 – English & Old English	3
422 – English etymology	2
428 – Standard English usage	1
510 – Mathematics	3
519 – Probabilities & applied mathematics	3
400 – Language	3
401 – Philosophy & theory	3
070 – News media, journalism & publishing	3
070 – News media, journalism & publishing	3
620 – Engineering & Applied operations	3
629 – Other branches of engineering	2
620 – Engineering & Applied operations	1
390 – Customs, etiquette, folklore	2
398 – Folklore	2
160 – Logic	2
160 – Logic	2
360 – Social services; association	2
362 – Social welfare problems & services	2
020 – Library & information sciences	2
025 – Library operations	1
021 – Library relationships	1
750 – Painting & paintings	2
759 – Geographical, historical, areas, persons treatment	1
751 – Techniques, equipment, forms	1
380 – Commerce, communications, transport	2
381 – Internal commerce (Domestic trade)	2
970 – General history of North America	2
977 – General history of North America; North central United States	1
974 – General history of North America; Northeastern United States	1
700 – Arts	2
709 – Historical, areas, persons treatment	1
700 – Arts	1
190 – Modern Western philosophy	1
191 – Modern Western philosophy of the United States and Canada	1
030 – Encyclopedias & books of facts	1
031 – Encyclopedias in American English	1
780 – Music	1
786 – Keyboard & other instruments	1
290 – Other & comparative religions	1
296 – Judaism	1
210 – Natural theology	1
210 – Natural theology	1
520 – Astronomy & allied sciences	1
523 – Specific celestial bodies & phenomena	1
950 – General history of Asia; Far East	1
952 – General history of Asia; Japan	1
710 – Civic & landscape art	1
712 – Landscape architecture	1
630 – Agriculture	1
635 – Garden crops (Horticulture)	1
500 – Sciences	1
501 – Philosophy & theory	1
Grand Total	776

I read a lot of management, personal finance, and psychology books. I enjoy reading them. I read them at breakfast, over lunch, before bed, on weekend afternoons. I’m not surprised by the proportions, although I’m a little surprised by the number – have I really checked out an average of eight nonfiction books a week? Gotten through more than 300 management-related books? Neat.

Time

The time data in my current system goes back to November 29, 2011. Excluding the nonfiction books that were returned before then (although still including the books I currently have checked out that I haven’t read yet), there are 727 nonfiction books checked out. Let’s assume I’ve read or skimmed through 80% of those (I’m probably closer to 90%) – that’s ~580 books. I’ve tracked 123.3 hours as “Discretionary – Productive – Nonfiction”. This undercounts the number of hours because I tend to read things over meals and during subway commutes, so let’s double that time to be in the right ballpark for multitasking. That’s a little less than half an hour per book… which is actually quite reasonable, considering I skim through most books in 10-15 minutes each and spend maybe two hours reading selected books in depth (the ones that I take notes on, for example).

This is what my nonfiction reading habit looks like, with the dark boxes indicating when I read more. (This doesn’t take into account reading while doing other things.)

That’s interesting… I read a lot more frequently when I was starting up my business in January/February 2012 (although I wonder what happened in April!). I read more sporadically now. I think it’s because I’m re-figuring-out my strategies for taking notes and applying ideas to my life.

How do I pick books to read?

The library releases lists of new books on the 15th or 16th of every month. I’ve written a small script that extracts the titles, authors, and IDs of the book into a text file that I can review. I delete the lines that I’m not interested in, and my script then requests the books that remain on the list. I monitor the new releases because I don’t want to wait for the usual press

When I ‘m learning about a topic, I tend to check out six or more books related to it. A wide variety of books lets me see different viewpoints, and I can focus on books of better quality.

I occasionally look at Amazon’s recommendations for other ideas, although the books are often not yet available at the library.

Sketchnoting a new release can have high impact, which is another reason why I monitor the new releases. I sometimes reach out to publishers for review copies as well.

The library doesn’t carry everything. I usually add other interesting releases to my Amazon wishlist. I rarely buy books, though, because there’s just such an interesting backlog that I haven’t yet gotten through. I buy books if there are clever illustrations that I’d like to use for ongoing inspiration, or if I want to give the book to a friend, or if it’s an older book that someone has recommended to me and the library doesn’t stock it. Now that I have a business, I usually file those books as business expenses.

So much for quantity. If I’m reading all that, what am I doing with it?

I use books to:

• Learn about different viewpoints and approaches, especially scenarios that I might not anticipate on my own
• Learn how to organize and communicate complex ideas
• Get shortcuts on explaining ideas – for example, I don’t have to explain outsourcing from scratch, because I can point people to the 4-Hour Work Week for starters

Many of the ideas I pick up from books resurface in my blog posts and experiments. Books help me recognize what’s going on in real life, because the authors have already come up with words for them. I also like applying the advice from books – much to learn.

