Nebraska farm income in 2007 by county

Format

A data frame with 93 observations on the following 4 variables.

county: county
crop: crop income, thousand dollars
animal: livestock and poultry income, thousand dollars
area: area of each county, square miles

Details

The variables for each county are:

Value of farm products sold - crops (NAICS) 2007 (adjusted)

Value of farm products sold - livestock, 2007 (adjusted).

Area in square miles.

Note: Cuming county is a very important beef-producing county. Some counties are not reported to protect privacy. Western Nebraska is dryer and has lower income. South-central Nebraska is irrigated and has higher crop income per square mile.

Source

U.S. Department of Agriculture-National Agriculture Statistics Service. https://censtats.census.gov/usa/usa.shtml

Examples

# \dontrun{

library(agridat)

data(nebraska.farmincome)
dat <- nebraska.farmincome

libs(maps, mapproj, latticeExtra)
# latticeExtra for mapplot

dat$stco <- paste0('nebraska,', dat$county)
# Scale to million dollars per county
dat <- transform(dat, crop=crop/1000, animal=animal/1000)

# Raw, county-wide incomes.  Note the outlier Cuming county
redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
mapplot(stco ~ crop + animal, data = dat, colramp=redblue,
        main="nebraska.farmincome",
        xlab="Farm income from animals and crops (million $ per county)",
        scales = list(draw = FALSE), 
        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
          projection = "mercator") )

# Now scale to income/mile^2
dat <- within(dat, {
  crop.rate <- crop/area
  animal.rate <- animal/area
})
# And use manual breakpoints.
mapplot(stco ~ crop.rate + animal.rate, data = dat, colramp=redblue,
        main="nebraska.farmincome: income per square mile (percentile breaks)",
        xlab="Farm income (million $ / mi^2) from animals and crops",
        scales = list(draw = FALSE), 
        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
          projection = "mercator"),
        # Percentile break points
        # breaks=quantile(c(dat$crop.rate, dat$animal.rate),
        #                 c(0,.1,.2,.4,.6,.8,.9,1), na.rm=TRUE)
        # Fisher-Jenks breakpoints via classInt package
        # breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)),
        #                       n=7, style='fisher')$brks
        breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31))

# }