Tag: statistics

Blog Stats for Q1 of 2014

I started keeping track of the stats on this blog in earnest back in late 2010. Back then, I was getting about 30-40 page views per day and as the year came to an end, I decided to see if I could get that number up to 100 page views per day by the end of 2011. That turned out to be easier than I thought. By April of 2011, I was seeing 100 page views/day and by the end of 2011, I was seeing 300 page views per day. For all of 2011, according to Google Analytics, I had about 80,000 page views. I was pleased, but came away worried that I wouldn’t be able to top it in 2012.

I needn’t have worried. In April 2012, I began my weekly Going Paperless posts and things kind of took off from there. I ended 2012 with more than 4 times the page views of 2011, about 347,000 of them! This was very close to an unheard of 1,000 page views per day. Of course, I thought, things would level out in 2013.

Well, the growth slowed, but did not stop. Last year, 2013, I had a total of about 840,000 page views, more than double what I had in 2012, and which amounts to about 2,300 page views per day. It seems that I had managed to produce content that a fair number of people liked and returned to consistently. I felt pretty good about this and one side-effect was that I became less obsessed with my blog stats.

But I didn’t forget them entirely. Yesterday, I took a look at the numbers for the first quarter of 2014 to see how they compared to 2013. So far, between January 1 and March 30, I’ve had 357,000 page views. That amounts to roughly 4,000 page views per day almost double 2013. You can see the difference in this chart:

2014 Blog Stats Q1

The blue line are the day-to-day page views for 2014. The orange line represents the page views for the same period last year. The plot is fairly spiky, and those spikes represent the days on which a new Going Paperless post comes out. (The big orange spike in early March of last year was when I was featured on Lifehacker’s “Ask an Expert” series.)

I don’t know if these numbers will hold steady for the rest of the year or not. They seem to be sinking slightly the last few weeks, but that happens sometimes. If the numbers do hold steady, I’ll come in at around 1.4 million page views for 2014. Of course, who knows. The blog audience has grown from year-to-year and it looks like that is continuing, but I imagine there is a point at which it will plateau.

All of the numbers listed above are based on data collected by Google Analytics. It does not count readers who read the blog solely via an RSS feed. I have some Feedburner numbers for this, but I take them with a grain of salt. According to Feeburner, I’ve had an additional 135,000 page views in 2014 from people who read the posts via RSS. If those numbers are right, then that bring the total page view fro Q1 to just under half a million page views.

Page views, of course, count the number of times a page is loaded. Google Analytics also counts visitors, and so far as I can tell, my unique visitors for Q1 of 2014 is also up from the same period last year:

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Blog Stats for 2012

Now that the final numbers are in, I can put together a post on the numbers for this blog in 2012. Those who aren’t interested in such things, feel free to skip over this. As to why I am interested in this, there are several reasons:

  1. The numbers provide some manner of objective measurement: visits, visitors, etc. Some valuable information can be extracted that might help me improve the blog in the coming year.
  2. The numbers can be compared to previous years to see changes, which is interesting to me.
  3. In general, I’m just kind of fascinated by these types of metrics.

I will try to indicate the source for all of my numbers as I go along. Keep in mind that different sources have slightly different numbers, but the differences are within a reasonable margin of error. Besides, I’m not using these numbers in a manufacturing or similar process so they don’t have to be precise. Mostly, I’m interested in the overall trends.

Sources of the data

I am making use of three primary sources of data, listed in order of priority:

  1. WordPress statistics that come via the WordPress JetPack plug-in. The same numbers are produced via WordPress’s site when I log in there with my account. This is my primary source of aggregate numbers. These numbers do not include subscribers to the RSS feed.
  2. Feedburner statistics that come from Google’s Feedburner. These include only those numbers that come from the RSS feed and do not include people who visit the blog directly. In other words, in terms of statistics, #1 and #2 can be considered mutually exclusive. (The same person may end up reading a post via RSS and then coming directly to the blog. In that case, the visit counts in both places.)
  3. Google Analytics. This is my primary source for demographic data on the blog, i.e., where people came from, what browser they used, how long they viewed the site, etc.

One other definitional clarification: I distinguish between page views and unique visitors. Mostly, when I am referring to numbers, I am referring to the page views, the total number of times pages on the blog were viewed. This is different from the number of unique visitors. The latter is often lower because the same person often views more than one page. I will try to be clear about when I am referring to views versus visitors.

The basics for 2012

Here is a chart that plots my monthly totals for 2012:



This is a stacked-chart. The purple area represents pages views on the blog. The blue area is page views via the RSS feed. The stacked total is the total number of views the for the month.

