From 73fd7036fa6a9f55ea4a157fef5da37fcb2e6781 Mon Sep 17 00:00:00 2001
From: iara <123iamela@gmail.com>
Date: Wed, 14 Apr 2021 17:35:20 -0300
Subject: [PATCH] Organizing and cleaning plots for publishing.

---
 plots.Rmd | 460 +++++-------------------------------------------------
 1 file changed, 41 insertions(+), 419 deletions(-)

diff --git a/plots.Rmd b/plots.Rmd
index a554028..afad82e 100644
--- a/plots.Rmd
+++ b/plots.Rmd
@@ -1,8 +1,6 @@
 ---
-title: "Plots"
-author: "Iara"
-date: "23 de julio de 2020"
-output: html_document
+title: "plots"
+author: "Iara Diamela Rodriguez"
 editor_options: 
   chunk_output_type: console
 ---
@@ -14,29 +12,31 @@ knitr::opts_chunk$set(echo = TRUE)
 load(".RData")
 
 require(igraph)
-require(dplyr)
+require(NetIndices)
+require(multiweb)
+
 require(ggplot2)
+
+require(dplyr)
 require(knitr)
 require(devtools)
-require(multiweb)
 require(cowplot)
-require(NetIndices)
+
 
 # Typography
 
 require(extrafont)
-#font_import()
-#loadfonts()
+font_import()
+loadfonts()
 loadfonts(device="win")
 
 # Color palette
 
-#colnet1 <- c("#833f8f", "#ccb7d5","#a8d9c3", "#088a4f")
-colnet <- c("#024828","#066e3f","#088a4f","#51b487","#85caab","#a8d9c3", #VERDE +oscuro-
-            "#ffffff", 
-            "#40004b","#683272","#833f8f","#9970ab","#b79ac4","#ccb7d5") #VIOLETA +oscuro-
+colnet <- c("#024828","#066e3f","#088a4f","#51b487","#85caab","#a8d9c3", #green +dark-
+            "#ffffff", #white
+            "#40004b","#683272","#833f8f","#9970ab","#b79ac4","#ccb7d5") #purple +dark-
 
