Tag Archives: personal analytics

Manifesto 43: Improving My Quantified Self

When it comes to quantified self, one question I frequently hear is “how can this data really help me”? It is a good question, especially since there are huge volumes of data about ourselves available, and it may not be obvious how to put it to use. I have used quantified self data to improve my writing, and help get more exercise, but it seems to me there is more I can be doing to use this data to improve.

I had been thinking about this a lot leading up to my birthday last week. As I approached my birthday, I began to think about the general areas of my life that I would like to improve, and see if there was a way that I could take advantage of data to help me make the improvements. So I put together a simple document in which I began to list the following:

  • The areas I wanted to improve
  • A simple statement or instruction to frame the improvement
  • An initial notion for how I might measure the improvement.

I called the document my “Manifesto:43.” I thought it might be interesting to others, so below are the major areas, along with the “instruction” I gave myself to keep in mind.

I have more detailed thoughts and actions in each of these areas, and I’ll tackle them in separate posts over the next few weeks, but for now, here are the major areas I’m looking to improve.

Play

Play with the kids whenever the opportunity presents itself.

Walk

Prefer walking over other modes of transportation where practical.

Write

Write every day, even if only for a few minutes.

Eat

Make healthy choices.

Disconnect

Make efficient use of online resources. Avoid unnecessary activity.

Simplify

Use the best tool for the job, but avoid overlapping tools.

Save

Look for opportunities to save more.

Relax

Don’t sweat the small stuff.

There are some overarching themes here. These things can be grouped in different ways to reflect overall priorities. For instance, grouping together “Play”, “Disconnect”, “Simplify” and “Relax”, you have what I think of as “family time.” Improving in those four areas helps improve family time. Grouping “Walk”, “Eat”, and “Relax” are all health-related.

For each of these areas, I produced simple examples of actions that I can take to make the improvements I am looking to make. I’ll drill down into those in a separate post. I have also attempted to identify quantifiable ways of measuring the improvements. In some instances (e.g. “play”) it is pretty hard. In others (“walk”, “write”, “save”) it is pretty easy. Some of the actions are one-time and others are ongoing. I’ve already taken some actions and although it is too early to say how well these changes are working, I am pretty happy with my overall framework for thinking about these things.

Stay-tuned for more.

How I Automatically Capture Driving Data From my @automatic Link in a Google Spreadsheet

I have been using the Automatic Link in my Kia Sorento since December. It is a good little device that plugs into your car’s data port and pulls out all kinds of interesting information about your driving habits. For a while, you needed the iPhone app to browse the data, and the data itself was not extractable in any easy way, but no longer.

A while back, the Automatic tracker became available on IFTTT, with a bunch of triggers that can be used in automation workflow. One of those triggers is when a new trip is completed. So I created a recipe in IFTTT that logs the data of each completed trip to a Google Spreadsheet. For now, it logs all of the data, even though I might not use all of it. The data is logged within 15 minutes of completing a “trip” (going from point a to point b and shutting of the engine). Here is a list of the data that gets collected in the spreadsheet:

  • Car
  • Start Time
  • End Time
  • Duration
  • Distance (miles)
  • Average MPG
  • Fuel volume consumed (gal)
  • Fuel cost (dollars)
  • Hard brake count1
  • Hard accel count2
  • Duration over 70 MPH (minutes)
  • Duration over 75 MPH (minutes)
  • Duration over 80 MPH (minutes)
  • Trip Map URL
  • Start Location Longitude
  • Start Location Latitude
  • Start Location Map URL
  • End Location Longitude
  • End Location Latitude
  • End Location Map URL

The spreadsheet looks something like this:

Automatic Link

The great thing about this is that, like the FitBit Flex or my Google Writing Tracker scripts, the data is collected automatically. This is, in my opinion, of critical importance for personal analytics, because any time you have to take for manual actions lessens the likelihood you’ll continue to collect the data. For this data, all I have to do is drive.

