Associating points with polygons in R

Some time ago I posted on how to find geographic coordinates given a list of village or city names in R. Somebody emailed me about how to do the reverse: the person had a list of villages in France along with the population in 2010, and wanted to find which administrative unit each village was located in. The problem boils down to associating points, the village coordinates, with polygons, the administrative division which they are a part of.

The village data look like this:


munic <- read.xls("France-Population.xlsx")
                  Name       long      lat pop_2010
1                 Aast -0.0887339 43.28919 182.5416
2           Abainville  5.4947440 48.53057 327.2407
3            Abancourt  1.7649060 49.69672 687.2479
4            Abancourt  3.2127010 50.23528 448.1252
5            Abaucourt  6.2579230 48.89637 285.9438
6 Abaucourt-Hautecourt  5.5405000 49.19700  93.0353

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Quick lookup for country codes

After more than half a decade at this, it has finally dawned upon me that instead of downloading the Correlates of War state system membership table, or the Gleditsch and Ward refinement of it, every time I wonder what country “338″ is, it might be easier to upload them to Google:

COW codes and state system membership

G&W codes and state system membership

And, for the sake of self-promoting completeness, code to produce panel data reflecting COW or G&W state system membership, and old Stata code to change country names to COW codes.

Defense doublethink

If you had to take a look at the chart below, what would you say about the overall trend in US defense spending? There’s a bump fairly early on for World War 2, but otherwise it seems to generally increase over time. I’m actually surprised to see that we spend more, in terms of constant US dollars, today than we did at the height of the Korean War, and in fact at any point in US history save World War 2.


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