Networks - Jaguaribe Basin
Vitoria Lima and Fred Windsor
06-2024
1 - Data standardisation
Setup
# Clear environment
rm(list=ls())
# Set the working directory
setwd("C:/Users/vitor/OneDrive/Doutorado PPGCB/Networks/Jaguaribe")
# Load the relevant libraries
library(dplyr); library(reshape2); library(magrittr); library(stringr); library(igraph); library(ggnetwork); library(tidyverse); library(bipartite); library(centiserve); library(gridExtra)##
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## combineLoading and curating data
# Read in the the data on fish-parasite interactions
fp <- read.csv("Parasites/parasites_jag.csv", header = TRUE, sep = ";")
# Read in the the data on fish-prey interactions
fd <- read.csv("Diet/diet_jag.csv", header = TRUE, sep = ";")
# Separate out the data on the fish individuals (e.g., mass, length)
fish <- select(fp, id:portion, -date, -sampling)
# Separate out the abiotic condition data
abiotic <- select(fp, id, site, date, transposition, season, portion, temp:nitrito)Standardise the interaction strengths for interactions
- Fish-parasite interactions
# Interactions need to be standardised for endo and ecto parasites separately
# as they will not interact with one another and occupy different niches
# Separate out the information on the parasites
parasites <- select(fp, id, species, transposition, season, portion,
contracaecum.endo:dolops.ecto)
# Separate the endoparasites
endo <- dplyr::select(parasites, contracaecum.endo:neoechinorhynchus.endo)
# Separate the ectoparasites
ecto <- dplyr::select(parasites, c_dig_ect.ecto:dolops.ecto)
# Calculate the total abundances of ectoparasites
parasites$ecto_sum <- rowSums(ecto)
# Calculate the total abundances of endoparasites
parasites$endo_sum <- rowSums(endo)
# Divide the diet contributions by the total to get a proportion (for endo parasites)
stand_endo_total <- parasites %>% mutate(across(contracaecum.endo:neoechinorhynchus.endo, ~ ./endo_sum))
# Divide the diet contributions by the total to get a proportion (for ecto parasites)
stand_ecto_total <- parasites %>% mutate(across(c_dig_ect.ecto:dolops.ecto, ~ ./ecto_sum))
#Remove the extra columns for endoparasites
stand_endo <- select(stand_endo_total, species, transposition, season, portion,
contracaecum.endo:neoechinorhynchus.endo)
#Remove the extra columns for ectoparasites
stand_ecto <- select(stand_ecto_total, species, transposition, season, portion,
c_dig_ect.ecto:dolops.ecto)
# Convert the NAs to zeros as they were generated by dividing 0 by 0 for endo and ecto
stand_endo[is.na(stand_endo)] <- 0
stand_ecto[is.na(stand_ecto)] <- 0
# Convert the variables to factors for endoparasites
stand_endo$species <- as.factor(stand_endo$species)
stand_endo$transposition <- as.factor(stand_endo$transposition)
stand_endo$season <- as.factor(stand_endo$season)
stand_endo$portion <- as.factor(stand_endo$portion)
# Convert the variables to factors for ectoparasites
stand_ecto$species <- as.factor(stand_ecto$species)
stand_ecto$transposition <- as.factor(stand_ecto$transposition)
stand_ecto$season <- as.factor(stand_ecto$season)
stand_ecto$portion <- as.factor(stand_ecto$portion)
#Select the columns to use with the mean information
fp_endo_mean <- aggregate(. ~ species + transposition + season + portion,
data = stand_endo,
function(x) mean(x, na.rm = TRUE))
fp_ecto_mean <- aggregate(. ~ species + transposition + season + portion,
data = stand_ecto,
function(x) mean(x, na.rm = T))- Fish-prey interactions
#Select the data to extract the mean
diet <- dplyr::select(fd, ephemeroptera:arachnida)
fd$diet_sum <- rowSums(diet)
# Divide the diet contributions by the total to get a proportion
stand_diet_total <- fd %>%
mutate(across(ephemeroptera:arachnida, ~ ./diet_sum))
#Select the variables and prey information
stand_diet <- stand_diet_total %>%
select(species, season, transposition, portion, ephemeroptera:arachnida)
#Change the NAs to 0
stand_diet[is.na(stand_diet)] <- 0
#Generate a dataframe with the variables and mean values
fd_mean <- aggregate(. ~ species + season + transposition + portion,
data = stand_diet,
function(x) mean(x, na.rm = T))
# Convert the variables to factors
fd_mean$species <- as.factor(fd_mean$species)
fd_mean$season <- as.factor(fd_mean$season)
fd_mean$transposition <- as.factor(fd_mean$transposition)
fd_mean$portion <- as.factor(fd_mean$portion)
# Combine the dataframes for ecto and endo
df_parasites <- left_join(fp_endo_mean, fp_ecto_mean,
by = c("species", "season", "transposition", "portion"))
# Combine the dataframes for parasites and variables
melted_par <- melt(df_parasites, id.vars = c("species", "season", "transposition", "portion"))
# Combine the dataframes for prey and variables
melted_diet <- melt(fd_mean, id.vars= c("species", "season", "transposition", "portion"))Individual level networks
# Create the total edgelist for all interactions
edgelist <- bind_rows(melted_par, melted_diet, .id = "type")
# Recode 1s and 2s to be Parasitism and Predation
edgelist1 <- edgelist %>%
mutate(type = ifelse(type == "1", "parasite", "prey"))
# Organize and recode the column names
edgelist2 <- edgelist1 %>%
select(species, variable, value, type, season, transposition, portion) %>%
rename(fish = "species", inverts = "variable", weight = "value")
#Remove the interactions with the weight 0 (non-interaction)
edgelist3 <- subset(edgelist2, weight != 0)
#Turn inverts into character
edgelist3$fish <- as.character(edgelist3$fish)
edgelist3$inverts <- as.character(edgelist3$inverts)
# Create a dataframe for the nodes where all the fish species, parasite and prey information,
# changing columns names to "fish", "parasite" and prey"
nodes <- data.frame(node = c(unique(edgelist3$fish), unique(edgelist3$inverts)),
type = c(rep("fish", length(unique(edgelist3$fish))),
rep("parasite", length(unique(melted_par$variable))),
rep("prey", length(unique(melted_diet$variable)))))
# Extract information on endo or ecto parasites to the nodes
nodes$para_type <- str_split(nodes$node, pattern = "\\.")
