R：用不同的颜色映射正负数

Question

I'm a journalist working to map the counties where the number of black farmers increased or decreased between 2002 and 2012. I am using R (3.2.3) to process and map the data.

I've been able to map the whole range of county-level gains and losses—which goes from negative 40 to positive 165—in a single color, but this makes it hard to see the pattern of gains and losses. What I'd like to do is make the losses all variations of a single color (say, blue), and render gains in variations of a second color (say, red).

The following code generates two separate (very simplified) maps for counties that saw positive and negative changes. Anyone know how to capture this information in two colors on a single map? Ideally, counties with a "Difference" value of 0 would appear in grey. Thanks for looking at this!

  df <- data.frame(GEOID = c("45001", "22001", "51001", "21001", "45003"), 
                        Difference = c(-10, -40, 150, 95, 20))

#Second part: built a shapefile and join.
counties <- readOGR(dsn="Shapefile", layer="cb_2015_us_county_5m")

#Join the data about farmers to the spatial data. 
counties@data <- left_join(counties@data, df)

#NAs are not permitted in qtm method, so let's replace them with zeros.  
counties$Difference[is.na(counties$Difference)] <- 0

#Here are the counties that lost black farmers.
loss.counties <- counties[counties$Difference < 0, ]
qtm(loss.counties, "Difference")

#Here are the counties that gained black farmers.
gain.counties <- counties[counties$Difference > 0, ]
qtm(gain.counties, "Difference")

Answer 1

Using the source data from your original post, here is a solution using ggplot as suggested in my comment above.

library(ggplot2)
library(ggmap)
library(maps)
library(dplyr)

# get data from 
# https://quickstats.nass.usda.gov/results/A68E27D5-E9B2-3621-8F1E-58829A551F32
df <- read.csv("nass_data.csv")
df$County <- tolower(df$County)
df$State <- tolower(df$State)

#Calculate the difference between the 2002 and 2012 census95, 
df <- df %>%
  filter(Domain == "TOTAL", Year == 2002 | Year == 2012) %>%
  group_by(County) %>%
  mutate(Difference = ifelse(is.na(Value-lag(Value)), 0, Value-lag(Value)))  %>%
  select(County, State, Difference)

#get map data for US counties and states
county_map <- map_data("county")
county_map$County <- county_map$subregion
county_map$State <- county_map$region

#Join the data about farmers to the spatial data. 
county_map <- left_join(county_map, df)

#plot using ggplot
ggplot(county_map, aes(x = long, y = lat, group=group)) +
  geom_polygon(aes(fill = Difference)) + 
  scale_fill_gradient2(midpoint = 0, mid="#eee8d5", high="#dc322f", low="#268bd2")

I'll note that your source data appear to be missing several counties throughout the country. Nonetheless, I think this gets you what you want.

Answer 2

It's probably better to bin this data. I made a snap judgment for what the bins should be, you should look at the data to see if it should be different. I also did the binning very manually to try to show what's going on.

Using FIPS code (the combo of the "ANSI" columns) can help in situations where county names are hard to match, hence why I did that here.

Folks tend to leave out AK & HI but there are some farms there it seems.

Also, red/blue are loaded colors and really should be avoided.

library(ggplot2)
library(maps)
library(maptools)
library(rgeos)
library(albersusa) # devtools::install_github("hrbrmstr/albersusa")
library(ggalt)
library(ggthemes)
library(dplyr)

df <- read.csv("347E31A8-7257-3AEE-86D3-4BE3D08982A3.csv")

df <- df %>%
  filter(Domain == "TOTAL", Year == 2002 | Year == 2012) %>%
  group_by(County) %>%
  mutate(delta=Value-lag(Value),
         delta=ifelse(is.na(delta), 0, delta),
         fips=sprintf("%02d%03d", State.ANSI, County.ANSI)) 

df$delta <- cut(df$delta, include.lowest=FALSE,
                breaks=c(-400, -300, -200, -100, -1, 1, 100, 200, 300, 400),
                labels=c("301 to 400 (losses)", "201 to 300", "101 to 200", "1 to 100",
                         "no gains/losses", 
                         "+1 to 100", "+101 to 200", "+201 to 300", "301 to 400 (gains)"))

counties <- counties_composite()
counties_map <- fortify(counties, region="fips")

gg <- ggplot()
gg <- gg + geom_map(data=counties_map, map=counties_map,
                    aes(x=long, y=lat, map_id=id),
                    color="#b3b3b3", size=0.15, fill="white")
gg <- gg + geom_map(data=df, map=counties_map,
                    aes(fill=delta, map_id=fips),
                    color="#b3b3b3", size=0.15)
gg <- gg + scale_fill_manual(name="Change since 2002\n(white = no data)",
                            values=c("#543005", "#8c510a", "#bf812d", "#dfc27d",
                                     "#e0e0e0",
                                     "#80cdc1", "#35978f", "#01665e", "#003c30"),
                            guide=guide_legend(reverse=TRUE))
gg <- gg + coord_proj(us_laea_proj)
gg <- gg + labs(x="Grey == no data", y=NULL)
gg <- gg + theme_map()
gg <- gg + theme(legend.position=c(0.85, 0.2))
gg <- gg + theme(legend.key=element_blank())
gg