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X - Functions_Plotting.R
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#' ####################################################################### #
#' PROJECT: [PhD; X - NETWORK PLOTTING]
#' CONTENTS:
#' - Functionality for plotting of networks
#' DEPENDENCIES:
#' -
#' AUTHOR: [Erik Kusch]
#' ####################################################################### #
# NETWORK PLOTTING =======================================================
# plotting a network from a three-column data frame
Plot.Network <- function(Plot_ls = NULL){
## column identification
## plot generation
Plots_ls <- as.list(rep(NA, length(Plot_ls)))
names(Plots_ls) <- names(Plot_ls)
for(Plot_Iter in 1:length(Plots_ls)){
plots_iter <- Plot_ls[[Plot_Iter]]
regplots_ls <- as.list(rep(NA, length(plots_iter)))
names(regplots_ls) <- names(plots_iter)
for(i in 1:length(plots_iter)){
Plot_df <- plots_iter[[i]]$data
name_plot <- names(plots_iter)[[i]]
if(startsWith(name_plot, "NDD")){
Plot_df$Sig <- ifelse(as.numeric(as.character(Plot_df$Sig)) < 1, FALSE, TRUE)
}
Plot_df[which(is.na(Plot_df[,4])),4] <- FALSE
Plot_df[,3][Plot_df[,3] == 0] <- NA
directed <- ifelse(names(plots_iter)[[i]] == "NDD_RIM", TRUE, FALSE)
regplots_ls[[i]] <- qgraph::qgraph(Plot_df[,1:3], # the three-column data frame of "Partner1", "Partner2", and "Effect"
bg = "white",
color = "white", # colour of nodes
layout = 'groups', # layout (I have found this one to be consistent across al plots)
esize = 7, # line width of strongest connection/edge
negCol = "#ba00bd", # colour of negative edges
posCol = "#d9db3b", # colour of positive edges
palette = "colorblind", # colour palette
fade = FALSE, # fading effect on edges relative to strength
directed = directed, # directed network or not
DoNotPlot = TRUE,
lty = Plot_df[,4]*-1+2, # significant interactions are solid, non-significant ones are dashed
title = name_plot
)
}
Plots_ls[[Plot_Iter]] <- regplots_ls
}
Plotting_df <- expand.grid(names(Plots_ls), names(regplots_ls))
layout(matrix(1:nrow(Plotting_df),
nrow = length(names(regplots_ls)),
ncol = length(names(Plots_ls)),
byrow = TRUE))
for(Plot_Iter in 1:nrow(Plotting_df)){
Plot_pos1 <- which(names(Plots_ls) == Plotting_df$Var1[Plot_Iter])
Plot_pos2 <- which(names(regplots_ls) == Plotting_df$Var2[Plot_Iter])
plot(Plots_ls[[Plot_pos1]][[Plot_pos2]])
}
}
# NETWORKS AS MATRICES ===================================================
# plotting of networks belonging to the same method as matrices
Plot.NetMat <- function(method = "COCCUR", data = YFDP_df, TreatmentOrder = c("Pre-Fire", "Post-Fire"), datareturn = FALSE){ ## currently only works for YFDP
plot_df <- data[, c(1:2, grep(colnames(data), pattern = method))]
if(method == "IF_REM"){
plot_df <- plot_df[, -grep(colnames(plot_df), pattern = "IF_REM_Assoc")]
}
if("Difference" %in% TreatmentOrder){
if(method == "HMSC"){
EffectCol <- c(grep(colnames(plot_df), pattern = "effects"), ncol(plot_df)+c(1,3,5) )
SigCol <- c(grep(colnames(plot_df), pattern = "Sig"), ncol(plot_df)+c(2,4,6) )
plot_df$Difference.abundance <- plot_df[, grep(x = colnames(plot_df), pattern = "abundance")[1]] - plot_df[, grep(x = colnames(plot_df), pattern = "abundance")[3]]
plot_df$Difference.abundance.Sig <- ifelse(rowSums(plot_df[, grep(x = colnames(plot_df), pattern = "abundance")[c(2,4)]]) == 2, TRUE, FALSE)
if(sum(grepl(pattern = "diametre", x = colnames(data))) == 0){
