diff --git a/server.R b/server.R
index 1b4db26..c217da7 100644
--- a/server.R
+++ b/server.R
@@ -11,6 +11,7 @@ library(shiny)
 # library(devtools)
 # install_github('MoBiodiv/mobsim')    # downloads the latest version of the package
 library(mobsim, lib.loc="./Library")
+library(ggplot2)
 
 # Define server logic for slider examples
 shinyServer(function(input, output, session) {
@@ -20,45 +21,215 @@ shinyServer(function(input, output, session) {
   # }
   
   # update range for species richness, an observed species has minimum one individual
-  observe({
-    updateSliderInput(session, "S", min=5, max=input$N, value=5, step=5)
-  })
+	observe({
+		updateSliderInput(session, "S", min=5, max=input$N, value=5, step=5)
+	})
+  # update the number of species in the drop down species list.
+	observe({
+		updateSelectInput(session, "species_ID", "Pick species ID", paste("species", 1:input$S, sep="_"))
+	})
+
+  values <- reactiveValues()
+  values$DT <- data.frame(x = numeric(),
+                         y = numeric(),
+                         species_ID = factor())
+
+output$select_sad_type <- renderUI({
+	if (!input$method_type %in% c("random_mother_points","click_for_mother_points"))	{
+		return()
+	} else {
+		selectizeInput("sad_type", "SAD Type", choices=c("lognormal"="lnorm","geometric"="geom","Fisher's log-series"="ls"))
+	}					
+})
+					
   
