README.md

In mysteryduck/ggrdiversity: Provides plotting functions for the package rdiversity

ggrdiversity

Plotting functions for the rdiversity package

# Define metacommunity
pop1 <- data.frame(a = c(1,3), b = c(1,1))
row.names(pop1) <- paste0("sp", 1:2)
pop1 <- pop1/sum(pop1)
meta1 <- rdiversity::metacommunity(pop1)
qs <- c(seq(0,1,.1),2:10, seq(20,100,10),Inf)

# Plot metacommunity beta diversity
b <- rdiversity::raw_beta(meta1)
mc <- rdiversity::metadiv(b, qs)
ggplot(mc)

# Plot subcommunity beta diversity
sc <- rdiversity::subdiv(b, qs)
ggplot(sc)

# Plot beta component
ind <- rdiversity::inddiv(b, qs)
ggplot(ind)

# Plot subcommunity and metacommunity beta diversity
res <- rdiversity::rdiv(list(sc, mc))
ggplot(res)

# Plot all subcommunity diversity measures
all_sc <- rdiversity::subdiv(meta1, qs)
ggplot(all_sc)

# Plot all metacommunity diversity measures
all_mc <- rdiversity::metadiv(meta1, qs)
ggplot(all_mc)

# Plot all diversity measures
all_res <- rdiversity::rdiv(list(all_sc, all_mc))
ggplot(all_res)

# Try a single population
pop2 <- c(1,3,4)
pop2 <- pop2/sum(pop2)
meta2 <- rdiversity::metacommunity(pop2)
sc <- rdiversity::sub_gamma(meta2, qs)
ggplot(sc)
mc <- rdiversity::meta_gamma(meta2, qs)
ggplot(mc)

# Plot branch-based phylogenetic diversity
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)
similarity <- rdiversity::phy2branch(tree, partition)
meta <- rdiversity::metacommunity(partition, similarity)
tm <- rdiversity::metadiv(meta, qs)
ggplot(tm)

test file

# Define metacommunity
pop1 <- data.frame(a = c(1,3), b = c(1,1))
row.names(pop1) <- paste0("sp", 1:2)
pop1 <- pop1/sum(pop1)
meta1 <- rdiversity::metacommunity(pop1)
qs <- c(seq(0,1,.1),2:10, seq(20,100,10),Inf)

# Plot metacommunity beta diversity
b <- rdiversity::raw_beta(meta1)
mc <- rdiversity::metadiv(b, qs)
ggplot(mc)

# Plot subcommunity beta diversity
sc <- rdiversity::subdiv(b, qs)
ggplot(sc)

# Plot beta component
ind <- rdiversity::inddiv(b, qs)
ggplot(ind)

# Plot subcommunity and metacommunity beta diversity
res <- rdiversity::rdiv(list(sc, mc))
ggplot(res)

# Plot all subcommunity diversity measures
all_sc <- rdiversity::subdiv(meta1, qs)
ggplot(all_sc)

# Plot all metacommunity diversity measures
all_mc <- rdiversity::metadiv(meta1, qs)
ggplot(all_mc)

# Plot all diversity measures
all_res <- rdiversity::rdiv(list(all_sc, all_mc))
ggplot(all_res)

# Try a single population
pop2 <- c(1,3,4)
pop2 <- pop2/sum(pop2)
meta2 <- rdiversity::metacommunity(pop2)
sc <- rdiversity::sub_gamma(meta2, qs)
ggplot(sc)
mc <- rdiversity::meta_gamma(meta2, qs)
ggplot(mc)

# Plot branch-based phylogenetic diversity
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)
similarity <- rdiversity::phy2branch(tree, partition)
meta <- rdiversity::metacommunity(partition, similarity)
tm <- rdiversity::metadiv(meta, qs)
ggplot(tm)

# Plot linearly transformed distance-based phylogenetic diversity
distance <- rdiversity::phy2dist(tree)
similarity <- dist2sim(distance, "l")
meta <- rdiversity::metacommunity(partition, similarity)
tm <- rdiversity::metadiv(meta, qs)
ggplot(tm)


# Plot exponentially transformed distance-based phylogenetic diversity
distance <- rdiversity::phy2dist(tree)
similarity <- dist2sim(distance, "e")
meta <- rdiversity::metacommunity(partition, similarity)
tm <- rdiversity::metadiv(meta, qs)
ggplot(tm)


# Plot taxonomic diversity
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)

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

partition <- cbind.data.frame(a = 1:4, b = 1:4)
rownames(partition) <- Species

distance <- tax2dist(lookup, taxDistance)
similarity <- dist2sim(distance, "linear")
meta <- rdiversity::metacommunity(partition, similarity)
tm <- rdiversity::metadiv(meta, qs)
ggplot(tm)


# # Plot taxonomic and naive diversity
# meta2 <- rdiversity::metacommunity(partition)
# tm2 <- rdiversity::metadiv(meta2, qs)
# res <- rdiv(list(tm, tm2))
# ggplot(res)

mysteryduck/ggrdiversity documentation built on May 9, 2019, 2:59 p.m.