plot_df$Difference.diametre <- plot_df[, grep(x = colnames(plot_df), pattern = "biomass")[1]] - plot_df[, grep(x = colnames(plot_df), pattern = "biomass")[3]]
plot_df$Difference.diametre.Sig <- ifelse(rowSums(plot_df[, grep(x = colnames(plot_df), pattern = "biomass")[c(2,4)]]) == 2, TRUE, FALSE)
}else{
plot_df$Difference.diametre <- plot_df[, grep(x = colnames(plot_df), pattern = "diametre")[1]] - plot_df[, grep(x = colnames(plot_df), pattern = "diametre")[3]]
plot_df$Difference.diametre.Sig <- ifelse(rowSums(plot_df[, grep(x = colnames(plot_df), pattern = "diametre")[c(2,4)]]) == 2, TRUE, FALSE)
}
plot_df$Difference.presence_absence <- plot_df[, grep(x = colnames(plot_df), pattern = "presence_absence")[1]] - plot_df[, grep(x = colnames(plot_df), pattern = "presence_absence")[3]]
plot_df$Difference.presence_absence.Sig <- ifelse(rowSums(plot_df[, grep(x = colnames(plot_df), pattern = "presence_absence")[c(2,4)]]) == 2, TRUE, FALSE)
}else{
EffectCol <- c(grep(colnames(plot_df), pattern = "effects"), ncol(plot_df)+1)
SigCol <- c(grep(colnames(plot_df), pattern = "Sig"), ncol(plot_df)+2)
plot_df$Difference <- plot_df[,EffectCol[1]] - plot_df[,EffectCol[2]]
plot_df$Difference.Sig <- ifelse(rowSums(plot_df[,SigCol[1:2]]) == 2, TRUE, FALSE)
}
}else{
EffectCol <- c(grep(colnames(plot_df), pattern = "effects"))
SigCol <- c(grep(colnames(plot_df), pattern = "Sig"))
}
effect_df <- reshape(plot_df,
direction = "long",
varying = list(names(plot_df)[EffectCol]),
v.names = "Value",
idvar = c("Partner1", "Partner2"),
timevar = "Condition",
times = names(plot_df)[EffectCol])
sig_df <- reshape(plot_df,
direction = "long",
varying = list(names(plot_df)[SigCol]),
v.names = "Sig",
idvar = c("Partner1", "Partner2"),
timevar = "Condition",
times = names(plot_df)[SigCol])
plot_df <- data.frame(Partner1 = effect_df$Partner1,
Partner2 = effect_df$Partner2,
Value = effect_df$Value,
Sig = sig_df$Sig,
Condition = effect_df$Condition)
plot_df$Condition <- gsub(plot_df$Condition, pattern = ".effects", replacement = "")
plot_df$Condition <- gsub(plot_df$Condition, pattern = paste0(method, "."), replacement = "")
if(method == "HMSC"){
plot_df$Model <- unlist(lapply(strsplit(plot_df$Condition, ".", fixed = TRUE), "[[", 2))
plot_df$Condition <- unlist(lapply(strsplit(plot_df$Condition, ".", fixed = TRUE), "[[", 1))
}
## reshuffling data so all values fall into upper diagonal
if(method != "IF_REM"){
for(Row_i in 1:nrow(plot_df)){
if(method == "HMSC"){
Op_pos <- which(plot_df$Partner2 == plot_df[Row_i, ]$Partner1 &
plot_df$Partner1 == plot_df[Row_i, ]$Partner2 &
plot_df$Condition == plot_df[Row_i, ]$Condition &
plot_df$Model == plot_df[Row_i, ]$Model)
}else{
Op_pos <- which(plot_df$Partner2 == plot_df[Row_i, ]$Partner1 &
plot_df$Partner1 == plot_df[Row_i, ]$Partner2 &
plot_df$Condition == plot_df[Row_i, ]$Condition)
}
if(!is.na(plot_df$Value[Op_pos])){
plot_df$Value[Row_i] <- plot_df$Value[Op_pos]
plot_df$Value[Op_pos] <- NA
plot_df$Sig[Row_i] <- plot_df$Sig[Op_pos]
plot_df$Sig[Op_pos] <- FALSE
}
}
}
levels(plot_df$Partner1) <- sort(levels(plot_df$Partner1))
levels(plot_df$Partner2) <- sort(levels(plot_df$Partner2))
gplot <- ggplot(plot_df, aes(x = Partner2, y = Partner1, fill = Value))
gplot <- gplot +
geom_tile(color = "white",
lwd = 1.5,
linetype = 1) +
## colours, look, and legend
geom_point(aes(shape = Sig), size = 2) +
scale_shape_manual(values=c(32, 15), na.translate = FALSE, name = "Significance") +
