###Final COVID analysis: create network graph from subthemes###
###Polly Compston last edited 01/04/2021###

####set up data and workspace####

.libPaths("C:/Users/pcompston/Desktop/epicollect")
setwd("C:/Users/pcompston/Desktop/epicollect/chickensandcovid/Final analysis/csv/")

library(tidyverse)
library(visNetwork)
library(igraph)
library (dplyr)
library(ggplot2)
library(RColorBrewer)

subthemes<-read.csv("master_subthemes.csv", header = TRUE, na.strings=c(""," ","NA") )
names (subthemes)



####Rename columns####

subthemes<- subthemes %>% rename (
                                  Consumer_demand_changes         = CONSUMER.DEMAND.CHANGES,                                           #Demand and market effects
                                  Reasons_for_demand_change       = REASONS.FOR.DEMAND.CHANGE,                                         #Demand and market effects
                                  Communications                  = INFORMATION.MANAGEMENT,                                            #Demand and market effects
                                  Change_in_chicken_sales         = CHANGES.IN.CHICKEN.or.CHICKEN.PRODUCT.SALES,                       #Demand and market effects
                                  Change_in_chicken_prices        = CHANGES.IN.CHICKEN.or.CHICKEN.MEAT.PRICES,                         #Demand and market effects
                                  Value_chain_business_closure   = VALUE.CHAIN.BUSINESS.CLOSURE,                                      #Value chain effects
                                  Supply_chain_functioning       = SUPPLY.CHAIN.FUNCTIONING,                                           #Value chain effects
                                  Links_to_other_chains           = LINKS.TO.OTHER.CHAINS,                                             #Value chain effects
                                  Requests_and_power              = REQUESTS.and.POWER,                                                #Value chain effects
                                  Trade                           = TRADE,                                                             #Trade
                                  Impact_on_labour                = LABOUR.AFFECTED.IN.VC.BUSINESS,                                    #Labour and livelihoods
                                  Economic_hardship               = ECONOMIC.HARDSHIP,                                                 #Labour and livelihoods
                                  Economic_support                = ECONOMIC.RELIEF.SUPPORT,                                           #Mitigation and interventions
                                  Food_donation                   = FOOD.DONATION,                                                     #Mitigation and interventions
                                  Normalise_market_dynamics       = EFFORTS.TO.NORMALISE.MARKET.DYNAMICS,                              #Mitigation and interventions
                                  Outbreak_management             = OUTBREAK.MANAGEMENT,                                               #Mitigation and interventions
                                  Coping_consumers                = COPING.CONSUMERS,                                                  #Coping
                                  Coping_processors               = COPING.PROCESSORS,                                                 #Coping 
                                  Coping_retailers_and_restaurants = COPING.RETAILERS.AND.RESTAURANTS,                                  #Coping 
                                  Coping_farmers                  = COPING.FARMERS,                                                    #Coping
                                  Epidemiological_information     = EPIDEMIOLOGICAL.INFORMATION.RELEVANT.TO.CHICKEN.MEAT.FOOD.SYSTEMS, #Epidemiological factors  
                                  Animal_welfare                  = ANIMAL.WELFARE                                                     #Animal welfare
                                  )     



####create nodes####
node1<- subthemes  %>% distinct(Consumer_demand_changes ) %>% rename (label =  Consumer_demand_changes  )
node2<- subthemes  %>% distinct(Reasons_for_demand_change) %>% rename (label = Reasons_for_demand_change)
nodes<- full_join(node1, node2, by = "label", na.omit = TRUE)

node3<- subthemes  %>% distinct(Communications  ) %>% rename (label = Communications)
nodes<- full_join(nodes, node3, by = "label", na.omit = TRUE)

node4<- subthemes  %>% distinct(Change_in_chicken_sales) %>% rename (label=Change_in_chicken_sales)
nodes<- full_join(nodes, node4, by = "label", na.omit = TRUE)

node5<- subthemes  %>% distinct(Change_in_chicken_prices) %>% rename (label=Change_in_chicken_prices)
nodes<- full_join(nodes, node5, by = "label", na.omit = TRUE)

node6<- subthemes  %>% distinct(Value_chain_business_closure) %>% rename (label= Value_chain_business_closure)
nodes<- full_join(nodes, node6, by = "label", na.omit = TRUE)

node7<- subthemes  %>% distinct(Supply_chain_functioning) %>% rename (label=Supply_chain_functioning)
nodes<- full_join(nodes, node7, by = "label", na.omit = TRUE)

node8<- subthemes  %>% distinct(Links_to_other_chains) %>% rename (label=Links_to_other_chains)
nodes<- full_join(nodes, node8, by = "label", na.omit = TRUE)