Some interesting observations:

  • Fewest page views took place in February with a total of 23,095.
  • Most views (best month) was December with a total of 107,691 (more on this later).
  • For the year, I averaged just under 59,000 page views per month

In April, I began my Going Paperless series of weekly posts and that is where things really began to take off. For instance, for the first three months of the year, I averaged about 29,000 views per month. From April through December, however, after I began my Going Paperless series, I averaged nearly 69,000 views per month.

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300,000 Page Views This Year!

Some time shortly after midnight, this site passed it’s 300,000th page view this year.  This does not include people who view the site via RSS or through LiveJournal, Tumblr, LinkedIn or other places that the site is crossposted. I’m talking about visits directly to the blog itself. That is just incredible to me, and I will illustrate why. Here are my month-to-month page views since inception back in February 2010, when I moved the site from LiveJournal into WordPress:

Blog Stats.PNG
Click to enlarge

When I started, I was getting less than 300 visits per month. You can see over the course of nearly 3 years that I’ve had a steady increase, followed by the occasional jump to the point where last month, I had over 48,000 views. You can see the progression somewhat more dramatically in the year-end totals. On the year, I’m averaging 1,011 page views per day. Last month, I averaged 1,600 per day.

Taking a look at Google Analytics, I see that since January, those 300,000 page views have been made by over 92,000 unique visitors. That’s a good-sized town! It’s just incredible to me.

People occasionally ask me how to get started blogging and how to get an audience. I try to tell them that you need to have something worth saying, of course, but even with that it takes patience and time. People sometimes don’t want to believe that, but it worked for me.

Perhaps most rewarding is the fact that people keep coming back. They leave me comments telling me how much they like my posts. It’s a love-fest, and like Isaac Asimov used to say about his own readers, I’ve got the best readers in the world! Thank you, thank you, thank you.

And keep coming back!


Some numbers from the first half of 2012

It’s hard to believe that the first half of 2012 is gone, bye-bye, seeyalater. I blinked and the year dashed by. Perception, I know, since time flows one second at a time, but still…

So a few interesting (to me) stats for the first half of the year, ending June 30:

Blogging and social networking:

  • I started 2012 with 442 Twitter followers. As of June 30, I was at an even 700.
  • I ended the first half of 2012 with just a hair over 150,000 direct visits to this site. That’s 150% more than all of 2011 combined.
  • Add in RSS hits and the number jumps to just under a quarter of a million visits.
  • I started 2012 with 144 active followers via Feedburner. As of June 30, I was at 417.


  • I read 134 pieces of short fiction
  • I read 16 books


  • I wrote 5 stories (complete drafts)
  • I made 4 submissions1
  • Committed more than 1.75 million keystrokes
Physical activity2
  • Walked 1,064,809 steps, or the equivalent of roughly 550 miles
  • Climbed the equivalent of 2,291 flights of stairs

Anyone else have interesting statistics for the first half of 2012?

  1. I sold one of those submissions, but word of that sale didn’t come until July 3, so it doesn’t count in the first half of the year.
  2. Based on 3 months worth of data.

Recent blog stats: an interesting trend

Every once in a while, visits to this here lil’ blog take a surprising jump. Usually it is a blip on the radar and the spike returns to roughly normal fairly rapidly. But over the course of the last two months, something has changed and the number of visitors has increased quite a bit. Quite a bit. Here, see for yourself. Here’s a chart that I put together plotting average daily visits since I converted the blog to WordPress back in February 2010 (and imported LiveJournal posts going back to 2005, for which I have no visit data).

chart_1 (1).png

Average daily visitors were always steadily increasing, which is generally how I like it. Slow, but steady. Late last year things started to pick up, but as you can see, there was another dip. Then, beginning last month, things really picked up and have stayed there. Indeed, May is outpacing April by half again, despite April being an outstanding month. At the moment, I’m seeing, on average, over 2,200 visitors each and every day!

That made the total visits to the blog explode, of course:

chart_2 (1).png

This chart shows total visits by month. Note that in both these charts, I’m counting only direct visitors to the blog. I am not including people who read the blog via RSS feeds. Those numbers tend to add about 1,000 visits per day. In the month of April alone, I had half as many visits as all of 2011. It is incredible!