# Annotate
nodes$para_type_modified <- rep(NA, nrow(nodes))
nodes$para_type_modified <- ifelse(grepl("endo", nodes$para_type, ignore.case=T),
"endo",
nodes$para_type_modified)
nodes$para_type_modified <- ifelse(grepl("ecto", nodes$para_type, ignore.case=T),
"ecto",
nodes$para_type_modified)
# Remove endo and ecto information after dot in the nodes names
nodes$node <- sub("\\..*", "", nodes$node)
# Extract information on endo or ecto parasites to the edgelist
edgelist3$inverts_type <- str_split(edgelist3$inverts, pattern = "\\.")
# Remove endo and ecto information after dot in the nodes names
edgelist3$inverts <- sub("\\..*", "", edgelist3$inverts)
# Change the fish types to native and invasive
nodes1 <- nodes %>%
mutate(fish_type = case_when(
node %in% c("a_ocellatus", "s_dissimilis", "c_monoculus", "o_niloticus") ~ "non-native",
TRUE ~ "native"))
# Creating a new column with all the information extracted and removing extras
nodes1$type_modified <- ifelse(nodes1$type == "fish", nodes1$fish_type, nodes1$para_type_modified)
# Removing unnecessary columns both on nodes1 and on edgelist3
nodes1$para_type <- NULL
nodes1$para_type_modified <- NULL
nodes1$fish_type <- NULL
edgelist3$inverts_type <- NULL
# Repeating the information of "prey" on type modified column
nodes1[nodes1$type == "prey", "type_modified"] <- "prey"2 - Bipartite network analysis
2.1 Fish-parasite interactions
# Change information on type column of edgelist3
edgelist_org <- edgelist3 %>%
mutate(type = ifelse(type == 1, "parasite", "prey"))
# Select only the fish-parasites interactions
edgelist_par <- subset(edgelist3, type == "parasite")
# Convert variables into characters
edgelist_par$season <- as.character(edgelist_par$season)
edgelist_par$transposition <- as.character(edgelist_par$transposition)
edgelist_par$portion <- as.character(edgelist_par$portion)
# Rename column from "inverts" to "parasite"
edgelist_par <- rename(edgelist_par, parasite=inverts)
# Organise the fish and parasite nodes and rename columns from "inverts" to "parasite"
nodes_par <- data.frame(node = c(unique(as.character(edgelist_par$fish)), unique(as.character(edgelist_par$parasite))),
type = c(rep("fish", length(unique(edgelist_par$fish))),
rep("parasite", length(unique(edgelist_par$parasite)))))
# Prepare the nodes coordinates
nodes_par[nodes_par$type == "fish", "x.coord"] <- seq(0, 1, length.out = sum(nodes_par$type == "fish"))
nodes_par[nodes_par$type == "parasite", "x.coord"] <- seq(0, 1, length.out = (sum(nodes_par$type == "parasite")))
nodes_par[nodes_par$type == "fish", "y.coord"] <- 0
nodes_par[nodes_par$type == "parasite", "y.coord"] <- 1
# Define the layout
lay_par <-as.matrix(nodes_par[, c("x.coord", "y.coord")])
# Create a list of edgelists for each variable (season, transposition and portion)
split_edgelists_par <- split(edgelist_par, interaction(edgelist_par$season, edgelist_par$transposition, edgelist_par$portion))
# A function to subset the nodes from the edgelists
node_subset_par <- function(x){
fish_names <- unique(x$fish)
parasite_names <- unique(x$parasite)
taxon_list <- unique(c(fish_names, parasite_names))
subset_y <- nodes_par[nodes_par$node %in% taxon_list,]
return(subset_y)
}
# Create a list of nodes for the networks
split_nodes_par <- lapply(split_edgelists_par, node_subset_par)
# Create bipartite networks for all of the nodes and edges
##### When "directed = T" the Katz's results are different but the plot in bipartite works
bipartite_par_nets <- mapply(function(x,y){graph_from_data_frame(x, vertices = y, directed = F)},
x = split_edgelists_par, y = split_nodes_par)
# Create a function to label nodes depending on their 'type' - for bipartite graph plots
create_ind_type <- function(x, y){
y$fish[is.na(y$fish)] <- "unknown"
y$parasite[is.na(y$parasite)] <- "unknown"
V(x)$type <- c(rep("fish",length(unique(y$fish))),rep("parasite",length(unique(y$parasite))))
return(x)
}
# Add a variable 'type' to the vertex properties
individual_net_list <- mapply(create_ind_type, x = bipartite_par_nets, y = split_edgelists_par, SIMPLIFY = F)
## Convert the edgelists to matrices for bipartite analyses
df_parasites2 <- df_parasites
cleaned_colnames <- sub("\\..*", "", colnames(df_parasites2))
colnames(df_parasites2) <- cleaned_colnames
split_matrices_par <- split(df_parasites2, interaction(df_parasites2$season, df_parasites2$transposition, df_parasites2$portion))
# Create function to clean the matrices
m_cleaner <- function(x) {
rownames(x) <- x$species
x1 <- select(x, -season, -species, -transposition, -portion)
return(x1)
}
# Prepare matrix to calculate the metrics
split_matrices1 <- lapply(split_matrices_par, m_cleaner)Calculate the metrics for Parasites