I only have a week of the data so far, but it has already confirmed what we already knew: we have an incredibly good commute to and from work. I live about 5 miles from the office (5.18 miles on the roads according to the Automatic Link). When we leave the house at 7:16 am (as we did yesterday), we arrive at my office at 7:28 for a total trip time of 13 minutes. (Kelly has to then catch the Yellow Line from my office to her office in the District.) Coming home. Our reverse commute in the evening takes 12 minutes, despite being right in the middle of rush hour.

There are a few things I am trying to tweak with the spreadsheet. One downside is that the data/time is entered as a text field instead of an actual date/time and that makes some charting difficult, but I’m working on some code that will convert this automatically. Then, once I have more data, producing some charts and plots similar to what I’ve done for writing and walking should be easy.

One thing I’ve learned from this that I’d never thought much about before is the cost of our commuting into the office. Looking at the fuel consumption of our commute and Automatic’s estimated fuel costs, our commute costs us $1.85/day. That amounts to $9.25/week, or assuming we work 48 weeks out of the year, $444 in fuel costs commuting to-and-from work each year.

That number is actually high because there are days when we both work from home, but I suppose the number wouldn’t be less than $400/year.

I’m looking forward to delving deeper into this data once I have more of it to make it more meaningful.

ETA: I’ve embedded my IFTTT recipe for this automation below, for easier access.

IFTTT Recipe: Export Automatic Trip Data to aGoogle Spreadsheet connects automatic to google-drive

  1. The tracker detects when you brake too hard as part of its system for analyzing fuel consumption performance.
  2. The tracker detects when you accelerate at a rate that burns fuel in a less-than-optimal way.

2 Useful Insights I’ve Gained from Personal Analytics Data: Sleep and Productivity

In writing about personal analytics and data collection, one question I get more frequently than most is: what do you get out of it? Today I thought I’d share 4 insights I’ve gained into my own behavior from scrutinizing the data that I collect.

For those who haven’t been following along, I am fascinated by what data about our everyday lives can tell us about our behaviors. The data is often referred to as “personal analytics” and the movement behind this kind of data collection and analysis is called the “quantified self” movement. I collect data in four major areas:

Areas of Tracking

I collect data in other areas, too, but the key point about these four areas is that the process is entirely automated. I just go about my day, and this data is collected without any intervention or action on my part. I’ve already written extensively about my walking and writing insights so today I’ll focus on what I’ve learned about my behavior when it comes to sleeping and overall productivity.

1. Restless nights and sleep efficiency

You know those nights where you feel like you are tossing and turning all night long, getting very little sleep? Turns out, I do sleep on those nights, at least according to my FitBit, but my “sleep efficiency” is down below 90%. Here is a one recent example:

Sleep Efficiency

I’ve been capturing this type of data for almost two years now and I’ve learned a few useful things about my sleep habits by looking closely at the data.

  1. When my sleep efficiency is >= 95%, it feels like a restful night’s sleep. This is true for me almost independent of the number of hours I actually sleep. If I only get 5 hours of sleep, but my sleep efficiency is, say, 97%, I still wake up feeling like I had a good night’s sleep.
  2. When my sleep efficiency is between 90-95%, it’s a pretty good night, but the number of hours is more of a factor. If I get, say 7 hours of sleep with a sleep efficiency of 92%, I feel pretty good in the morning. On the other hand, if I get 5-1/2 hours of sleep with a 92% efficiency, then I don’t feel nearly as well-rested. According to the data, the time threshold is around 6 hours.
  3. When my sleep efficiency is less than 90%, I feel like I had a restless night’s sleep, regardless of hours actually slept.

I’ve been able to take this data and put together a chart of my sleep quality, based on two variables, sleep efficiency, and hours of actual sleep (vs. hours in bed).

Sleep Quality

I should not that I do not track how I feel each morning when I wake up. But on mornings when I felt particularly good or poor, I’ve checked it against the data from my FitBit and it is fairly consistent. For me, therefore, the above chart is a good representation of the quality of my sleep based on the two inputs.

