Plotting Biome using RBedrock

am_class7.R

## Today we will work on making maps with ggplot2.

# Install the latest version of rbedrock
devtools::install_github("reedacartwright/rbedrock")

# Load tidyverse and rbedrock
library(tidyverse)
library(rbedrock)

# Open World Database
dbpath <- "ClassSMP"
db <- bedrockdb(dbpath)

# Biome Table contains information about biomes in the game
list_biomes()

# Biomes are stored in two different datasets right now
chunk_keys <- get_keys(db, starts_with="@0:0:0") %>% parse_chunk_keys()
chunk_keys

# Mapping one chunk --------------------------------------------------

# Lets extract 1.18 biome information for one chunk:
dat <- get_cnc_biomes_value(db, 0, -10, 0)
dim(dat)

# dat contains per-block biome information. The number of y-values varies by chunk.
# We're going to extract the biomes at y=-64 and turn them into a table.
m <- dat[,1,]
a <- arrayInd(1:256,dim(m))-1
df <- data.frame(x=a[,1]+16*0, z=a[,2]+16*-10, biome=c(m))

# Plot the data using raster
gg <- ggplot(data=df,aes(x=x+0.5,y=z+0.5,fill=biome)) + geom_raster()
gg

# Let's clean up the plot
gg <- ggplot(data=df,aes(x=x+0.5,y=z+0.5,fill=biome)) + geom_raster()
gg <- gg + coord_fixed() + scale_y_reverse()
gg <- gg + labs(x="x", y="z")
gg

# Let's add proper colors
biome_col <- list_biomes() %>% select(name, color) %>% deframe()

gg <- ggplot(data=df,aes(x=x+0.5,y=z+0.5,fill=biome)) + geom_raster()
gg <- gg + coord_fixed() + scale_y_reverse()
gg <- gg + labs(x="x", y="z")
gg <- gg + scale_fill_manual(values=biome_col)
gg

## Challenge #1
## Which of the following overworld chunks has 5 biomes?
##  1,-12; 4,3; 9,9; 14,12; -16,-6
##
## Which of the following overworld chunks contains a forest biome?
##  1,-12; 4,3; 9,9; 14,12; -16,-6

# Mapping multiple chunks --------------------------------------------

# grid of 1024 chunks
g <- expand_grid(x=-16:15,z=-16:15)

# Helper function to process each chunk as we did above
biomes_to_df <- function(x, key) {
    pos <- chunk_origins(key)
    m <- x[,1,]
    a <- arrayInd(1:256,dim(m))-1
    df <- data.frame(x=a[,1]+pos[1], z=a[,2]+pos[2], biome=c(m))
    df
}

# extract the data and process it
dat <- get_cnc_biomes_data(db, x=g$x, z=g$z, dimension=0)
df <- dat %>% compact() %>% imap_dfr(biomes_to_df)

# Let's start with our graph we had before
gg <- ggplot(data=df,aes(x=x+0.5,y=z+0.5,fill=biome)) + geom_raster()
gg <- gg + coord_fixed() + scale_y_reverse()
gg <- gg + labs(x="x", y="z")
gg <- gg + scale_fill_manual(values=biome_col)
gg

# Let's add some guide lines and a title
breaks64 <- seq(-256,256,64)
breaks128 <- seq(-256,256,128)

gg <- ggplot(data=df,aes(x=x+0.5,y=z+0.5,fill=biome)) + geom_raster()
gg <- gg + coord_fixed()
gg <- gg + scale_y_reverse(breaks=breaks64)
gg <- gg + scale_x_continuous(breaks=breaks64)
gg <- gg + labs(x="x", y="z", title="Biome Map of Spawn")
gg <- gg + scale_fill_manual(values=biome_col)
gg <- gg + geom_vline(xintercept=breaks128)
gg <- gg + geom_hline(yintercept=breaks128)
gg <- gg + theme_minimal()
gg

## Challenge #2
## Draw a map of the chunk region x = 0 to 15 and z = 16 to 31

# Filling in missing data --------------------------------------------

# If there is time let's try to use 2dMaps to fill in missing data.