-# Plots theme
+# Plots' theme
 
 theme_new <- theme_minimal() +
              theme(axis.title.x=element_text(size=18, face="bold", family="Lato", margin=margin(t=8, r=0, b=0, l=0)),
@@ -54,131 +54,9 @@ theme_new <- theme_minimal() +
 ```
 
 
-### Plot food webs ###
-```{r plotFW, echo=FALSE, message=FALSE}
-
-#---- Plot fw by TL and species colored by topological role
-
-ptl <- function (g, topoFrame, vertexLabel = FALSE, vertexSizeFactor = 5, modules = FALSE, lMat = NULL, bpal = NULL, maxTL = NULL, edge.width = NULL, ...) 
-  {
-  deg <- degree(g, mode = "all")
-  V(g)$size <- log10(deg) * vertexSizeFactor + vertexSizeFactor
-  V(g)$frame.color <- "white"
-  V(g)$color <- "orange"
-  E(g)$color <- "gray65"
-  E(g)$width <- 0.5
-  tl <- TrophInd(get.adjacency(g, sparse = F))
-  if (!vertexLabel) 
-    V(g)$label <- NA
-  if (inherits(g, "mgraph") && ("Trophic" %in% unique(unlist(edge.attributes(g))))) {
-    tt <- subgraph.edges(g, E(g)[E(g)$type == "Trophic"])
-    tl <- TrophInd(get.adjacency(tt, sparse = F))
-  }
-  else {
-    tl <- TrophInd(get.adjacency(g, sparse = F))
-  }
-  if (is.null(lMat)) {
-    lMat <- matrix(nrow = vcount(g), ncol = 2)
-    lMat[, 2] <- jitter(tl$TL, 0.1)
-    if (modules) {
-      if (count_components(g) > 1) {
-        if (!is.named(g)) 
-          V(g)$name <- (1:vcount(g))
-        dg <- components(g)
-        V(g)$membership = 0
-        for (comp in unique(dg$membership)) {
-          g1 <- induced_subgraph(g, which(dg$membership == 
-            comp))
-          m <- cluster_spinglass(g1, weights = weights)
-          if (length(m$membership) == 0) 
-            m$membership <- 1
-          V(g)[V(g1)$name]$membership <- m$membership + 
-            max(V(g)$membership)
-        }
-        m$membership <- V(g)$membership
-      }
-      else {
-        m <- cluster_spinglass(g, weights = weights)
-      }
-      df <- data.frame(tl = tl$TL, m = m$membership)
-      df <- df %>% mutate(m = dense_rank(ave(tl, m, FUN = max)))
-      lMat[, 1] <- jitter(df$m, 1)
-    }
-    else {
-      lMat[, 1] <- runif(vcount(g))
-    }
-  }
-   
-  colnet <- c("#833f8f", "#ccb7d5","#a8d9c3", "#088a4f")
-  #(network connector -high connectivity between and within modules-, module connector -few links between modules-,   module specialist -few links in its module-, module hub -high # links in its module-)
-  hc <- topoFrame %>% mutate(type = factor(type)) %>% arrange(node) %>% mutate(col=as.numeric(type), TL=tl[,1]) 
-  V(g)$color <- colnet[hc$col]
-  
-  plot(g,
-       edge.arrow.size = 0.4, 
-       vertex.label.color = "black", 
-       edge.curved = 0.2, 
-       layout = lMat, 
-       vertex.label.cex = 1, 
-       vertex.label.family = "Lato", 
-       vertex.label.font = 3)
-    
-  maxnew <- max(tl$TL)
-  minnew <- min(tl$TL)
-  maxold <- 1
-  minold <- -1
-  t2 <- function(x) (maxold - minold)/(maxnew - minnew) * 
-      (x - maxnew) + maxold
-  tlseq <- seq(1, ifelse(is.null(maxTL), maxnew + 1, maxTL), 
-      by = 1)
-  par(family = "Lato")
-  axis(side = 2, at = t2(tlseq), labels=tlseq, las=1, col=NA, col.ticks=NULL, cex.axis=1, lwd.ticks=1)
-  }
-
-# Plot networks
-tiff("2020/Figures/fw.tif", width=12, height=8, units="in", res=300)
-par(mfrow=c(1,2), mar=c(0,2,0,0), oma=c(0,1.5,2.3,0.5), family = "Lato")
