Sifting Through My Mobile Phone Logs

Oct 9, 2007

When I was in NYC and my wife was in Buffalo, New York we talked on the phone almost every day, usually around ten in the evening. I was at my friend’s place one night, and at 10:05pm, my wife called.

The first thing she said was, “Where are you?”

I told her I was at my friend’s.

My wife quickly replied, “Ha! I knew it!”

Confused, I asked, “How did you know?”

“Because otherwise, you would have called me at exactly 9:58.”

Am I really that predictable? First it was the Chinese food, and now I had been accused of call time predictability. Of course there was only one way to put this dispute to rest — look at the data.

The Analysis

Luckily, my carrier, Verizon wireless, offers call logs in spreadsheet form. I was only interested in chats with my wife while I was in NYC, so I sorted all of my phone calls by time and got rid of the records that weren’t her. After some data cleaning and adjustments, I threw the data at R (a statistical computing language) with all of my might, and it kindly provided me the graph below.

Not Calling Her at the Same Time Every Night

The Findings

As expected, I didn’t call at 9:58 every night. In fact, the most calls were at 9:57. Ha. So there. Alright, maybe my call times were slightly predictable, but definitely not to the extent suggested. Most calls occurred some time between 9:30 and 10:30 with some scattered calls late at night and during the afternoon.

Data wins again. Data 2, over-generalization 0.

Have you looked at your call logs lately?


  • Lovely graphic – but a few minor criticisms:

    * what’s with the x limits?

    * the caption regarding the bin size could be a little clearer – the bins are 1 minute wide, correct? And it’s the time the call started?

    * some thing doesn’t feel right with the y-axis ticks – they look awfully like rotated bins

  • Yeah, I really should have extended the x-axis a bit to include midnight to 11:59pm instead of only my own call times. That’ll teach me to work on things late at night.

  • I really like these clever types of analysis…there is not enough appreciation of statistics as an *investigative* tool (using human traces etc.)! But give it a few years and we’ll see ;-)


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