.Rhistory

else graphics::lines(x = coord[ind, 1], y = coord[ind, 2])
}
# Disturbed states
if(type == "points"){
if(!is.null(cluster)) graphics::points(x = coord[ind[2], 1], y = coord[ind[2], 2],
pch = 15, col = cluster[ind][2])
else graphics::points(x = coord[ind[2], 1], y = coord[ind[2], 2], pch = 15)
}
if(type %in% c("mixed", "points")){
# Pre-disturbance states
if(!is.null(cluster)) graphics::points(x = coord[ind[1], 1], y = coord[ind[1], 2],
pch = 16, col = cluster[ind][1])
else graphics::points(x = coord[ind[1], 1], y = coord[ind[1], 2], pch = 16)
# Final points
if(!is.null(cluster)) graphics::points(x = coord[ind[3], 1], y = coord[ind[3], 2],
pch = 17, col = cluster[ind][3])
else graphics::points(x = coord[ind[3], 1], y = coord[ind[3], 2], pch = 17)
}
# Arrows
if(type == "arrows"){
graphics::lines(x = coord[ind, 1], y = coord[ind, 2], col = ind[1])
shape::Arrows(x0 = coord[ind[2], 1], y0 = coord[ind[2], 2],
x1 = coord[ind[3], 1], y1 = coord[ind[3], 2],
col = ind[1])
}
}
add.legend == TRUE
is.null(cluster.name)
if(is.null(cluster.name)) cluster.name = rep("", length(drawSites))
cluster.name
!is.null(cluster)
if(!is.null(cluster)){
drawCluster <- cluster[drawID]
drawClustername <- cluster.name[drawID]
legend.table <- reshape2::dcast(data.frame(drawSurveys, drawClustername, drawCluster),
drawClustername + drawCluster ~ drawSurveys,
value.var = "drawClustername", length)
} else {legend.table <- data.frame(drawClustername = "", drawCluster = 1, X1 = 1, X2 = 1, X3 = 1)}
legend.table
if(is.character(cluster)) legend.table$drawCluster <- as.character(legend.table$drawCluster)
legend.table
for(i in 1:nrow(legend.table)){
legend.table[i, 3] <- ifelse(legend.table[i, 3] > 0, legend.table$drawCluster[i], 0)
legend.table[i, 4] <- ifelse(legend.table[i, 4] > 0, legend.table$drawCluster[i], 0)
legend.table[i, 5] <- ifelse(legend.table[i, 5] > 0, legend.table$drawCluster[i], 0)
}
legend.table
par(mar = c(0, 0, 0, 0))
plot.new()
!is.null(cluster)
# if(!is.null(cluster)){
text("Pre-disturbance\nstate", x=0.3, y=0.75, adj = c(0.5, 0.5), cex = 0.7)
text("Disturbed\nstate", x=0.62, y=0.75, adj = c(0.5, 0.5), cex = 0.7)
text("Final\nstate", x=0.87, y=0.75, adj = c(0.5, 0.5), cex = 0.7)
ypos = seq(0.65, 0.05, length.out = nrow(legend.table))
for(i in 1:nrow(legend.table)){
text(legend.table[i, 1], x = 0.05, y = ypos[i], adj = c(0, 0), cex = 0.7)
points(x = 0.3, y = ypos[i], pch = 16, col = legend.table[i, 3])
points(x = 0.87, y = ypos[i], pch = 17, col = legend.table[i, 5])
if(type == "points"){
points(x = 0.62, y = ypos[i], pch = 15, col = legend.table[i, 4])
} else if(type == "mixed"){
points(x = 0.62, y = ypos[i], pch = "__", col = legend.table[i, 4])
}
}
library(persistR)
data("post_disturbance")