-ptl(p, hub_connp, vertexLabel=FALSE)
-ptl(b, hub_connb, vertexLabel=FALSE)
-
-# Add axis-y legend
-mtext("Trophic level", side=2, outer=TRUE, line=0, cex=1.25, font=2)
-par(fig=c(0,1,0,1), family="Lato", new=TRUE)
-
-# Plots titles
-plot(0, 0, type="n", bty="n", xaxt="n", yaxt="n")
-mtext("Beagle Channel", side=3, outer=TRUE, line=0.5, cex=1.75, font=2, adj=1)
-par(fig=c(0,1,0,1), family="Lato", new=TRUE)
-
-plot(0, 0, type="n", bty="n", xaxt="n", yaxt="n")
-mtext("Potter Cove", side=3, outer=TRUE, line=0.5, cex=1.75, font=2, adj=0)
-par(fig=c(0,1,0,1), oma=c(0,0,0,0), mar=c(0,0,1,0), new=TRUE)
-
-# Add legend
-plot(0, 0, type="n", bty="n", xaxt="n", yaxt="n")
-colnet1 <- c("#833f8f", "#ccb7d5","#a8d9c3", "#088a4f")
-legend("top", legend = c("Network connector", "Module connector", "Module specialist", "Module hub"), col=colnet1, pch=19, cex=0.8, xpd=TRUE, inset=c(0,0), title="Topological roles:")
-
-dev.off()
-```
-
-```{r}
-
-#---- Plot fw by TL
-svg("2020/Figures/fwp.svg", width=8, height=8)
-par(mfrow=c(1,1), mar=c(0,2,0,0), oma=c(0,1.5,2.3,0.5), family = "Lato")
-plot_troph_level(p, vertexSizeFactor = 6, maxTL = 4, edge.color="white")
-dev.off()
-
-svg("2020/Figures/fwb.svg", width=8, height=8)
-par(mfrow=c(1,1), mar=c(0,2,0,0), oma=c(0,1.5,2.3,0.5), family = "Lato")
-plot_troph_level(b, vertexSizeFactor = 6, maxTL = 4, edge.color="white")
-dev.off()
-
-```
+### Food web plot by trophic level and modules ###
 
-```{r}
-#---- Plot fw by TL and mod
+```{r plotFW, echo=FALSE, message=FALSE}
 
 troph.net1 <- TrophInd(get.adjacency(p, sparse=F), Dead=c("Necromass", "Fresh detritus", "Aged detritus"))
 layout.matrix.1 <- matrix(nrow=length(V(p)), ncol=2)
@@ -187,13 +65,13 @@ troph.net2 <- TrophInd(get.adjacency(b, sparse=F), Dead=c("Necromass", "Fresh de
 layout.matrix.2 <- matrix(nrow=length(V(b)), ncol=2)
   
 # Add colors with topological roles to nodes 
-colnet <- c("#833f8f", "#ccb7d5","#a8d9c3", "#088a4f")
+colnet1 <- c("#833f8f", "#ccb7d5","#a8d9c3", "#088a4f")
   
 hcp <- hub_connp %>% mutate(type = factor(type)) %>% arrange(node) %>% mutate(col=as.numeric(type), TL=troph.net1[,1]) 
-V(p)$color <- colnet[hcp$col]
+V(p)$color <- colnet1[hcp$col]
 
 hcb <- hub_connb %>% mutate(type = factor(type)) %>% arrange(node) %>% mutate(col=as.numeric(type), TL=troph.net2[,1]) 
-V(b)$color <- colnet[hcb$col]
+V(b)$color <- colnet1[hcb$col]
   
 # Transform y-axis coordinates
 maxnewp <- max(hcp$TL)
@@ -215,7 +93,7 @@ layout.matrix.1[,1] <- jitter(mod_by_red[[1]]$membership, 1.2) # randomly assign
 layout.matrix.2[,2] <- jitter(troph.net2$TL, 0.4)
 layout.matrix.2[,1] <- jitter(mod_by_red[[2]]$membership, 1.4)
 
-svg("2020/Figures/fw_mod_topo2.svg", width=20, height=10)
+svg("Results/fw_mod_TL.svg", width=20, height=10)
 par(mfrow=c(1,2), mar=c(0,0.5,0,0), oma=c(0,1.5,2.3,0.5), family = "Lato")
 require(prettyGraphs)
 
@@ -245,7 +123,7 @@ axis(side=2, at=t1(1:5), labels=1:5, las=1, col=NA, col.ticks=1)
 
 dev.off()
 
-#svg("2020/Figures/fw_legend.svg", width=5, height=5)
+#svg("Results/fw_legend.svg", width=5, height=5)
 #par(mfrow=c(1,1), mar=c(0,2,0,0), oma=c(0,1.5,2.3,0.5), family = "Lato")
 # Add legend
 #plot(0, 0, type="n", bty="n", xaxt="n", yaxt="n")
@@ -258,10 +136,6 @@ dev.off()
 