# Create a dataframe to visualize the results
para_net_metrics <- data.frame(Site=rep(c("upper","middle","lower"), 4),
Season=c(rep("dry", 6),rep("wet", 6)),
Transposition = c(rep("post",6), rep("pre", 6)))Network level
- Connectance
# Create a column on the dataframe to show results
para_net_metrics$Connectance <- NA
# Apply the function to show the Connectance values
para_net_metrics$Connectance <- unlist(lapply(split_matrices1, function(x){networklevel(x, index = "weighted connectance")}))- Nestedness
para_net_metrics$Nestedness <- NA
para_net_metrics$Nestedness <- unlist(lapply(split_matrices1, function(x){networklevel(x, index = "NODF")}))Organise the results
# Function to extract names from matrices
species_names <- function(x){
x1 <- x
x2 <- bipartite::empty(x1)
names<- c(colnames(x2),rownames(x2))
return(names)
}
# List of species across sites
species_names_list <- lapply(split_matrices1, species_names)
# Dataframe for individual metrics
para_node_metrics <- data.frame(ID = unlist(species_names_list),
Season = substr(names(unlist(species_names_list)), 1, 3),
Transposition = substr(names(unlist(species_names_list)), 5, 7),
Portion = substr(names(unlist(species_names_list)), 10, 12))
#Extract fish species for the Type column
species_fish <- unique(fish$species)
#Create Type column
para_node_metrics$Type <- NA
#Apply "Fish" and "Parasite" on Type column
for (i in 1:nrow(para_node_metrics)) {
if (para_node_metrics$ID[i] %in% species_fish) {para_node_metrics$Type[i] <- "fish"} else
{para_node_metrics$Type[i] <- "parasite"}
}
#Reorganize columns
para_node_metrics <- para_node_metrics[, c("ID", "Type", "Season", "Transposition", "Portion")]Calculate the node-based values
- Degree distribution
para_node_metrics$Degree <- NA
para_node_metrics$Degree <- unlist(lapply(split_matrices1, function(x){specieslevel(x, index = "degree")}))- Katz’s centrality (for individuals)
para_node_metrics$Katz <- NA
para_node_metrics$Katz <- unlist(lapply(bipartite_par_nets, function(x){katzcent(x)}))2.2 Fish-prey interactions
## Organise the fish-prey edgelist
# Select only the fish-prey interactions
edgelist_prey <- subset(edgelist3, type == "prey")
# Convert variables into characters
edgelist_prey$season <- as.character(edgelist_prey$season)
edgelist_prey$transposition <- as.character(edgelist_prey$transposition)
edgelist_prey$portion <- as.character(edgelist_prey$portion)
# Rename column from "inverts" to "prey"
edgelist_prey <- rename(edgelist_prey, prey = inverts)
# Organise the fish and prey nodes
nodes_prey <- data.frame(node = c(unique(edgelist_prey$fish), unique(as.character(edgelist_prey$prey))),
type = c(rep("fish", length(unique(edgelist_prey$fish))),
rep("prey", length(unique(edgelist_prey$prey)))))
# Prepare the nodes coordinates
nodes_prey[nodes_prey$type == "fish", "x.coord"] <- seq(0, 1, length.out = sum(nodes_prey$type == "fish"))
nodes_prey[nodes_prey$type == "prey", "x.coord"] <- seq(0, 1, length.out = (sum(nodes_prey$type == "prey")))
nodes_prey[nodes_prey$type == "fish", "y.coord"] <- 0
nodes_prey[nodes_prey$type == "prey", "y.coord"] <- 1
lay_prey <-as.matrix(nodes_prey[, c("x.coord", "y.coord")])
# Create a list of edgelists for each variable (site, season and transposition)
split_edgelists_prey <- split(edgelist_prey, interaction(edgelist_prey$season,
edgelist_prey$transposition,
edgelist_prey$portion))
# A function to subset the nodes from the edgelists
node_subset_prey <- function(x){
fish_names <- unique(x$fish)
prey_names <- unique(x$prey)
taxon_list <- unique(c(fish_names, prey_names))
subset_y <- nodes_prey[nodes_prey$node %in% taxon_list,]
return(subset_y)
}
# Create a list of nodes for the networks
split_nodes_prey <- lapply(split_edgelists_prey, node_subset_prey)
# Create bipartite networks for all of the nodes and edges
bipartite_prey_nets <- mapply(function(x,y){graph_from_data_frame(x, vertices = y, directed = F)}, x = split_edgelists_prey, y = split_nodes_prey)
# Convert the edgelists to matrices for bipartite analyses
split_matrices_prey <- split(fd_mean, interaction(fd_mean$season, fd_mean$transposition, fd_mean$portion))
# Prepare matrix to calculate the metrics
split_matrices_prey1 <- lapply(split_matrices_prey, m_cleaner)Calculate the metrics for Prey
# Create a dataframe to visualize the results
prey_net_metrics <- data.frame(Site=rep(c("upper","middle","lower"), 4),
Season=c(rep("dry", 6),rep("wet", 6)),
Transposition = c(rep("post",6), rep("pre", 6)))- Connectance
# Create a column on the dataframe to show results
prey_net_metrics$Connectance <- NA
# Apply the function to show the Connectance values
prey_net_metrics$Connectance <- unlist(lapply(split_matrices_prey1, function(x){networklevel(x, index = "weighted connectance")}))- Nestedness
