move functions in new file

exploration/GR3D_Rdescription/GR3Dmetapopulation.R

+
+#=============================================================================================================
+# metapopulation identification
+# =============================================================================================================
+computeAutochtonousRate = function(data){
+  data %>% filter(migrationBasin == originBasin) %>% 
+    select(-originBasin) %>% 
+    inner_join(data %>% group_by(year, migrationBasin) %>% 
+                 summarise(totalRun = sum(effective), .groups = 'drop'), by = c('migrationBasin', 'year')) %>% 
+    mutate(autochtonousRate = effective / totalRun) %>% 
+    select(year, migrationBasin, autochtonousRate)
+}
+
+computeHomingRate = function(data){
+  data %>% filter(migrationBasin == originBasin) %>% 
+    select(-migrationBasin) %>% 
+    inner_join(data %>% group_by(year, originBasin) %>% 
+                 summarise(production = sum(effective), .groups = 'drop'), by = c('originBasin', 'year')) %>% 
+    mutate(homingRate = effective / production) %>% 
+    select(year, originBasin, homingRate)
+}
+
+# comparison between autochtonous rate and homing rate
+# computeAutochtonousRate(exchangesData) %>% 
+#   inner_join(computeHomingRate(data = exchangesData), by =  c('migrationBasin' = 'originBasin', 'year'))
+
+
+
+identyMetapopulation = function(exchangesData, threshold = .95, verbose = FALSE) {
+  # exchangesData {year, migrationBasin, originBasin)}
+  
+  exchangesDataUpdated = exchangesData
+  
+  # initialise the connection table between basin and metapopulation
+  metapopulation = exchangesDataUpdated %>% 
+    distinct(year, basin = migrationBasin) %>% 
+    mutate(metapop = basin)
+  
+  iteration = 1
+  
+  # find the basin with the minimum autochtonous rate for the first time,
+  basinWithMinAutochtonousRate = computeAutochtonousRate(data = exchangesDataUpdated) %>%
+    group_by(year) %>%
+    slice(which.min(autochtonousRate))
+  
+  # loop while autochtonousRate is still < 0.95
+  
+  while (basinWithMinAutochtonousRate$autochtonousRate < threshold ) {
+    #  while (iteration <= 4 ) {
+    if (verbose) cat(iteration, ": ", basinWithMinAutochtonousRate$autochtonousRate, '\n' )
+    
+    # basinWithMinAutochtonousRate will be merged with origin basin sending the maximum number of fish in this basin
+    metapopsToBeMerged <-   exchangesDataUpdated %>% 
+      inner_join(basinWithMinAutochtonousRate %>% select(-autochtonousRate), 
+                 by = c('year', 'migrationBasin' )) %>%
+      filter(migrationBasin != originBasin) %>% # to avoid self merging
+      group_by(year) %>% 
+      slice(which.max(effective)) %>% 
+      ungroup() %>% 
+      select(year, migrationBasin, originBasin)
+    
+    # update the connection table between basin and metapopulation by merging metapopulation
+    for (i in 1:nrow(metapopsToBeMerged)) {
+      mergedName = paste0("M", iteration, '_',i)
+      metapopsToBeMerged_i <- metapopsToBeMerged %>% slice(i) 
+      if (verbose) cat("\t", metapopsToBeMerged_i$migrationBasin, ' + ', metapopsToBeMerged_i$originBasin, ' = ', mergedName, "\n")
+      
+      metapopulation <- metapopulation %>% 
+        mutate(metapop = if_else(year == metapopsToBeMerged_i$year &
+                                   metapop %in%  (metapopsToBeMerged_i %>% select(ends_with("Basin")) %>% unlist(use.names = FALSE)), 
+                                 mergedName,
+                                 metapop)) 
+    }
+    # sum by migration and origin basins according to updated metapopulations
+    # computed with initial exchangesData
+    exchangesDataUpdated <- exchangesData %>% 
+      #sum up on migration basins
+      inner_join(metapopulation, 
+                 by = c('year', 'migrationBasin' = 'basin')) %>% 
+      group_by(year, metapop, originBasin) %>% 
+      summarise(effective = sum(effective), .groups = 'drop') %>% 
+      rename(migrationBasin = metapop) %>% 
+      # sum on origine basins
+      inner_join(metapopulation, 
+                 by = c('year', 'originBasin' = 'basin')) %>% 
+      group_by(year, metapop, migrationBasin) %>% 
+      summarise(effective = sum(effective), .groups = 'drop') %>% 
+      rename(originBasin = metapop)
+    
+    # minimum of autochtonous rate
+    basinWithMinAutochtonousRate = computeAutochtonousRate(data = exchangesDataUpdated) %>%
+      group_by(year) %>%
+      slice(which.min(autochtonousRate))
+    
+    iteration = iteration + 1
+  }
+  return(list(metapopulation = metapopulation, exchangesData = exchangesDataUpdated))
+}
+
+
+

