Month: December 2013

Here you can see that I interacted primarily with my immediate colleague and manager the most, then other management, and the remainder further down the line are a mix which includes email to myself and from office operations. Also of note – I don’t actually receive that much email (I’m more of a “in the weeds” type of guy) or, as I said, much has gone into the appropriate folders.

Time-Series Analysis
The above graphs show a very simplistic and high level view of what proportion of email I was receiving from who (with a suitable level of anonymity, I hope). More interesting is a quick and simple analysis of patterns in time of the volume of email I received – and I’m pretty sure you already have an idea of what some of these patterns might be.

When doing data analysis, I always feel it is important to first visualize as much of the data as practically possible – in order to get “a feel” for the data and avoid making erroneous conclusions without having this overall familiarity (as I noted in an earlier post). If a picture is worth thousand words then a good data visualization is worth a thousand keystrokes and mouse clicks.

Below is a simple scatter plot all the emails received by day, with the time of day on the y-axis:

This scatterpolot is perhaps not immediately particulary illuminating, however it already shows us a few things worth noting:

• the majority of emails appear in a band approximately between 8 AM and 5 PM
• there is increased density of email in the period between the end of July and early October, after which there is a sparse interval until mid-month / early November
• there appears to be some kind of periodic nature to the volume of daily emails, giving a “strip-like” appearance (three guesses what that periodic nature is…)

We can look into this further by considering the daily volume of emails, as below. The black line is a 7 day moving average:

We can see the patterns noted above – the increase in daily volume after 7/27 and the marked decrease mid-October. Though I wracked my brain and looked thoroughly, I couldn’t find a specific reason why there was an increase over the summer – this was just a busy time for projects (and probably not for myself sorting email). The marked decrease in October corresponds to a period of bench time, which you can see was rather short-lived.

As I noted previously in analyzing communications data, the distribution of this type of information is exponential in nature and usually follows a log-normal distribution. As such, a moving average is not the greatest measure of central tendency – but a decent approximation for our purposes. Still, I find the graph a little more digestible when depicted with a logarithmic y-axis, as below:

Lastly we consider the periodic nature of the emails which is noted in the initial scatterplot. We can look for patterns by making a standard heatmap with the weekday as the column and hour of day as the row, as below:

You can clearly see that the the majority of work email occurs between the hours of 9 to 5 (shocking!). However some other interesting points of note are the bulk of email in the mornings at the begiinning of the week, fall-off after 5 PM at the end of the week (Thursday & Friday) and the messages received Saturday morning. Again, I don’t really receive that much email, or have spirited a lot of it away into folders as I noted at the beginning of the article (this analysis does not include things like automated emails and reports, etc.)

Email Size & Attachments
Looking at file attachments, I believe the data are more skewed than the rest, as the clean-up of large emails is a semi-regular task for the office worker (as not many have the luxury of an unlimited email inbox capacity – even executives) so I would expect that values on the high end to have largely been removed. Nevertheless it still provides a rough approximation of how email sizes are distributed and what proportion have attachments included.

First we look at the overall proportion of email left in my inbox which has attachments – of the 4,217 emails, 2914 did not have an attachment (69.1%) and 1303 did (30.9%).

Examining the size of emails (which includes the attachments) in a histogram, we see a familiar looking distribution, which here I have further expanded by making it into a Pareto chart. (note that the scale on the left y-axis is logarithmic):

Here we can see that of what was left in my inbox, all messages were about 8 MB in size or less, with the vast majority being 250K or less. In fact 99% of the email was less than 1750KB, and 99.9% less than 6MB.

Conclusion