I frequently recommend books to other people. Visual book reviews make that easier. I try to slow down and recognize books that I’ll probably recommend to others so that I can make visual book reviews of them while I have the book. Sometimes I’ll take quick text notes for myself and then use that for reference. If I find myself recommending the book frequently, then I’ll check it out again and make better notes.

I don’t review my book notes much, relying instead on situations to trigger my memories. When I come across something that’s related to a book I’ve read, or I talk to someone who could benefit from a book recommendation, I dig through my visual and text-based notes.

Next steps

“Better” isn’t about reading more books – it’s about being able to apply, organize, and share what I’m learning from those books. I want to learn more about doing good research: identifying a topic to explore, synthesizing insights from multiple sources, and adding personal experiences or ideas. The process might look like this:

• Outline a topic for research
• Search the library catalogue for resources; also check bibliographies, Amazon, and other recommendations
• File properly-cited notes so that I can give credit later on
• Compare and organize ideas from different sources
• Test ideas in small experiments
• Write blog posts, then articles, then e-books

Visual book reviews are another way for me to grow. In addition to making visual book reviews of interesting new releases, I’d like to revisit the books that were a big influence on me in order to make visual reviews of them too.

Yay for Quantified Self and tracking. Onward!

(Also, if you’re curious about tracking your own library use: I can probably extend Quantified Awesome to support other libraries with online interfaces, but you’re going to need to walk me through how your library works.)

Update from August 22, 2013: Here’s a presentation I’m putting together for Quantified Self Toronto.

It’s amazing how little tweaks give you a whole new sense of the data. I’ve been using Cal-HeatMap to look at my blogging history. I figured I’d build it into Quantified Awesome to make it even easier to analyze how I spend my time. 1.9 hours later, here’s what I have. All totals are reported for the past 12-month period by default (as of this writing, July 19 2012 to July 19 2013, including the day’s activities), but it adjusts depending on the filter settings.

Here’s me working on the Quantified Awesome system:

Instead of just a table of log entries or a summary of numbers, I can see the gaps and sprints in my activity.

Here’s the one for Discretionary – Productive:

Pretty consistent, actually.

and Discretionary – Play:

February must’ve been when I had a new video game to tinker around with. Plenty of opportunities to relax.

Here’s my Business – Earn graph:

and Business – Build:

I’ve been biking pretty regularly, mostly on Tuesdays and Thursdays…

In contrast, I take the subway only if it’s winter or really rainy, if I’m going somewhere far or steeply uphill, or if my bike is flat (as it was yesterday).

Neato. I should definitely do this for groceries too, now that I’ve loaded my grocery receipts into Quantified Awesome! (No public link yet for that data, sorry. =) ) I also want to figure out how to speed things up enough so that I can do quartile analysis and then use that to colour the scale…

Calendar heatmaps for the win!

David Achkar has a great blog post sharing his observations from 42 days of time-tracking using Google Calendar and a few scripts for export and analysis. Since it’s fun to be able to compare numbers, I thought I’d reflect on 2013 so far.

Like David, I spend about half of my life on “survival”-type activities (48%): sleep, routines, exercise, walking, and so on. I include planning in my Personal category, even though that might be more of a discretionary activity, because planning helps keep me sane. I count my bike commutes as part of this category as well, because I think of it as exercise. Without the bike commutes, exercise, and planning, the part of my week used for survival activities is down to 44%.

I don’t think that’s a bad proportion at all. After all, you’ve got to sleep sometime. =) While some people can get along fine on four hours of sleep (hello, Papa!), I know I need my 8-9 hours of sleep, because I feel fuzzy when I don’t get it. Assuming I sleep an average of 8.5 hours a day—which turns out to be the actual result from my 2013 numbers—that leaves me with 15.5 hours of awake-time for awesomeness. Of those waking hours, I use:

• 36% for business,
• 19% for personal routines,
• 12% for chores and other unpaid work,
• 11% for socializing (family and others),
• 11% for productive discretionary activities,
• 8% for relaxation and enjoyment,
• and 3% for other activities.

So that’s roughly 58% of waking hours for good stuff, which is plenty of time to get things done. And the chores are pretty good for me, too – cooking and tidying are relaxing. =) I don’t mind. If anything, I should probably increase my “overhead” and spend more time exercising and wandering.

Choosing your time

David talks about being aware of and consciously choosing activities instead of simply reacting to whatever comes our way. It’s one of the nifty unexpected benefits of time-tracking: once you put a name to the time you’re spending, it becomes easier to recognize other things as not that activity. Working? Facebook doesn’t count. Relaxing? Checking e-mail doesn’t count.

