Add the rest of functions and tests

R/RN_dims.R

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+#' Title
+#'
+#' @param int_data
+#' @param cover_data
+#' @param int_type
+#'
+#' @returns
+#' @export
+#'
+#' @examples
+#'
+#' # RN_dims_UNI() modified to enter the interaction and cover data
+#The size of a network can be described by the number of nodes (*N*) and links (*L*), and its complexity is #proportional to network connectance. *C* can be estimated on different ways depending on the type of #network, but it always measures the proportion of links relative to the maximum number of links that could #be possible in the network. In the case of recruitment networks we use the formula $C = L /(N^2-N)$ since #the node "Open" does not act as a recruit (i.e. Open is represented by a row of zeroes in the adjacency #matrix).
+#For facilitation and competition that are bipartite networks, the nodes in eahc guild are #provided( nurse/facilitated, Canopy (depressor)/recruit) seprately, and open is not a node
+#'
+RN_dims <- function(int_data,cover_data, int_type=c("rec","fac","comp")){
+
+  if(int_type=="rec"){
+
+    int_data <-comm_to_RN_UNI(int_data,cover_data)
+
+    # FUNCTION
+    df <- int_data
+    n_nodes <- length(unique(c(df$Canopy, df$Recruit)))
+    n_links <- sum(df$Pcr)
+    connectance <- n_links/(n_nodes^2 - n_nodes)
+
+    out <- data.frame(c(n_nodes, n_links, connectance))
+    colnames(out) <- c("Value")
+    rownames(out) <- c("Num. Nodes", "Num. Links", "Connectance")
+    return(out)
+
+  }
+
+  if(int_type=="fac"){
+
+    int_data <-suppressWarnings(RN_to_matrix(int_data,cover_data, int_type="fac",weight="Pcr"))
+
+    df <-int_data
+    n_nodes_nurse <-dim(df)[2]
+    n_nodes_facilitated <-dim(df)[1]
+    n_links <- sum(df)
+    connectance <-n_links/(n_nodes_nurse*n_nodes_facilitated)
+
+    out <- data.frame(c(n_nodes_nurse,n_nodes_facilitated, n_links, connectance))
+    colnames(out) <- c("Value")
+    rownames(out) <- c("Num. Nurse sp","Num. Facilitated sp", "Num. Links", "Connectance")
+    return(out)
+
+  }
+
+
+  if(int_type=="comp"){
+
+    int_data <-suppressWarnings(RN_to_matrix(int_data,cover_data, int_type="comp",weight="Pcr"))
+
+    df <-int_data
+    n_nodes_canopy_depressing <-dim(df)[2]
+    n_nodes_recruit_depressed <-dim(df)[1]
+    n_links <- sum(df)
+    connectance <-n_links/(n_nodes_canopy_depressing*n_nodes_recruit_depressed)
+
+    out <- data.frame(c(n_nodes_canopy_depressing, n_nodes_recruit_depressed, n_links, connectance))
+    colnames(out) <- c("Value")
+    rownames(out) <- c("Num. Canopy depressing sp","Num. Recruit depressed sp", "Num. Links", "Connectance")
+    return(out)
+
+
+  }
+
+
+}
+

