#'
#' This is a list of R functions with the main functionalities of
#' the chagasnws package.
#'
#' The name chagasnws make reference to the Chagas Networks.
#'
#' The main package dependence is with the igraph package.
#'
#' This function epsilon estimates the epsilon association used to build
#' association link between species.
#'
#' @param nij = Number of cells or units in which both species A and B ocurrs.
#' @param ni = Number of cells or units in which the A species ocurrs.
#' @param nj = Number of cells or units in which the B species ocurrs.
#' @param n  = Number of cell grids or units in which A and B can ocurr.
#' @examples
#' epsilon(5,20,36,1900)
#' @author Leopoldo Valiente-Banuet
#' @author Carlos Ibarra-Cerdeña
#' @references
#' \url{https://peerj.com/current_paper}
#' @seealso
#' \url{http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0005725#s4}
#' @export
epsilon <- function ( nij, ni, nj, n) {
   message_sent = FALSE
   if ((!is.numeric(nij)) | (!is.numeric(ni)) | (!is.numeric(nj)) | (!is.numeric(n))) {
      message ("nij, ni, nj and N must be numeric")
      message_sent = TRUE
   }
   if ((nij > ni) | (nij > nj) | (nij > n)){
      message ("nij must be numeric and lower than ni, nj and N")
      message_sent = TRUE
   }
   if ((ni > n) | (nj > n)){
      message ("ni and nj must be numeric and lower than N")
      message_sent = TRUE
   }
   Prob.Bi <- ni / n
   Prob.Bi.condicional.Ij <- nij / nj
   if (message_sent == TRUE){
      epsilon = NA
   } else {
      epsilon <- (nj * (Prob.Bi.condicional.Ij - Prob.Bi)) / sqrt(nj * Prob.Bi * (1 - Prob.Bi))
   }
   return(epsilon)
}

# The functions included in this script can be used as full procedures o
# cab be used as code snipets.

# An example to produce the graphs of the networks with different values of epsilon
# is shown as follows.
# The rest of the script presents R funtions.

# This function produces five network graphs for epsilons of 2, 4, 6, 8 and 10.

generate_network_graphs <- function() {
nij <- 3 # Minimum number of species coincidences
epsilon <- 2 # Select all significant epsilons biger or lower than 2.
# This values depend on the implementation. Here we connect to a PostgreSQL/PostGIS
# database
q <- paste("select idvector as source, idmammal as target, ni, ni, nij, n, epsilon from epsilons ",
                 " where (epsilon >= ",epsilon," or epsilon <= -",epsilon,") and nij >= ",nij,";",sep="")
datos <- dbGetQuery(con, q)

# The networks graphs that we present corresponde to the next values of epsilons.

epsilons_graficas <- c(2,4,6,8,10)

for ( i in 1:length(epsilons_graficas)) {

   graph_name_file = paste('graph_',epsilons_graficas[i],'.png',sep="")
   png(filename = graph_name_file, width = 1000, height = 1000,  bg="transparent", res=80)
   #par(mfrow=c(3,2))
   par(cex=0.9)
   epsi <- epsilons_graficas[i]
   datos_i <- datos[datos$epsilon >= epsi | datos$epsilon <= -epsi,]
   graph_df = data.frame(triatomine = datos_i$source, mammal = datos_i$target, epsilon = datos_i$epsilon)
   species_target_network_tmp = c(unique(datos_i$source), unique(datos_i$target))
   speciestxt <- paste(species_target_network_tmp,sep=" ",collapse = ", ")
   # Again her it depends on the implementation. For us it is a database table especiestraits
   # with al the information about factors like T. cruzi and Synanthropy.
   q_traits <- paste("SELECT idespecie, tcruzi, synanthropic, class from especiestraits where idespecie in (",
      speciestxt,");",sep="")
   species_traits <- dbGetQuery(con,q_traits)

   species_traits$size <- rep(5,length(species_traits$idespecie))
   species_traits$color <- rep(colors()[257],length(species_traits$idespecie))
   species_traits$shape <- rep("circle",length(species_traits$idespecie))
   #species_styles <- get_network_nodes_style(graphica)
   graphica <- igraph::graph.data.frame(d=graph_df, vertices=species_traits)
   graphica <-as.undirected(graphica, mode="collapse")  # undirected graph