node9<- subthemes  %>% distinct(Requests_and_power) %>% rename (label=Requests_and_power)
nodes<- full_join(nodes, node9, by = "label", na.omit = TRUE)

node10<- subthemes  %>% distinct(Trade) %>% rename (label = Trade)
nodes<- full_join(nodes, node10, by = "label", na.omit = TRUE)

node11<- subthemes  %>% distinct(Impact_on_labour) %>% rename (label = Impact_on_labour)
nodes<- full_join(nodes, node11, by = "label", na.omit = TRUE)

node12<- subthemes  %>% distinct(Economic_hardship) %>% rename (label = Economic_hardship)
nodes<- full_join(nodes, node12, by = "label", na.omit = TRUE)

node13<- subthemes  %>% distinct(Economic_support) %>% rename (label = Economic_support)
nodes<- full_join(nodes, node13, by = "label", na.omit = TRUE)

node14<- subthemes  %>% distinct(Food_donation) %>% rename (label = Food_donation)
nodes<- full_join(nodes, node14, by = "label", na.omit = TRUE)

node15<- subthemes  %>% distinct(Normalise_market_dynamics) %>% rename (label = Normalise_market_dynamics)
nodes<- full_join(nodes, node15, by = "label", na.omit = TRUE)

node16<- subthemes  %>% distinct(Outbreak_management) %>% rename (label = Outbreak_management)
nodes<- full_join(nodes, node16, by = "label", na.omit = TRUE)

node17<- subthemes  %>% distinct(Coping_consumers) %>% rename (label = Coping_consumers)
nodes<- full_join(nodes, node17, by = "label", na.omit = TRUE)

node18<- subthemes  %>% distinct(Coping_processors) %>% rename (label = Coping_processors)
nodes<- full_join(nodes, node18, by = "label", na.omit = TRUE)

node19<- subthemes  %>% distinct(Coping_retailers_and_restaurants) %>% rename (label = Coping_retailers_and_restaurants)
nodes<- full_join(nodes, node19, by = "label", na.omit = TRUE)

node20<- subthemes  %>% distinct(Coping_farmers) %>% rename (label = Coping_farmers)
nodes<- full_join(nodes, node20, by = "label", na.omit = TRUE)

node21<- subthemes  %>% distinct(Epidemiological_information) %>% rename (label = Epidemiological_information)
nodes<- full_join(nodes, node21, by = "label", na.omit = TRUE)

node22<- subthemes  %>% distinct(Animal_welfare) %>% rename (label = Animal_welfare)
nodes<- full_join(nodes, node22, by = "label", na.omit = TRUE)

nodes <- na.omit(nodes)
nodes<-nodes%>% tibble::rowid_to_column ("id")
nodes$id<-sub("^", "s", nodes$id)



nodes

####create edges####

edgelist1   <- subthemes %>%group_by(  Consumer_demand_changes,Reasons_for_demand_change)       %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Reasons_for_demand_change")
edgelist2   <- subthemes %>%group_by(  Consumer_demand_changes,Communications)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Communications")
edgelist3   <- subthemes %>%group_by(  Consumer_demand_changes,Change_in_chicken_sales)         %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Change_in_chicken_sales")
edgelist4   <- subthemes %>%group_by(  Consumer_demand_changes,Change_in_chicken_prices)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Change_in_chicken_prices")
edgelist5   <- subthemes %>%group_by(  Consumer_demand_changes,Value_chain_business_closure)   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Value_chain_business_closure")
edgelist6   <- subthemes %>%group_by(  Consumer_demand_changes,Supply_chain_functioning)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Supply_chain_functioning")
edgelist7   <- subthemes %>%group_by(  Consumer_demand_changes,Links_to_other_chains)           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Links_to_other_chains")
edgelist8   <- subthemes %>%group_by(  Consumer_demand_changes,Requests_and_power)              %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Requests_and_power")
edgelist9   <- subthemes %>%group_by(  Consumer_demand_changes,Trade)                           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Trade")
edgelist10  <- subthemes %>%group_by(  Consumer_demand_changes,Impact_on_labour)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Impact_on_labour")
edgelist11  <- subthemes %>%group_by(  Consumer_demand_changes,Economic_hardship)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Economic_hardship")
edgelist12  <- subthemes %>%group_by(  Consumer_demand_changes,Economic_support)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Economic_support")
edgelist13  <- subthemes %>%group_by(  Consumer_demand_changes,Food_donation)                   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Food_donation")
edgelist14  <- subthemes %>%group_by(  Consumer_demand_changes,Normalise_market_dynamics)       %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Normalise_market_dynamics")
edgelist15  <- subthemes %>%group_by(  Consumer_demand_changes,Outbreak_management)             %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Outbreak_management")
edgelist16  <- subthemes %>%group_by(  Consumer_demand_changes,Coping_consumers)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Coping_consumers")
edgelist17  <- subthemes %>%group_by(  Consumer_demand_changes,Coping_processors)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Coping_processors")
edgelist18  <- subthemes %>%group_by(  Consumer_demand_changes,Coping_retailers_and_restaurants) %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Coping_retailers_and_restaurants")
edgelist19  <- subthemes %>%group_by(  Consumer_demand_changes,Coping_farmers)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Coping_farmers")
edgelist20  <- subthemes %>%group_by(  Consumer_demand_changes,Epidemiological_information)     %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Epidemiological_information")
edgelist21  <- subthemes %>%group_by(  Consumer_demand_changes,Animal_welfare)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Consumer_demand_changes", "B" = "Animal_welfare")