Many of the visitors are coming by to read my Going Paperless posts that I have been doing in my capacity as Evernote’s Paperless Lifestyle Ambassador, and , of course, that pleases me enormously. The feedback I’ve gotten on those posts has been overwhelmingly positive. But it seems like folks who come by to read those posts, are looking at other things as well, and that also pleases me. I can’t imagine this ever-increasing pace can be sustained, but it’s certainly fun while it lasts.

So I just wanted to say to all of the new visitors, and the old visitors who keep coming back: thank you for stopping by. It’s a pleasure to have you here and I am humbled by the activity I’ve seen in my little corner of the web.

Personal analytics: 7 years of personal email activity

Last month, I posted some analytics on my behavior over the course of 12 years of work emails that I’ve sent. At the time, I had data for just my work email and just for sent messages. I wanted to look at my personal email activity as well, but there were two things preventing me:

  1. I didn’t have the time.
  2. I wanted to do it all in Mathematica, which I am slowly teaching myself from scratch.

Well, I found a little bit of time, and I only required a little time because Paul-Jean Letourneau, a lead developer at WolframAlpha, wrote a post on how to use Mathematica to do the kind of email analytics that Stephen Wolfram posted about last month. This was great because the post contained all of the code needed to do this kind of analysis and all I need is some good code examples to quickly learn a new system. The code provided worked almost without change on my own MacBook instance of Mathematica. I had to make a few minor changes to get the mailboxes I wanted. And I had to add the following line to increase the heap space for Java:

ReinstallJava[CommandLine -> "java", JVMArguments -> "-Xmx3024m"]

Without that line, the code executed fine for sent mail, but ultimately resulted in an out-of-memory error for incoming mail.

The resulting data is a pretty good look at my personal email use over the last 7 years. We’ll start with email that I’ve sent. This goes back only to 2009 because that is when I switched from Panix to Gmail. The code looked at my Sent Mail folder in Gmail and looked for email sent from my Gmail address. I had years of imported mail from Panix, but the sent messages are from a different email address and I decided not to move things around or change the code to include these. It’s still 3 years of sent mail data which is good enough for some analysis. Here is the diurnal plot of my sent email, a total of 4,382 messages:

dirunal sent mail.png

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Personal analytics: a year of online activity

Here is another look at some personal analytics data. In this chart, you can see my online activity plotted for 2011:

Online Activity 2011.PNG
Click to enlarge

The activity is color-coded and broken into six categories: Facebook, Twitter, WordPress, Instagram, Mobile Photos and Check-ins. Note that this is not a complete set of online activity, but it is everything that gets routed through Facebook. For instance, when I take a picture with Instagram, it gets posted to Facebook. When I make a blog post, it gets posted to Facebook. This doesn’t capture things like Twitter replies, direct messaging, commenting on posts, etc.  Still, I think it makes for an interesting picture. You can see that I’m busiest online in the mornings. Often times that’s when I’m replying to stuff that has come in overnight. And a lot of scheduled posts through WordPress come out in the mornings.

Right around mid-August there is an almost vertical line running through the day. It starts red (tweets) and then turns blue (photos) just before 9am. That is when the Little Miss was born. Beginning in September, you’ll see the later night activity more or less curtailed. This is when I started going to bed earlier and earlier to help the Little Man get used to sleeping back in his own room.

How did I get this data? I used Facebook’s Download functionality to download a zipped copy of all of my Facebook data (including Wall posts). From the downloaded archive, there is an HTML file that represents all of your Wall activity. I wrote a Perl script to parse out dates from the entries and also classify them, mostly based on the icon associated with the entry. Anything that wasn’t one of the 5 main online activities got automatically classified as “Facebook.”


10 days of personal analytics data

Last week I wrote about how I was impressed by Stephen Wolfram’s article on personal analytics. I provided some stats on my work-related email analytics. But Wolfram’s article impressed me so much that I have started collecting additional personal analytics data, including steps, using a FitBit activity monitor, and keystrokes using some keylogging software. Both of these are physical activities (typing is physical) that I do throughout the day, so yesterday, having accumulated ten days worth of this data, I created a timeplot of both to begin to illustrate my physical patterns throughout the day.

steps and strokes.PNG

Blue dots represent steps (as measured by my FitBit device). Red dots represent my keystrokes at work only. With only 10 days of data, it is difficult to discern real patterns–other than the fact that I don’t tend to walk and type at the same time. But there are some obvious things that show up. For instance, every workday at 10am, I take a 15 minute walk around the block to give myself a break from the keyboard and clear my head. I did’t take my walk on Monday, March 12, but I did the rest of the week and you can see the small string of blue dots sandwiched between the red dots at 10am. Then, too, since the time has changed, we’ve resumed our evening walks around 6pm. You can also see at 5pm, my walk over to the Little Man’s school to pick him up.