   # colors and shapes

   colors_and_shapes <- get_network_nodes_style(graphica,5)

   shapes_class <- c("circle","square") # circles for Triatomine and squares for Mammals

   # Uses the color palette categorical_pal to choose the colors.

   colors_class <- c(categorical_pal(8)[1], # orange dark for synanthropic and T. cruzi positive
                     categorical_pal(8)[4], # yellow for synanthropic species
                     categorical_pal(8)[3], # green for species with T. cruzi
                     categorical_pal(8)[2]  # blue for the non synanthropic and non T. cruzi
   )

   species_class_colors <- igraph::get.vertex.attribute(graphica, 'class')
   species_class_colors[species_class_colors == 'Vector'] = colors_class[4]
   species_class_colors[species_class_colors == 'Mammal'] = colors_class[4]

   species_class_shapes <- igraph::get.vertex.attribute(graphica,'class')
   species_class_shapes[species_class_shapes == 'Vector'] = shapes_class[1]
   species_class_shapes[species_class_shapes == 'Mammal'] = shapes_class[2]

   species_tcruzi <- igraph::get.vertex.attribute(graphica,'tcruzi')
   species_synant <- igraph::get.vertex.attribute(graphica,'synanthropic')
   species_class <- igraph::get.vertex.attribute(graphica,'class')
   species_specie <- igraph::get.vertex.attribute(graphica,'name')

   for (i in 1:length(species_tcruzi)){
      if (species_class[i] == 'Vector'){
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")){
            species_class_colors[i] = colors_class[4]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")) {
            species_class_colors[i] = colors_class[2]
         }
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "'t")) {
            species_class_colors[i] = colors_class[3]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "t")) {
            species_class_colors[i] = colors_class[1]
         }
      } else if (species_class[i] == 'Mammal'){
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")){
            species_class_colors[i] = colors_class[4]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "t")){
            species_class_colors[i] = colors_class[1]
         }
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "t")){
            species_class_colors[i] = colors_class[3]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")){
            species_class_colors[i] = colors_class[2]
         }
      }
   }

   graphica <- igraph::set.vertex.attribute(graphica,"shape", V(graphica), species_class_shapes)
   graphica <- igraph::set.vertex.attribute(graphica,"color", V(graphica), species_class_colors)

   E(graphica)$color  <- categorical_pal(8)[8]
   E(graphica)$lty = 1
   plot(graphica, layout=layout.kamada.kawai, )
   dev.off()
}

# End of example of the precedure mode.
# The rest in this script are R functions by themself.

#' This function generates a graph for an estimated network and send it to print.
#' The examples for this function are:
#' plot_network() - to plot all the epsilons bigger or equal to two, for all the species,
#'                   without filtering with the coocurrences.
#' plot_network(sel_triatomines = c(289)) -
#' To plot the Triatoma pallidipenis interaction network
#'
#' @param sel_triatomines - Defines which triatomines to include in a list. Default is all .
#' @param sel_mammals - Define which mammals to include in a list. Default is all.
#' @param d_nij - Defines the minimum co-ocurrences to include by cell. 3 is the default
#' @param epsilonp - Defines the umbral of the epsilon to include. 2 is the minimum default.
#' @param style - Defines if the network is represented with the default style.
#'
#' @return - Plots a metwork
#' @import igraph
#'
#' @examples
#' plot_network()
#' plot_network(sel_triatomines = c(289)) # The interactions network of Triatoma pallidipennis
#' @export
plot_network <- function(sel_triatomines='All', sel_mammals='All',d_nij=3, epsilonp=2, style=TRUE) {

