Building Maps with choropleth and ggplot

Data: Census 2000, number of jobs by industry by county

#
# library(choroplethr)
# library(choroplethrMaps)
# library(ggplot2)
# library(gridExtra)
#
# employment = read.csv("data/census_2000_employment.csv")
# crops = read.csv("data/econ_census_2012_farming.csv")
#
# head(employment)
#
# farming = data.frame(
#   region = employment$STCOU,
#   value = employment$Farming
#   )
#
# county_choropleth(farming, title = "Farming Employment", legend = "Jobs")
#
#
# choro = CountyChoropleth$new(farming)
# choro$title = "Southern Farmers"
# choro$set_num_colors(1)
# choro$ggplot_scale = scale_fill_gradientn("Jobs Count", colors = c("#F6EAE1", "darkgreen"))
# choro$set_zoom(c("texas", "louisiana", "mississippi", "alabama", "georgia", "arkansas",
#                  "tennessee", "south carolina", "florida", "oklahoma", "north carolina"))
# choro$render()
#
# ## Also without the gradient
# choro = CountyChoropleth$new(farming)
# choro$title = "Southern Farmers"
# choro$legend = "Jobs Count"
# choro$set_zoom(c("texas", "louisiana", "mississippi", "alabama", "georgia", "arkansas",
#                  "tennessee", "south carolina", "florida", "oklahoma", "north carolina"))
# choro$render()
#
# ## fill in NA with 0 to prevent NULL counties from standing out
# crops[is.na(crops)] = 0
#
# ## create the individual data sets
# corn = data.frame(region = crops$FIPS, value = crops$Corn)
# wheat = data.frame(region = crops$FIPS, value = crops$Wheat)
# cotton = data.frame(region = crops$FIPS, value = crops$Cotton)
# peanuts = data.frame(region = crops$FIPS, value = crops$Peanuts)
# peaches = data.frame(region = crops$FIPS, value = crops$Peaches)
# oranges = data.frame(region = crops$FIPS, value = crops$Oranges)
#
# ## list of states we want on the map
# states =  c("texas", "louisiana", "mississippi", "alabama", "georgia", "arkansas",
#             "tennessee", "south carolina", "florida", "oklahoma", "north carolina")
#
# ## create each graph object
# g1 = CountyChoropleth$new(corn); g1$title = "Corn % of Farmable Land"
# g1$set_num_colors(1); g1$set_zoom(states)
# g1$ggplot_scale = scale_fill_gradientn("%", colors = c("gray", "darkgreen"))
#
# g2 = CountyChoropleth$new(wheat); g2$title = "Wheat % of Farmable Land"
# g2$set_num_colors(1); g2$set_zoom(states)
# g2$ggplot_scale = scale_fill_gradientn("%", colors = c("gray", "darkgreen"))
#
# g3 = CountyChoropleth$new(cotton); g3$title = "Cotton % of Farmable Land"
# g3$set_num_colors(1); g3$set_zoom(states)
# g3$ggplot_scale = scale_fill_gradientn("%", colors = c("gray", "darkgreen"))
#
# g4 = CountyChoropleth$new(peanuts); g4$title = "Peanuts % of Farmable Land"
# g4$set_num_colors(1); g4$set_zoom(states)
# g4$ggplot_scale = scale_fill_gradientn("%", colors = c("gray", "darkgreen"))
#
# g5 = CountyChoropleth$new(peaches); g5$title = "Peaches % of Farmable Land"
# g5$set_num_colors(1); g5$set_zoom(states)
# g5$ggplot_scale = scale_fill_gradientn("%", colors = c("gray", "darkgreen"))
#
# g6 = CountyChoropleth$new(oranges); g6$title = "Oranges % of Farmable Land"
# g6$set_num_colors(1); g6$set_zoom(states)
# g6$ggplot_scale = scale_fill_gradientn("%", colors = c("gray", "darkgreen"))
#
# ## render each object in order to call as a group
# g1 = g1$render(); g2 = g2$render(); g3 = g3$render()
# g4 = g4$render(); g5 = g5$render(); g6 = g6$render()
#
# grid.arrange(g1, g2, g3, g4, g5, g6, nrow = 3, ncol = 2)