guides(shape = FALSE) +
coord_fixed() +
guides(fill = guide_colourbar(barwidth = 2,
barheight = 15,
title = "Association")) +
## axes
theme_bw() +
theme(axis.text.x=element_text(angle = -40, hjust = 0))
if(all(plot_df$Value[!is.na(plot_df$Value)] > 0)){
gplot <- gplot + scale_fill_gradient(high = "#5ab4ac")
}else{
if(all(plot_df$Value[!is.na(plot_df$Value)] < 0)){
gplot <- gplot + scale_fill_gradient(high = "#d8b365")
}else{
gplot <- gplot + scale_fill_gradient2(low = "#5ab4ac", high = "#d8b365")
}
}
if(method != "IF_REM"){
gplot <- gplot + labs(x = "", y = "")
}else{
gplot <- gplot + labs(x = "Subject", y = "Actor")
}
if(method != "HMSC"){
gplot <- gplot + facet_wrap(~factor(Condition, levels = TreatmentOrder))
}else{
if(sum(grepl(pattern = "diametre", x = colnames(data))) == 0){
HMSCOrder <- c("presence_absence", "abundance", "biomass")
}else{
HMSCOrder <- c("presence_absence", "abundance", "diametre")
}
gplot <- gplot + facet_wrap(~ factor(Model, levels = HMSCOrder) +
factor(Condition, levels = TreatmentOrder)
)
}
if(datareturn){
return(plot_df)
}else{
return(gplot)
}
}
# # NETWORKS AS MATRICES ACROSS SCALES =====================================
# # plotting of networks belonging to the same method as matrices across scales
# Plot.NetMat.Cross <- function(data = NULL,
# ALLRegionOrder = c("Pre-Fire", "Yosemite", "Temperate Broadleaf & Mixed Forests"),
# ALLMethodOrder = c("COCCUR", "NETASSOC", "HMSC", "NDD-RIM")){
#
# plot_ls <- as.list(rep(NA, length(ALLRegionOrder)))
# names(plot_ls) <- ALLRegionOrder
#
# for(region in ALLRegionOrder){
# print(region)
# plot_df <- data_ls[[region]]
# plot_df <- plot_df[, -grep(colnames(plot_df), pattern = "_Assoc")]
#
# EffectCol <- c(grep(colnames(plot_df), pattern = "effects"))
# SigCol <- c(grep(colnames(plot_df), pattern = "Sig"))
# # plot_df$Difference <- plot_df[,EffectCol[1]] - plot_df[,EffectCol[2]]
# # plot_df$Difference.Sig <- ifelse(rowSums(plot_df[,SigCol[1:2]]) == 2, TRUE, FALSE)
#
# effect_df <- reshape(plot_df,
# direction = "long",
# varying = list(names(plot_df)[EffectCol]),
# v.names = "Value",
# idvar = c("Partner1", "Partner2"),
# timevar = "Condition",
# times = names(plot_df)[EffectCol])
#
# sig_df <- reshape(plot_df,
# direction = "long",
# varying = list(names(plot_df)[SigCol]),
# v.names = "Sig",
# idvar = c("Partner1", "Partner2"),
# timevar = "Condition",
# times = names(plot_df)[SigCol])
#
# plot_df <- data.frame(Partner1 = effect_df$Partner1,
# Partner2 = effect_df$Partner2,
# Value = effect_df$Value,
# Sig = sig_df$Sig,
# Condition = effect_df$Condition)
# plot_df$Condition <- unlist(lapply(strsplit(plot_df$Condition, split = "\\.") , "[[", 1))
#
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#
# }
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# for(method in ALLMethodOrder){
# print(method)
#
#
# method_ls <- lapply(names(data), FUN = function(x){
# x <- data[[x]]
# x[, c(1:2, grep(colnames(x), pattern = method))]
# })
#
# do.call(cbind, method_ls)
#
# plot_df <- plot_ls[[]]
#
# plot_df <- data[, c(1:2, grep(colnames(data), pattern = method))]
#
# if("Difference" %in% ALLRegionOrder){
# if(method == "HMSC"){
#
# if(sum(grepl(pattern = "diametre", x = colnames(data))) == 0){
# plot_df$Difference.diametre <- plot_df[, grep(x = colnames(plot_df), pattern = "biomass")[1]] - plot_df[, grep(x = colnames(plot_df), pattern = "biomass")[3]]