   if ((!missing(sel_triatomines)) | (!missing(sel_mammals))) {
      if ((!missing(sel_triatomines)) & (missing(sel_mammals))) {
         if ("numeric" %in% class(sel_triatomines)) {
            cus <- cooccurrs[cooccurrs$idtriatomine %in% sel_triatomines,]
         } else if ("list" %in% class(sel_triatomines)) {
            cus <- cooccurrs[cooccurrs$idtriatomine %in% unlist(sel_triatomines),]
         } else {
            print(class(sel_triatomines))
         }
      } else if ((missing(sel_triatomines)) & (!missing(sel_mammals))) {
         if ("numeric" %in% class(sel_triatomines)) {
            cus <- cooccurrs[cooccurrs$idmammal %in% sel_mammals,]
         } else if ("list" %in% class(sel_triatomines)) {
            cus <- cooccurrs[cooccurrs$idmammal %in% unlist(sel_mammals),]
         } else {
            print(class(sel_mammals))
         }
      } else if ((!missing(sel_triatomines)) & (!missing(sel_mammals))) {
         if ("numeric" %in% class(sel_triatomines)) {
            sps_a = sel_triatomines
         } else if ("list" %in% class(sel_triatomines)) {
            sps_a = unlist(sel_triatomines)
         } else if (("character" %in% class(sel_triatomines)) &
                    (sel_triatomines == "All")) {
            sps_a = "All"
         } else {
            sps_a = "notAll"
         }
         if ("numeric" %in% class(sel_mammals)) {
            sps_b = sel_mammals
         } else if ("list" %in% class(sel_mammals)) {
            sps_b = unlist(sel_mammals)
         } else if (("character" %in% class(sel_mammals)) &
                    (sel_mammals == "All")) {
            sps_b = "All"
         } else {
            sps_b = "notAll"
         }
         if ((class(sps_a) == "character" && sps_a == "All")
               && (class(sps_b) == "character" & sps_b == "All")) {
            cus <- cooccurrs
         } else if (class(sps_a) == "numeric" & class(sps_b) == "numeric") {
            cus <- cooccurrs[cooccurrs$idtriatomine %in% sps_a &
                                cooccurrs$idmammal %in% sps_b,]
         }
      }
   } else {
      cus <- cooccurrs
   }
   idmammals_l <- c()
   idtriatominos_l <- c()
   epsilons_l <- c()

   for (i in 1:dim(cus)[1]){
      if ((d_nij != 1) & (d_nij > 1)) {
         cus <- cus[cus$nij>=d_nij,]
      }
      epsilons_est <- epsilon(cus[i,]$nij, cus[i,]$ni, cus[i,]$nj, cus[i,]$n)
      if (epsilons_est >= epsilonp) {
         s <- length(epsilons_l) + 1
         epsilons_l[s] <- epsilons_est
         idmammals_l[s] <- cus[i,]$idmammal
         idtriatominos_l[s] <- cus[i,]$idtriatomine
      }
   }

   graph_df = data.frame(idmammal = idmammals_l, idtriatomine = idtriatominos_l, epsilon = epsilons_l)
   species_traits <- get_species_traits(graph_df)
   graphica <- igraph::graph.data.frame(d=graph_df,vertices=species_traits)
   graphica <-as.undirected(graphica,mode="collapse")  # means undirected graph

   colors_and_shapes <- get_network_nodes_style(graphica,5)
   legend(2000, 9.5, # places a legend at the appropriate place
          legend(1,1,fill = unique(as.vector(matrixValues[matrixValues>0])),
                 legend=unique(as.vector(matrixValues[matrixValues>0])))
          graphica <- igraph::set.vertex.attribute(graphica,"shape",
                                                   V(graphica),colors_and_shapes[[2]])
          graphica <- igraph::set.vertex.attribute(graphica,"color",
                                                   V(graphica),colors_and_shapes[[1]])

          send_network_to_plot(graphica, position = 0.0, scale = FALSE, scale_variable = "",
                               main = '', factor_scale = 20)
          return <- graphica
}

