Tag: quantified self

Manifesto 43: Improving My Quantified Self

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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.

January by the numbers: A winter slowdown?

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January seemed like a pretty slow month when it come to writing, reading, and walking. In fact, it may have been my slowest month on record when it comes to my FitBit data. I walked a total of about 198,000 steps in the month of January. This may sound like a lot. It comes to roughly 90 miles worth of walking. But it is dramatically lower than nearly any other month on record since I started using a FitBit–which is nearly 3 years now.

January 2015 FitBit

I am to walk 15,000 steps per day. But as you can see, there was only one day in the entire month that I hit my goal. For the other 30 days, I didn’t even come close. How much of a difference was this from a typical month? Well, December was pretty typical and here is what December looked like:

December 2014 FitBit

A big part of the drop in steps was due to how busy I was, and a little of it was the result of uncooperative weather. I am perfectly willing to walk in the rain, or the cold, when it is hot, or when it is snowing. But when you combine two tough conditions, it gets too hard for me. We had a lot of cold and wind. And some snow and wind. And some bitter cold and snow. And because of that, I didn’t get out as much as I would have liked. Last January I walked 340,000 steps, and the weather was probably more cooperative.

Writing in January

I was extremely busy in the day job in January working hard to get ready for a big software rollout. That meant longer than usual hours, and it also meant that I was tired and had less energy by the time I got home from work. All of this contributes to how much I can write. Still, all things considered, I didn’t do too badly. I wrote nearly 18,000 words in January, almost all of them on the novel draft, although there was a little nonfiction here and there.

January 2015 writing

This is down significantly from December where I wrote about 30,000 words. On the other hand, despite how busy things were, my consecutive day writing streak remained in tact all through the month. As of today, my streak stands at 559 consecutive days.

I had two items published in January, an editorial in the March 2015 issue of Analog, and a new story, “Meet and Greet” in the January 2015 issue of InterGalactic Medicine Show. That helped make up for the lower word counts.

Reading

I didn’t finish a single book in the month of January. Usually I average between 50-60 hours of audiobook listening per month. In December, while on vacation, I got more than 70 hours of audiobook listening in. But in January I managed only a meager 18 hours of audiobook listening.

Audiobook Listening

How much audiobook listening I do is highly correlated to how much walking I do, because I typically do both at the same time. Since my walking was down, it makes sense that my audiobook listening was down.

And now, February is here. If the weather cooperates, I am sure to do better in all three categories this month.

Charting Twitter Follower Counts Over Time

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There was a time, two or three years ago, when I paid close attention to Twitter statistics, and in particular, that ever important Number of Followers. It’s a number so in vogue that I’ve seen in mentioned in half a dozen TV shows. Somewhere along the way, I pretty much stopped paying attention. The last time that I can remember really watching the number was nearly a year ago, when I was about to head off on vacation. The only reason I kept an eye on it then was because I was about to it 2,222 followers.

Well, it’s a year later, and for some reason, the number caught my eye today, probably because it is creeping close to 3,000 followers. The thing is, it has been a climb, but a very slow one. When I looked at my follower count today, it stood at 2,975. That’s an increase of 732 followers over the course of an entire year! Wil Wheaton I am not.

Back in August, I was playing around with the APIs of many services, including Twitter. I decided to write a little script that would capture changes in my Twitter follower count over time. My script grabs my follower count once per hour–24 times a day–and stores the data in a comma separated file. With more than four full months of data, I thought I’d plot it out today. Here is what it looks like:

Twitter followers over time

The chart begins with me at about 2805 followers and ends with me at 2975 followers. That is a change of 170 followers over 4 months. Or about 1.4 new followers per day on average.

In August there is a big jump–due in large part to my articles at The Daily Beast and 99U. But then things pretty much smooth out and go sideways. I wanted to see if I could predict if I would hit 3,000 followers (I have 25 more to go) before the end of the year. But generating a trend for this chart doesn’t work well because the data is skewed in August.

Still, I can predict a range. The difference in follower counts over the last year is 732. That is about 2 new followers per day. Over the last 4 months, that number is 1.4 new followers per day.