Tracking your time manually adds a tiny bit of friction to switching tasks (you need to track it yourself, after all!), but this turns out to be a good thing. It encourages you to put off distractions until you legitimately track it as that, and if you’re going to do that, you might as well do that for at least five minutes. As it happens, postponing distractions makes them less tempting.

Busyness

I look at my work time mainly as a way of keeping it in check. =) I’m delighted to see that my average business-related time per week is 39:29 in 2013 so far and 39:51 in 2012, amazingly close to my target of 40. (How do I manage that? Boggle.) 

2013 has an average of 18:13 billable hours a week. This is down from 19:49 in 2012, which is good because I’ve been moving towards focusing on my own things. I’ll try to bring this down to less than 8 hours a week next year, to see what that’s like. If I can get one to three good things done each day, that’s enough.

Focus

It turns out that I can actually concentrate in long stretches, and that I can arrange my time to accommodate these spans if needed. I tend to favour 0-2 hour sprints, though. Flow feels great – but it’s also dangerously seductive, and limiting it might be worth a good idea.

Category	< 1 hour	1 hour	2 hours	3 hours	4 hours	5 hours	6 hours	>= 7 hours
Business – Build – Book review	4	3
Business – Build – Coding	39	17	6	3	1		1
Business – Build – Delegation	12	1
Business – Build – Drawing	46	15	7	1
Business – Build – Learn	13	2	2		1
Business – Build – Paperwork	69	18	2			1
Business – Build – Plan	14	4	3		1
Business – Build – Quantified Awesome	34	18	6	2	3	1
Business – Build – Research	10	4		1
Discretionary – Productive – Emacs	33	19	5		2	2		1
Discretionary – Productive – Gardening	36	3
Discretionary – Productive – Japanese	45	11	1	1
Discretionary – Productive – Nonfiction	20	7	2				1
Discretionary – Productive – Outlining	4	1
Discretionary – Productive – Sewing	1	1
Discretionary – Productive – Tracking	4
Discretionary – Productive – Writing	153	38	6	1	1		1

(I posted a similar analysis in 2011.)

Since practically all of my meetings are discretionary, I don’t need to make a special effort to clear large blocks of my day for concentrated work. Even when the day stretches before me without a calendar entry in sight, I usually don’t spend it all doing One Thing. I shift from one activity to another when I reach a good stopping point, following my interests or energy. Besides, food is important, so I usually interrupt my work for lunch or a snack. No marathon sessions for me!

(One year, I got so carried away programming that I forgot to make sure I drank regularly, and I fainted from dehydration. Other times, I’ve forgotten to take care of important things. So… right. I’ll pick moderation even if task-switching cuts into efficiency.)

Urgency

Very little in my life is urgent, so I’m rarely stressed. That’s partly because I have the freedom to minimize commitments and to recover from mistakes. I usually answer my e-mail within a week or two.  I could probably earn more or do more if I was more responsive or went looking for more commitments, but I don’t want to give up my creative time by shackling myself to e-mail or schedule expectations.

Other thoughts

Time data is an amazing thing to have, and it’s well worth tracking. I’m looking forward to more analyses from David. If you track and analyze your time, I’d love to hear from you too!

David Achkar: A Life Logged: Surprises and Insights

Summary: Cost per serving: CAD 1.25-1.50, time per serving: ~30 minutes(!)

Since people were curious, here’s the rough recipe we used for the last batch of wontons:

Amount	Ingredient	Cost / source
generous knob	ginger, peeled and finely chopped	left over from previous
6+ cloves	garlic, peeled and finely chopped	pantry
small handful	cilantro, finely chopped	from the garden
two bunches	green onions, finely chopped	CAD 1.14
1 large bag	small shrimp, raw, unpeeled, 70/90 – peel and chop	CAD 10.00
~2.5kg	ground pork	CAD 15.61
6 packages	wonton wrappers	CAD 8.94
	soy sauce	pantry
	sesame oil	pantry
	salt and pepper	pantry

Sauté the ginger and garlic, then mix everything together (except the wonton wrappers, of course). Set out a small bowl of water, a plate, and a teaspoon.

For each package do:

• For each wrapper do:
  • Hold the wrapper in the shape of a diamond.
  • Place a teaspoon of filling a little above the middle of the wrapper.
  • Wet the top two edges, then fold the bottom half up to meet the top half. Press out air bubbles.
  • Wet one of the outside corners, and fold the two outside corners together.
  • Place the wonton on the plate.
• Boil the wontons for about a minute and a half, then cool in a bowl of water. Sample a few for quality control. Drain and pack into small containers, 250-265g per container (15-17 wontons, average of 16.8g per wonton). Label and freeze.

If you want to quantify your wonton production, the easiest way is to count them as you’re about to boil them.