 ```{r plotTrait, echo=FALSE, message=FALSE, results='asis'}
 
-colnet <- c("#024828","#066e3f","#088a4f","#51b487","#85caab","#a8d9c3", #VERDE +oscuro-
-            "#ffffff", 
-            "#40004b","#683272","#833f8f","#9970ab","#b79ac4","#ccb7d5") #VIOLETA +oscuro-
-
 #--- Modules vs habitat 
 
 #POTTER
@@ -328,18 +202,18 @@ col2 <- plot_grid(hfg[[3]], hfg[[4]], ncol=1, nrow=2)
 col3 <- plot_grid(hfg[[5]], hfg[[6]], ncol=1, nrow=2, labels = c("(b)", "(d)"), label_size=15, label_fontfamily="Lato", hjust=0.25, vjust=2)
 plot_grid(col1, col2, col3, ncol=3, rel_widths=c(1,0.35,1), scale=0.98)
 
-ggsave("2020/Figures/HFG_PCBC.svg", width=13, height=12)
-#ggsave("C:/Users/iamia/Google Drive (iaradiamelar@gmail.com)/Escrito/Paper/HFG_PCBC.tiff", width=13, height=12, units="in", dpi=1000)
-
+ggsave("Results/HFG_PCBC.svg", width=13, height=12)
 
 rm(ph,bh,pfg,bfg,hfg,col1,col2,col3)
 
 ```
 
-```{r}
+```{r, echo=FALSE, message=FALSE, results='asis'}
+
 # Total spp x traits plots
 
 #-- Habitat
+
 #POTTER
 cpH_t <- cpH %>% group_by(Habitat) %>% summarise(Total=(sum(Freq)/110)*100)
 ggplot(cpH_t, aes(x=1, y=Total, fill=Habitat)) +
@@ -353,9 +227,9 @@ ggplot(cpH_t, aes(x=1, y=Total, fill=Habitat)) +
        coord_polar(theta="y") + 
        labs(y="", x="") +
        theme_void()
-ggsave("2020/Figures/cpH_t.svg", width=5, height=5)
+ggsave("Results/cpH_t.svg", width=5, height=5)
 
-#BEALGE
+#BEAGLE
 cbH_t <- cbH %>% group_by(Habitat) %>% summarise(Total=(sum(Freq)/145)*100)
 ggplot(cbH_t, aes(x=1, y=Total, fill=Habitat)) +
        geom_bar(stat="identity", show.legend=FALSE) +
@@ -368,9 +242,10 @@ ggplot(cbH_t, aes(x=1, y=Total, fill=Habitat)) +
        coord_polar(theta="y") + 
        labs(y="", x="") +
        theme_void()
-ggsave("2020/Figures/cbH_t.svg", width=5, height=5)
+ggsave("Results/cbH_t.svg", width=5, height=5)
 
 #-- Functional group
+
 #POTTER
 cpFG_t <- cpFG %>% group_by(FG) %>% summarise(Total=(sum(Freq)/110)*100)
 ggplot(cpFG_t, aes(x=1, y=Total, fill=FG)) +
@@ -381,7 +256,7 @@ ggplot(cpFG_t, aes(x=1, y=Total, fill=FG)) +
        coord_polar(theta="y") + 
        labs(y="", x="") +
        theme_void()
-ggsave("2020/Figures/cpFG_t.svg", width=5, height=5)
+ggsave("Results/cpFG_t.svg", width=5, height=5)
 
 #BEAGLE
 cbFG_t <- cbFG %>% group_by(FG) %>% summarise(Total=(sum(Freq)/145)*100)
@@ -393,98 +268,28 @@ ggplot(cbFG_t, aes(x=1, y=Total, fill=FG)) +
        coord_polar(theta="y") + 
        labs(y="", x="") +
        theme_void()
-ggsave("2020/Figures/cbFG_t.svg", width=5, height=5)
+ggsave("Results/cbFG_t.svg", width=5, height=5)
 
 rm(cpH_t,cbH_t,cpFG_t,cbFG_t)
-```
-
-
-### Plot stability (Q, QSS) metrics ###
-
-```{r plotStability, echo=FALSE, message=FALSE, results='asis'}
-
-datp <- ti %>% filter(Network=="Potter")
-simp <- tiMIC %>% filter(Network=="Potter")
-
-datb <- ti %>% filter(Network=="Beagle")
-simb <- tiMIC %>% filter(Network=="Beagle")
-
-# Trophic coherence
-
-q13 <- quantile(simp$Q, c(0.005,0.995))
-q14 <- quantile(simb$Q,c(0.005,0.995))
-