How does this help?

Continue reading 2 Useful Insights I’ve Gained from Personal Analytics Data: Sleep and Productivity

Some Interesting Vacation Stats

Here are some interesting stats from my vacation, which lasted 23 days from start to finish:

Writing/Blogging

I wrote 22,461 words of fiction while on vacation. That amounts to an average of just under 1,000 words/day. My best day was December 28, when I wrote over 3,600 words. My worst day was December 15, while on the road, where I wrote 288 words. All told, my vacation fiction writing looked like this:

Vacation Writing

That amounts to 16-1/2 hours I spent writing fiction while on vacation.

I wrote 20,465 words worth of blog posts while on vacation. That brings my grand total of vacation writing, fiction and blogging to about 43,000 words. Not bad for 3 weeks.

The data for these numbers come from my Google Writer Tracker scripts.

Walking

I walked a total of just over 265,000 steps, or about 120 miles on my vacation.  That might sound like a lot but I only hit my daily goal of 15,000 steps on 6 out of the 23 days I was on vacation. Here is what the day-to-day breakdown of my walking looks like:

Vacation Walking

The data for my walking comes from my FitBit Flex.

Driving

I made use of my new Automatic Link to track the driving we did on our vacation. Based on the numbers provided by that device, we drove about 2,218 miles on our 3 weeks of vacation. Of course, that includes the driving we did from Virgina to Florida and back, and indeed, that makes up most of the driving we did. Our Kia Sorento averages about 29 MPG on the long drives, although at one point, it did hit 30 MPG.


Of course, none of these stats reflect how much fun we had, or how much we enjoyed our vacation, but that’s what the memories (to say nothing of the photos) are for.

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2013 By the Numbers: Activity and Sleeping

I have been using a FitBit device since early 2012. At first, I had a FitBit Ultra which I used every day until I finally lost it on a walk in early March of 2013. In May of 2013, I got a FitBit Flex. Since it is much harder to lose the Flex, or forget it (you wear it on your writs and it is waterproof) I have excellent data from May going forward. Also, since it is much easier to track sleep with the Flex, I have excellent data for that as well. Keep in mind, however, that for nearly two months, between March and May, I have some missing data.

Activity (Walking, Level of Activity, etc.)

It is recommended that a person gets 30 minutes of “very active” activity each day. This might be a vigorous workout, it might be a brisk walk, jog, whatever. If you get 30 minutes, you are doing yourself some good. How “very active” is defined is a little vague, but FitBit bases their definition of “very active minutes” on METs (metabolic equivalents):

All Fitbit trackers calculate very active minutes using metabolic equivalents (METs). MET is a unit used to represent the amount of oxygen used by a body during physical activity; therefore, MET can be used as an indicator for intensity of physical activities.

The trackers break your activity into four categories:

  1. Sedentary
  2. Lightly active
  3. Fairly active
  4. Very active

Of course, a FitBit also records steps and distances. For the first few months of the year, my daily goal was to hit 10,000 steps, or roughly 5 miles of walking each day total. Here is what my year of walking (in steps) looked like:

Steps 2013

The blue line represents my actual steps each day. The red line represents a 7-day rolling average. You’ll note the gap in the March – April timeframe. This is where I lost my FitBit Ultra and before I bought my Flex.

I walked a total of 3,596,000 steps in 2013. That amounts to about 1,633 miles, or a little more than halfway across the continental United States. Excluding the period which I’d lost my FitBit I averaged 11,415 steps per day,  or close to 6 miles per day.