#' This function gets the species attributes for the network nodes.
#'
#' # A dataframe for the graph.
#' @param species_graph_df
#'
#' @return
#'
#' @examples
#' get_species_traits(graph_df)
#'
get_species_traits <- function(species_graph_df = '') {
   triat <- triatomine
   mamm <- mammals
   names(triat) <- c('id','cl','gen','sp','lon','lat','tc','syn')
   names(mamm) <- c('id','cl','gen','sp','lon','lat','tc','syn')
   species_traits <- rbind(triat,mamm)
   species_traits <- species_traits[species_traits$id %in% species_graph_df$idtriatomine |
                                       species_traits$id %in% species_graph_df$idmammal,]
   species_traits <- species_traits[c('id','cl','sp','tc','syn')]
   names(species_traits) <- c('especie','class','specie_name','tcruzi','synanthropic')
   return(species_traits)
}

#' This function set the default nodes style in the graph.
#'
#' @param gu - the graph with all of their attributes
#' @param size - the defatul size for the nodes
#'
#' @return
#' @import igraph
get_network_nodes_style <- function(gu,size=5) {

   shapes_class <- c("circle","square") # circles for Triatomine and squares for Mammals

   colors_class <- c(categorical_pal(8)[1], # orange dark for synanthropic and T. cruzi positive
                     categorical_pal(8)[4], # yellow for synanthropic species
                     categorical_pal(8)[3], # green for species with T. cruzi
                     categorical_pal(8)[2]  # blue for the non synanthropic and non T. cruzi
   )

   species_class_colors <- igraph::get.vertex.attribute(gu,'class')
   species_class_colors[species_class_colors == 'triatomine'] = colors_class[4]
   species_class_colors[species_class_colors == 'mammal'] = colors_class[4]

   species_class_shapes <- igraph::get.vertex.attribute(gu,'class')
   species_class_shapes[species_class_shapes == 'triatomine'] = shapes_class[1];
   species_class_shapes[species_class_shapes == 'mammal'] = shapes_class[2];

   species_tcruzi <- igraph::get.vertex.attribute(gu,'tcruzi')
   species_synant <- igraph::get.vertex.attribute(gu,'synanthropic')
   species_class <- igraph::get.vertex.attribute(gu,'class')
   species_specie <- igraph::get.vertex.attribute(gu,'specie_name')

   for (i in 1:length(species_tcruzi)){
      if (species_class[i] == 'triatomine'){
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")){
            species_class_colors[i] = colors_class[4]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")) {
            species_class_colors[i] = colors_class[2]
         }
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "'t")) {
            species_class_colors[i] = colors_class[3]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "t")) {
            species_class_colors[i] = colors_class[1]
         }
      } else if (species_class[i] == 'mammal'){
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")){
            species_class_colors[i] = colors_class[4]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "t")){
            species_class_colors[i] = colors_class[1]
         }
         if ((species_synant[i] == 'FALSE' | species_synant[i] == "f") &&
             (species_tcruzi[i] == 'TRUE' | species_tcruzi[i] == "t")){
            species_class_colors[i] = colors_class[3]
         }
         if ((species_synant[i] == 'TRUE' | species_synant[i] == "t") &&
             (species_tcruzi[i] == 'FALSE' | species_tcruzi[i] == "f")){
            species_class_colors[i] = colors_class[2]
         }
      }
   }

   return(list(species_class_colors,species_class_shapes))
}

#' Receive the network properly and show in the screen with scale and styles.
#'
#' @param gu - Graph to plot
#' @param position - position of the legends in the network.
#' @param main - title for the network
#' @param scale - to scale or not to scale the nodes.
#' @param scale_variable - the variable of the network properties to scale
#' @param factor_scale - a number to make more or less evident the scaling
#' @param clase -
#'
#' @return
#'
#' @examples
#' @export
send_network_to_plot <- function(gu,
                                 position = 0.3, main = "",
                                 scale = FALSE,
                                 scale_variable = 'betweenness',
                                 factor_scale = 5,
                                 clase = 'Vector'
                                 )
   {

