Global Trends Deserve Global Charts and a Note on Chart Infrastructure

In the next few series of posts I plan to do something I've largely shied away from in my career: cross-sectional data.  This data is important because it helps to remind us that the mean is just that - the mean.  It hides import variation in the data. Just look at the graph below for an example!
So in the next few posts I will focus on the world, at fixed points in time, to delve in and help describe whats happening globally.  For this exercise, I will rely on Treasury TIC data, the BIS for short-term rates and Inflation, and the IMF and OECD for a smorgasbord of other data.  I hope you come to enjoy what follows.

Building a chart infrastructure

What is chart infrastructure? It is a set of charts that can be easily transferred to new problems.  It's all about efficiency and "reproduce ability" - that's what statistical programming is all about (in my view).  A proper chart infrastructure requires proper data, for geographical data it's all about making sure the names of locations in your data-set match-up with your coordinate 'crosswalk' files.  This files has things like state abbreviations and states spelled out in full - in order to cover the bases and making merging in new data-sets easier.

Chart Infrastructure as geographical or otherwise, also means setting up a data process that allows for the easy and swift update of charts.  Therefore, having a proper chart infrastructure requires having a proper data infrastructure.  For example, take the charts below.

A. I save each chart as a 6 x 11 so that each chart takes up the exact same area in my work across all platforms.
B. For each area I have a set of fixed latitudes and longitudes, so that in future plots involving Europe or South America will be comparable to the eye.
C. Each plot is made easily update-able by the data flow infrastructure.

These points are recommended for those building chart-packs.













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