edgelistA <- rbind(edgelist1,
                   edgelist2,
                   edgelist3,
                   edgelist4,
                   edgelist5,
                   edgelist6,
                   edgelist7,
                   edgelist8,
                   edgelist9,
                   edgelist10,
                   edgelist11,
                   edgelist12,
                   edgelist13,
                   edgelist14,
                   edgelist15,
                   edgelist16,
                   edgelist17,
                   edgelist18,
                   edgelist19,
                   edgelist20,
                   edgelist21)


edgelist22  <- subthemes %>%group_by( Reasons_for_demand_change,Communications)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Communications")
edgelist23  <- subthemes %>%group_by( Reasons_for_demand_change,Change_in_chicken_sales)         %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Change_in_chicken_sales")
edgelist24  <- subthemes %>%group_by( Reasons_for_demand_change,Change_in_chicken_prices)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Change_in_chicken_prices")
edgelist25  <- subthemes %>%group_by( Reasons_for_demand_change,Value_chain_business_closure)   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Value_chain_business_closure")
edgelist26  <- subthemes %>%group_by( Reasons_for_demand_change,Supply_chain_functioning)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Supply_chain_functioning")
edgelist27  <- subthemes %>%group_by( Reasons_for_demand_change,Links_to_other_chains)           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Links_to_other_chains")
edgelist28  <- subthemes %>%group_by( Reasons_for_demand_change,Requests_and_power)              %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Requests_and_power")
edgelist29  <- subthemes %>%group_by( Reasons_for_demand_change,Trade)                           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Trade")
edgelist30  <- subthemes %>%group_by( Reasons_for_demand_change,Impact_on_labour)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Impact_on_labour")
edgelist31  <- subthemes %>%group_by( Reasons_for_demand_change,Economic_hardship)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Economic_hardship")
edgelist32  <- subthemes %>%group_by( Reasons_for_demand_change,Economic_support)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Economic_support")
edgelist33  <- subthemes %>%group_by( Reasons_for_demand_change,Food_donation)                   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Food_donation")
edgelist34  <- subthemes %>%group_by( Reasons_for_demand_change,Normalise_market_dynamics)       %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Normalise_market_dynamics")
edgelist35  <- subthemes %>%group_by( Reasons_for_demand_change,Outbreak_management)             %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Outbreak_management")
edgelist36  <- subthemes %>%group_by( Reasons_for_demand_change,Coping_consumers)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Coping_consumers")
edgelist37  <- subthemes %>%group_by( Reasons_for_demand_change,Coping_processors)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Coping_processors")
edgelist38  <- subthemes %>%group_by( Reasons_for_demand_change,Coping_retailers_and_restaurants) %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Coping_retailers_and_restaurants")
edgelist39  <- subthemes %>%group_by( Reasons_for_demand_change,Coping_farmers)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Coping_farmers")
edgelist40  <- subthemes %>%group_by( Reasons_for_demand_change,Epidemiological_information)     %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Epidemiological_information")
edgelist41  <- subthemes %>%group_by( Reasons_for_demand_change,Animal_welfare)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Reasons_for_demand_change", "B" = "Animal_welfare")

edgelistB <- rbind( edgelist22,
                    edgelist23,
                    edgelist24,
                    edgelist25,
                    edgelist26,
                    edgelist27,
                    edgelist28,
                    edgelist29,
                    edgelist30,
                    edgelist31,
                    edgelist32,
                    edgelist33,
                    edgelist34,
                    edgelist35,
                    edgelist36,
                    edgelist37,
                    edgelist38,
                    edgelist39,
                    edgelist40,
                    edgelist41)


