## -----------------------------------------------------------------------------
# Load the package into R
library(rdiversity)

# Initialise data
partition <- data.frame(a = c(1, 1), b = c(2, 0), c = c(3, 1))
row.names(partition) <- c("cows", "sheep")

## -----------------------------------------------------------------------------
# Generate metacommunity object
meta <- metacommunity(partition = partition)

## -----------------------------------------------------------------------------
# Initialise similarity matrix
s <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
row.names(s) <- c("cows", "sheep")
colnames(s) <- c("cows", "sheep")

# Generate similarity object
s <- similarity(similarity = s, dat_id = "my_taxonomic")

# Generate metacommunity object
meta <- metacommunity(partition = partition, similarity = s)

## -----------------------------------------------------------------------------
# Initialise distance matrix
d <- matrix(c(0, 0.7, 0.7, 0), nrow = 2)
row.names(d) <- c("cows", "sheep")
colnames(d) <- c("cows", "sheep")

# Generate distance object
d <- distance(distance = d, dat_id = "my_taxonomic")

# Convert the distance object to similarity object (by means of a linear or exponential transform)
s <- dist2sim(dist = d, transform = "linear")

# Generate metacommunity object
meta <- metacommunity(partition = partition, similarity = s)

## -----------------------------------------------------------------------------
# Initialise data
partition <- data.frame(a = c(1, 1), b = c(2, 0), c = c(3, 1))
row.names(partition) <- c("cows", "sheep")

# Generate a metacommunity object
meta <- metacommunity(partition)

# Calculate diversity
norm_sub_alpha(meta, 0:2)

## -----------------------------------------------------------------------------
# Initialise data
partition <- data.frame(a = c(1, 1), b = c(2, 0), c = c(3, 1))
row.names(partition) <- c("cows", "sheep")

# Generate a metacommunity object
meta <- metacommunity(partition)

# Calculate the species-level component for normalised alpha
component <- norm_alpha(meta)

# Calculate normalised alpha at the subcommunity-level
subdiv(component, 0:2)

# Likewise, calculate normalised alpha at the metacommunity-level
metadiv(component, 0:2)

## -----------------------------------------------------------------------------
# Calculate all subcommunity diversity measures
subdiv(meta, 0:2)

# Calculate all metacommunity diversity measures
metadiv(meta, 0:2)

## -----------------------------------------------------------------------------
# Taxonomic lookup table
Species <- c("tenuifolium", "asterolepis", "simplex var.grandiflora", "simplex var.ochnacea")
Genus <- c("Protium", "Quararibea", "Swartzia", "Swartzia")
Family <- c("Burseraceae", "Bombacaceae", "Fabaceae", "Fabaceae")
Subclass <- c("Sapindales", "Malvales", "Fabales", "Fabales")
lookup <- cbind.data.frame(Species, Genus, Family, Subclass)

# Partition matrix
partition <- matrix(rep(1, 8), nrow = 4)
colnames(partition) <- LETTERS[1:2]
rownames(partition) <- lookup$Species

## -----------------------------------------------------------------------------
values <- c(Species = 0, Genus = 1, Family = 2, Subclass = 3, Other = 4)

## -----------------------------------------------------------------------------
d <- tax2dist(lookup, values)

## -----------------------------------------------------------------------------
s <- dist2sim(d, "linear")

## -----------------------------------------------------------------------------
meta <- metacommunity(partition, s)

## -----------------------------------------------------------------------------
meta_gamma(meta, 0:2)

## -----------------------------------------------------------------------------
# Example data
tree <- ape::rtree(4)
partition <- matrix(1:12, ncol = 3)
partition <- partition / sum(partition)

## -----------------------------------------------------------------------------
d <- phy2dist(tree)

## -----------------------------------------------------------------------------
s <- dist2sim(d, "linear")

## -----------------------------------------------------------------------------
meta <- metacommunity(partition, s)

## -----------------------------------------------------------------------------
meta_gamma(meta, 0:2)

## -----------------------------------------------------------------------------
tree <- ape::rtree(4)
partition <- matrix(1:12, ncol = 3)
partition <- partition / sum(partition)
colnames(partition) <- letters[1:3]
row.names(partition) <- paste0("sp", 1:4)
tree$tip.label <- row.names(partition)

## -----------------------------------------------------------------------------
s <- phy2branch(tree, partition)

## -----------------------------------------------------------------------------
meta <- metacommunity(partition, s)

## -----------------------------------------------------------------------------
meta_gamma(meta, 0:2)

## ----eval = FALSE-------------------------------------------------------------
# library(rdiversity)
# vcf_file <- system.file("extdata", "pinf_sc50.vcf.gz", package = "pinfsc50")
# # read in twice: first for the column names then for the data
# tmp_vcf <- readLines(vcf_file)
# vcf_data <- read.table(vcf_file, stringsAsFactors = FALSE)
# # filter for the columns names
# vcf_names <- unlist(strsplit(tmp_vcf[grep("#CHROM", tmp_vcf)], "\t"))
# names(vcf_data) <- vcf_names
# partition <- cbind.data.frame(A = c(rep(1, 9), rep(0, 9)), B = c(rep(0, 9), rep(1, 9)))
# partition <- partition / sum(partition)

## ----eval = FALSE-------------------------------------------------------------
# d <- gen2dist(vcf)

## ----eval = FALSE-------------------------------------------------------------
# s <- dist2sim(d, transform = "l")

## ----eval = FALSE-------------------------------------------------------------
# rownames(partition) <- rownames(s@similarity)
# meta <- metacommunity(partition, s)

## ----eval = FALSE-------------------------------------------------------------
# norm_meta_beta(meta, 0:2)

## -----------------------------------------------------------------------------
partition <- matrix(sample(6), nrow = 3)
rownames(partition) <- paste0("sp", 1:3)
partition <- partition / sum(partition)

d <- matrix(c(0, .75, 1, .75, 0, .3, 1, .3, 0), nrow = 3)
rownames(d) <- paste0("sp", 1:3)
colnames(d) <- paste0("sp", 1:3)
d <- distance(d, "my_taxonomy")
s <- dist2sim(d, "linear")

meta <- metacommunity(partition, s)

## -----------------------------------------------------------------------------
partition <- matrix(sample(6), nrow = 3)
rownames(partition) <- paste0("sp", 1:3)
partition <- partition / sum(partition)

s <- matrix(c(1, .8, 0, .8, 1, .1, 0, .1, 1), nrow = 3)
rownames(s) <- paste0("sp", 1:3)
colnames(s) <- paste0("sp", 1:3)
s <- similarity(s, "my_functional")

meta <- metacommunity(partition, s)

## -----------------------------------------------------------------------------
tree <- ape::rtree(5)
tree$tip.label <- paste0("sp", 1:5)

partition <- matrix(rep(1, 10), nrow = 5)
row.names(partition) <- paste0("sp", 1:5)
partition <- partition / sum(partition)
s <- phy2branch(tree, partition)
meta <- metacommunity(partition, s)

new_partition <- matrix(sample(10), nrow = 5)
row.names(new_partition) <- paste0("sp", 1:5)
new_partition <- new_partition / sum(new_partition)

new_meta <- repartition(meta, new_partition)