Starting with the low number, and considering that as of today there are 27 days left in the year, then 1.4 * 27 = 37.8 new followers by the end of the year–which would put me over the 3,000 follower mark, with 3013 followers.

If we take the larger number, we get 2 * 27 = 54 new followers by the end of the year. This also puts me over the 3,000 follower mark, with 3,029 followers.

Of course, the trend is volatile. The number can go down, or it might go up significantly. In any case, now I have reasonable confidence that I will pass the 3,000 follower mark by the end of the year. And with that I can stop paying attention once again–until January 1, of course, when I will just have to check to see how my prediction panned out.

5 Tips for FitBit Newcomers

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With the holidays approaching quickly, people are beginning to think about New Year’s resolutions. Getting into better shape is always one of the more popular resolutions. And with the explosion of wearable tech devices–like a FitBit–on the market, I imagine there will be a lot of people eager to improve their fitness with the help of their new device. With that in mind, here are a few tips I’d offer for getting started with your FitBit (or similar) device in the new year. These tips come from my own experience. I’ve used a FitBit Flex almost constantly for the last 2-1/2 years, tracking more than 10 million steps.

1. Spend the first week or two establishing a baseline

A FitBit device doesn’t automatically improve your health or fitness simply by wearing it. What it does do is provide an effortless way of collecting data about your physical activity and sleep behaviors. For me, one of the most difficult challenges in trying to improve myself has always been measuring that improvement. And to measure improvement, you need to set a baseline.

When I first got my FitBit, I spent about 2 weeks, just going about my normal behavior, and trying to forget that I had the new device. This allows you to establish a baseline and from that, you can set realistic goals.

From your baseline, you can see how much walking you do in a day–and even when you do that walking. If you find that your baseline is 4,800 steps per day you might try upping it to something reasonable like, 6,500 or 7,000 steps per day.  The baseline will also tell you when you are not being active during the day, and might help you to plan times when you can be more active. Below is an example of a day’s activity for me.

A typical day's activity

 

Your baseline will also include an estimate of how many calories you burn throughout the day, and this can help in determining how many calories you should consume.

It is worth spending time that first week or two wearing your device and not worrying about it because the baseline will prove to be a valuable calibration tool in the long run.

2. Identify common milestones

Once I established my baseline and set some goals, I found that it was useful to have a few pieces of information handy to help me meet my goals each day. For instance, since everyone’s stride is different, I thought it would be useful to know how many step it took me to go one mile. I used my FitBit device to help figure this out, and it turned out that I typically take about 2,200 steps in a mile. How is this helpful?

Well, my current goal is 7.5 miles per day. If I happen to be at, say 13,000 steps, and know that I need about 2,000 more to make my goal, I know that all I have to do is walk one mile.

It also helps to know how far a mile is. For instance, I know that one walk around the city block on which my office building resides is just about 1 mile.

If you don’t think in terms of steps or distance, but instead, think of calories, you can identify similar milestones. For instance, you might learn that you burn 600 calories walking one mile a normal pace. I find these milestones useful in helping me make ad hoc adjustments to my activity throughout the day.

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Novel Status and Writing Stats for November 2014

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For many writers, November is NaNoWriMo, which means a month of highly productive writing, often resulting in 50,000 words or more in 30 days, a rather remarkable feat. I wanted to take a moment to congratulate everyone who successfully completed the NaNoWriMo challenge, and also to everyone who attempted it, but didn’t complete it. Writing words is (for me, at least) the hardest part.

I did not participate in NaNoWriMo this year. As I have explained, I think NaNoWriMo is extremely useful in teaching people how to write every day. I have learned that trick, however. Today is my 498th consecutive day of writing. That said, I used NaNoWriMo to jump start the second draft of my novel, in the hopes of breaking through the struggles I’ve had with it. Overall, I think I was successful.

I wrote just under 25,000 words in November. That’s half of what is expected for NaNoWriMo, but it is up 10,000 words from October. It brings my 2014 word count to 280,000 words. Here is what November looked like for me:

Writing Stats, November 2014

I spent a total of 21 hours 45 minutes writing in November, and averaged about 45 minutes per day. I mention this stat to once again emphasize the fact that large blocks of time are not required to write every day. Here is what my day-to-day time spent writing looked like in November:

Time spent writing, November 2014

Finally, here is what my year-to-date looks like (month-to-month) compared to last year. (This year is blue and 2013 is red. Note that my data for 2013 begins in March.)