But not all of these steps are equal. Some are more active than others and if we look at the my “very active” minutes each day, we get a chart that looks like this:

Very Active 2013

This shows that after May, I was well above the recommended 30 minutes of vigorous activity each day, often hitting 90 or even 120 minutes. This is largely thanks to the three walks I take each day, encouraged by the fact that (a) it is a break from work and (b) I can listen to audio books while I work (see the next post in this series). And now, for my sleeping data:

Continue reading 2013 By the Numbers: Activity and Sleeping

2013 by the Numbers: Writing

I have already written about this in more detail here and here, but as a final end-of-year recap, I wanted to provide some interesting charts that summarize my fiction writing for 2013. I wrote a total of 274,373 words of fiction in 20131. These numbers come from my automated writing tracker scripts for Google Docs, which are available on GitHub for anyone who wants to use them. What made up these words? Well…

  • A novel draft, totaling 95,000 words, but which included some false starts of perhaps another 30,000 words.
  • A 5,500 word  epic fantasy story which consisted of 3 complete drafts.
  • A 6,300 word science fiction story which consisted of 3 complete drafts.
  • A 1,200 word flash story which consisted of 4 complete drafts.
  • A (so far) 8,000 word novella which has consisted of 3 previous false starts totaling around 20,000 words.
  • About 10,000 words of nonfiction articles (including drafts)

I ended 2013 on a 163 day writing streak, and having written 306 out of the last 308 days of the year. Here is what the monthly word count looked like for 2013 beginning in March (where my data begins): Writing 2013

If you combine this to my blogging for 2013, you get a chart that looks like this: Writing & Blogging

All combined, I wrote more than half a million words. That seems remarkable to me, just looking at the numbers, especially considering that I have a full time day job and two little kids that keep me busy. Here is what my day-to-day writing looks like for the last 308 days: Words per Day 2013

At a more practical level, it’s easier to look at the 7-day moving average over the last 308 days. I’ve added my daily goal for comparison so you can see where the 7-day moving average is above or below the goal: Moving Avg 2013

A few other interesting writing-related numbers:

  • My longest consecutive streak: 164 days, and counting
  • Longest streak where I met or exceed my goal: 33 days
  • My best day: August 15, where I wrote 5,384 words in a single day

See also

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  1. A very small fraction of this was actually nonfiction, but it made up less than 10,000 words of the total

2013 By the Numbers: Blogging

I‘ve been tracking quite a few areas of “personal analytics” for well over a year and so I have a fairly complete data set for some parts of my life in 2013. Here are some of the numbers for folks who like following along with this type of stuff. Keep in mind that there are still roughly 12 hours in the year for more data to accumulate, but I think it is safe to say that it won’t alter these totals by much.

Rather than combining all of this in one long post, I plan on doing a post for each of 6 areas:

  1. Blogging (this post!)
  2. Writing and publishing
  3. Activity and sleeping
  4. Reading
  5. Driving
  6. Email and social networking

I begin with blogging.


This blog has over 830,000 page views in 2013, more than double from last year’s total, although the comparison is slightly skewed. Last year, I mostly relied on WordPress’s stats package, while this year, I switched entirely over to Google Analytics for its richer feature set. The WordPress packaged seemed to have a higher count by 10% or so, but I’m declaring Google Analytics numbers official. So 830,000 page views it is.

The year started off strong, with both January and February seeing over 100,000 visits per month. For a while, I thought I might crack 1 million page views, but things slowed down a bit, averaging between 60,000 – 70,000 page view a month for most of the rest of the year. This month, things jumped again, and I’ll finish December with just about 90,000 page views.

Page views are different from visits. I visit is a complete session and can contain multiple page views by the same visitor. If you want to look at things from the visit perspective, I had just about half a million distinct visits in 2013. Do that math and that makes for 1.8 page views per visit.

This was a strange year for RSS, which Google retiring Reader. I have roughly 2,000 subscribers to the RSS feed for the blog, according to Feedburner. And according to Feedly, I have nearly 800 readers in that application. This adds another 150,000 or so page views, which I suppose gets me to my 1 million mark, but in truth, I’m looking to hit that milestone directly through my Google Analytics. So I’m sticking with the 830,000 page views for now.