-g7 <- ggplot(tiMIC, aes(Q)) + 
-      geom_histogram(data=tiMIC, aes(fill=Network), bins=50, show.legend=TRUE) +
-      scale_fill_manual(values=c(colnet[12], colnet[5]), labels=c("Beagle Channel", "Potter Cove")) +
-      xlab("Trophic coherence") + 
-      ylab("Frecuency") + 
-      geom_segment(data=datp, aes(x=Q, xend=Q, y=45, yend=0), colour=colnet[2], size=1.3, arrow=arrow(length=unit(0.25, "cm"), type="open", angle=30)) +
-      geom_text(data=datp, aes(x=Q, y=-19, label=round(Q, 2)), size=4.25, family="Lato", fontface="bold", colour=colnet[2], nudge_y=0, nudge_x=0.011) +
-      geom_segment(data=datp, aes(x=q13[1], xend=q13[1], y=35, yend=0), colour=colnet[2], size=1.25, linetype=1) +
-      geom_segment(data=datp, aes(x=q13[2], xend=q13[2], y=35, yend=0), colour=colnet[2], size=1.25, linetype=1) + 
-      geom_segment(data=datb, aes(x=Q, xend=Q, y=45, yend=0), colour=colnet[9], size=1.3, arrow=arrow(length=unit(0.25, "cm"), type="open", angle=30)) +
-      geom_text(data=datb, aes(x=Q, y=-19, label=round(Q, 2)), size=4.25, family="Lato", fontface="bold", colour=colnet[9], nudge_y=0, nudge_x=-0.012) +
-      geom_segment(data=datb, aes(x=q14[1], xend=q14[1], y=35, yend=0), colour=colnet[9], size=1.25, linetype=1) +
-      geom_segment(data=datb, aes(x=q14[2], xend=q14[2], y=35, yend=0), colour=colnet[9], size=1.25, linetype=1) + 
-      theme_new + 
-      coord_cartesian(ylim=c(-8, 580)) + 
-      guides(fill=guide_legend(reverse=TRUE)) +
-      theme(legend.position="top")
-
-# Quasi-sign stabilty
-
-require(binom)
-df <- ti %>% dplyr::select(Network, QSS) %>% group_by(Network) %>% do(binom.confint(.$QSS*10000, 10000, methods="wilson"))
-
-df$Network <- factor(df$Network, levels=c("Potter", "Beagle"))
-levels(df$Network) <- c("Potter Cove", "Beagle Channel")
-
-gqss <- ggplot(df, aes(Network, mean, color=Network)) + 
-        geom_point() + 
-        geom_errorbar(aes(ymin=lower, ymax=upper), width=0.15) +
-        xlab("") +      
-        ylab("Quasi-sign stability") + 
-        scale_colour_manual(values=c(colnet[3], colnet[10])) + 
-        theme_new + 
-        theme(legend.position="none", 
-              axis.text.x=element_text(size=16, face="plain", family="Lato", margin=margin(t=8, r=0, b=0, l=0)),
-              axis.title.y=element_text(size=18, face="bold", family="Lato", margin=margin(t=8, r=0, b=0, l=0))) +
-        coord_cartesian(ylim=c(-0.00055, 0.039)) 
-
-gqss <- gqss + coord_flip() + theme(axis.text.y=element_text(size=16, angle=90, hjust=0.5),
-                                    axis.text.x=element_text(size=10))
-
-# Add all plots into a single plot
-  
-plot_grid(g7, gqss,
-          nrow=1, ncol=2, 
-          labels=c("(a)","(b)"), label_size=15, label_fontfamily="Lato", hjust=0, vjust=5.1, 
-          scale=0.98, 
-          align=c("hv", "hv"),
-          axis=c("tb"))
-
-ggsave("C:/Users/iamia/Google Drive (iaradiamelar@gmail.com)/Escrito/Paper/Q_QSS.tiff", width=11, height=5.5, units="in", dpi=1000)
-
-
-#rm(datp,datb,simp,simb,q13,q14,g7,df,gqss)
 