Words Per Month

Novel status

With 25,000 words under my belt in November, and with my goal to get the second draft of my novel jump-started, one might think that I’ve written about a quarter of the novel in the month. The truth is, I have written far less.

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FitBit Milestone: Ten Million Steps!

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This morning at around 9:15 am Eastern Standard Time, I surpassed 10 million steps on my FitBit device. Here is what it looked like after I passed this milestone.

10 million steps

For those wondering, 10 million steps comes out to about 4,600 miles.

4600 miles

 

According to Google Maps, that about the distance from Washington, D.C. to the crater of Vesuvius in Naples, Italy.

Vesuvius

The 10 million steps covers 2 FitBit devices spread over more than 2-1/2 years of tracking. I used a FitBit Ultra from early March 2012 until I lost it a year later in March 2013. I went a month and a half without a FitBit device and then I got my FitBit Flex in May 2013, and have been using that ever since. You can see that gap when I was missing my device in the chart below. The chart shows my steps for every day in the 2-1/2 years it took to accumulate 10 million steps. The red line is a 7-day moving average.

Ten Million Steps Over Time
Click to enlarge

I clearly began to pick up the pace when I got my FitBit Flex, going from an average of 10,000 steps per day to 15,000 steps per day. I’ve done fairly well at maintaining that pace, which amounts to about 5.5 million steps per year.

On my single best day, back in May 2014, I walked over 31,000 steps in a single day. It was exhausting.

In any case, it was pretty exciting to see the numbers flip from 7 figures to 8 figures this morning. Of course, at this pace, it will take close to 20 years before the 8 figures flip up to 9 figures and I reach 100 million steps. Stay-tuned…

 

How I Used RescueTime to Baseline My Activity in 2014 and Set Goals for 2015

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Since early in the year, I have been using Rescue Time on all of my computers to track how much time I spend in various applications, websites, and documents. Rescue Time is great because you install it, and it runs in the background, without ever needing me to take any action. Like a FitBit device, it just collects data as I go about my day. Rescue Time has a nice reporting interface, but it also has a very useful API that allows me to pull specific data and look at it interesting ways.

Tracking the time I spend writing

For instance, I’ve always wanted to get a good measurement of the time I spend writing each day. That said, I didn’t want to have to remember to “clock-in” or “clock-out.” It seemed to me that Rescue Time could help with this because it is constantly tracking my activity, and Rescue Time should therefore be able to tell me how much time I spend writing. After some exploration of the API, I found out how to pull the information I needed from Rescue Time, and now, I have scripts that can automatically produce a chart of the time I spend writing each day. Here’s an example of the last 60 days of my writing:

Time Spend Writing

The top 10 tools I’ve used in 2014

As part of my effort to simplify the tools and technology I use, and to automate as much as I can, a baseline of what exactly I use would be a helpful starting point. Fortunately, RescueTime captures all of this data and has some canned reports that show just where I’ve spent my time in front of they keyboard. I started using RescueTime in January, so this data covers a period of January to the present, nearly a full year. Here, then, are the top 10 tools I’ve used on all computers during that time.

RescueTime - All Activities
Click to enlarge

Twitter is number one on the list, and while that surprised me at first, I quickly realized that I am constantly jumping in and out of Twitter, in an effort to keep up with those friends and colleagues that I follow. (I rarely post from Twitter. I use Buffer for that.) Still, 221 hours for the better of the year is quite a bit of time spent in Twitter. Red items are those that Rescue Time considers “unproductive.” Twitter can certainly be a distraction, but I wouldn’t consider all of it unproductive.

Next on the list at 219 hours, much to my dismay, is Microsoft Outlook. This is what I use at the day job, and it is among the worst email programs I’ve encountered. The thing is, I’ve also been using it since it first existed, and there’s no way of getting away from it. What it tells me is that a great deal of my job–too much, I think–is spent dealing with email messages, and calendar appointments.