   n <- length(V(gu))
   if (scale == TRUE){

      # It can include network properties to scale the node sizes.
      centralities_local <- centralities_target_network
      species_class_names<-get.vertex.attribute(gu,'name')
      #names(centralities_vectors)
      orden_especies_vertices <- data.frame(idespecie=species_class_names)
      row_v <- data.frame()
      for (i in 1:length(centralities_local$idespecie)){
         row_v <- rbind(row_v,centralities_local[centralities_local$idespecie==as.character(orden_especies_vertices$idespecie[i]),])
      }
      row.names(row_v) <- NULL
      centralities_local_ordenadas <- row_v
      row.names(centralities_local_ordenadas) <- NULL

      scale_factor <- scale_variable
      if (clase == 'mammal') {
         V(gu)[V(gu)[class=="Mammal"]]$size <- eval(parse(text=paste("scale(centralities_local_ordenadas$",scale_factor,",
                                                  scale=TRUE,
                                                  center = FALSE) * ",factor_scale,"",sep="")))
      } else if (clase == 'triatomine') {
         V(gu)[V(gu)[class=="Vector"]]$size <- eval(parse(text=paste("scale(centralities_local_ordenadas$",scale_factor,",
                                                  scale=TRUE,
                                                   center = FALSE) * ",factor_scale,"",sep="")))
      }
   }

   set.seed(233)
   par(cex=0.5)
   title = main
   atributtes_lis <- set_network_nodes_style(gu,5)
   plot(gu,
        vertex.label.dist=c(rep(position, n)),
        layout=layout.kamada.kawai,
        main=title
   )
}

#' This function plots the centroids of distributions of the triatomine and mammals species.
#' To runs a Mexico shapefile contour map is needed. It is included as a dataset in the package chagasnws.
#'
#' @return
#'
#' @examples
#' plot_centroides()
#' plot_centroides('triatomine')
#' plot_centroides('mammals')
#' @export
plot_centroids <- function(centroids="") {
   basic_colors_species = c("red","navy blue")
   data(mexico)
   plot.new()
   if (centroids == "") {
      plot(mexico, main="Species distribution centroids", xlim=c(-126,-77), ylim=c(10, 36))
      add_interactions()
      styles_centroids_triat = style_centroids(triatomine$idtriatomine,'triatomine','both')
      points(triatomine$lon_centroid,triatomine$lat_centroid,pch=22,cex=1,col=unlist(styles_centroids_triat),
             bg=unlist(styles_centroids_triat))
      styles_centroids_mamm = style_centroids(mammals$idmammal,'mammals','both')
      points(mammals_filtered$lon_centroid,mammals_filtered$lat_centroid,pch=21,cex=1,col=unlist(styles_centroids_mamm),
             bg=unlist(styles_centroids_mamm))
   } else if (centroids == 'triatomine'){
      plot(mexico, main="Triatomine species distributions centroids", xlim=c(-126,-77), ylim=c(10, 36))
      points(triatomine$lon_centroid,triatomine$lat_centroid,pch=22,cex=1,col=basic_colors_species[1],
             bg=basic_colors_species[1])
   } else if (centroids == 'mammals'){
      plot(mexico, main="Mammals species distribution centroids", xlim=c(-126,-77), ylim=c(10, 36))
      points(mammals_filtered$lon_centroid,mammals_filtered$lat_centroid,pch=21,cex=1,col=basic_colors_species[2],
             bg=basic_colors_species[2])
   }
}

#' Creates and plot communities graphs.
#'
#' @return
#' @export
#'
#' @examples
create_and_plot_communities <- function() {

   # Get the species and species traits for the target network
   # This function returns idtriatomine, idmammal and epsilon for the species.
   # This function is not included because it depends on the implementation.
   # We have a here a connection to a db.
   # get_species_epsilons_target_network_f('epsilonsr',nij=nij)  # species data

   cooccurrs <- get_species_epsilons_target_network_f('epsilonsr',nij=nij)  # species data