-  output$ui <- renderUI({
-    if (is.null(input$sad_type))
-      return()
-    switch(input$sad_type,
-           "lnorm"=sliderInput("coef", label="CV(abundance), i.e. standard deviation of abundances divided by the mean abundance",value=1, min=0, max=5, step=0.1, ticks=F),
-           "geom"=sliderInput("coef",label="Probability of success in each trial. 0 < prob <= 1",value=0.5,min=0,max=1,step=0.1, ticks=F),
-            "ls"=textInput("coef",label="Fisher's alpha parameter",value=1)
-    )
-  })
-   
+output$CVslider <- renderUI({
+	if (!input$method_type %in% c("random_mother_points","click_for_mother_points") | is.null(input$sad_type))	
+		return()
+	switch(input$sad_type,
+		"lnorm"=sliderInput("coef", label="CV(abundance), i.e. standard deviation of abundances divided by the mean abundance",value=1, min=0, max=5, step=0.1, ticks=F),
+		"geom"=sliderInput("coef",label="Probability of success in each trial. 0 < prob <= 1",value=0.5,min=0,max=1,step=0.1, ticks=F),
+		"ls"=textInput("coef",label="Fisher's alpha parameter",value=1)
+	)
+})
+
+output$text_spat_agg <- renderUI({
+	if (!input$method_type %in% c("random_mother_points","click_for_mother_points"))	{
+		return()
+	} else {
+		textInput(inputId="spatagg", label="Spatial Aggregation (mean distance from mother points)", value = 0.1)
+	}			
+})
+
+
+output$spatdist <- renderUI({
+	if (input$method_type != "random_mother_points")	{
+		return()
+	} else {
+		selectizeInput(inputId="spatdist", "Cluster parameter", choices = c("Number of mother points"="n.mother", "Number of clusters"="n.cluster"))
+	}					
+})
+
+output$spatcoef <- renderUI({
+	if (input$method_type != "random_mother_points")	{
+		return()
+	} else {
+		textInput(inputId="spatcoef",label="Integer values separated by commas", value="1")
+	}					
+})
+
+
+
+output$species_ID_input <- renderUI({
+	if (input$method_type != "click_for_mother_points")	{
+		return()
+	} else {
+		selectInput("species_ID", "Pick species ID", paste("species", 1:input$S, sep="_"))
+	}
+})
+
+output$on_plot_selection <- renderPlot({
+  if (is.null(input$method_type)) {
+		return()
+	} else {
+		if(input$method_type=="click_for_mother_points") {
+				color_vector <- rainbow(input$S)
+				par(mex=0.7, mar=c(3,3,1,1))
+				plot(x=values$DT$x, y=values$DT$y, col=color_vector[values$DT$species_ID], xlim=c(0,1), ylim=c(0,1), xlab="", ylab="", las=1, asp=1, pch=20)
+				abline(h=c(0,1), v=c(0,1), lty=2)
+		}
+	}
+})
+
+
+output$rem_point_button <- renderUI({
+	if (input$method_type != "click_for_mother_points")	{
+		return()
+	} else {
+		actionButton("rem_point", "Remove Last Point")
+	}
+})
+
+output$rem_all_points_button <- renderUI({
+	if (input$method_type != "click_for_mother_points")	{
+		return()
+	} else {
+		actionButton("rem_all_points", "Remove All Points")
+	}
+})
+
+
+output$info <- renderUI({
+	if (input$method_type != "click_for_mother_points")	{
+		return()
+	} else {
+		verbatimTextOutput("info", placeholder=F)
+	}
+})
+
+
+output$community <- renderUI({
+	if (input$method_type != "uploading_community_data")	{
+		return()
+	} else {
+		fileInput(inputId="sim.com", label="Choose rData community File", multiple = FALSE,
+					accept = "", width = NULL,
+					buttonLabel = "Browse...", placeholder = "No file selected")	# c("text/csv", "text/comma-separated-values,text/plain", ".csv")
+	}
+})
+
+
+ 
+observeEvent(input$plot_click, { # The data.frame could be a matrix with already existing data: "no clustering" to allow users not to cluster some species.
+add_row = data.frame(x = input$plot_click$x,
+						 y = input$plot_click$y,
+						 species_ID = factor(input$species_ID, levels = paste("species", 1:input$S, sep="_")))
+values$DT = rbind(values$DT, add_row)
+})
+	 
+
+observeEvent(input$rem_all_points, {
+ rem_row = values$DT[-(1:nrow(values$DT)), ]
+ values$DT = rem_row
+})
+	 
+observeEvent(input$rem_point, {
+ rem_row = values$DT[-nrow(values$DT), ]
+ values$DT = rem_row
+})
+
+## point coordinates
+output$info <- renderText({
+	xy_str <- function(e) {
+		if(is.null(e)) return("NULL\n")
+		paste0("x=", round(e$x, 1), " y=", round(e$y, 1), "\n")
+	}
+	xy_range_str <- function(e) {
+		if(is.null(e)) return("NULL\n")
+		paste0("xmin=", round(e$xmin, 1), " xmax=", round(e$xmax, 1), 
+				 " ymin=", round(e$ymin, 1), " ymax=", round(e$ymax, 1))
+	}
+	paste0(
+		"click: ", xy_str(input$plot_click),
+		"brush: ", xy_range_str(input$plot_brush)
+	)
+})
+		
   ## plot theme
+   
   output$InteractivePlot <- renderPlot({
     input$Restart
     
     isolate({
-      set.seed(229376)
-    
-      n.mother <- ifelse(input$spatdist=="n.mother", as.numeric(input$spatcoef), NA)
-      n.cluster <- ifelse(input$spatdist=="n.cluster", as.numeric(input$spatcoef), NA)
-      
-      sim.com <- switch(input$sad_type,
-                        "lnorm"=sim_thomas_community(s_pool = input$S, n_sim = input$N, 
-                                                     sigma=input$spatagg, mother_points=n.mother, cluster_points=n.cluster,
-                                                     sad_type = input$sad_type, sad_coef=list(cv_abund=input$coef),
-                                                     fix_s_sim = T),
-                        "geom"=sim_thomas_community(s_pool = input$S, n_sim = input$N,                                                    sigma=input$spatagg, mother_points=n.mother, cluster_points=n.cluster, sad_type = input$sad_type, 
-                                                    sad_coef=list(prob=input$coef),
-                                                    fix_s_sim = T),
-                        "ls"=sim_thomas_community(s_pool = input$S, n_sim = input$N,                                                    sigma=input$spatagg, mother_points=n.mother, cluster_points=n.cluster,
-                                                  sad_type = input$sad_type, 
-                                                  sad_coef=list(N=input$N,alpha=as.numeric(input$coef)),
-                                                  fix_s_sim = T)
-                        
-      )
       