# Display pre-disturbance, disturbed, and final states as orange,
# blue, and red points, respectively, for a set of selected sites.
color = rep(c("orange", "blue", "red", "black", "purple", "green"),
length(unique(post_disturbance$sites))/2)
cluster.name = rep(c("orange", "blue", "red", "black", "purple", "green"),
length(unique(post_disturbance$sites))/2)
selected.sites = c(3, 17, 66, 86, 132, 157)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = color,
type = "points", main = 'type = "points"',
onlyDraw = selected.sites,
zoom = TRUE,
add.legend = TRUE,
cluster.name = cluster.name)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
type = "points", main = 'type = "points"',
onlyDraw = selected.sites,
zoom = TRUE, add.legend = TRUE)
devtools::document()
library(persistR)
library(persistR)
library(persistR)
library(persistR)
library(persistR)
??layout
??plot.new
??par
library(persistR)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE)
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
library(persistR)
library(persistR)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
library(persistR)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
library(persistR)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
library(persistR)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
abundance = post_disturbance$abundance
sites = post_disturbance$sites
surveys = post_disturbance$surveys
cluster = post_disturbance$cluster
type = "mixed"
lwd.factor = 20
zoom = TRUE
add.legend = TRUE
onlyPaths = NULL
axes = c(1, 2)
species = FALSE
cluster.name = NULL
if(length(sites) != nrow(abundance)){
stop("Length of 'sites' must be equal to the number of abundance rows")
}
if(length(surveys) != nrow(abundance)){
stop("Length of 'surveys' must be equal to the number of abundance rows")
}
if(length(cluster) != nrow(abundance)){
stop("Length of 'cluster' must be equal to the number of abundance rows")
}
if(any(table(sites) != 3)){
stop("Each site must be surveyed three times")
}
# PCA of abundance matrix
pca <- vegan::rda(abundance)
# Coordinates of PCA
scores.pca <- vegan::scores(pca, choices = axes, display = c("sites", "species"))
coord <- as.data.frame(scores.pca$sites)
coord.sp <- as.data.frame(scores.pca$species)
# Define pathway codes
sit.surv <- data.frame(sites = sites, surveys = surveys, cluster = cluster)
cluster.df <- reshape2::dcast(
sit.surv,
sites ~ surveys, value.var = "cluster"
)
cluster.df$pathways <- apply(cluster.df[, -1], 1, paste, collapse = "")
cluster.df <- merge(sit.surv[, 1:2], cluster.df, all.x = T)
cluster.df <- cluster.df[order(match(
paste(cluster.df$sites, cluster.df$surveys),
paste(sit.surv$sites, sit.surv$surveys)
)), ]
# Pathways following each site and survey
pathways <- cluster.df$pathways
# Subset selected pathways
if(!is.null(onlyPaths)){
for(i in 1:length(onlyPaths)){
if(!is.na(onlyPaths[i])){
cluster.df <- cluster.df[cluster.df[, (i+2)] == onlyPaths[i], ]
}
}
}
pathways
# Proportion of plots for each pathway to set lwd
if(!is.null(lwd.factor)){
prop <- lwd.factor * table(unique(cluster.df)$pathways)/sum(table(unique(cluster.df)$pathways))
} else {prop <- table(unique(cluster.df)$pathways)/table(unique(cluster.df)$pathways)}
# Calculate average coordinates by state and cluster
avg.coord <- reshape2::melt(unique(cluster.df[, 3:ncol(cluster.df)]),
id = "pathways",
variable.name = "surveys", value.name = "cluster")
avg.coord <- avg.coord[order(avg.coord$pathways, avg.coord$surveys),]
for(i in 1:nrow(avg.coord)){
id <- which(pathways == avg.coord$pathways[i] & surveys == avg.coord$surveys[i])
avg.coord$x[i] <- mean(coord[id, 1])
avg.coord$y[i] <- mean(coord[id, 2])
}
avg.coord <- merge(avg.coord, data.frame(prop),
by.x = "pathways", by.y = "Var1", all.x = T)
# Pathways that will be drawn
pathID <- unique(cluster.df$pathways)
drawID <- which(avg.coord$pathways %in% pathID)
drawPath1 <- avg.coord$pathways[drawID]
drawSurveys <- avg.coord$surveys[drawID]
pathID
drawID
drawPath1 <- avg.coord$pathways[drawID]
drawPath1
drawSurveys
# Set plot space if add.legend == TRUE
if(add.legend == TRUE){
layout(mat = matrix(c(1, 2), nrow = 1, ncol = 2, byrow = T), widths = c(1, 1/2))
}
par(mar = c(4, 4, 3, 0))
# Draw empty plot with zoom (if zoom = TRUE)
if(zoom == TRUE){
graphics::plot(pca, type="n",
xlab = paste0("PC ", axes[1], " (", round(100*pca$CA$eig[axes[1]]/sum(pca$CA$eig)),"%)"),
ylab = paste0("PC ", axes[2], " (", round(100*pca$CA$eig[axes[2]]/sum(pca$CA$eig)),"%)"),
xlim = c(min(avg.coord[drawID, "x"]), max(avg.coord[drawID, "x"])),
ylim = c(min(avg.coord[drawID, "y"]), max(avg.coord[drawID, "y"])), ...)
} else {
graphics::plot(pca, type = "n",
xlab = paste0("PC ", axes[1], " (", round(100*pca$CA$eig[axes[1]]/sum(pca$CA$eig)),"%)"),
ylab = paste0("PC ", axes[2], " (", round(100*pca$CA$eig[axes[2]]/sum(pca$CA$eig)),"%)"), ...)
