bivariate raster plots in R

Question

I have challenge in plotting a bivariate raster data in one plot with one legend for both variables. my first layer is a continuous variable ranging between -2 and 2 while the second layer is a categorical variable (in years form 1980 to 2011). I need help in ploting the data as one rastr plot with a color scheme and legend which shows both variables as shown here . I appreciate your help.

r <- raster(ncols=100, nrows=100)
r[] <- runif(ncell(r))
crs(r) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"


r1 <- raster(ncols=100, nrows=100)
r1[] <- 1980:2011
crs(r1) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"

dta=stack(r,r1)

Answer 1

有关示例，请参阅?raster::plot ，或执行spplot(dta)

Answer 2

I successfully applied the code from the site you mentioned.

kpacks <- c("classInt", 'raster', 'rgdal',
            'dismo', 'XML', 'maps', 'sp')
new.packs <- kpacks[!(kpacks %in% installed.packages()[, "Package"])]
if (length(new.packs))
  install.packages(new.packs)
lapply(kpacks, require, character.only = T)
remove(kpacks, new.packs)


r <- raster(ncols = 100, nrows = 100)
r[] <- runif(ncell(r))
crs(r) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"


r1 <- raster(ncols = 100, nrows = 100)
r1[] <- sample(1980:2011, 10000, replace = T)
crs(r1) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"

dta = stack(r, r1)
plot(dta)

colmat <-
  function(nquantiles = 10,
           upperleft = rgb(0, 150, 235, maxColorValue = 255),
           upperright = rgb(130, 0, 80, maxColorValue = 255),
           bottomleft = "grey",
           bottomright = rgb(255, 230, 15, maxColorValue = 255),
           xlab = "x label",
           ylab = "y label") {
    my.data <- seq(0, 1, .01)
    my.class <- classIntervals(my.data, n = nquantiles, style = "quantile")
    my.pal.1 <- findColours(my.class, c(upperleft, bottomleft))
    my.pal.2 <- findColours(my.class, c(upperright, bottomright))
    col.matrix <- matrix(nrow = 101, ncol = 101, NA)
    for (i in 1:101) {
      my.col <- c(paste(my.pal.1[i]), paste(my.pal.2[i]))
      col.matrix[102 - i, ] <- findColours(my.class, my.col)
    }
    plot(
      c(1, 1),
      pch = 19,
      col = my.pal.1,
      cex = 0.5,
      xlim = c(0, 1),
      ylim = c(0, 1),
      frame.plot = F,
      xlab = xlab,
      ylab = ylab,
      cex.lab = 1.3
    )
    for (i in 1:101) {
      col.temp <- col.matrix[i - 1, ]
      points(
        my.data,
        rep((i - 1) / 100, 101),
        pch = 15,
        col = col.temp,
        cex = 1
      )
    }
    seqs <- seq(0, 100, (100 / nquantiles))
    seqs[1] <- 1
    col.matrix <- col.matrix[c(seqs), c(seqs)]
  }

col.matrix <-
  colmat(
    nquantiles = 10,
    upperleft = "blue",
    upperright = "yellow",
    bottomleft = "green",
    bottomright = "red",
    xlab = "My x label",
    ylab = "My y label"
  )


bivariate.map <-
  function(rasterx,
           rastery,
           colormatrix = col.matrix,
           nquantiles = 10) {
    quanmean <- getValues(rasterx)
    temp <- data.frame(quanmean, quantile = rep(NA, length(quanmean)))
    brks <-
      with(temp, quantile(temp, na.rm = TRUE, probs = c(seq(0, 1, 1 / nquantiles))))
    r1 <-
      within(
        temp,
        quantile <-
          cut(
            quanmean,
            breaks = brks,
            labels = 2:length(brks),
            include.lowest = TRUE
          )
      )
    quantr <- data.frame(r1[, 2])
    quanvar <- getValues(rastery)
    temp <- data.frame(quanvar, quantile = rep(NA, length(quanvar)))
    brks <-
      with(temp, quantile(temp, na.rm = TRUE, probs = c(seq(0, 1, 1 / nquantiles))))
    r2 <-
      within(temp,
             quantile <-
               cut(
                 quanvar,
                 breaks = brks,
                 labels = 2:length(brks),
                 include.lowest = TRUE
               ))
    quantr2 <- data.frame(r2[, 2])
    as.numeric.factor <- function(x) {
      as.numeric(levels(x))[x]
    }
    col.matrix2 <- colormatrix
    cn <- unique(colormatrix)
    for (i in 1:length(col.matrix2)) {
      ifelse(is.na(col.matrix2[i]),
             col.matrix2[i] <- 1,
             col.matrix2[i] <- which(col.matrix2[i] == cn)[1])
    }
    cols <- numeric(length(quantr[, 1]))
    for (i in 1:length(quantr[, 1])) {
      a <- as.numeric.factor(quantr[i, 1])
      b <- as.numeric.factor(quantr2[i, 1])
      cols[i] <- as.numeric(col.matrix2[b, a])
    }
    r <- rasterx
    r[1:length(r)] <- cols
    return(r)
  }

my.colors = colorRampPalette(c("white", "lightblue", "yellow", "orangered", "red"))
plot(
  r,
  frame.plot = F,
  axes = F,
  box = F,
  add = F,
  legend.width = 1,
  legend.shrink = 1,
  col = my.colors(255)
)
map(interior = T, add = T)

bivmap <- bivariate.map(r, r1, colormatrix = col.matrix, nquantiles = 10)

# Plot the bivariate map:

plot(
  bivmap,
  frame.plot = F,
  axes = F,
  box = F,
  add = F,
  legend = F,
  col = as.vector(col.matrix)
)
col.matrix