   # This function retrieves the species traits, specifically tcruzi and synanthropic boolean traits
   # This function returns the idspecies, tcruzi, synathropic and class for all the species in the network
   # This function is not included because depends on the implementation. We have a here a connection to a db.
   get_species_traits_f(adjust_nodes = FALSE, source = 'general_network', node_class = 'Triatomine-Mammal')

   graphica <- graph.data.frame(d=species_epsilons_target_network,vertices=species_traits_target_network)
   # Creates an indirected graph
   graphica <-as.undirected(graphica,mode="collapse")  # undirected graph
   # Adjacency matrix
   matriza <- get.adjacency(graphica)

   # Calls the community function according to Newman to separate in just two communities
   mem <- community.newman(graphica)
   V(graphica)$color <- ifelse(mem < 0, "grey", "green") # Assigned default colors to communities 1 and 2
   V(graphica)$community <- ifelse(mem < 0, 1, 2)
   V(graphica)$community <- ifelse(mem < 0, 2, 1)  # a la inversa

   # To weights the nodes
   V(graphica)$width <- 2
   # To weight the nodes we can adjust the size we use some properties nodes in the graph
   # like is shown here.
   # E(graphica)$weight <- edge.betweenness(graphica)

   # V(graphica)$community == 1
   V(graphica)[ V(graphica)[community == 1] ]$shape

   marks1 <- c(V(graphica)[ V(graphica)[community == 1] ])
   marks2 <- c(V(graphica)[ V(graphica)[community == 2] ])

   E(graphica)$color  <- categorical_pal(8)[8]
   E(graphica)$lty = 1

   # Synanthropic or intermediate nodes
   intermediacion <- E(graphica)[ V(graphica)[color=="grey"] %--% V(graphica)[color=="green"] ] $color <- colors_class[1]
   E(graphica)[ V(graphica)[color=="grey"] %--% V(graphica)[color=="green"] ]$lty <- 4
   E(graphica)[ V(graphica)[color=="green"] %--% V(graphica)[color=="grey"] ]

   # Color assigned to the edges
   E(graphica)[color=="grey"]$color <- 'darkgrey'

   # Change the node shapes
   V(graphica)[ V(graphica)[class == 'Vector'] ]$shape <- shapes_class[1]
   V(graphica)[ V(graphica)[class == 'Mammal'] ]$shape <- shapes_class[2]

   #
   # Changes the color for the nodes according if they belong to the communities 1 and 2
   #

   for (i in V(graphica)){
      if (V(graphica)[i]$color == "grey"){  # Nodes in Community 1
         if (V(graphica)[i]$synanthropic == TRUE){
            if (V(graphica)[i]$tcruzi == TRUE){
               V(graphica)[i]$color <- colors_class[1]
            } else if (V(graphica)[i]$tcruzi == FALSE){
               V(graphica)[i]$color <- colors_class[3]
            }
         } else if (V(graphica)[i]$synanthropic == FALSE){
            if (V(graphica)[i]$tcruzi == TRUE){
               V(graphica)[i]$color <- colors_class[2]
            } else if (V(graphica)[i]$tcruzi == FALSE){
               V(graphica)[i]$color <- colors_class[4]
            }
         }
      } else if (V(graphica)[i]$color == "green"){ # Nodes in Community 2
         if (V(graphica)[i]$synanthropic == TRUE){
            if (V(graphica)[i]$tcruzi == TRUE){
               V(graphica)[i]$color <- colors_class[1]
            } else if (V(graphica)[i]$tcruzi == FALSE){
               V(graphica)[i]$color <- colors_class[3]
            }
         } else if (V(graphica)[i]$synanthropic == FALSE){
            if (V(graphica)[i]$tcruzi == TRUE){
               V(graphica)[i]$color <- colors_class[2]
            } else if (V(graphica)[i]$tcruzi == FALSE){
               V(graphica)[i]$color <- colors_class[4]
            }
         }
      }
   }