prey_net_metrics$Nestedness <- NA
prey_net_metrics$Nestedness <- unlist(lapply(split_matrices_prey1, function(x){networklevel(x, index = "NODF")}))Organise the resuls
# Function to extract names from matrices
species_names <- function(x){
x1 <- x
x2 <- bipartite::empty(x1)
names<- c(colnames(x2),rownames(x2))
return(names)
}
# List of species across sites
species_names_list <- lapply(split_matrices1, species_names)
# Apply the function to list species across sites
species_names_prey <- lapply(split_matrices_prey1, species_names)
# Dataframe for individual metrics
prey_node_metrics <- data.frame(ID = unlist(species_names_prey),
Season = substr(names(unlist(species_names_prey)), 1, 3),
Transposition = substr(names(unlist(species_names_prey)), 5, 7),
Portion = substr(names(unlist(species_names_prey)), 10, 12))
#Extract fish species for the Type column
species_fish <- unique(fish$species)
#Create Type column
prey_node_metrics$Type <- NA
#Apply "Fish" and "Prey" on Type column
for (i in 1:nrow(prey_node_metrics)) {
if (prey_node_metrics$ID[i] %in% species_fish) {prey_node_metrics$Type[i] <- "fish"} else
{prey_node_metrics$Type[i] <- "prey"}
}
#Reorganize columns
prey_node_metrics <- prey_node_metrics[, c("ID", "Type", "Season", "Transposition", "Portion")]Calculate the node-based values
- Degree distribution
prey_node_metrics$Degree <- NA
prey_node_metrics$Degree <- unlist(lapply(split_matrices_prey1, function(x){specieslevel(x, index = "degree")}))- Katz’s centrality (for individuals)
prey_node_metrics$Katz <- NA
prey_node_metrics$Katz <- unlist(lapply(bipartite_prey_nets, function(x){katzcent(x)}))3 - Network plotting
Tripartite plotting
# Create some x coordinates to use in plotting later
nodes1[nodes1$type == "fish", "x.coord"] <- seq(0, 1, length.out = sum(nodes1$type == "fish"))
nodes1[nodes1$type == "parasite", "x.coord"] <- seq(0, 1, length.out = (sum(nodes1$type == "parasite")))
nodes1[nodes1$type == "prey", "x.coord"] <- seq(0, 1, length.out = (sum(nodes1$type == "prey")))
# Create some y coordinates to use in plotting later
nodes1[nodes1$type == "fish", "y.coord"] <- 2
nodes1[nodes1$type == "parasite", "y.coord"] <- 3
nodes1[nodes1$type == "prey", "y.coord"] <- 1
# Adjust the height values
nodes1$height_adj <- (nodes1$y.coord - min(nodes1$y.coord)) / (max(nodes1$y.coord) - min(nodes1$y.coord))Create the network graphs
- Pre/Upper
edges_PRU <- subset(edgelist3, transposition == "pre" & portion == "upper")
graph_PRU <- graph_from_data_frame(edges_PRU, directed = T)
nodes_PRU <- nodes1[nodes1$node %in% c(unique(edges_PRU$fish), unique(edges_PRU$inverts)),]
lay_PRU <- as.matrix(nodes_PRU[, c("x.coord", "y.coord")])
network_PRU <- ggnetwork:::fortify.igraph(graph_PRU, layout = lay_PRU, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_PRU$height <- (nodes_PRU$y.coord - min(nodes_PRU$y.coord)) / (max(nodes_PRU$y.coord) - min(nodes_PRU$y.coord))
plot_PRU <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_PRU) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_PRU) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Pre-transposition (Upper)")
plot_PRU
- Pre/Middle
edges_PRM <- subset(edgelist3, transposition == "pre" & portion == "middle")
graph_PRM <- graph_from_data_frame(edges_PRM, directed = T)
nodes_PRM <- nodes1[nodes1$node %in% c(unique(edges_PRM$fish), unique(edges_PRM$inverts)),]
lay_PRM <- as.matrix(nodes_PRM[, c("x.coord", "y.coord")])
network_PRM <- ggnetwork:::fortify.igraph(graph_PRM, layout = lay_PRM, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_PRM$height <- (nodes_PRM$y.coord - min(nodes_PRM$y.coord)) / (max(nodes_PRM$y.coord) - min(nodes_PRM$y.coord))
plot_PRM <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_PRM) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_PRM) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Pre-transposition (Middle)")
plot_PRM
- Pre/Lower
edges_PRL <- subset(edgelist3, transposition == "pre" & portion == "lower")
graph_PRL <- graph_from_data_frame(edges_PRL, directed = T)
nodes_PRL <- nodes1[nodes1$node %in% c(unique(edges_PRL$fish), unique(edges_PRL$inverts)),]
lay_PRL <- as.matrix(nodes_PRL[, c("x.coord", "y.coord")])
network_PRL <- ggnetwork:::fortify.igraph(graph_PRL, layout = lay_PRL, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_PRL$height <- (nodes_PRL$y.coord - min(nodes_PRL$y.coord)) / (max(nodes_PRL$y.coord) - min(nodes_PRL$y.coord))
plot_PRL <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_PRL) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_PRL) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Pre-transposition (Lower)")
plot_PRL
- Post/Upper
