honors to Kyle Walker per usual, for his handy hack of the ACS package.

Orange County’s GINI index as of the American Census Survey’s recent 2010-2014 estimates.

{% highlight r %} library(dplyr) library(acs14lite) library(CartoDB) library(rgdal) library(acs) {% endhighlight %}

Set up permissions (get API keys from ACS, CartoDB)

{% highlight r %} set_api_key(‘zzzzzz’) cartodb(‘username’,‘apikey’) {% endhighlight %}


{% highlight r %} acs.lookup(endyear=2014, keyword=“Gini”) #use ACS package to look up correct table numbers oc<- acs14(geography = ‘tract’, variable = c(‘B19083_001E’, ‘B19083_001M’), state = ‘CA’, county=‘Orange’) oc_tracts <- tracts(‘CA’, ‘Orange County’, cb = TRUE) oc2<- oc %>% mutate(geoid = paste0(state, county, tract), gini = round(100 *(B19083_001E),1), moe= round(100 *(B19083_001M),1)) %>% select (geoid, gini, moe) oc_tracts2 <- geo_join(oc_tracts, oc2, “GEOID”, “geoid”) r2cartodb(oc_tracts2, ‘oc’) {% endhighlight %}

No big surprise, Newport Beach’s pretty wealthy. Digging deeper in ACS data with CartoDB sounds promising though, it’s a lot easier to visualize disparities.