+		set.seed(229376)
+		
+		if(input$method_type != "uploading_community_data") {
+		
+			spatagg_num <- as.numeric(unlist(strsplit(trimws(input$spatagg), ",")))
+			spatcoef_num <- as.numeric(unlist(strsplit(trimws(input$spatcoef), ",")))
+		 
+			if(input$spatdist=="n.mother") n.mother <- spatcoef_num else n.mother <- NA
+			if(input$spatdist=="n.cluster") n.cluster <- spatcoef_num else n.cluster <- NA
+			
+			simulation_parameters <- switch(input$method_type,
+									"random_mother_points"=list(mother_points=n.mother,
+																		cluster_points=n.cluster,
+																		xmother=NA,
+																		ymother=NA),
+									"click_for_mother_points"=list(mother_points=NA,
+																		cluster_points=NA,
+																		xmother=tapply(values$DT$x, values$DT$species_ID, list),
+																		ymother=tapply(values$DT$y, values$DT$species_ID, list))
+									)
+									
+			# if(input$method_type == "click_for_mother_points") {	# if the user does not set any point for some species: no clustering
+				# species_list <- paste("species", 1:input$S, sep="_")
+				# missing_species <- species_list[!species_list %in% values$DT$species_ID]
+				# if(length(missing_species) > 0) {
+					# xmother[missing_species] <- "no_clustering"
+					# ymother[missing_species] <- "no_clustering"
+				# }
+				# xmother[is.na(xmother)] <- "no clustering"
+				# ymother[is.na(ymother)] <- "no clustering"
+			# }
+			
+			sim.com <- switch(input$sad_type,
+							"lnorm"=sim_thomas_community(s_pool = input$S, n_sim = input$N, 
+								sigma=spatagg_num, mother_points=simulation_parameters$mother_points, cluster_points=simulation_parameters$cluster_points, xmother=simulation_parameters$xmother, ymother=simulation_parameters$ymother,
+								sad_type = input$sad_type, sad_coef=list(cv_abund=input$coef),
+								fix_s_sim = T),
+							"geom"=sim_thomas_community(s_pool = input$S, n_sim = input$N,
+								sigma=spatagg_num, mother_points=simulation_parameters$mother_points, cluster_points=simulation_parameters$cluster_points, xmother=simulation_parameters$xmother, ymother=simulation_parameters$ymother,
+								sad_type = input$sad_type, sad_coef=list(prob=input$coef),
+								fix_s_sim = T),
+							"ls"=sim_thomas_community(s_pool = input$S, n_sim = input$N,
+								sad_type = input$sad_type, sad_coef=list(N=input$N,alpha=as.numeric(input$coef)),
+								sigma=spatagg_num, mother_points=simulation_parameters$mother_points, cluster_points=simulation_parameters$cluster_points, xmother=simulation_parameters$xmother, ymother=simulation_parameters$ymother,
+								fix_s_sim = T)
+						)
+		}
+		
       layout(matrix(c(1,2,3,
                       4,5,6), byrow = T, nrow = 2, ncol = 3),
              heights = c(1,1), widths=c(1,1,1))
diff --git a/ui.R b/ui.R
index dfb7275..7cd5144 100644
--- a/ui.R
+++ b/ui.R
@@ -13,47 +13,72 @@ library(mobsim)
 