R/RN_heatmap.R

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+#' Title
+#'
+#' @param int_data
+#' @param cover_data
+#' @param int_type
+#' @param weight
+#' @param scale_top
+#'
+#' @returns
+#' @export
+#'
+#' @examples
+#'
+#'
+RN_heatmap <- function(int_data,cover_data,int_type=c("rec","fac","comp"), weight = c("Pcr","Fcr","Dcr","Dro","Ns", "NintC", "NintA", "RII"), scale_top = 1) {
+
+  if(int_type=="rec"){
+
+    index<-suppressWarnings(associndex(int_data,cover_data,expand="yes",rm_sp_no_cover="allsp"))
+    data<-comm_to_RN_UNI(int_data,cover_data)
+    data$Dcr<-data$Fcr/data$Ac
+
+    # calculate Dro for each recuit species
+    open_rows <- data[data$Canopy == "Open", ]
+    ratios <- tapply(open_rows$Fcr / open_rows$Ac, open_rows$Recruit, mean)
+    data$Dro <- ratios[data$Recruit]
+
+
+    data$int<-paste(data$Canopy,data$Recruit, sep="_")
+    index$int<-paste(index$Canopy,index$Recruit, sep="_")
+    db<-merge(data[,c("int","Canopy","Recruit","Fcr","Icr","Pcr","Dcr","Dro")],index[,c("int","Ns", "NintC", "NintA", "RII")],   by="int", all.x=T)
+    db[is.na(db)]<-0
+
+    int_data<-db
+
+    if (weight %in% c("Ns", "NintC", "NintA", "RII")) {
+      warning("Since the index specified in the 'weight' argument is defined relative to 'Open', rows or columns labeled 'Open' are mathematically zero and not biologically meaningful")}
+
+    require(ggplot2)
+    # manually set node order
+    canopy_order <- unique(int_data$Canopy)
+    canopy_order <- canopy_order[!canopy_order %in% c('Open')]
+    canopy_order <- c("Open", canopy_order)
+    int_data$Canopy2 <- factor(int_data$Canopy, levels = canopy_order)
+    recruit_order <- sort(unique(int_data$Canopy), decreasing = TRUE)
+    recruit_order <- recruit_order[!recruit_order %in% c('Open')]
+    recruit_order <- c(recruit_order, "Open")
+    int_data$Recruit2 <- factor(int_data$Recruit, levels = recruit_order)
+
+
+    # Make weight variable
+    int_data$weight_v <- as.numeric(int_data[[weight]])
+
+    # Lowest (non-zero) and highest values of the weighting variable
+    highest_W <- max(int_data$weight_v)
+    lowest_W <- min(int_data$weight_v[int_data$weight_v>0])
+
+    # Plot the heatmap
+    p<-ggplot(int_data, aes(Canopy2, Recruit2, fill= weight_v)) +
+      geom_tile(aes(height = 1, alpha = weight_v != 0),
+                colour = "gray", linewidth = 0.25) +
+      scale_alpha_manual(values = c("TRUE" = 1, "FALSE" = 0), guide = "none") +
+      scale_x_discrete(position = "top") +
+      labs(fill = weight,x = "Canopy", y = "Recruit") +
+      theme(panel.grid = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=0))
+
+    return(p)
+
+  }
+
+
+
+  if(int_type=="fac"){
+
+    data<-comm_to_RN_BI(int_data,cover_data)
+    data$int<-paste(data$Canopy,data$Recruit, sep="_")
+    df<-suppressWarnings(int_significance_BI (data))
+    df<-df[df$Effect_int=="Enhancing",]
+    fac_int<-paste(df$Canopy,df$Recruit, sep="_")
+    db<-suppressWarnings(associndex_UNISITE_BI(comm_to_RN_BI(int_data,cover_data)))
+    db$int<-paste(db$Canopy,db$Recruit, sep="_")
+    db<-merge(db, data[,c("int","Icr","Pcr")], by="int", all.x=T)
+    db<-db[db$int%in%fac_int,]
+
+    int_data<-db
+
+    require(ggplot2)
+    # manually set node order
+    canopy_order <- unique(int_data$Canopy)
+    int_data$Canopy2 <- factor(int_data$Canopy, levels = canopy_order)
+    recruit_order <- sort(unique(int_data$Recruit), decreasing = TRUE)
+    int_data$Recruit2 <- factor(int_data$Recruit, levels = recruit_order)
+
+    # Add recruitment density as another weighting variable
+    int_data$Dcr <- int_data$Fcr/int_data$Ac
+
+    # Make weight variable
+    int_data$weight_v <- as.numeric(int_data[[weight]])
+
+    # Lowest (non-zero) and highest values of the weighting variable
+    highest_W <- max(int_data$weight_v)
+    lowest_W <- min(int_data$weight_v[int_data$weight_v>0])
+
+    # Plot the heatmap
+    p<-ggplot(int_data, aes(Canopy2, Recruit2, fill= weight_v)) +
+      geom_tile(aes(height = 1, alpha = weight_v != 0),
+                colour = "gray", linewidth = 0.25) +
+      scale_alpha_manual(values = c("TRUE" = 1, "FALSE" = 0), guide = "none") +
+      scale_x_discrete(position = "top") +
+      labs(fill = weight,x = "Canopy", y = "Recruit") +
+      theme(panel.grid = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=0))
+
+    return(p)
+
+  }
+
+
+  if(int_type=="comp"){
+
+
+    data<-comm_to_RN_UNI_COMP(int_data,cover_data)
+    data$int<-paste(data$Canopy,data$Recruit, sep="_")
+    df<-suppressWarnings(int_significance_BI_COMP (data))
+    df<-df[df$Effect_int=="Depressing",]
+    comp_int<-paste(df$Canopy,df$Recruit, sep="_")
+    db<-suppressWarnings(associndex_UNISITE_BI_COMP(comm_to_RN_UNI_COMP(int_data,cover_data)))
+    db$int<-paste(db$Canopy,db$Recruit, sep="_")
+    db<-merge(db, data[,c("int","Icr","Pcr")], by="int", all.x=T)
+    db<-db[db$int%in%comp_int,]
+
+    int_data<-db
+
+    require(ggplot2)
+    # manually set node order
+    canopy_order <- unique(int_data$Canopy)
+    int_data$Canopy2 <- factor(int_data$Canopy, levels = canopy_order)
+    recruit_order <- sort(unique(int_data$Recruit), decreasing = TRUE)
+    int_data$Recruit2 <- factor(int_data$Recruit, levels = recruit_order)
+
+    # Add recruitment density as another weighting variable
+    int_data$Dcr <- int_data$Fcr/int_data$Ac
+
+    # Make weight variable
+    int_data$weight_v <- as.numeric(int_data[[weight]])
+
+    if(weight%in% c("Fcr","Icr","Pcr","Dcr","Dro")){
+      # Lowest (non-zero) and highest values of the weighting variable
+      highest_W <- max(int_data$weight_v)
+      lowest_W <- min(int_data$weight_v[int_data$weight_v>0])
+    }
+
+
+    if(weight %in% c("Ns", "NintC", "NintA", "RII")){
+      nonzero_vals <- int_data$weight_v[int_data$weight_v != 0]
+      highest_W <- max(nonzero_vals)
+      lowest_W <- min(nonzero_vals)
+    }
+    # Plot the heatmap
+    p<-ggplot(int_data, aes(Canopy2, Recruit2, fill= weight_v)) +
+      geom_tile(aes(height = 1, alpha = weight_v != 0),
+                colour = "gray", linewidth = 0.25) +
+      scale_alpha_manual(values = c("TRUE" = 1, "FALSE" = 0), guide = "none") +
+      scale_x_discrete(position = "top") +
+      labs(fill = weight,x = "Canopy", y = "Recruit") +
+      theme(panel.grid = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=0))
+
+    return(p)
+
+
+  }
+
+}
+
+

