library( RCurl )
library( jsonlite )
library( pander )
library( dplyr )fieldnm <- "DP03_0119PE" # poverty
state <- "36" # NY
county <- "067" # Onondaga County
APIkey <- "your,key" # http://api.census.gov/data/key_signup.html
We will retrieve data for the following three variables from Onondaga County in NY.
fieldnm <- "DP03_0119PE" # poverty
state <- "36" # NY
county <- "067" # Onondaga County
APIkey <- "your.api.key.here"
paste0( "http://api.census.gov/data/2013/acs5/profile/?get=", fieldnm,
"&for=tract:*&in=state:", state,
"+county:", county,
"&key=", "your.api.key.here" )
The data is returned in a format that looks like this:
fieldnm <- "DP03_0119PE" # poverty
state <- "36" # NY
county <- "067" # Onondaga County
resURL <- paste("https://api.census.gov/data/2013/acs/acs5?get=",fieldnm,
"&for=tract:*&in=state:",state,"+county:",county,"&key=",
APIkey,sep="")
poverty.json <- getURL( resURL, ssl.verifypeer = FALSE )
head( poverty.json )## [1] "[[\"NAME\",\"B01001_001E\",\"state\"],\n[\"Alabama\",\"4830620\",\"01\"],\n[\"Alaska\",\"733375\",\"02\"],\n[\"Arizona\",\"6641928\",\"04\"],\n[\"Arkansas\",\"2958208\",\"05\"],\n[\"California\",\"38421464\",\"06\"],\n[\"Colorado\",\"5278906\",\"08\"],\n[\"Connecticut\",\"3593222\",\"09\"],\n[\"Delaware\",\"926454\",\"10\"],\n[\"District of Columbia\",\"647484\",\"11\"],\n[\"Florida\",\"19645772\",\"12\"],\n[\"Georgia\",\"10006693\",\"13\"],\n[\"Hawaii\",\"1406299\",\"15\"],\n[\"Idaho\",\"1616547\",\"16\"],\n[\"Illinois\",\"12873761\",\"17\"],\n[\"Indiana\",\"6568645\",\"18\"],\n[\"Iowa\",\"3093526\",\"19\"],\n[\"Kansas\",\"2892987\",\"20\"],\n[\"Kentucky\",\"4397353\",\"21\"],\n[\"Louisiana\",\"4625253\",\"22\"],\n[\"Maine\",\"1329100\",\"23\"],\n[\"Maryland\",\"5930538\",\"24\"],\n[\"Massachusetts\",\"6705586\",\"25\"],\n[\"Michigan\",\"9900571\",\"26\"],\n[\"Minnesota\",\"5419171\",\"27\"],\n[\"Mississippi\",\"2988081\",\"28\"],\n[\"Missouri\",\"6045448\",\"29\"],\n[\"Montana\",\"1014699\",\"30\"],\n[\"Nebraska\",\"1869365\",\"31\"],\n[\"Nevada\",\"2798636\",\"32\"],\n[\"New Hampshire\",\"1324201\",\"33\"],\n[\"New Jersey\",\"8904413\",\"34\"],\n[\"New Mexico\",\"2084117\",\"35\"],\n[\"New York\",\"19673174\",\"36\"],\n[\"North Carolina\",\"9845333\",\"37\"],\n[\"North Dakota\",\"721640\",\"38\"],\n[\"Ohio\",\"11575977\",\"39\"],\n[\"Oklahoma\",\"3849733\",\"40\"],\n[\"Oregon\",\"3939233\",\"41\"],\n[\"Pennsylvania\",\"12779559\",\"42\"],\n[\"Rhode Island\",\"1053661\",\"44\"],\n[\"South Carolina\",\"4777576\",\"45\"],\n[\"South Dakota\",\"843190\",\"46\"],\n[\"Tennessee\",\"6499615\",\"47\"],\n[\"Texas\",\"26538614\",\"48\"],\n[\"Utah\",\"2903379\",\"49\"],\n[\"Vermont\",\"626604\",\"50\"],\n[\"Virginia\",\"8256630\",\"51\"],\n[\"Washington\",\"6985464\",\"53\"],\n[\"West Virginia\",\"1851420\",\"54\"],\n[\"Wisconsin\",\"5742117\",\"55\"],\n[\"Wyoming\",\"579679\",\"56\"],\n[\"Puerto Rico\",\"3583073\",\"72\"]]"
That’s not very helpful! Let’s reformat the json output.
poverty <- json.to.data( poverty.json )
head( poverty ) %>% pander| NAME | B01001_001E | state |
|---|---|---|
| Alabama | 4830620 | 01 |
| Alaska | 733375 | 02 |
| Arizona | 6641928 | 04 |
| Arkansas | 2958208 | 05 |
| California | 38421464 | 06 |
| Colorado | 5278906 | 08 |
json.to.data <- function( x )
{
a.matrix <- fromJSON(x) # converts json table to a matrix
c.names <- a.matrix[ 1 , ] # column names are the first row
a.matrix <- a.matrix[ -1 , ]
my.dat <- data.frame( a.matrix, stringsAsFactors=F )
names( my.dat ) <- c.names
# my.dat[,1] <- as.numeric( as.character( my.dat[,1] ) )
# > names( my.dat )
# [1] "DP03_0119PE" "state" "county" "tract"
return( my.dat )
}