 # Define UI for slider demo application
   
-fluidPage(
-  titlePanel("Visualization of biodiversity pattern"),
-    
-  sidebarLayout(
-    
-    sidebarPanel(
-      
-      # Slider inputs
-      sliderInput("N", "Number of individuals",
-                  min=10, max=5000, value=1000, step=50, ticks=F),
-      
-      sliderInput("S", "Species Richness",
-                  min=10, max=500, value=50, step=10, ticks=F),
-      
-      selectizeInput("sad_type", "SAD Type", choices=c("lognormal"="lnorm","geometric"="geom","Fisher's log-series"="ls")),
-      #,
-      #               options = list(placeholder = 'Please select an option below',onInitialize = I('function() { this.setValue(""); }')),
+navbarPage("Visualization of biodiversity pattern",
+	tabPanel("Plot", 
 
-      # This outputs the dynamic UI component
-      uiOutput("ui"),
+		fluidPage(
+			fluidRow(
+				column(width=4,
+					# Slider inputs
+					sliderInput("N", "Number of individuals",
+									min=10, max=5000, value=500, step=50, ticks=F),
+					
+					sliderInput("S", "Species Richness",
+									min=5, max=500, value=5, step=5, ticks=F),
+					
+					selectizeInput("method_type", label="Method", choices=c("Random mother points"="random_mother_points", "Click for mother points"="click_for_mother_points", "User community file"="uploading_community_data"), selected="Random mother points", multiple=FALSE)
 
-      sliderInput("spatagg", "Spatial Aggregation (mean distance from mother points)",
-                  min = 0, max = 2, value = 0.1, step= 0.01, ticks=F),
-      
-      selectizeInput("spatdist", "Cluster parameter", choices = c("Number of mother points"="n.mother", "Number of clusters"="n.cluster")),
-      
-      textInput("spatcoef",""),
-      
-      # Action button
-      actionButton(inputId="Restart",label="Restart Simulation")#,
-      
-      # Check box
-#      checkboxInput('keep', 'Keep last simulation', FALSE)      
-    ),
-    
-    mainPanel(
-      tabsetPanel(
-        tabPanel("Introduction", includeMarkdown("introduction.md")),
-        tabPanel("Plot", plotOutput("InteractivePlot", height="600px",width="750px"))
-      )
-    )  
-  )
-  
+				),
+				column(width=4,
+					uiOutput("select_sad_type"),
+					# This outputs the dynamic UI (user input) component (CV(abundance))
+					uiOutput("CVslider"),
+					
+					#sliderInput("spatagg", "Spatial Aggregation (mean distance from mother points)", min = 0, max = 2, value = 0.1, step= 0.01, ticks=F),
+					uiOutput("text_spat_agg"),
+											
+					uiOutput("spatdist"),
+					uiOutput("spatcoef"),
+					
+					uiOutput("community")
+				),
+				
+				column(width=4,
+					uiOutput("species_ID_input"),
+					plotOutput("on_plot_selection",
+						click = "plot_click",
+						brush = "plot_brush"
+					),
+					
+					uiOutput("rem_point_button"),
+					uiOutput("rem_all_points_button"),
+					
+					# tableOutput("table"),
+					uiOutput("info")
+				)
+			)
+		),
+		
+		
+		fluidRow(
+			column(width=6, offset=5,
+				# Action button
+				actionButton(inputId="Restart",label="Restart Simulation")#,
+				
+				# Check box
+				# checkboxInput('keep', 'Keep last simulation', FALSE) 
+			),
+			column(width=1)				
+		),
+		
+		fluidRow(
+			column(width=8, offset=3,
+				plotOutput("InteractivePlot", height="600px",width="750px")
+			),
+			column(width=1)
+		)
+	),
+	
+	tabPanel("Introduction", includeMarkdown("introduction.md"))
+	# another tabPanel "Saved simulation" with the previous sim?
 )
\ No newline at end of file