edges_POU <- subset(edgelist3, transposition == "post" & portion == "upper")
graph_POU <- graph_from_data_frame(edges_POU, directed = T)
nodes_POU <- nodes1[nodes1$node %in% c(unique(edges_POU$fish), unique(edges_POU$inverts)),]
lay_POU <- as.matrix(nodes_POU[, c("x.coord", "y.coord")])
network_POU <- ggnetwork:::fortify.igraph(graph_POU, layout = lay_POU, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_POU$height <- (nodes_POU$y.coord - min(nodes_POU$y.coord)) / (max(nodes_POU$y.coord) - min(nodes_POU$y.coord))
plot_POU <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_POU) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_POU) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Post-transposition (Upper)")
plot_POU 
- Post/Middle
edges_POM <- subset(edgelist3, transposition == "post" & portion == "middle")
graph_POM <- graph_from_data_frame(edges_POM, directed = T)
nodes_POM <- nodes1[nodes1$node %in% c(unique(edges_POM$fish), unique(edges_POM$inverts)),]
lay_POM <- as.matrix(nodes_POM[, c("x.coord", "y.coord")])
network_POM <- ggnetwork:::fortify.igraph(graph_POM, layout = lay_POM, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_POM$height <- (nodes_POM$y.coord - min(nodes_POM$y.coord)) / (max(nodes_POM$y.coord) - min(nodes_POM$y.coord))
plot_POM <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_POM) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_POM) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Post-transposition (Middle)")
plot_POM
- Post/Lower
edges_POL <- subset(edgelist3, transposition == "post" & portion == "lower")
graph_POL <- graph_from_data_frame(edges_POL, directed = T)
nodes_POL <- nodes1[nodes1$node %in% c(unique(edges_POL$fish), unique(edges_POL$inverts)),]
lay_POL <- as.matrix(nodes_POL[, c("x.coord", "y.coord")])
network_POL <- ggnetwork:::fortify.igraph(graph_POL, layout = lay_POL, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_POL$height <- (nodes_POL$y.coord - min(nodes_POL$y.coord)) / (max(nodes_POL$y.coord) - min(nodes_POL$y.coord))
plot_POL <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_POL) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_POL) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Post-transposition (Lower)")
plot_POL
- Plot as a grid (by transposition and portion)
grid_plot_tri <- grid.arrange(plot_PRU, plot_PRM, plot_PRL, plot_POU, plot_POM, plot_POL, nrow = 2)
# Save image
#ggsave("C:/Users/Windows 10/OneDrive/Doutorado PPGCB/Networks/Jaguaribe/Results/Plots/Networks/Fish-parasite-prey_Transp_Port.png",
# plot = grid_plot_tri,
# dpi = 1200)3.1 Bipartite plotting
- Fish x Parasites – Pre/Upper
edges_par_PRU <- subset(edgelist3, type == "parasite" & transposition == "pre" & portion == "upper")
graph_par_PRU <- graph_from_data_frame(edges_par_PRU, directed = T)
nodes_par_PRU <- nodes1[nodes1$node %in% c(unique(edges_par_PRU$fish), unique(edges_par_PRU$inverts)),]
lay_par_PRU <- as.matrix(nodes_par_PRU[, c("x.coord", "y.coord")])
network_par_PRU <- ggnetwork:::fortify.igraph(graph_par_PRU, layout = lay_par_PRU, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_par_PRU$height <- (nodes_par_PRU$y.coord - min(nodes_par_PRU$y.coord)) / (max(nodes_par_PRU$y.coord) - min(nodes_par_PRU$y.coord))
plot_par_PRU <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_par_PRU) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_par_PRU) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59")) +
theme_blank() +
ggtitle("Parasites (Pre/Upper)")
plot_par_PRU
- Pre/Middle
edges_par_PRM <- subset(edgelist3, type == "parasite" & transposition == "pre" & portion == "middle")
graph_par_PRM <- graph_from_data_frame(edges_par_PRM, directed = T)
nodes_par_PRM <- nodes1[nodes1$node %in% c(unique(edges_par_PRM$fish), unique(edges_par_PRM$inverts)),]
lay_par_PRM <- as.matrix(nodes_par_PRM[, c("x.coord", "y.coord")])
network_par_PRM <- ggnetwork:::fortify.igraph(graph_par_PRM, layout = lay_par_PRM, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_par_PRM$height <- (nodes_par_PRM$y.coord - min(nodes_par_PRM$y.coord)) / (max(nodes_par_PRM$y.coord) - min(nodes_par_PRM$y.coord))
plot_par_PRM <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_par_PRM) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_par_PRM) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59")) +
theme_blank() +
ggtitle("Parasites (Pre/Middle)")
plot_par_PRM
- Pre/Lower
edges_par_PRL <- subset(edgelist3, type == "parasite" & transposition == "pre" & portion == "lower")
graph_par_PRL <- graph_from_data_frame(edges_par_PRL, directed = T)
nodes_par_PRL <- nodes1[nodes1$node %in% c(unique(edges_par_PRL$fish), unique(edges_par_PRL$inverts)),]
lay_par_PRL <- as.matrix(nodes_par_PRL[, c("x.coord", "y.coord")])