# plot_df$Difference.diametre.Sig <- ifelse(rowSums(plot_df[, grep(x = colnames(plot_df), pattern = "biomass")[c(2,4)]]) == 2, TRUE, FALSE)
# }else{
# plot_df$Difference.diametre <- plot_df[, grep(x = colnames(plot_df), pattern = "diametre")[1]] - plot_df[, grep(x = colnames(plot_df), pattern = "diametre")[3]]
# plot_df$Difference.diametre.Sig <- ifelse(rowSums(plot_df[, grep(x = colnames(plot_df), pattern = "diametre")[c(2,4)]]) == 2, TRUE, FALSE)
# }
#
# }else{
# EffectCol <- c(grep(colnames(plot_df), pattern = "effects"), ncol(plot_df)+1)
# SigCol <- c(grep(colnames(plot_df), pattern = "Sig"), ncol(plot_df)+2)
# plot_df$Difference <- plot_df[,EffectCol[1]] - plot_df[,EffectCol[2]]
# plot_df$Difference.Sig <- ifelse(rowSums(plot_df[,SigCol[1:2]]) == 2, TRUE, FALSE)
# }
# }else{
# EffectCol <- c(grep(colnames(plot_df), pattern = "effects"))
# SigCol <- c(grep(colnames(plot_df), pattern = "Sig"))
# }
#
# effect_df <- reshape(plot_df,
# direction = "long",
# varying = list(names(plot_df)[EffectCol]),
# v.names = "Value",
# idvar = c("Partner1", "Partner2"),
# timevar = "Condition",
# times = names(plot_df)[EffectCol])
#
# sig_df <- reshape(plot_df,
# direction = "long",
# varying = list(names(plot_df)[SigCol]),
# v.names = "Sig",
# idvar = c("Partner1", "Partner2"),
# timevar = "Condition",
# times = names(plot_df)[SigCol])
#
# plot_df <- data.frame(Partner1 = effect_df$Partner1,
# Partner2 = effect_df$Partner2,
# Value = effect_df$Value,
# Sig = sig_df$Sig,
# Condition = effect_df$Condition)
# plot_df$Condition <- gsub(plot_df$Condition, pattern = ".effects", replacement = "")
# plot_df$Condition <- gsub(plot_df$Condition, pattern = paste0(method, "."), replacement = "")
# if(method == "HMSC"){
# plot_df$Condition <- gsub(plot_df$Condition, pattern = ".biomass", replacement = "")
# plot_df$Condition <- gsub(plot_df$Condition, pattern = ".diametre", replacement = "")
# }
#
# if(method == "NETASSOC" | method == "NDD-RIM"){
# gplot <- ggplot(plot_df, aes(x = Partner2, y = Partner1, fill = Value))
# }else{
# gplot <- ggplot(plot_df, aes(x = Partner1, y = Partner2, fill = Value))
# }
# gplot <- gplot +
# geom_tile(color = "white",
# lwd = 1.5,
# linetype = 1) +
# ## colours, look, and legend
# geom_point(aes(shape = as.factor(Sig)), size = 4) +
# scale_shape_manual(values=c(32, 15), na.translate = FALSE, name = "Significance") +
# guides(shape = FALSE) +
# coord_fixed() +
# ## axes
# theme_bw() +
# theme(axis.text.x=element_text(angle = -20, hjust = 0),
# text = element_text(size=15))
#
# if(all(plot_df$Value[!is.na(plot_df$Value)] > 0)){
# gplot <- gplot + scale_fill_gradient(high = "#d9db3b")
# }else{
# if(all(plot_df$Value[!is.na(plot_df$Value)] < 0)){
# gplot <- gplot + scale_fill_gradient(high = "#ba00bd")
# }else{
# gplot <- gplot + scale_fill_gradient2(low = "#ba00bd", high = "#d9db3b")
# }
# }
#
# if(method != "NDD-RIM"){
# gplot <- gplot + labs(x = "", y = "") +
# guides(fill = guide_colourbar(barwidth = 2,
# barheight = 15,
# title = "Association"))
# }else{
# gplot <- gplot + labs(y = "Actor", x = "Subject") +
# guides(fill = guide_colourbar(barwidth = 2,
# barheight = 15,
# title = "Interaction \n Strength"))
# }
# gplot <- gplot + facet_wrap(~factor(Condition, levels = ALLRegionOrder))
# plot_ls[[method]] <- gplot + theme(plot.margin = unit(c(0, 0, 0, 0), "cm"))
#
# }
# return(plot_grid(plotlist=plot_ls, ncol = 1, labels = ALLMethodOrder))
# }
# NETWORK TOPOLOGIES =====================================================
## for calculation and visualisation of topology metrics
Calc.Topology <- function(data_ls = Comparable_ls,