I am also typing at the keyboard for much of the workday. The gaps are either things like lunch breaks or meetings where I can’t really be typing. Once I’ve accumulated more data (several months at least) the patterns should become much more discernible.

Why collect this data? Three reasons:

  1. It’s easy to do. It takes no additional effort on my part. My FitBit device collects and uploads my activity data without needing me to do anything. The same is true for my keylogger. So I’m not spending any additional effort, with the exception of preparing some data collection mechanisms, which was a one-time, early-on activity.
  2. I’m a data-person. Much of my job deals with helping others answer questions through the use of data and data mashups and I’ve learned that you can learn incredibly useful things from data, if you pay attention to it.
  3. I find it interesting, looking at the patterns and seeing what’s there.

I noted that many of the comments on Stephen Wolfram’s post were from people asking how he collected all of his data. In his post, he mentioned that all of this data is collected through automated systems, and that is key. If you had to manually enter this information, it would be too cumbersome. But being able to collect it without thinking about it makes it much easier and more useful. Here is how I collect the data shown in the chart above.

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Personal analytics

I was impressed by a recent post by Stephen Wolfram discussing his “personal analytics”; that is, data that is automatically collected about ones self and then analyzed for interesting trends. Wolfram has been capturing such data for two decades and some of what he had to report was fascinating, especially if you like numbers. One of the plots Wolfram presented was a timeplot of the email he’s sent and received. That was data that I had readily available, at least for my day job, so I constructed a similar plot for all of the work-related email I’ve sent since January 1, 2000; that is, for the last dozen years. Keep in mind this is just the mail that I’ve sent, not mail that I’ve received. In that 12 year period, I’ve sent over 47,000 email messages. And in looking at the plot, you can see some pretty cool trends:

Email Analytics.PNG
1 dot for each email. Click to enlarge


The times plotted along the y-axis are in eastern time. So the first interesting thing that this plot shows is my move from Santa Monica to the Washington, D.C. metro area back in the summer of 2002. If you look at that time frame, you’ll see that I start sending emails a few hours earlier in the day–because I’ve changed time zones. I also start going into the office later, around 7am, whereas I was getting into the office between 5:30 and 6:00am in Santa Monica. From 2003 to about summer of 2008 I was pretty consistent in the times I started work. Then in the summer of 2008, it starts to get closer to 8am. Since then, it has gradually moved back toward 7am, although you can see scattered throughout the dozen years times in which I was sending email in the middle of the night.

From 2000 to 2002, there is a kind of white band at the 3-4pm range–my lunch hour in Santa Monica; and this band shifts to the noon-1 pm range when I moved to Washington. This plot demonstrates that I’m fairly good about keeping my lunch hour to myself. It’s when I get reading done and it’s a good mental break during the day.

Scattered throughout the plot are a number of “vertical” stripes. Many of these represent vacations, when I was away from the office and not sending email. For instance, there is a band in the summer of 2007, when I was in Europe for nearly a month. There is another one in the morning hours of the summer of 2009–when the Little Man was born and I was taking the mornings off for a while. And another in the summer of 2011, when the Little Miss was born and I took a month off.

I find plots like these fascinating. Stephen Wolfram has taken this kind of analysis much farther than I possibly could. That said, I capture quite a bit of data. Now that it has come to my attention that there is some useful information that can be extracted from these type of personal analytics in the aggregate, I’ll report on more of my own as I have time to look at it and analyze it.

Some remedial math for Capital One bank

Capital One Bank’s latest ad campaign annoys me and it has nothing to do with Jerry Stiller, who I think is a great comic actor and who I loved in Seinfeld.

Capital One’s latest campaign talks about how great their checking account is because it pays 5 times the national average. Five times the national average. That sounds pretty remarkable if you don’t have the slightest grip on even the most remedial math. Furthermore, it is a classic example of how statistics can be deceiving while the underlying statement remains perfectly true.

I suspect that most checking accounts these days pay no interest. A no interest checking account has an interest rate of 0%. Let’s pretend for a moment, therefore, that the national average for checking accounts is 0%. We learn very early in our schooling (about the time we start learning the multiplication tables, what, second, third grade) that zero times any number is zero. With that in mind, Captial One bank pays 5 x 0% which equals exactly 0% interest. Note that the statement, five times the national average is true because 0 times any number is 0.