R/RN_to_matrix.R

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+#' Title
+#'
+#' @param int_data
+#' @param cover_data
+#' @param int_type
+#' @param weight
+#'
+#' @returns
+#' @export
+#'
+#' @examples
+#' #This function returns a matrix with canopy species as columns and recruits as rows, containing the association index specified in the weight argument. If int_type is set to "rec," all possible interactions between species with known coverage are displayed. If set to "fac" (or "comp"), a nonzero value appears when recruits associate with canopy plants more (or less) than expected based on their coverage; otherwise, the value is zero.
+#'
+RN_to_matrix<- function(int_data,cover_data, int_type=c("rec", "fac","comp"), weight = c("Fcr","Dcr","Dro","Ns", "NintC", "NintA", "RII")){
+
+  if (!"Open" %in% int_data$Canopy) stop("ERROR: your data does not contain a node named Open or it is spelled differently.")
+
+  if(int_type=="rec"){
+
+    index<-suppressWarnings(associndex(int_data,cover_data,expand="yes",rm_sp_no_cover="allsp"))
+    data<-comm_to_RN_UNI(int_data,cover_data)
+    data$Dcr<-data$Fcr/data$Ac
+
+    # calculate Dro for each recruit species
+    open_rows <- data[data$Canopy == "Open", ]
+    ratios <- tapply(open_rows$Fcr / open_rows$Ac, open_rows$Recruit, mean)
+    data$Dro <- ratios[data$Recruit]
+
+
+    data$int<-paste(data$Canopy,data$Recruit, sep="_")
+    index$int<-paste(index$Canopy,index$Recruit, sep="_")
+    db<-merge(data[,c("int","Canopy","Recruit","Fcr","Icr","Pcr","Dcr","Dro")],index[,c("int","Ns", "NintC", "NintA", "RII")],   by="int", all.x=T)
+    db[is.na(db)]<-0
+    net<-suppressWarnings(RN_to_matrix_UNI(db, weight))
+
+    if (weight %in% c("Ns", "NintC", "NintA", "RII")) {
+      warning("Since the index specified in the 'weight' argument is defined relative to 'Open', rows or columns labeled 'Open' are mathematically zero and not biologically meaningful")
+    }
+  }
+
+  if(int_type=="fac"){
+
+    data<-comm_to_RN_BI(int_data,cover_data)
+    data$int<-paste(data$Canopy,data$Recruit, sep="_")
+    df<-suppressWarnings(int_significance_BI (data))
+
+    if(nrow(df[df$Effect_int=="Enhancing",])==0){
+
+      stop("There is not any recruitment enhancing interaction")
+    }else{
+
+      df<-df[df$Effect_int=="Enhancing",]
+      fac_int<-paste(df$Canopy,df$Recruit, sep="_")
+      db<-suppressWarnings(associndex_UNISITE_BI(comm_to_RN_BI(int_data,cover_data)))
+      db$int<-paste(db$Canopy,db$Recruit, sep="_")
+      db<-merge(db, data[,c("int","Icr","Pcr")], by="int", all.x=T)
+      db<-db[db$int%in%fac_int,]
+      net<-suppressWarnings(RN_to_matrix_BI(db, weight))
+    }
+
+  }
+
+  if(int_type=="comp"){
+
+    data<-comm_to_RN_UNI_COMP(int_data,cover_data)
+    data$int<-paste(data$Canopy,data$Recruit, sep="_")
+    df<-suppressWarnings(int_significance_BI_COMP (data))
+
+    if(nrow(df[df$Effect_int=="Depressing",])==0){
+
+      stop("There is not any recruitment depressing interaction")
+    }else{
+
+      df<-df[df$Effect_int=="Depressing",]
+      comp_int<-paste(df$Canopy,df$Recruit, sep="_")
+      db<-suppressWarnings(associndex_UNISITE_BI_COMP(comm_to_RN_UNI_COMP(int_data,cover_data)))
+      db$int<-paste(db$Canopy,db$Recruit, sep="_")
+      db<-merge(db, data[,c("int","Icr","Pcr")], by="int", all.x=T)
+      db<-db[db$int%in%comp_int,]
+      net<-suppressWarnings(RN_to_matrix_BI(db, weight))
+    }
+  }
+
+
+  return(net)
+}
+