Google Docs is next on the list at 205 hours. The vast majority of this time–probably 90% or more–is spent writing. Ideally, I’d like to see this move up to number one over the next year.

Gmail follows at 169 hours. It’s still a lot of time to be spending reading and writing email messages, but that number is almost certainly down from what it would have been the previous year, thanks to a great deal of automation I’m able to do with Gmail using tools like Boomerang, for instance.

From there, things begin to drop off pretty rapidly. Facebook shows up in 7th place, but even that seems like too much to me.

Using the RescueTime baseline to find more time to write

With actual numbers in hand based on my behavior, I can begin to change my behavior and measure that change over time. First and foremost on the list is a tradeoff: more writing time for less social media time.

My Twitter and Facebook time totaled 310 hours in 2014 to-date. My writing time totaled under 200 hours. I could easily get more time for writing by cutting back on social media. Cutting back doesn’t necessarily mean no participating. Tools like Buffer have allowed me to schedule tweets and Facebook posts head of time. Whenever I post to my blog, it gets automatically posted to various social media outlets. What I think I need to do is make better use of the time I spend reading my social media feeds.

Right now, I read stuff throughout the day in a very fragmented fashion. I only follow people on Twitter that I am interested in keeping up with. I know that conventional wisdom is that if you want more followers, you follow everyone. But I honestly don’t know how people with 17,000 followers and who follow 19,000 people can keep up with it all. Probably they don’t even try to. Yes, there are lists that I could build, but that takes time to create and manage, and I’m looking to spend less time here, not more.

It seems to me that a fair number would be to spend half of the time in social media that I spend on writing. This year, the hours for both categories gives me a total of about 500 hours. So if I have 500 hours to spend between social media and writing, and I want to spend double the time writing than on social media, then let’s assume w represents the time I want to spend writing:

0.5w + w = 500

This simplifies to:

1.5w = 500

And solving for w, we find that,

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More Lessons from My Writing Streak: Accept the Slumps, But Keep Writing

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I mentioned earlier in the week that I was not formally participating in NaNoWriMo this year, but that I was using the spirit of the event to jump start the second draft of my novel, and try to break out of a writing slump that I’d been in for the last month or so. While it has only been five days, I think I am finally emerging from that slump.

Emerging from a slump

The chart above shows the last 30 days of my writing. The last five are in the red box. It’s clearly the most productive 5 days I have had all month. Moreover, the 1,200 words I wrote yesterday were more than I’d written in day since September 20. Most pleasing to me of all is that my 7-day moving average is on the rise again, after a long and steady decline.

While it is nice to see that I am recovering from this writing slump, I was particularly stressed out by it. One thing I’ve learned over the course of my (now) 472 consecutive days of writing is to accept the slumps… but to keep writing every day.

What is a writing slump?

In baseball, hitters get into slumps when they remain hitless at the plate for many consecutive at-bats. For me, a writing slump is similar, but different. I’m still writing every day, just not producing as much as I’d like, or to the quality that I’d like to be producing. Since July 22, 2013, I’ve averaged 900 words/day. Ideally, I’d like to write at least 500 words every day. I don’t sweat the days where I don’t make 500 words, but when multiple days of less than 500 words pile up, I begin to start thinking in terms of a slump.

For the purposes of a clear personal definition, let me define a writing slump as any 30 day period where my moving average falls below 500 words/day for that period. Let’s define being “hot” as any 30 day period where my moving average is above 1,000 words/day. Based on that definition, here is a chart that identifies my slumps and hot spots:

 

Writing Slump and Hot Spots

You can see from this data, which contains 30-day moving averages, that I’ve only recently hit what I define as a slump. Otherwise, I’ve mostly been within my “average” range (a 30-day moving average of 500-1,000 words). I’ve also had two significant periods where I’ve been “hot,” with a 30-day moving average exceeding 1,000 words day.

This may seem overly analytical, but the numbers tell me not to stress about slumps. They happen, but they don’t last. The same is true for those hot streaks. The important thing is to keep writing every day, to push through the streaks, to keep hacking away when the words seem hard. Eventually, in my experience, the work pays off, and I make a breakthrough.

What causes these slumps?