network_par_PRL <- ggnetwork:::fortify.igraph(graph_par_PRL, layout = lay_par_PRL, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_par_PRL$height <- (nodes_par_PRL$y.coord - min(nodes_par_PRL$y.coord)) / (max(nodes_par_PRL$y.coord) - min(nodes_par_PRL$y.coord))
plot_par_PRL <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_par_PRL) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_par_PRL) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59")) +
theme_blank() +
ggtitle("Parasites (Pre/Lower)")
plot_par_PRL
- Post/Upper
edges_par_POU <- subset(edgelist3, type == "parasite" & transposition == "post" & portion == "upper")
graph_par_POU <- graph_from_data_frame(edges_par_POU, directed = T)
nodes_par_POU <- nodes1[nodes1$node %in% c(unique(edges_par_POU$fish), unique(edges_par_POU$inverts)),]
lay_par_POU <- as.matrix(nodes_par_POU[, c("x.coord", "y.coord")])
network_par_POU <- ggnetwork:::fortify.igraph(graph_par_POU, layout = lay_par_POU, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_par_POU$height <- (nodes_par_POU$y.coord - min(nodes_par_POU$y.coord)) / (max(nodes_par_POU$y.coord) - min(nodes_par_POU$y.coord))
plot_par_POU <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_par_POU) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_par_POU) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59")) +
theme_blank() +
ggtitle("Parasites (Post/Upper)")
plot_par_POU
- Post/Middle
edges_par_POM <- subset(edgelist3, type == "parasite" & transposition == "post" & portion == "middle")
graph_par_POM <- graph_from_data_frame(edges_par_POM, directed = T)
nodes_par_POM <- nodes1[nodes1$node %in% c(unique(edges_par_POM$fish), unique(edges_par_POM$inverts)),]
lay_par_POM <- as.matrix(nodes_par_POM[, c("x.coord", "y.coord")])
network_par_POM <- ggnetwork:::fortify.igraph(graph_par_POM, layout = lay_par_POM, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_par_POM$height <- (nodes_par_POM$y.coord - min(nodes_par_POM$y.coord)) / (max(nodes_par_POM$y.coord) - min(nodes_par_POM$y.coord))
plot_par_POM <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_par_POM) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_par_POM) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59")) +
theme_blank() +
ggtitle("Parasites (Post/Middle)")
plot_par_POM
- Post/Lower
edges_par_POL <- subset(edgelist3, type == "parasite" & transposition == "post" & portion == "lower")
graph_par_POL <- graph_from_data_frame(edges_par_POL, directed = T)
nodes_par_POL <- nodes1[nodes1$node %in% c(unique(edges_par_POL$fish), unique(edges_par_POL$inverts)),]
lay_par_POL <- as.matrix(nodes_par_POL[, c("x.coord", "y.coord")])
network_par_POL <- ggnetwork:::fortify.igraph(graph_par_POL, layout = lay_par_POL, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_par_POL$height <- (nodes_par_POL$y.coord - min(nodes_par_POL$y.coord)) / (max(nodes_par_POL$y.coord) - min(nodes_par_POL$y.coord))
plot_par_POL <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_par_POL) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_par_POL) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59")) +
theme_blank() +
ggtitle("Parasites (Post/Lower)")
plot_par_POL
- Plot as a grid for Parasites (by transposition and portion)
grid_plot_par <- grid.arrange(plot_par_PRU, plot_par_PRM, plot_par_PRL, plot_par_POU, plot_par_POM, plot_par_POL, nrow = 2)
# Save image
#ggsave("C:/Users/Windows 10/OneDrive/Doutorado PPGCB/Networks/Jaguaribe/Results/Plots/Networks/Fish-parasite_Transp_Port.png",
# plot = grid_plot_par,
# dpi = 1200)- Fish x Prey – Pre/Upper
edges_prey_PRU <- subset(edgelist3, type == "prey" & transposition == "pre" & portion == "upper")
graph_prey_PRU <- graph_from_data_frame(edges_prey_PRU, directed = T)
nodes_prey_PRU <- nodes1[nodes1$node %in% c(unique(edges_prey_PRU$fish), unique(edges_prey_PRU$inverts)),]
lay_prey_PRU <- as.matrix(nodes_prey_PRU[, c("x.coord", "y.coord")])
network_prey_PRU <- ggnetwork:::fortify.igraph(graph_prey_PRU, layout = lay_prey_PRU, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_prey_PRU$height <- (nodes_prey_PRU$y.coord - min(nodes_prey_PRU$y.coord)) / (max(nodes_prey_PRU$y.coord) - min(nodes_prey_PRU$y.coord))
plot_prey_PRU <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_prey_PRU) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_prey_PRU) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Prey (Pre/Upper)")
plot_prey_PRU
- Pre/Middle
edges_prey_PRM <- subset(edgelist3, type == "prey" & transposition == "pre" & portion == "middle")
graph_prey_PRM <- graph_from_data_frame(edges_prey_PRM, directed = T)
nodes_prey_PRM <- nodes1[nodes1$node %in% c(unique(edges_prey_PRM$fish), unique(edges_prey_PRM$inverts)),]