   # Scale the nodes size
   V(graphica)$size <- scale(abs(mem), 5, 12)

   # Create a tiff file with marks for the communities.

   set.seed(130)

   name_graph_comunidades <- paste("RplotComunidadesPeerJ_epsilon_",epsilon,"_nij_",nij,".tiff",sep="")
   tiff(filename = name_graph_comunidades,
        width = 1600, height = 1600, units = "px", pointsize = 12,
        compression = c("none"),
        bg = "white", res = NA,
        type = c("cairo", "Xlib", "quartz"))
   par(cex=0.9)
   l  <- layout.kamada.kawai # Used Layout to graph the comminities
   marks <- list(marks1,marks2) # Add mark for gropus of nodes
   shapis <- list(1,1)
   par(cex=0.9)
   plot(graphica,layout=l, mark.groups = marks, mark.shape = shapis, mark.col =
           c(adjustcolor("#CC79A7",0.2), adjustcolor("#F0E442",0.2)),
        mark.border = c("#CC79A7","#F0E442"), margin(c(0.5)))
   dev.off()
}

#' Function to build the communities according to the references.
#'
#' @param g is a graph object
#'
#' @return
#' @references
#' https://pdfs.semanticscholar.org/1d27/44b83519657f5f2610698a8ddd177ced4f5c.pdf
#' http://www.pnas.org/content/103/23/8577.abstract
#' @examples
community.newman <- function(g){
   deg <- degree(g)
   ec <- ecount(g)
   B <- get.adjacency(g) - outer(deg, deg, function(x,y) x*y/2/ec)
   diag(B) <- 0
   eigen(B)$vectors[,1]
}

#' Function to scale the nodes in the communities graphs.
#'
#' @param v
#' @param a
#' @param b
#'
#' @return
#'
scale <- function(v,a,b){
   v <- v-min(v);
   v <- v/max(v);
   v <- v*(b-a);
   v+a
}

#' These are the main datasets of species co-occurences of triatomines and
#' mammals asssociated with the Chagas disease in Mexico.
#'
#' A dataset containing the historic co-occurrences of triatome species and
#' more than 500 mammals species in Mexico.
#'
#' The count of co-occurences is made over a 0.25 grad grid.
#' These counts include the occurences of the species where
#' both co-occurr in at least 3 cells of the grid.
#'
#' @format A data frame with 658 rows and 6 variables:
#' \describe{
#'   \item{idvector}{integer id for the triatomine species}
#'   \item{idmammal}{integer id for the mammal species}
#'   \item{ni}{number of cells of a grid or units in which }
#'   \item{nj}{number of cells of a grid or units in which }
#'   \item{nij}{number of cells of a grid or units in which }
#'   \item{n}{number of cells of a grid or units in which }
#' }
"cooccurrs"

#' Dataset of triatomine vectors names occurring in Mexico.
#'
#' A dataset based on the historic occurrences of triatome species in Mexico.
#'
#' @format A data frame with 26 rows and 6 variables:
#' \describe{
#'   \item{idvector}{integer id for the triatomine species}
#'   \item{genus}{Genus of the triatomine species}
#'   \item{species}{Name of the species of triatomine}
#'   \item{lon_centroid}{Median longitude of the centroid of their species geographic range}
#'   \item{lat_centroid}{Median latitude of the centroid of their species geographic range}
#' }
"triatomine"

#' Dataset of mammal species occurring in Mexico associated to the triatomines.
#'
#' A dataset based on the historic occurrences of mammals species in Mexico.
#'
#' @format A data frame with 26 rows and 6 variables:
#' \describe{
#'   \item{idvector}{integer id for the triatomine species}
#'   \item{genus}{Genus of the triatomine species}
#'   \item{species}{Name of the species of triatomine}
#'   \item{lon_centroid}{Median longitude of the centroid of their species geographic range}
#'   \item{lat_centroid}{Median latitude of the centroid of their species geographic range}
#' }
"mammals"

#' Dataset from a Mexico shapefile to plot the centroid distributions
#' and species interactions in a geographic representation.
#'
#' @format A data frame with 825 geometry IDs
#' \describe{
#' }
"mexico"