Download USDA data here. The formatted file I used below is here (in excel format, although I read into R as csv file). The census data is read from url as below.
California has a ton of absolute number of farmer's markets, but Vermont takes the cake by far with number of markets per capita. Iowa comes in a distant second behind Vermont in markets per capita.
######## Farmer's Markets ############# setwd("/Mac/R_stuff/Blog_etc/USDAFarmersMarkets") # Set to your working directory, this is where you want to call files from and write files to install.packages(c("ggplot2", "RCurl")) # install all packags required below require(ggplot2) # plyr is libraried along with ggplot2, as ggplot2 uses plyr (as well as package reshape) functions # read market data markets <- read.csv("farmmarkets.csv") markets$state <- as.factor(gsub("Wyoming ", "Wyoming", markets$LocAddState)) # there was a typo for Wyoming markets <- na.omit(markets) str(markets) # read population census data popcen <- read.csv("http://www.census.gov/popest/national/files/NST_EST2009_ALLDATA.csv") popcen <- popcen[,c(4,5,6,17)] str(popcen) # summarize markets_ <- ddply(markets, .(state), summarise, markets_n = length(LocAddState) ) markets_pop_ <- merge(markets_, popcen[,-1], by.x = "state", by.y = "NAME") # merge two data sets markets_pop_$marketspercap <- markets_pop_$markets_n/markets_pop_$POPESTIMATE2009 # create column of markets per capita markets_pop_$markets_n_st <- markets_pop_$markets_n/max(markets_pop_$markets_n) markets_pop_$marketspercap_st <- markets_pop_$marketspercap/max(markets_pop_$marketspercap) # plot ggplot(melt(markets_pop_[,-c(2:5)]), aes(x = state, y = value, fill = variable)) + geom_bar(position = "dodge") + coord_flip() ggsave("fmarkets_barplot.jpeg")
Note: the x-axis here is standardized value of number of markets (markets_n_st) and number of markets per capita (marketspercap_st).
# maps try_require("maps") states <- map_data("state") markets_pop_$statelow <- tolower(markets_pop_$state) survey_sum_map <- merge(states, markets_pop_, by.x = "region", by.y = "statelow") survey_sum_map <- survey_sum_map[order(survey_sum_map$order), ] str(survey_sum_map) qplot(long, lat, data = survey_sum_map, group = group, fill = markets_n, geom = "polygon", main = "Total farmer's markets") + scale_fill_gradient(low="green", high="black") ggsave("fmarkets_map_green.jpeg")
qplot(long, lat, data = survey_sum_map, group = group, fill = marketspercap, geom = "polygon", main = "Farmer's markets per person") + scale_fill_gradient(low="green", high="black") ggsave("fmarkerspercap_map_green.jpeg")