I wrote 488 blog posts in 2013. I don’t have a total word count, but I can give a rough estimate, since I started tracking this explicitly in February. It comes to about 260,000 words, and this is completely independent of my fiction and nonfiction writing. Here is what the month-by-month breakdown looks like, beginning in February:

Blogging in 2013

That about covers blogging. In the next post in the series, I’ll have some numbers for my fiction and nonfiction writing for 2013.

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A Typical Weekday in 2013 Based on Quantified Self Data

Yesterday, I happened to notice that on my Audible app, I passed 30 days of listening to audiobooks in 2013. That means I spent 1/12th of my year listening to books. I thought that was a pretty amazing statistic, and I wondered how much of my day I could piece together based on data that I collect. Turns out, I have enough data to capture over 80% of what occupied my time on a typical weekday.

Using that data, I put together the following chart. This is what a typical weekday looked like for me in 2013. I used weekday, because there are more than twice as many weekdays as weekends and I generally know that I am working at the day job during the day. Put another way, I have more reliable data for weekdays than weekend.

Typical Weekday in 2013

Working down the list, here are the sources for the data that makes up the chart:

  • Sleeping: data comes from my FitBit Flex. I used the aggregate data for hours slept each night.
  • Reading: data comes from the Audible app. Obviously, this counts only audiobooks and not reading I do using traditional, e-books, or online, but I think it is a pretty good estimate.
  • Writing: data comes from my Google Writing Tracker scripts. Back in October I added some code that allows me to track the time I spend writing. The percentage, not displayed here, is about 3.2% of my day.
  • Working: I typically work 8 hours a day a the day job and that is the number I used here.
  • Walking: data comes from my FitBit Flex. I only used “very active” minutes aggregated for the year. Very active minutes are my “exercise” walks each day. The number does not include the general walking around I do throughout the day outside my walks.
  • Untracked: the 19.6% of my time that is untracked includes everything else that goes on during a typical weekday: commuting to and from work, picking up the kids from school, eating dinner, family time, getting kids ready for bed, etc.

I think this makes a pretty accurate, to say nothing of interesting, picture of a typical weekday in 2013. Even more interesting will be to compare this data to what it will look like for 2014.

 

The Personal Analytics of My Vacation

Last week, the family and I spent the week up in Maine on vacation. We had a lot of fun, and of course, the vacation ended far too quickly. I wrote while I was on vacation, I blogged a bit, I read some, and I walked a bit. It seemed to me that since I was on vacation, I’d be able to do a lot more of those things that I normally do. So you can imagine my surprise to find that I did far less in almost every category.

Since I have automated scripts that collect data on my writing, blogging, walking and reading, I decided to look at the data and separate it out into “vacation” and “non-vacation” days. To do this, I aggregated the data to produce an “average” week when I am not on vacation, as well as an “average” week when I am on vacation. The results are pretty interesting.

Fiction writing

When I am not on vacation, I have averaged about 800 words/day of fiction (and my consecutive-day streak currently stands at 134 days). On vacation, while I did write every day, my average dropped down to about 540 words/day. Here is the comparative breakdown by day of week:

Fiction Writing

Blue indicates days when I am not on vacation and red represent days when I am on vacation. Sunday is skewed slightly by the fact that I crammed in an extra effort upon returning from vacation. Technically, the last Sunday of my vacation should count for both on and off vacation, but I gave myself the benefit of the doubt.

Blogging

Blogging suffered quite a bit while I was on vacation, with multiple consecutive days going by without a blog post (a rare thing for me when I am not on vacation). Here is what the comparative chart looks like for my blogging:

Blogging

Tuesdays are skewed because I publish my Going Paperless posts on Tuesdays, more often than not, and this continued while I was on vacation. But you can see that I had no posts at all on Monday, Wednesday, or Friday (either the first or second Friday) of my vacation.