}
graphics::plot(pca, type="n",
xlab = paste0("PC ", axes[1], " (", round(100*pca$CA$eig[axes[1]]/sum(pca$CA$eig)),"%)"),
ylab = paste0("PC ", axes[2], " (", round(100*pca$CA$eig[axes[2]]/sum(pca$CA$eig)),"%)"),
xlim = c(min(avg.coord[drawID, "x"]), max(avg.coord[drawID, "x"])),
ylim = c(min(avg.coord[drawID, "y"]), max(avg.coord[drawID, "y"]))
)
for(i in 1:length(pathID)){
# sites and surveys may not be in order
ind = drawID[which(drawPath1 == pathID[i])]
ind = ind[order(drawSurveys[which(drawPath1 == pathID[i])])]
# Linking lines
if(type == "mixed"){
graphics::lines(x = avg.coord[ind, "x"], y = avg.coord[ind, "y"],
col = avg.coord$cluster[ind][2], lwd = avg.coord$Freq[ind][1])
}
# Disturbed states
if(type == "points"){
graphics::points(x = avg.coord[ind[2], "x"], y = avg.coord[ind[2], "y"],
pch = 15, col = avg.coord$cluster[ind][2])
}
if(type %in% c("mixed", "points")){
# Pre-disturbance states
graphics::points(x = avg.coord[ind[1], "x"], y = avg.coord[ind[1], "y"],
pch = 16, col = avg.coord$cluster[ind][1])
# Post disturbance points
graphics::points(x = avg.coord[ind[3], "x"], y = avg.coord[ind[3], "y"],
pch = 17, col = avg.coord$cluster[ind][3])
}
# Arrows
if(type == "arrows"){
graphics::lines(x = avg.coord[ind, "x"], y = avg.coord[ind, "y"],
lwd = avg.coord$Freq[ind][1], col = ind[1])
shape::Arrows(x0 = avg.coord[ind[2], "x"], y0 = avg.coord[ind[2], "y"],
x1 = avg.coord[ind[3], "x"], y1 = avg.coord[ind[3], "y"],
arr.lwd = avg.coord$Freq[ind][1], col = ind[1])
}
}
drawSites <- sites[drawID]
drawSites
if(is.null(cluster.name)) cluster.name = rep("", length(drawSites))
cluster.name
# Define legend in a dataframe
drawCluster <- cluster[drawID]
drawCluster
drawClustername <- cluster.name[drawID]
drawClustername
legend.table <- reshape2::dcast(data.frame(drawSurveys, drawClustername, drawCluster),
drawClustername + drawCluster ~ drawSurveys,
value.var = "drawClustername", length)
legend.table
drawSurveys
drawCluster
cluster
drawPath1
drawID
drawPath1
avg.coord
pathID
drawID
cluster
legend.table
drawSurveys
drawCluster
cluster[drawID]
drawID
drawPath1
drawSites
avg.coord
drawCluster <- avg.coord$cluster[drawID]
drawCluster
# Define legend in a dataframe
drawClustername <- cluster.name[drawID]
legend.table <- reshape2::dcast(data.frame(drawSurveys, drawClustername, drawCluster),
drawClustername + drawCluster ~ drawSurveys,
value.var = "drawClustername", length)
legend.table
# Convert colors into character if needed
if(is.character(cluster)) legend.table$drawCluster <- as.character(legend.table$drawCluster)
# Specify colors only when they are plotted
for(i in 1:nrow(legend.table)){
legend.table[i, 3] <- ifelse(legend.table[i, 3] > 0, legend.table$drawCluster[i], 0)
legend.table[i, 4] <- ifelse(legend.table[i, 4] > 0, legend.table$drawCluster[i], 0)
legend.table[i, 5] <- ifelse(legend.table[i, 5] > 0, legend.table$drawCluster[i], 0)
}
par(mar = c(0, 0, 0, 0))
plot.new()
# Draw state names
text("Pre-disturbance\nstate", x=0.3, y=0.75, adj = c(0.5, 0.5), cex = 0.7)
text("Disturbed\nstate", x=0.62, y=0.75, adj = c(0.5, 0.5), cex = 0.7)
text("Final\nstate", x=0.87, y=0.75, adj = c(0.5, 0.5), cex = 0.7)
# Draw symbols
ypos = seq(0.65, 0.05, length.out = nrow(legend.table))
for(i in 1:nrow(legend.table)){
text(legend.table[i, 1], x = 0.05, y = ypos[i], adj = c(0, 0), cex = 0.7)
points(x = 0.3, y = ypos[i], pch = 16, col = legend.table[i, 3])
points(x = 0.87, y = ypos[i], pch = 17, col = legend.table[i, 5])
if(type == "points"){
points(x = 0.62, y = ypos[i], pch = 15, col = legend.table[i, 4])
} else if(type == "mixed"){
points(x = 0.62, y = ypos[i], pch = "__", col = legend.table[i, 4])
}
}
library(persistR)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
data("post_disturbance")
# Display individual pathways using a different color for each
# cluster
head(post_disturbance$cluster)
pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Individual pathways",
zoom = TRUE,
add.legend = TRUE,
cluster.name = post_disturbance$cluster)
# Summarize individual into average pathways
avg.pathways(abundance = post_disturbance$abundance,
sites = post_disturbance$sites,
surveys = post_disturbance$surveys,
cluster = post_disturbance$cluster,
type = "mixed",
main = "Average pathways",
lwd.factor = 20,
zoom = TRUE,
add.legend = TRUE)
library(persistR)
library(persistR)
library(persistR)
library(persistR)
library(persistR)
library(persistR)
citation("persistR")
library(persistR)