Each package contained an average of 70 wrappers (stdev: 5, mode: 74) and took the two of us roughly an hour to process and boil (~1.5-2 person-minutes per wonton). The cost per wonton worked out to $0.08 per wonton (maybe $0.09 considering the pantry ingredients), which means each serving costs about 30 minutes of labour (not including grocery-shopping) and less than $1.50 in raw ingredients.

Thirty minutes seems like a lot for a serving that disappears pretty quickly, but the time is both relationship-time and movie-watching time for us, so it works out. And the wontons are yuuuummy – much better than the frozen ones you can get in the store. (Texture! Flavour! Smug satisfaction!) We like them even more than the ones you can get in a restaurant. =) We usually have the wontons with udon noodles and soup, although we occasionally snack on plain wontons seasoned with soy sauce.

Lots of the freezer recipes we come across are geared to Western tastes, so we like collecting Asian recipes that freeze well too: wontons, Japanese croquettes, okonomiyaki, beef bulgogi… So nice to be able to pull something out of the freezer and enjoy it any time!

The other Quantified Self Toronto organizers and I have been thinking about following up on the “slow data” workshop idea from the QS Conference in Europe this year, which Eric Boyd is really keen on. The idea is that self-tracking takes time to plan, to get data, to get back into collecting data after you’ve fallen out of the habit, to analyze data, to revise your experiment based on what you learned… so although 15-minute bursts of inspiration are great for showing people what people are working on, wouldn’t it be nice to go through an extended workshop with support at just the right moments? Based on our survey results, people might even be willing to pay for monthly or semi-monthly workshops.

I’m interested in tracking time much more than I’m interested in health or other popular self-tracking topics, so I’d love to experiment with building resources and workshops for people who are interested in tracking time as well. The payoff? I’d love to be able to compare questions, data, and conclusions.

Here’s what that workshop might look like:

Session 1: The Whys and Hows of Tracking Time

• Discuss objectives and motivations for tracking time. Plan possible questions you want to ask of the data (which influences which tools to try and how to collect data). Recommend a set of tools based on people’s interests and context (paper? iPhone? Android? Google Calendar?).
• Resources: Presentations on time-tracking, recommendations for tools, more detail on structuring data (categories, fields); possible e-mail campaign for reminders
  Output: Planning worksheet for participants to help people remember their motivations and structure their data collection; habit triggers for focused, small-scale data collection, buddying up for people who prefer social accountability

Session 2: Staying on the Wagon + Preliminary Analysis

• Checking in to see if people are tracking time the way they want to. Online and/or one-on-one check-ins before the workshop date, plus a group session on identifying and dealing with obstacles (because it helps to know that other people struggle and overcome these things). Preliminary analysis of small-scale data.
• Resources: Frequently-encountered challenges and how to deal with them; resources on habit design; tool alternatives
• Output: Things to try in order to support habit change; larger-scale data collection for people who are doing well

Session 3: Analyzing your data

• Massaging your data to fit a common format; simple analyses and interpretation
• Resources: Common analysis format and some sample charts/instructions; maybe even a web service?
• Output: Yay, charts!

Session 4: More ways you can slice and dice your data

• Bring other questions you’d like to ask, and we’ll show you how to extract that out of your data (if possible – and if not, what else you’ll probably need to collect going forward). Also, understanding and using basic statistics
• Resources: Basic statistics, uncommon charts
• Output: More analyses!

Session 5: Making data part of the way you live

• Building a personal dashboard, integrating your time data into your decisions
• Outcome: Be able to make day-to-day decisions using your time data; become comfortable doing ad-hoc queries to find out more

Session 6: Designing your own experiments

• Designing experiments and measuring interventions (A/B/A, how to do a blind study on yourself)
• Outcome: A plan for changing one thing and measuring the impact on time

Session 7: Recap, Show & Tell

• Participants probably have half a year of data and a personal experiment or two – hooray! Share thoughts and stories, inspire each other, and figure out what the next steps look like.
• Outcome: Collection of presentations

Does that progression make sense?

Eric thinks this would work out as a local workshop here in Toronto. I’m curious about what it would be like as a virtual workshop, too. We might even be able to experiment with both. Is this something you might be interested in? If you’re a QS organizer, would you like to give it a try in your own meetup?

I’d love to hear from you! Leave a comment below, or sign up with your e-mail address so that we can talk about it in e-mail. =)

[contact-form subject=’Quantified Time Workshop’][contact-field label=’Your e-mail’ type=’email’ required=’1’/][contact-field label=’Where would you like the workshop?’ type=’radio’ options=’Toronto-based,Online’/][contact-field label=’Is there anything else you%26#039;d like to learn about in terms of time tracking?’ type=’textarea’/][/contact-form]

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!