I think there are two things that caused my recent slump (where my 30-day moving average fell below 500 words/day).

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A Look at My Reading in 2014 (So Far, Not Much Science Fiction)

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It occurred to me recently that I haven’t been reading much science fiction. Strictly speaking, I haven’t been writing much of it either. My more recent stories have been more along the lines of mainstream alternate histories, with a slightly (barely detectable) element of science fiction to them. This isn’t anything intentional. I just go where the stories take me, and lately, they haven’t been taking me into the galaxy. But I thought it was strange that I wasn’t reading much science fiction either, so I decided to look at what I’ve read so far in 2014.

To-date, I’ve read 30 books in 2014, and it has been a fairly eclectic year. Back when I was a kids and would check books out of the library, there was a requirement to check out nonfiction as well as fiction. Over time, that developed into a habit, and for the early years of my reading list, I kept a pretty good balance of fiction-to-nonfiction. Then, I drifted. Some years, I read a lot of fiction, other years, a lot of nonfiction. This year, the balance seems to have returned.

Type of books

16 out of 30 books to-date have been nonfiction. That comes out to about 53%. Drilling into the categories of books that I’ve read this year, things get more interesting.

Category of Books

Almost a third of all of the books I’ve read are biographies (which include memoirs as well). 9 biographies to-date. But that is more than half of the nonfiction reading that I’ve done this year. The next biggest category is “mainstream” fiction; that is, books that don’t fall into the usual genre categories. A Prayer for Owen Meany by John Irving is one example. 13% of the books I’ve read this year (4) have been on baseball. Science fiction makes up only 10% (3) of the books that I have read in 2014.

The vast majority of my reading these days is via audio book. Indeed, a full 90% (27) of the books that I’ve read so far this year have been audio books.

Format of book

2 of the books have been e-books. And I read 1 paper book this year.

Finally, there are the re-reads. Occasionally, I re-read books that I particularly enjoy. This year was no exception.

Format of book

About 25% of the books that I’ve read have been re-reads. I can accept that ratio. Some years its lower and some years its higher. I sometimes think that with the limited time I have for reading, I should always read something I’ve never read before. But then I think, ah, what the heck, I read for fun, to learn, to relax, why not re-read something I really enjoy every now and then?

I don’t rate the books that I read. My list of books that I’ve read since 1996 has some bold titles, which indicates books that I would recommend to others. That’s about as close as I get to rating them. So far this year, I’ve marked 18 of the 30 books that I’ve read (60%) as “recommended.” That seemed pretty high to me, so I went to look at past years. Here is how they line up:

Recommended Books by Year

Why such an increase in the last 2 years? It goes coincide with when I started listening to audio books, so perhaps the voice actor’s performance changes my perception of the book. But I like to think I’ve just gotten better at selecting books I think I will enjoy reading.

With 12 weeks remaining in 2014, I’d estimate completing another 10 books before the year is out. It’s possible the number will be higher. Several of the books I’ve read this year have been very long, and that tends to skew things. Still, I don’t see an uptick in the fiction ration. It may hold the same, but I’m pretty content with nonfiction at the moment. I’ve learned to just go with the flow, and read whatever I feel like reading. It all works itself out in the end.

Practical Statistical Modeling: The Dreaded After-School Carpool Pickup

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The Little Man started kindergarten this week. It meant a new school, and the new school has one of these well-organized systems of picking up your kids at the end of the day. But it can be a bit intimidating the first time. Basically, it works like this:

You arrive at the school and pull around to a side parking area. Four lanes are set up. Each lane holds about 10 cars, and the lanes are filled in order, first lane first, then the second, and so on. When all lanes are filled, the area is closed. Cars that don’t make the first round, line up in the upper parking areas for the second round. The students are released, the go to their cars. When all students are in the cars, the lanes are released in order. This is then repeated for the second round.

It’s very organized and efficient, but there if you want to be in that first round, or that first lane, you have to get there pretty early. As someone who doesn’t really want to sit in the car for half an hour, I decided I’d get there early on the first two days to capture data about when cars arrive, and build a statistical model based on that. Which is exactly what I did. I arrived early, getting into the first half of the first lane, and then noted the arrival times, and lane positions of the other cars in the first round. I did this for two days, and then built my model.

