If we make the points smaller, cities and interstates are clearly visible, at least once you leave the Northeast Megalopolis: G = g + scale_y_continuous(limits = c(25,50), breaks = NA) G = g + theme_bw() + scale_x_continuous(limits = c(-125,-66), breaks = NA)
# simplify display and limit to the "lower 48" G = ggplot(data=zipcode) + geom_point(aes(x=longitude, y=latitude, colour=region)) Zipcode$region = substr(zipcode$zip, 1, 1) So let’s take a look at how they’re distributed by first digit: Now we wouldn’t be R users if we didn’t try to do something with data, even if it’s just a lookup table of zip codes. > somedata = merge(somedata, zipcode, by.x='zip', by.y='zip') > somedata$zip = clean.zipcodes(somedata$postal) It strips off “ZIP+4” suffixes, attempts to restore missing leading zeroes, and replaces anything with non-digits (like non-U.S. The package also includes a clean.zipcodes() function to help clean up zip codes in your data. Note that the ‘zip’ column is a string, not an integer, in order to preserve leading zeroes - a sensitive topic for those of us in the Northeast… 🙂 One you load the package, the database is available in the ‘zipcode’ ame: Since others may find it useful, too, I have packaged it into the ‘zipcode’ package now available on CRAN.
I have been using the database a lot recently to correlate with survey respondents, so I have saved it as an R ame. Census Bureau upon which it is based, so a lot of work has gone into it. It contains nearly 10,000 more records than the ZIP Code Tabulation Areas file from the U.S. You may know that I am a fan of the CivicSpace US ZIP Code Database compiled by Schuyler Erle of Mapping Hacks fame. Sillywabbit4562 on Data source to map Zip codes t…Īrnaud on slides from my R tutorial on T…Ībraham on slides from my R tutorial on T… J k lakshna on slides from my R tutorial on T…
#Civicspace us zip code database Bluetooth#
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