Reading

I thought I’d get a lot more reading done on vacation because, you know, vacation. Also because I figured I’d be able to listen to audio books on the drive to and from Maine. That was partially true, but the results still surprised me. Here is the comparative breakdown of my daily hours of reading, both for a typical week and my vacation week:

Reading

This is a striking difference to me. Sunday was more because the Sunday we drove home, I listened to audio books for the better part of the drive. But look at the other days! I normally spend nearly 3-1/2 hours reading on Mondays, but didn’t even manage half an hour while on vacation! I guess I was busy vacationing!

Walking

I figured that while on vacation, we’d visit a lot of places and therefore do a lot of walking. We did go to a lot of places, but it turns out, unless you make a deliberate effort, you don’t necessarily do a whole lot of walking. Here is the comparative breakdown of steps (via my FitBit Flex) for a typical week and my vacation week:

Walking

This is almost pathetic! I barely walked half of what I normally walked, and on some days (like Fridays) it was a lot worse!


For some reason, I imagined that vacation means more time to do things like reading and writing and walking. But I suppose my imagination didn’t factor in that we had to entertain two little kids, as well as find fun things to do for ourselves.

On the flip side, I guess you can say that when I go on vacation, I really do go on vacation. The work, even the fun work, slacks off and I tend to engage in the things that we are interested in doing, sacrificing the reading or walking or writing in their place.

The Personal Analytics of My Writing and Reading

It has been a little over 2 months since I put in place automated processes for capturing data about my daily writing, blogging and reading. And given that I have reading, written, and blogged nearly every day for the last two months, it seems like a good time to share some of the numbers with you. First, a quick summary for folks who might not have been following along with this. In addition to having automated scripts to collect my daily activity, like data from my FitBit device, I decided I wanted to collect some data about my other daily activity, particularly my writing and reading. I was able to automate most of this so that I don’t have to think about it.

Collecting the data

I do my fiction writing in Google Docs. I have written a set of Google App scripts that do the following:

  1. Capture each day’s writing in Evernote. This includes highlighting any changes and deletions I’ve made so that I have a complete record of what I did on any given day.
  2. Count how many new words I wrote each day and record them in a Google Spreadsheet.
  3. Summarize my daily writing in an almanac entry that gets sent to Evernote.

Here is what my almanac note for yesterday’s writing looks like:

Daily Almanac for April 30

In addition to the scripts mentioned above, I have another script that grabs how many words I wrote on my blog for a given day. It does this by parsing the data from my RSS feed.

The only part of my process that is not automated is the daily update of my reading data. Since the vast majority of my reading these last few months has been via Audible, I use the stats produced from the Audible app on my iPhone. Each morning, I update a Google Spreadsheet with the previous days stats. While this is a manual process (for now) it takes less than a minute each morning.

Audible Stats
My daily Audible stats

All of this data resides in Google Spreadsheets, and with the exception of the Audible data, I don’t have to do anything to collect it. I write each day, I blog each day, and the data is collected automatically. That is important because I don’t want to have to spend my time gathering it manually.

Examining the data

First, some basic information about my writing over the last two months:

Month

This table shows how much I’ve written and read over the course of the last two months. As of yesterday, I’ve written fiction for 63 consecutive days. The same is not true for blogging. Still, I’ve written nearly 60,000 words of fiction and 43,000 words of blogging for a grand total of over 100,000 words in 2 months.

When it comes to my fiction writing, I just try to write every day. More and more, I am for at least 500 words. There are days that I don’t hit that mark and others that I far exceed it. In the last two months, it has averaged out to a little over 900 words per day of fiction writing and 700 words per day of blogging.

Many professional full-time writers aim for 2,000 words per day. That’s roughly 10 pages. Between my fiction writing and blogging I am maintaining 1,600 words per day. The thing is, I am not a full time writer. I have a full time day job, and two little kids on top of that. So I think 1,600 words per day is pretty darn good. Of course, only 900 of that is fiction, but if I converted the time I spent blogging to writing fiction (something that I have no immediate plans to do), I could come close to that 2,000 words per day while still doing everything else I do.