Of course, there are some banks who pay interest on checking accounts. My bank is one such bank so I checked to see what the interest rate was. It turns out that it is 0.01% on any part of the balance over $2,000. That means I earn no interest on the first $2,000 in the account. On anything beyond that, I earn one one-hundreth of a percent. Put in a way people can better understand, to earn one dollar in interest for the month, I’d need a balance of  $12,000 in my checking account. For one measly dollar in interest.

Okay, so Capital One pays five times the national average. Since many banks pay no interest and some pay 0.01% interest, the national average will be somewhere between 0 and 0.01%. Let’s split the difference and call the national average 0.005%. That’s one five-thousandth of a percent. I multiply this number by 5 and get 0.025%. Exactly one quarter of a percent interest on my checking account. If  I am very luck, I might earn a dollar or two each month. Of course, there are account fees to consider, which would easily wipe out these earnings, so what’s the point?

Mostly, though, my gripe is with how much of a big deal the ad campaign makes FIVE TIME THE NATIONAL AVERAGE seem. When you are talking about such incredibly low interest rates to begin with, while the underlying statement is true, the campaign itself seems intentionally deceiving.

My blog stats for the first 8 months of 2011

One of my goals for this year was to improve the posts that I was writing in order to provide more useful content for folks visiting the site. The easiest measure of this is to look at the total number of visits to the site 1. A few months back I posted in some detail about the stats on the site so far this year. With two thirds of the year completed, I wanted to provide an update as to how the site is doing.

Last year, I averaged about 35 hits/day and had a grand total of 5,004 hits on the blog. My goal was to triple that by the end of 2011. As you will see, I have rather exceeded my expectations.

My data comes from two sources:

  1. The native WordPress Stats application that comes as part of the WordPress JetPack.
  2. Feedburner stats 2.

Below is a table listing the totals for the first 8 months of the year, broken down by direct hits and Feedburner hits.

Month Direct Feedburner Total
January 2,664 2,664
February 2,741 547 3,288
March 3,217 6,710 9,927
April 3,274 9,340 12,614
May 4,712 5,572 10,284
June 6,081 9,123 15,204
July 7,323 21,830 29,153
August 7,702 15,295 22,997
Total 37,714 68,417 106,131


As you can see, from direct hits alone, I am doing far better than I imagined. My average daily hits in August was 248, not counting what comes through Feedburner. For the entire 8 months, the average daily hits is about 160/day, well above what I was aiming for. When you add Feedburner into the mix, things really shoot up. Indeed, I have passed 100,000 hits in just the first 8 months of the year, which astounds me.

One of the most important lessons I’ve learned in all of this is not to stress the day-to-day numbers. For the first several months, I was obsessed with checking my stats (the truth is, I still check them several times a day) and I was concerned every time I saw a slow day. I no longer worry about that. What I focus on is attempting to raise the number of hits from month to month and as far as direct hits to the blog goes, I’ve done that for each of the first 8 months of the year.

Of course, I just write the posts. A lot of the success that I have had this year is thanks to the folks that have come to the site and liked what they saw enough to tell others about it. I also have places like SF Signal to thanks for signal-boosting some of my posts, giving them more visibility than they might otherwise have received. Thanks to everyone who has visited the site! I hope to keep providing more good content and maybe see these numbers go up a bit more before the end of the year.

  1. Note that I say this is the easiest measure, not necessarily the most accurate when it comes to quality.
  2. I didn’t register this site with Feedburner until late February so data for January and most of February is missing

My 2008 Almanac

Here is the roundup for 2008:


  • Short fiction pieces started: 5
  • Short fiction pieces finished: 2
  • Novels started: 1
  • Novels finished: 0
  • Short fiction words: ~25,000
  • Novel words: 2,700
  • Total fiction: 27,700 words
  • Story submissions: 3
  • Rewrite requests: 1
  • Rejections: 2
  • Outstanding: 1
  • Science fiction conventions: 2
  • Blog posts: 170,000 words


  • Total books: 20
  • Total words: 3,067,000
  • Total pages: 7,891
  • % fiction: 52.3%
  • % non-fiction: 47.7%
  • Average time to read a book: 8.1 days
  • Average # of words/day: 38,349


  • Actual miles flown: 14,490
  • Miles driven: ~5,000
  • Countries visited: 4

Social Networking

  • 112 friends on Facebook
  • 41 friends on LiveJournal

Other Major Milestones

  • Got engaged (May)
  • Moved to Virginia (Jul)
  • Got married (Oct)
  • Expecting our first baby
  • A new niece

Happy New Year, everyone!