Category Archives: quantified

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Quantified Awesome: Adding calendar heatmaps to categories

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:

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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:

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Pretty consistent, actually.

and Discretionary – Play:

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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:

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and Business – Build:

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

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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).

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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!

Quantified Time: Comparing notes

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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

Wontonomics: Dumpling numbers

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!

Planning a Quantified Self workshop on time tracking

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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. =)

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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!

Poll: How often would you like to receive e-mail updates? Also, quantifying my blog posting history

I’ve been posting practically every day for the past 3.5 years, and I write about a variety of topics. I’ve been thinking of ways to make it easier for people to keep in touch without E-mail newsletters seem to be a Thing. Right now, the e-mail subscription form on my blog is the default provided by WordPress, so people get daily updates (which is probably a bit much). I’ve been thinking of making it easier to subscribe to weekly or monthly updates. Would you find something like that useful? I’d really appreciate it if you could answer this poll!

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(Don’t see the poll? Try viewing this post on my website.)

Aside: I was curious about just how long I’ve been keeping up with this ~1 post a day thing, so I graphed my blog posting history. It turns out that I’ve been pretty consistent, although there were days when I didn’t have anything new posted. I schedule my blog posts using Editorial Calendar and I sometimes send people sneak previews of upcoming posts using the Share a Draft plugin. This lets me smooth out the spikiness of my writing habit into a more predictable publishing schedule.

blog-posting-history

To generate this graph, I extracted the timestamps of all my published posts with the following SQL query:

SELECT UNIX_TIMESTAMP(post_date) FROM wp_posts WHERE post_type='post' AND post_status='publish' 
INTO OUTFILE '/tmp/timestamps.txt';

… and then I graphed it with cal-heatmap, removed in-between labels in GIMP, and used Autodesk Sketchbook Pro to hand-write new labels. =)