On top of all of that, I still manage to get in about 100 hours per month of reading. This is only possible because I started using Audible back in late February, which allows me to read book while I do other things throughout my day. I can read on my morning walks. I can read when I pick the Little Man up from school. I can read when I am doing chores around the house. I can read while I am doing yard work or grocery shopping.  Turns out, I manage to read a little over 3 hours each and every day.

Day-to-day

Plotting all of this data over time allows me to see what a typical day looks like for me when it comes to my writing, blogging, and reading. Below I’ve stacked plots of my fiction writing, blogging and reading over the last two months. Look down across all of them, you can see some interesting things. For instance, surprisingly, it looks like on days when I write a lot of fiction, I also do a fair amount of blogging. I wouldn’t have thought that was the case:

FictionWriting
Blogging
Reading

Not only that, but my daily reading also follows the pattern. Peak writing days also appear to be peak reading days. Maybe I get into some kind of zone. Maybe the writing feeds the reading or vice versa. I did a few scatter plots to take a look at this more closely.

Continue reading The Personal Analytics of My Writing and Reading

One Full Year of FitBit Pedometer Data

I am currently away on an Internet Vacation. I’ll be back online on March 31. I have written one new post for each day of my Vacation so that folks don’t miss me too much while I am gone. But keep in mind, these posts have been scheduled ahead of time. Feel free to comment, as always, but note that since I am not checking email, I will likely not be replying to comments until I am back from my Vacation on March 31. With that said, enjoy!


I got my FitBit Ultra device on March 8, 2012, a little over a year ago now. I bought the device after reading Stephen Wolfram’s post on personal analytics. I like data and I though this would be interesting data to look at. Of course, on the day I got my device, I had virtually no data. Now it has been a year and I have a wealth of data. It’s really remarkable how much you can learn from data like this. And what’s key about this type of data is there is no effort to collect it, other than remembering to clip on your FitBit device each morning.

I thought, therefore, that I’d break this into two posts. In this post, I’ll discuss my year’s worth of pedometer data. In tomorrow’s post, I’ll describe how I collected and processed the data, as I imagine this question will come up.

Let’s start with the basic information and get that out of the way: I am working off of 363 days worth of data. (I am actually doing the analysis on March 7, and I didn’t start using the device until March 9, 2012, so strictly speaking this is not quite a year’s worth of data.) There are 18 days for which I have no data. These are day that I forgot to clip on my FitBit device. That’s actually not too bad. It amounts to just under 5% of the total days.

From March 9, 2012 through March 6, 2013 I walked 2,734,142 steps. Let’s call it 2.7 million to keep things simple. Across all those days–even the days for which I have no data, I averaged 7,511 steps. That’s somewhat below my goal. My goal had been to try to walk 10,000 steps every day.

How often did I meet my daily goal? I walked more than 10,000 steps on 77 days, or about once every five days overall.

The 2.7 million steps comes out to 1,320 miles, or an average of just over 3.5 miles each day.

The FitBit device also counts flights of stairs climbed. In the last year, I climbed 5,255 flights of stairs, for an average of more than 14 flights of stairs per day.

What about my best days? Well, for steps (and distance) my best day was December 17, 2012, when I walked 25,056 steps (11.3 miles). It was one of the days we were at Disney World with the kids. The following day was also well above 20,000 steps.

That’s all interesting factual information that I was able to derive from the basic data that FitBit supplies me with. What about some additional data? The FitBit device actually records how many steps you take each minute. The reports available on the website report out the data day-by-day, however. You have to request special permission to have access to the API that gives you your “intraday” (that is, minute-by-minute) data. Of course, you also have to know how to write code and use the FitBit API to get at this data. Fortunately, I was granted access to the data, and I happen to know how to write code. Early on, I wrote a Google App script that pulled my minute-by-minute data into a Google Spreadsheet each night. It is from that data that the following charts and plots are derived. I should also mention that this post by William Sehorn at Mathematica really helped me pull these charts together. (All of this part of the analysis was done in Mathematica.)