Constructing the model

The  model was fairly simple. I used a negative number to represent the number of minutes before dismissal (a kind of t-minus 10 minutes) that a car arrived. With that number, I gave the number of the car. So at t-21 minutes, car 16 and 17 arrived. Since each lanes holds 10 cars, it’s pretty easy to determine which lane (and which slot in a lane) the car is in. I ran a correlation on my data and got a very strong correlation: 0.951. The r-squared came to 0.905. I then plotted the data in a scatterplot, and annotated it to better illustrate the lanes. Here is what the results look like:

Carpool Model
Click to enlarge

As you can see, the data makes it clear that in order to make the first round, I’d need to arrive no later than 7 minutes before dismissal. If I want to be in the first lane, I need to arrive no later than 24 minutes before dismissal.

Adding practicality

Of course, it would be a little more practical if the model told me when to leave the house. I hadn’t thought to note the time I left the house and arrived at the school each day, but it didn’t matter. I grabbed the data from my Automatic Link device, and was able to determine that it took, on average, 6 minutes to drive from the house to the school. To be safe, I added 1 minute to this number, and then came up with the following table:

Departure Times

So now, I know that if I want to be in that first round of pickups, I need to leave the house no later than 14 minutes before the students are dismissed. That information could end up saving quite a bit of time over the course of the year. I tend to like to get places early, but I have to balance that against other things I need to get done. Knowing that I don’t have to leave the house half an hour early buys me an extra 15 minutes/day. That doesn’t sound like much, but, I can write a page and a half in 15 minutes. So it’s something.

Introducing open.jamierubin.net

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With all of the data I collect about myself, I’ve been wanting to put together a kind of open dashboard that provides a window into the data through interesting visualizations. While my short term plans are nothing like the amazing things happening over at Aprilzero, I have put a very early prototype together of what I am calling open.jamierubin.net.

open.jamierubin.net

Right now, all the site does is make a live query to my Google Doc Writing Tracker spreadsheet, and renders the data in a chart on the site. Clicking on the links above the chart, you can see either the last 30 days of my writing data plotted out, or go back to the beginning of time (over 500 days).

For each visualization I publish, I plan to include a link to the “HOWTO” which will include the code I used and how I pulled the data I needed to make the visualization. That way, if others want to give it a try, there will be at least some documentation.

Eventually, I will come up with a framework for the site, and begin pulling in other data as well. For now, this is a quick-and-dirty prototype of what is possible with just a little bit of code. Take a peek at it and let me know what you think.

The Daily Almanac Has Been Added to My Google Writing Tracker

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One of the most frequent requests I get regarding my Google Writing Tracker is to make my Daily Almanac available as part of those scripts. The wait is over. Today, I pushed out the Daily Almanac the Google Writing Tracker project on GitHub.

For those who don’t know, my Google Writing Tracker is a set of script that automate the process of tracking what I write every day. Since I do all of my writing in Google Docs, these scripts run automatically each night, look at what I wrote, tally up the stats and record them in a spreadsheet. They also email me a copy of all of my writing for that day, including differences from the previous day.

Along with those scripts, I built another script that I call my Daily Almanac. This script culls that spreadsheet that is populated by my Writing Tracker scripts and gives me a summary report each night. The report tells me how much I wrote that day, and breaks it down for me. It also identifies any streaks I may have set (369 consecutive days of writing as of today) and any records I may have set. (The most words I’ve written in a day, etc.) I set up my Daily Almanac to send the nightly email to Evernote so that I have a nice record there of my day-to-day writing activity. Here is what a typical Daily Almanac entry looks like:

Daily Almanac July 23

The Daily Almanac is now available for anyone who wants to use it with the Google Writing Tracker. I have checked it in to the project on GitHub, and I’ve updated the README file with detailed steps for setting it up.

As always, this is a use-at-your-own-risk thing. I just don’t have the time to support these scripts. The best I can do is make them available for others who want to give them a try, and encourage folks to add to improve upon them. Be sure to read the instructions carefully, and if you do find any bugs, feel free to open up an issue in the GitHub project. I may not fix it any time soon, but at least it will get tracked.