 ```
 
-### Plot topology metrics ###
+
+### Plot structure and stability metrics ###
 
 ```{r plotTopology, echo=FALSE, message=FALSE, results='asis'}
 
-datp <- ti %>% filter(Network=="Potter")
-simp <- tiMIC %>% filter(Network=="Potter")
+datp <- ti %>% filter(Network=="Potter") #empiric metrics for Potter
+simp <- tiMIC %>% filter(Network=="Potter") #curveball randomizations metrics for Potter
+
+datb <- ti %>% filter(Network=="Beagle") #empiric metrics for Beagle
+simb <- tiMIC %>% filter(Network=="Beagle") #curveball randomizations metrics for Beagle
 
-datb <- ti %>% filter(Network=="Beagle")
-simb <- tiMIC %>% filter(Network=="Beagle")
 
 # Mean trophic level
 
-q1 <- quantile(simp$TLmean, c(0.005,0.995))
-q2 <- quantile(simb$TLmean, c(0.005,0.995))
+q1 <- quantile(simp$TLmean, c(0.005,0.995)) #95% confidence interval for Potter mean TL
+q2 <- quantile(simb$TLmean, c(0.005,0.995)) #95% confidence interval for Beagle mean TL
 
 #legend1 <- get_legend(g1)
 g1 <- ggplot(tiMIC, aes(TLmean)) + 
@@ -578,193 +383,10 @@ row3 <- plot_grid(NULL, ggg[[5]], NULL, ncol=3, rel_widths=c(0.25,0.5,0.25))
 
 plot_grid(row3, row1, row2, ncol=1, rel_heights=c(0.15,1,1))
 
-ggsave("C:/Users/Iara Diamela/Downloads/curveball.tiff", width=11, height=12, units="in", dpi=300)
+ggsave("Results/curveball.svg", width=11, height=12, units="in", dpi=300)
 
 #rm(datp,datb,simp,simb)
 #rm(q1,q2,q3,q4,q5,q6,q7,q8,q9,q10)
 #rm(g1,g2,g3,g4,g5,ggg,row1,row2,row3,row4)
 