Shared_abbr = Shared_abbr,
Sig = TRUE){
Plotting_df <- expand.grid(names(data_ls), names(data_ls[[1]]))
# output objects
Base_df <- data.frame(Species = NA,
Value = NA,
Model = NA,
Treatment = NA)
output_ls <- list(Centrality = list(Strength = Base_df,
Eigenvector = Base_df),
Modularity = Base_df[,-1],
adj = c())
# loop over models here
for(i in 1:nrow(Plotting_df)){
print(paste("#####", i))
name_Region <- as.character(Plotting_df$Var1[[i]])
name_Model <- as.character(Plotting_df$Var2[[i]])
data_i <- data_ls[[name_Region]][[name_Model]]
data_i <- data_i[data_i$Shared == 2, ]
## network building
## only keep singificant interactions when Sig == TRUE
if(Sig){
data_i$Sig[is.na(data_i$Sig)] <- FALSE
if(startsWith(name_Model, "NDD")){
SigPos <- as.numeric(as.character(data_i$Sig)) > 0.5
}else{
SigPos <- data_i$Sig
}
if(sum(SigPos)>0){
data_i <- data_i[SigPos,]
}else{
data_i <- NA
strength_df <- data.frame(Species = NA,
Value = NA,
Model = NA,
Treatment = NA)
rownames(strength_df) <- c()
colnames(strength_df) <- colnames(Base_df)
output_ls$Centrality$Strength <- rbind(output_ls$Centrality$Strength, strength_df)
output_ls$Centrality$Eigenvector <- rbind(output_ls$Centrality$Eigenvector, strength_df)
output_ls$Modularity <- rbind(output_ls$Modularity, c(NA, name_Model, name_Region))
next()
}
}else{
data_i <- data_i
}
data_i <- na.omit(data_i[,c("Partner1", "Partner2", "Value")])
colnames(data_i)[3] <- "weight"
if(name_Model == "NDD_RIM"){directed=TRUE}else{directed=FALSE}
g <- igraph::graph_from_data_frame(d = data_i, directed = directed)
adj <- igraph::as_adjacency_matrix(g, attr = "weight")
g_mod <- g
if(!is.null(igraph::E(g)$weight)){
igraph::E(g)$weight <- abs(igraph::E(g)$weight)
}
## Node-Level Topology ----
### Strength
print("Node Strength")
strength <- range01(sort(strength(g), decreasing = TRUE))
strength_df <- data.frame(Species = names(strength),
Value = strength,
Model = name_Model,
Treatment = name_Region)
rownames(strength_df) <- c()
colnames(strength_df) <- colnames(Base_df)
output_ls$Centrality$Strength <- rbind(output_ls$Centrality$Strength, strength_df)
### Eigenvector Centrality
print("Eigenvectors")
ecvent <- sort(evcent(g, scale = TRUE)$vector, decreasing = TRUE)
ecvent_df <- data.frame(Species = names(ecvent),
Value = ecvent,
Model = name_Model,
Treatment = name_Region)
rownames(ecvent_df) <- c()
colnames(ecvent_df) <- colnames(Base_df)
output_ls$Centrality$Eigenvector <- rbind(output_ls$Centrality$Eigenvector, ecvent_df)
## Network-Level Topology ----
### Modularity
print("Modularity")
Fun.Modu <- function(g_mod){
igraph::E(g_mod)$weight[is.na(igraph::E(g_mod)$weight)] <- 0
e <- simplify(g_mod)
fc <- try(cluster_spinglass(g_mod, implementation = "neg", spins = 1e2), silent = TRUE)
if(class(fc) == "try-error"){
# stop()
modularity <- NA
rm(fc)
# fc <- cluster_optimal(g)
# modularity <- modularity(e, membership = fc$membership)
}else{
modularity <- fc$modularity
}
return(modularity)
}
Modu_infer <- Fun.Modu(g_mod)
output_ls$Modularity <- rbind(output_ls$Modularity, c(Modu_infer, name_Model, name_Region))
}
## cleaning output object
output_ls$Centrality$Strength <- output_ls$Centrality$Strength[-1,]
output_ls$Centrality$Strength$Value[which(output_ls$Centrality$Strength$Value == "NaN")] <- 1
