Categorizing my expenses

In order to analyse my expenses, a classification scheme is necessary. I need to identify categories that are meaningful to me. I decided to go with the “Classification of Individual Consumption by Purpose” (COICOP), for three reasons:

• It is made by people who have thought more about consumption classification than I ever will.
• It is feasible to assign bank transactions and tracked cash spendings to one of the 12 top level categories.
• It is widely used by statistics divisions, e.g. the Federal Statistical Office of Germany, Eurostat, and the UN. This means I can do social comparisons: In which categories do I spend more money than the average? Do the prices I pay rise faster than the price indices suggest?

So I classified my last year’s expense data according to COICOP. Here is a chart showing the portions of the categories for each month:

For me, the holidays, prepared in August and traveled in September (shown as unknown expenses), are much more dominant than I expected. Except for the new glasses in September I did not make any larger investments.

I like this kind of chart more than stacked bar charts because the history for each category is very visible. This chart is called inkblot chart. I stumbled on it on junk charts, asked how to implement it in R on StackOverflow, and included a revised version in the latest pft package. See below for more information.

Tracking my expenses

One new-year resolution I made last year was to understand where my money goes. From previous experiments I know that expense tracking has to be as simple as possible. My approach is to

• Use my cash card as often as possible. This automatically tracks the date and some information on the vendor.
• Use twitter to track my cash expenses. This supplements the bank account statement data.
• Edit, enrich, merge and visualise the two data sources with R. Because it is fun playing with R!

Now after more than one year of expense tracking, I can now analyse the results. The first result however, was disappointing. My cash tracking with twitter was not as complete as I thought it is. Below is a figure that displays the sum tracked with twitter divided by the sum withdrawn from my bank account for each month of 2011.

If I had tracked my cash expenses completely, the ratio would be around 100, the gray dashed line. However, it is systematically below. For September, there is an explanation: I was on holidays and did intentionally not track the expenses. But even considering that, there remain 18 percent of my cash spendings unexplained!

More analysis results will follow. If you are interested in technical aspects of the expense tracking, such as importing the tweets and bank statements, read on. However, there is no R code today, since there is no example data.