lay_prey_PRM <- as.matrix(nodes_prey_PRM[, c("x.coord", "y.coord")])
network_prey_PRM <- ggnetwork:::fortify.igraph(graph_prey_PRM, layout = lay_prey_PRM, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_prey_PRM$height <- (nodes_prey_PRM$y.coord - min(nodes_prey_PRM$y.coord)) / (max(nodes_prey_PRM$y.coord) - min(nodes_prey_PRM$y.coord))
plot_prey_PRM <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_prey_PRM) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_prey_PRM) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Prey (Pre/Middle)")
plot_prey_PRM
- Pre/Lower
edges_prey_PRL <- subset(edgelist3, type == "prey" & transposition == "pre" & portion == "lower")
graph_prey_PRL <- graph_from_data_frame(edges_prey_PRL, directed = T)
nodes_prey_PRL <- nodes1[nodes1$node %in% c(unique(edges_prey_PRL$fish), unique(edges_prey_PRL$inverts)),]
lay_prey_PRL <- as.matrix(nodes_prey_PRL[, c("x.coord", "y.coord")])
network_prey_PRL <- ggnetwork:::fortify.igraph(graph_prey_PRL, layout = lay_prey_PRL, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_prey_PRL$height <- (nodes_prey_PRL$y.coord - min(nodes_prey_PRL$y.coord)) / (max(nodes_prey_PRL$y.coord) - min(nodes_prey_PRL$y.coord))
plot_prey_PRL <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_prey_PRL) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_prey_PRL) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Prey (Pre/Lower)")
plot_prey_PRL
- Post/Upper
edges_prey_POU <- subset(edgelist3, type == "prey" & transposition == "post" & portion == "upper")
graph_prey_POU <- graph_from_data_frame(edges_prey_POU, directed = T)
nodes_prey_POU <- nodes1[nodes1$node %in% c(unique(edges_prey_POU$fish), unique(edges_prey_POU$inverts)),]
lay_prey_POU <- as.matrix(nodes_prey_POU[, c("x.coord", "y.coord")])
network_prey_POU <- ggnetwork:::fortify.igraph(graph_prey_POU, layout = lay_prey_POU, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_prey_POU$height <- (nodes_prey_POU$y.coord - min(nodes_prey_POU$y.coord)) / (max(nodes_prey_POU$y.coord) - min(nodes_prey_POU$y.coord))
plot_prey_POU <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_prey_POU) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_prey_POU) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Prey (Post/Upper)")
plot_prey_POU
- Post/Middle
edges_prey_POM <- subset(edgelist3, type == "prey" & transposition == "post" & portion == "middle")
graph_prey_POM <- graph_from_data_frame(edges_prey_POM, directed = T)
nodes_prey_POM <- nodes1[nodes1$node %in% c(unique(edges_prey_POM$fish), unique(edges_prey_POM$inverts)),]
lay_prey_POM <- as.matrix(nodes_prey_POM[, c("x.coord", "y.coord")])
network_prey_POM <- ggnetwork:::fortify.igraph(graph_prey_POM, layout = lay_prey_POM, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_prey_POM$height <- (nodes_prey_POM$y.coord - min(nodes_prey_POM$y.coord)) / (max(nodes_prey_POM$y.coord) - min(nodes_prey_POM$y.coord))
plot_prey_POM <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_prey_POM) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_prey_POM) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Prey (Post/Middle)")
plot_prey_POM
- Post/Lower
edges_prey_POL <- subset(edgelist3, type == "prey" & transposition == "post" & portion == "lower")
graph_prey_POL <- graph_from_data_frame(edges_prey_POL, directed = T)
nodes_prey_POL <- nodes1[nodes1$node %in% c(unique(edges_prey_POL$fish), unique(edges_prey_POL$inverts)),]
lay_prey_POL <- as.matrix(nodes_prey_POL[, c("x.coord", "y.coord")])
network_prey_POL <- ggnetwork:::fortify.igraph(graph_prey_POL, layout = lay_prey_POL, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_prey_POL$height <- (nodes_prey_POL$y.coord - min(nodes_prey_POL$y.coord)) / (max(nodes_prey_POL$y.coord) - min(nodes_prey_POL$y.coord))
plot_prey_POL <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_prey_POL) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 4.5, data = nodes_prey_POL) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Prey (Post/Lower)")
plot_prey_POL
- Plot as a grid for Prey (by transposition and portion)
grid_plot_prey <- grid.arrange(plot_prey_PRU, plot_prey_PRM, plot_prey_PRL, plot_prey_POU, plot_prey_POM, plot_prey_POL, nrow = 2)
# Save image
#ggsave("C:/Users/Windows 10/OneDrive/Doutorado PPGCB/Networks/Jaguaribe/Results/Plots/Networks/Fish-prey_Transp_Port.png",
# plot = grid_plot_prey,
# dpi = 1200)- Plot by transposition – Pre-transposition
edges_transp_pre <- subset(edgelist3, transposition == "pre")
graph_transp_pre <- graph_from_data_frame(edges_transp_pre, directed = T)
nodes_transp_pre <- nodes1[nodes1$node %in% c(unique(edges_transp_pre$fish), unique(edges_transp_pre$inverts)),]