exploration/NEA_sensitivity_analysis/metapopulationIdentification.R

-library(tidyverse)
-
-computeAutochtonousRate = function(data){
-  data %>% filter(migrationBasin == originBasin) %>% 
-    select(-originBasin) %>% 
-    inner_join(data %>% group_by(year, migrationBasin) %>% 
-                 summarise(totalRun = sum(effective), .groups = 'drop'), by = c('migrationBasin', 'year')) %>% 
-    mutate(autochtonousRate = effective / totalRun) %>% 
-    select(year, migrationBasin, autochtonousRate)
-}
-
-computeHomingRate = function(data){
-  data %>% filter(migrationBasin == originBasin) %>% 
-    select(-migrationBasin) %>% 
-    inner_join(data %>% group_by(year, originBasin) %>% 
-                 summarise(production = sum(effective), .groups = 'drop'), by = c('originBasin', 'year')) %>% 
-    mutate(homingRate = effective / production) %>% 
-    select(year, originBasin, homingRate)
-}
-
-# comparison between autochtonous rate and homing rate
-# computeAutochtonousRate(exchangesData) %>% 
-#   inner_join(computeHomingRate(data = exchangesData), by =  c('migrationBasin' = 'originBasin', 'year'))
-
-
-
-identyMetapopulation = function(exchangesData, threshold = .95, verbose = FALSE) {
-  exchangesDataUpdated = exchangesData
-  
-  # initialise the connection table between basin and metapopulation
-  metapopulation = exchangesDataUpdated %>% 
-    distinct(year, basin = migrationBasin) %>% 
-    mutate(metapop = basin)
-  
-  iteration = 1
-  
-  # find the basin with the minimum autochtonous rate for the first time,
-  basinWithMinAutochtonousRate = computeAutochtonousRate(data = exchangesDataUpdated) %>%
-    group_by(year) %>%
-    slice(which.min(autochtonousRate))
-  
-  # loop while autochtonousRate is still < 0.95
-  
-  while (basinWithMinAutochtonousRate$autochtonousRate < threshold ) {
-    #  while (iteration <= 4 ) {
-    if (verbose) cat(iteration, ": ", basinWithMinAutochtonousRate$autochtonousRate, '\n' )
-    
-    # basinWithMinAutochtonousRate will be merged with origin basin sending the maximum number of fish in this basin
-    metapopsToBeMerged <-   exchangesDataUpdated %>% 
-      inner_join(basinWithMinAutochtonousRate %>% select(-autochtonousRate), 
-                 by = c('year', 'migrationBasin' )) %>%
-      filter(migrationBasin != originBasin) %>% # to avoid self merging
-      group_by(year) %>% 
-      slice(which.max(effective)) %>% 
-      ungroup() %>% 
-      select(year, migrationBasin, originBasin)
-    
-    # update the connection table between basin and metapopulation by merging metapopulation
-    for (i in 1:nrow(metapopsToBeMerged)) {
-      mergedName = paste0("M", iteration, '_',i)
-      metapopsToBeMerged_i <- metapopsToBeMerged %>% slice(i) 
-      if (verbose) cat("\t", metapopsToBeMerged_i$migrationBasin, ' + ', metapopsToBeMerged_i$originBasin, ' = ', mergedName, "\n")
-      
-      metapopulation <- metapopulation %>% 
-        mutate(metapop = if_else(year == metapopsToBeMerged_i$year &
-                                   metapop %in%  (metapopsToBeMerged_i %>% select(ends_with("Basin")) %>% unlist(use.names = FALSE)), 
-                                 mergedName,
-                                 metapop)) 
-    }
-    # sum by migration and origin basins according to updated metapopulations
-    # computed with initial exchangesData
-    exchangesDataUpdated <- exchangesData %>% 
-      #sum up on migration basins
-      inner_join(metapopulation, 
-                 by = c('year', 'migrationBasin' = 'basin')) %>% 
-      group_by(year, metapop, originBasin) %>% 
-      summarise(effective = sum(effective), .groups = 'drop') %>% 
-      rename(migrationBasin = metapop) %>% 
-      # sum on origine basins
-      inner_join(metapopulation, 
-                 by = c('year', 'originBasin' = 'basin')) %>% 
-      group_by(year, metapop, migrationBasin) %>% 
-      summarise(effective = sum(effective), .groups = 'drop') %>% 
-      rename(originBasin = metapop)
-    
-    # minimum of autochtonous rate
-    basinWithMinAutochtonousRate = computeAutochtonousRate(data = exchangesDataUpdated) %>%
-      group_by(year) %>%
-      slice(which.min(autochtonousRate))
-    
-    iteration = iteration + 1
-  }
-  return(list(metapopulation = metapopulation, exchangesData = exchangesDataUpdated))
-}
+require(tidyverse)
 
+source(GR3Dmetapopulation)
 # upload data =====
 # longer table of effective of different origin basins in each migration basin 
 exchangesData <- read_csv("../../data/output/northeastamerica/effectiveFluxes_1-observed.csv")
 
+
 exchangesData %>% group_by(year, basin = originBasin) %>% 
   summarise(production = sum(effective), .groups = 'drop') %>% 
   inner_join(exchangesData %>% group_by(year, basin = migrationBasin) %>%