First, what does a typical day look like for me, in terms of walking? If you take my minute-by-minute data and aggregate it all, you can build a picture of what a typical day looks like. Mine looks like this:

FitBit Daily Distribution

There are a number of interesting observations that can be drawn from this data:

  1. I am generally up an moving about shortly after 6am. Remember this is aggregated for the entire year, so it includes weekends.
  2. I’ve been pretty good about going on my 10am walk every day. Right about the time I got my FitBit device, I started taking a mile-long walk at 10am every morning as a way to clear my head and get some fresh air. That big spike at 10am represents the aggregate data for my walk.
  3. I also walk to pick up the Little Man from his school after work. That spike is also pretty obvious at the 5pm mark or so.
  4. After work, I’m pretty active with the kids.
  5. Once the kids are in bed, I’m pretty much done, too. There is very little activity after 8pm, you’ll notice.

Another way of looking at the data is to look at what is known as a diurnal plot. This is one where date is plotted on the x-axis and time is plotted on the y-axis. The intersection is where you are actively taking steps. Rather than plot this at 1-minute intervals, the data has been aggregated to 5-minute intervals to make it a little easier to see. Here is my diurnal plot for the entire year:

Continue reading One Full Year of FitBit Pedometer Data

My 500th Book Since 1996

I have officially read 500 books since I started keeping my list on January 1, 1996. The lucky #500 turned out to be Jack McDevitt and Mike Resnick’s The Cassandra Project1. In the 17 years that it took me to hit 500 books 6,139 days have elapsed, which means that I finished a book, on average, once every 12 days or so. This is a rough average. I don’t put books on my list that I don’t finish and I’m sure there were many of those. Some books are longer than others. I used to try to keep track of the total words read, but I gave that up years ago. I do have page counts, which provide a rough relative estimate of volume–better anyway that just the number of books.

To celebrate my accomplishment. Here is a small statistical breakdown of the 500 books I read over the last 17 years.

First, here are the yearly totals for the 500 books that I’ve read:

Books Read.PNG

For actual count, my best year was 1999 where I read a total of 42 books. Then, beginning in about 2004, there is a steady drop off in the number of books, bottoming out in 2007. It was during this period that I was really focusing on writing and trying to sell stories. In 2007, I sold my first story and began trying to read a lot more short fiction, which ate up some of the time I had to read book. The spike in 2011 was thanks to my Vacation in the Golden Age, where I counted each issue of Astounding that I read as a single book, because I read them cover-to-cover and they approximated between 80,000 – 100,000 words each.

Of course, the number of books tell only part of the story since books can vary dramatically in length. If we use page count as a very rough measure of length2, here is what the last 17 years look like:

Pages Read.PNG

The plot is somewhat different from raw book count. For one thing, while I read 42 books in 1999, the I read the most pages (just about 16,000) in 2001. I was reading fewer books, but they were somewhat longer. At present, I’m hovering around 10,000 pages/year, two-thirds of my peak. And again, this is because I am reading a lot more short fiction in the magazines which doesn’t get captured on this particular list.

One of the things I set out to do when I started keeping the list was to try, overall, to balance my reading between fiction and nonfiction. I started out doing a pretty good job, averaging 51% fiction over the first 7 years. But as I focused more and more on my own fiction writing, the trend started shifting clearly in favor of fiction and that 51% has been more like 66% in the last 10 years. Overall, here is the percent of fiction I’ve read (in pages) over 17 years. I’ve added a trend line so you can see where things are headed:

Percent Fiction.PNG

Continue reading My 500th Book Since 1996

  1. You can read my review of the book next month over at InterGalactic Medicine Show.
  2. Rough, because font size can also vary from book to book and so a 300 page book with a large font can be as long as a 150 page book with a much smaller font.