lay_transp_pre <- as.matrix(nodes_transp_pre[, c("x.coord", "y.coord")])
network_transp_pre <- ggnetwork:::fortify.igraph(graph_transp_pre, layout = lay_transp_pre, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_transp_pre$height <- (nodes_transp_pre$y.coord - min(nodes_transp_pre$y.coord)) / (max(nodes_transp_pre$y.coord) - min(nodes_transp_pre$y.coord))
plot_transp_pre <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_transp_pre) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 6, data = nodes_transp_pre) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Pre-transposition")
plot_transp_pre
- Post-transposition
edges_transp_post <- subset(edgelist3, transposition == "post")
graph_transp_post <- graph_from_data_frame(edges_transp_post, directed = T)
nodes_transp_post <- nodes1[nodes1$node %in% c(unique(edges_transp_post$fish), unique(edges_transp_post$inverts)),]
lay_transp_post <- as.matrix(nodes_transp_post[, c("x.coord", "y.coord")])
network_transp_post <- ggnetwork:::fortify.igraph(graph_transp_post, layout = lay_transp_post, scale = F )## Warning in format_fortify(model = model, nodes = nodes, weights = "none", :
## duplicated edges detectednodes_transp_post$height <- (nodes_transp_post$y.coord - min(nodes_transp_post$y.coord)) / (max(nodes_transp_post$y.coord) - min(nodes_transp_post$y.coord))
plot_transp_post <- ggplot() +
geom_edges(alpha = 0.2, aes(x = xend, y = yend, xend = x, yend = y), data = network_transp_post) +
geom_nodes(aes(x = x.coord, y = y.coord, shape = factor(type), colour = factor(type_modified)), size = 6, data = nodes_transp_post) +
#geom_label(data = nodes1, aes(x = x.coord, y = height, label = node), inherit.aes = F) +
scale_shape_manual(guide = "none", values = c("fish" = 16, "parasite" = 17, "prey" = 15)) +
scale_size_manual(guide = "none", values = c(10,8, 9)) +
scale_colour_manual(guide = "none", name = "", values = c("non-native" = "#91bfdb", "native" = "#4575b4",
"ecto" = "#d73027", "endo" = "#fc8d59",
"prey" = "#fee090")) +
theme_blank() +
ggtitle("Post-transposition")
plot_transp_post
- Plot as a grid (by transposition)
grid_plot_transp <- grid.arrange(plot_transp_pre, plot_transp_post, nrow = 1)
# Save image
#ggsave("C:/Users/Windows 10/OneDrive/Doutorado PPGCB/Networks/Jaguaribe/Results/Plots/Networks/Fish-parasite-prey_Transp.png",
# plot = grid_plot_transp,
# dpi = 1200)4 - Metrics plotting
- Fish x Parasites
# Organise the visualization
para_net_metrics$Transposition <-
factor(para_net_metrics$Transposition, levels = c("pre", "post"))
para_net_metrics$Site <-
factor(para_net_metrics$Site, levels = c("upper", "middle", "lower"))- Boxplot for Nestedness
ggplot(para_net_metrics) +
aes(x = Transposition, y = Nestedness) +
geom_boxplot(fill = "#112446") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Boxplot for Connectance
ggplot(prey_net_metrics) +
aes(x = Transposition, y = Connectance) +
geom_boxplot(fill = "#112446") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Connectance with bars for portions
ggplot(para_net_metrics) +
aes(x = Transposition, fill = Site, weight = Connectance) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "GnBu", direction = 1) +
labs(title = "Connectance") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Nestedness with bars for portions
ggplot(para_net_metrics) +
aes(x = Transposition, fill = Site, weight = Nestedness) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "GnBu", direction = 1) +
labs(title = "Nestedness") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Fish x Prey
# Organise the visualization
prey_net_metrics$Transposition <-
factor(prey_net_metrics$Transposition, levels = c("pre", "post"))
prey_net_metrics$Site <-
factor(prey_net_metrics$Site, levels = c("upper", "middle", "lower"))- Boxplot for Nestedness
ggplot(prey_net_metrics) +
aes(x = Transposition, y = Nestedness) +
geom_boxplot(fill = "#112446") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Boxplot for Connectance
ggplot(prey_net_metrics) +
aes(x = Transposition, y = Connectance) +
geom_boxplot(fill = "#112446") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Connectance with bars for portions
ggplot(prey_net_metrics) +
aes(x = Transposition, fill = Site, weight = Connectance) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "GnBu", direction = 1) +
labs(title = "Connectance") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))
- Nestedness with bars for portions
ggplot(prey_net_metrics) +
aes(x = Transposition, fill = Site, weight = Nestedness) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "GnBu", direction = 1) +
labs(title = "Nestedness") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, hjust = 0.5))