thallada.github.io/_posts/2013-04-09-visualizing-laundry-usage.md

---
title: Visualizing Laundry Usage
layout: post
redirect_from: "/blog/visualizing-laundry-usage/"
---

George Mason University uses a system called
[eSuds](http://www.usatech.com/esuds/) to control the laundry machine
transactions in the dorms. What makes eSuds really cool though, is that it
keeps track of the status of every machine and displays it on a
[website](http://gmu.esuds.net/) so students can check how full the machines
are before making the trek down to the laundry rooms. The system emails each
student when their laundry is finished as well.

The only problem is that their user interface is pretty atrocious. I wrote up a
[usability analysis](https://gist.github.com/thallada/5351114) of the site for
my *SWE 205: Software Usability Analysis and Design* class, but most people
agree it's a pretty painful interface to use ([just see for
yourself](http://gmu.esuds.net/)). The thing is, most of the information that's
on the website could be reduced to a few charts. I'm a big fan of simplifying
data, so I thought: why not?

I decided to create the visualizations with [pygal](http://pygal.org/), because
the charts it spits out are absolutely gorgeous and well... it's in python,
which made it easy for me to dive right in. I'll probably try out
[d3js](http://d3js.org/) for my next visualization project though, it looks a
whole lot more advanced.

### Current laundry usage charts

I created an [app](/laundry) in [Django](https://www.djangoproject.com/) to
display current laundry machine usage charts for all of the laundry rooms on
George Mason's campus. All of the data is scraped from the eSuds site using
[Beautiful Soup](http://www.crummy.com/software/BeautifulSoup/) and updated
every time you refresh the page.

<div style="text-align: center"><a href="/laundry" alt="See it in action"><img
src="/img/blog/laundry_preview.png" /></a></div>

The site will save which laundry room you select so when you come back you will
immediately see the chart for your laundry room.

You can see the code for this on my
[GitHub](https://github.com/thallada/personalsite/) (look in the "laundry"
folder).

The point was to make this as dead simple and easy to use as possible. Do you
think I succeeded?

### Weekly laundry usage chart

Knowing the *current* laundry machine usage is nice for saving a wasted trip
down to the laundry room, but what if you wanted to plan ahead and do your
laundry when you know other people are less likely to do laundry? That's why I
recorded the laundry usage with a [cronjob](http://en.wikipedia.org/wiki/Cron)
every 15 minutes for an entire week: to get an idea of when there is a high
probability of open machines.

<embed type="image/svg+xml" src="/img/blog/record.svg">

This one is a little interactive.

As you can see, the laundry usage jumps around all over the place very quickly.
This definitely provides evidence to some previous frustrations I've had when I
had checked eSuds, saw that most machines were open, and arrived in the laundry
room to suddenly find that no machines were open.

**So when is the best time to do laundry?**

After analyzing the data for a bit, I noticed that there still seemed to be
quite a bit of usage around midnight and 1 AM, which, I suppose, would be
expected of college students. However, after about 2 AM the laundry usage
consistently teeters off until about 10 AM. So I guess there's no way around
it; if you want to have the laundry room to yourself, you'll have to be the
early bird.

Also, I should note that this was during the week of spring break. I'm
currently working on recording data over a few normal weeks and then compiling
it into one average week in order to see the patterns more clearly. I'll post
again once I've done that.

This was a lot of fun and I expect to make more data visualizations in the
future.

**Let me know what you think!**

EDIT: Check out the [comments on
Reddit](http://www.reddit.com/r/gmu/comments/1c1ehg/i_dont_like_esuds/).