 ```
-
-### Plot smallworld ###
-
-```{r plotSmallworld, echo=FALSE, message=FALSE, results='asis'}
-
-#ti$Network <- factor(ti$Network, levels=c("Potter", "Beagle"))
-#levels(ti$Network) <- c("Potter Cove", "Beagle Channel")
-#smallWorldness$Network <- factor(smallWorldness$Network, levels=c("Potter", "Beagle"))
-#levels(smallWorldness$Network) <- c("Potter Cove", "Beagle Channel")
-
-# Clustering coefficient
-
-gsmw_cc <- ggplot(ti, aes(Clustering, x=Network, color=Network)) + 
-           geom_point(aes(size=2)) +
-           geom_line(data=smallWorldness, aes(Network, clus.coef), size=1.5) + 
-           scale_colour_manual(values=c(colnet[3], colnet[10])) +
-           xlab("") +      
-           ylab("Clustering coefficient") +
-           theme_new + 
-           theme(legend.position="none", 
-                  axis.text.x=element_text(size=16, face="plain", family="Lato", margin=margin(t=8, r=0, b=0, l=0)),
-                  axis.title.y=element_text(size=18, face="bold", family="Lato", margin=margin(t=8, r=0, b=0, l=0))) 
-
-gsmw_cc <- gsmw_cc + coord_flip() + theme(axis.text.y=element_text(size=16, angle=90, hjust=0.5),
-                                          axis.text.x=element_text(size=10))
-        
-# Characteristic path length
-
-gsmw_cpl <- ggplot(ti, aes(PathLength, x=Network, color=Network)) + 
-            geom_point(aes(size=2)) +
-            geom_line(data=smallWorldness, aes(Network, cha.path), size=1.5) + 
-            scale_colour_manual(values=c(colnet [3], colnet[10])) +
-            xlab("") +      
-            ylab("Characteristic path length") +
-            theme_new + 
-            theme(legend.position="none", 
-                  axis.text.x=element_text(size=16, face="plain", family="Lato", margin=margin(t=8, r=0, b=0, l=0)),
-                  axis.title.y=element_text(size=18, face="bold", family="Lato", margin=margin(t=8, r=0, b=0, l=0))) 
-        
-
-gsmw_cpl <- gsmw_cpl + coord_flip() + theme(axis.text.y=element_blank(),
-                                            axis.text.x=element_text(size=10))
-
-# Add all plots into a single plot
-
-plot_grid(gsmw_cc, gsmw_cpl,
-          nrow=1, ncol=2, 
-          labels=c("(a)","(b)"), label_size=15, label_fontfamily="Lato", hjust=-0.7, vjust=2, 
-          scale=0.98, 
-          align=c("hv", "hv"),
-          axis=c("tb"))
-
-ggsave("C:/Users/iamia/Google Drive (iaradiamelar@gmail.com)/Escrito/Paper/smallworld.tiff", width=11, height=5.5, units="in", dpi=1000)
-
-```
-
-```{r}
-
-dgp <- data.frame(degree(p)) %>% mutate(Name="Potter")
-colnames(dgp) <- c("Degree", "Network")
-dgp.histogram <- as.data.frame(table(dgp))
-dgp.histogram[,1] <- as.numeric(dgp.histogram[,1])
-
-dgb <- data.frame(degree(b)) %>% mutate(Name="Beagle")
-colnames(dgb) <- c("Degree", "Network")
-dgb.histogram <- as.data.frame(table(dgb))
-dgb.histogram[,1] <- as.numeric(dgb.histogram[,1])
-
-dg <- bind_rows(dgp.histogram, dgb.histogram)
-
-
-ggplot(dg, aes(x=Degree, y=Freq, color=Network)) + geom_point() + theme_new +
-  scale_x_continuous("Degree\n(nodes with this amount of connections)",
-                     breaks = c(1, 3, 10, 30, 100, 300),
-                     trans = "log10") +
-  scale_y_continuous("Frequency\n(how many of them)",
-                     breaks = c(1, 3, 10, 30, 100, 300, 1000),
-                     trans = "log10")
- 
-rm(dgp,dgb.histogram,dgb.histogram,dg)
-
-```
-
-
-### Plot degree distribution###
-```{r}
-require(poweRlaw)
-
-#---POTTER
-p1 <- degree(p)
-
-# Power law
-m1 <- displ$new(p1)
-est <- estimate_xmin(m1)
-m1$setXmin(est)
-m1$setXmin(1)
-alf <- estimate_pars(m1)
-m1$setPars(alf)
-
-# Log normal
-m2 <- dislnorm$new(p1)
-est2 <- estimate_xmin(m2)
-m2$setXmin(est2)
-m2$setXmin(1)
-alf2 <- estimate_pars(m2)
-m2$setPars(alf2)
-
-# Poisson
-m3 <- dispois$new(p1)
-est3 <- estimate_xmin(m3)
-m3$setXmin(est3)
-m3$setXmin(1)
-alf3 <- estimate_pars(m3)
-m3$setPars(alf3)
-
-# Exponential
-m4 <- disexp$new(p1)
-est4 <- estimate_xmin(m4)
-m4$setXmin(est4)
-m4$setXmin(1)
-alf4 <- estimate_pars(m4)
-m4$setPars(alf4)
-
-
-#---BEAGLE
-b1 <- degree(b)
-
-# Power law
-m5 <- displ$new(b1)
-est <- estimate_xmin(m5)
-m5$setXmin(est)
-m5$setXmin(1)
-alf5 <- estimate_pars(m5)
-m5$setPars(alf5)
-
-# Log normal
-m6 <- dislnorm$new(b1)
-est6 <- estimate_xmin(m6)
-m6$setXmin(est6)
-m6$setXmin(1)
-alf6 <- estimate_pars(m6)
-m6$setPars(alf6)
-
-# Poisson
-m7 <- dispois$new(b1)
-est7 <- estimate_xmin(m7)
-m7$setXmin(est7)
-m7$setXmin(1)
-alf7 <- estimate_pars(m7)
-m7$setPars(alf7)
-
-# Exponential
-m8 <- disexp$new(b1)
-est8 <- estimate_xmin(m8)
-m8$setXmin(est8)
-m8$setXmin(1)
-alf8 <- estimate_pars(m8)
-m8$setPars(alf8)
-
-
-# Plots
-#tiff("C:/Users/Iara Diamela/Downloads/degree.tif", width=18, height=10, units="in", res=300)
-svg("2020/Figures/degree.svg", width=18, height=10)
-par(mfrow=c(1,2), mar=c(5,5,3,2), oma=c(1,1,1,1), family = "Lato", cex.axis=1.2, cex.lab=1.8)
-
-plot(m1, xlim=c(1,60), ylab="Cumulative distribution", xlab="Degree", main="Potter Cove", cex.main=2.2, pch=19, col=colnet[4], cex=1.4, ylim=c(0.01,1))
-lines(m1, lty=1) #power
-lines(m2, lty=2) #lognorm
-lines(m3, lty=3) #pois
-lines(m4, lty=4, lwd=2.5) #exp
-legend(c(0.02,0.0235), c("Power-Law", "Log Normal", "Poisson", "Exponential"), lty= c(1,2,3,4), box.lty=0, cex=1.5)
-
-plot(m5, xlim=c(1,60), ylab="", xlab="Degree", main="Beagle Channel", cex.main=2.2, pch=19, col=colnet[11], cex=1.4, ylim=c(0.01,1))
-lines(m5, lty=1) #power
-lines(m6, lty=2) #lognorm
-lines(m7, lty=3) #pois
-lines(m8, lty=4) #exp
-
-dev.off()
-
-#rm(b1,p1,m1,m2,m3,m4,m5,m6,m7,m8,alf,afl2,alf3,alf4,alf5,alf6,alf7,alf8,est,est2,est3,est4,est5,est6,est7,est8)
-```
-