output_ls$Centrality$Eigenvector <- output_ls$Centrality$Eigenvector[-1,]
output_ls$Centrality$Eigenvector$Value[which(output_ls$Centrality$Eigenvector$Value == "NaN")] <- 1
output_ls$Modularity <- output_ls$Modularity[-1,]
output_ls$Modularity$Value <- as.numeric(output_ls$Modularity$Value)
## Modularity ----
gplot_df <- output_ls$Modularity
Mod_gg <- ggplot(gplot_df, aes(x = factor(Model,
levels = names(data_ls[[1]])),
y = factor(Treatment,
levels = names(data_ls)),
fill = Value)
) +
geom_tile(color = "black",
lwd = 0.5,
linetype = 1) +
coord_fixed() +
guides(fill = guide_colourbar(barwidth = 2,
barheight = 15,
title = "Modularity")) +
theme_bw() +
theme(axis.text.x=element_text(angle = -20, hjust = 0)) +
scale_fill_gradient2(low = "#5ab4ac", high = "#d8b365") +
labs(y = "Treatment") +
scale_size_continuous(range = c(2, 10), breaks=pretty_breaks(8), name = "Effect Size") +
guides(size = guide_legend(override.aes = list(shape = 25), order = 3))
## Strength ----
# difference calculation
output_ls$Centrality$Strength <- na.omit(output_ls$Centrality$Strength)
NodeTopo_df <- expand.grid(Shared_abbr, names(data_ls[[1]]), names(data_ls))
colnames(NodeTopo_df) <- colnames(output_ls$Centrality$Strength)[-2]
NodeTopo_df$Value <- NA
match_vec <- match(with(output_ls$Centrality$Strength, paste(Species, Model, Treatment)),
with(NodeTopo_df, paste(Species, Model, Treatment)))
NodeTopo_df$Value[match_vec] <- output_ls$Centrality$Strength$Value
Strength_gg <- ggplot(NodeTopo_df,
aes(x = factor(Model, levels = names(data_ls[[1]])),
y = Species, fill = Value)) +
geom_tile(color = "black",
lwd = 0.5,
linetype = 1) +
coord_fixed() +
geom_label(label= round(NodeTopo_df$Value, 2)) +
guides(fill = guide_colourbar(barwidth = 2,
barheight = 15,
title = "Node Strength")) +
## axes
theme_bw() +
theme(axis.text.x=element_text(angle = -20, hjust = 0)) +
facet_wrap(~factor(Treatment, levels = names(data_ls))) +
scale_fill_gradient2(low = "#5ab4ac", high = "#d8b365")
# ## Eigenvector ----
# # difference calculation
# data2 <- output_ls$Centrality$Eigenvector
# if(length(levels(factor(data2$Treatment))) == 2){
# for(k in unique(data2$Model)){
# diff_ls <- split(data2[data2$Model == k,], data2[data2$Model == k,"Treatment"])
# diff_ls <- lapply(diff_ls, FUN = Sort.DF, Column = "Species")
# Diffs <- diff_ls[[TreatmentOrder[1]]]$Value - diff_ls[[TreatmentOrder[2]]]$Value
#
#
# data3 <- data.frame(Species = diff_ls[[TreatmentOrder[1]]]$Species,
# Value = Diffs,
# Model = rep(k, length(Diffs)),
# Treatment = rep("Difference", length(Diffs)))
# data2 <- rbind(data2, data3)
# }
# }
# output_ls$Centrality$Eigenvector <- data2
# Eigenvector_gg <- ggplot(output_ls$Centrality$Eigenvector, aes(x = factor(Model, levels = ModelOrder), y = Species, fill = Value)) +
# geom_tile(color = "black",
# lwd = 0.5,
# linetype = 1) +
# coord_fixed() +
# geom_label(label= round(output_ls$Centrality$Eigenvector$Value, 2)) +
# guides(fill = guide_colourbar(barwidth = 2,
# barheight = 15,
# title = "Eigenvector Centrality")) +
# ## axes
# theme_bw() +
# theme(axis.text.x=element_text(angle = -20, hjust = 0)) +
# facet_wrap(~factor(Treatment, levels = TreatmentOrder)) +
# scale_fill_gradient2(low = "#5ab4ac", high = "#d8b365")
#
plot_ls <- list(Strength = Strength_gg,
# Eigenvector = Eigenvector_gg,
Modularity = Mod_gg
#, Nestedness = Nest_gg
)
return(list(plots = plot_ls,
numbers = output_ls)
)
}