edgelist42  <- subthemes %>%group_by( Communications,Change_in_chicken_sales)         %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Change_in_chicken_sales")
edgelist43  <- subthemes %>%group_by( Communications,Change_in_chicken_prices)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Change_in_chicken_prices")
edgelist44  <- subthemes %>%group_by( Communications,Value_chain_business_closure)   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Value_chain_business_closure")
edgelist45  <- subthemes %>%group_by( Communications,Supply_chain_functioning)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Supply_chain_functioning")
edgelist46  <- subthemes %>%group_by( Communications,Links_to_other_chains)           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Links_to_other_chains")
edgelist47  <- subthemes %>%group_by( Communications,Requests_and_power)              %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Requests_and_power")
edgelist48  <- subthemes %>%group_by( Communications,Trade)                           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Trade")
edgelist49  <- subthemes %>%group_by( Communications,Impact_on_labour)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Impact_on_labour")
edgelist50  <- subthemes %>%group_by( Communications,Economic_hardship)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Economic_hardship")
edgelist51  <- subthemes %>%group_by( Communications,Economic_support)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Economic_support")
edgelist52  <- subthemes %>%group_by( Communications,Food_donation)                   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Food_donation")
edgelist53  <- subthemes %>%group_by( Communications,Normalise_market_dynamics)       %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Normalise_market_dynamics")
edgelist54  <- subthemes %>%group_by( Communications,Outbreak_management)             %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Outbreak_management")
edgelist55  <- subthemes %>%group_by( Communications,Coping_consumers)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Coping_consumers")
edgelist56  <- subthemes %>%group_by( Communications,Coping_processors)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Coping_processors")
edgelist57  <- subthemes %>%group_by( Communications,Coping_retailers_and_restaurants) %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Coping_retailers_and_restaurants")
edgelist58  <- subthemes %>%group_by( Communications,Coping_farmers)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Coping_farmers")
edgelist59  <- subthemes %>%group_by( Communications,Epidemiological_information)     %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Epidemiological_information")
edgelist60  <- subthemes %>%group_by( Communications,Animal_welfare)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Communications", "B" = "Animal_welfare")


edgelistC <- rbind(edgelist42,
                   edgelist43,
                   edgelist44,
                   edgelist45,
                   edgelist46,
                   edgelist47,
                   edgelist48,
                   edgelist49,
                   edgelist50,
                   edgelist51,
                   edgelist52,
                   edgelist53,
                   edgelist54,
                   edgelist55,
                   edgelist56,
                   edgelist57,
                   edgelist58,
                   edgelist59,
                   edgelist60)


edgelist61  <- subthemes%>%group_by( Change_in_chicken_sales,Change_in_chicken_prices)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Change_in_chicken_prices")
edgelist62  <- subthemes%>%group_by( Change_in_chicken_sales,Value_chain_business_closure)   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Value_chain_business_closure")
edgelist63  <- subthemes%>%group_by( Change_in_chicken_sales,Supply_chain_functioning)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Supply_chain_functioning")
edgelist64  <- subthemes%>%group_by( Change_in_chicken_sales,Links_to_other_chains)           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Links_to_other_chains")
edgelist65  <- subthemes%>%group_by( Change_in_chicken_sales,Requests_and_power)              %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Requests_and_power")
edgelist66  <- subthemes%>%group_by( Change_in_chicken_sales,Trade)                           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Trade")
edgelist67  <- subthemes%>%group_by( Change_in_chicken_sales,Impact_on_labour)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Impact_on_labour")
edgelist68  <- subthemes%>%group_by( Change_in_chicken_sales,Economic_hardship)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Economic_hardship")
edgelist69  <- subthemes%>%group_by( Change_in_chicken_sales,Economic_support)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Economic_support")
edgelist70  <- subthemes%>%group_by( Change_in_chicken_sales,Food_donation)                   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Food_donation")
edgelist71  <- subthemes%>%group_by( Change_in_chicken_sales,Normalise_market_dynamics)       %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Normalise_market_dynamics")
edgelist72  <- subthemes%>%group_by( Change_in_chicken_sales,Outbreak_management)             %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Outbreak_management")
edgelist73  <- subthemes%>%group_by( Change_in_chicken_sales,Coping_consumers)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Coping_consumers")
edgelist74  <- subthemes%>%group_by( Change_in_chicken_sales,Coping_processors)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Coping_processors")
edgelist75  <- subthemes%>%group_by( Change_in_chicken_sales,Coping_retailers_and_restaurants) %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Coping_retailers_and_restaurants")
edgelist76  <- subthemes%>%group_by( Change_in_chicken_sales,Coping_farmers)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Coping_farmers")
edgelist77  <- subthemes%>%group_by( Change_in_chicken_sales,Epidemiological_information)     %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Epidemiological_information")
edgelist78  <- subthemes%>%group_by( Change_in_chicken_sales,Animal_welfare)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_sales", "B" = "Animal_welfare")

edgelistD <- rbind( edgelist61,
                    edgelist62,
                    edgelist63,
                    edgelist64,
                    edgelist65,
                    edgelist66,
                    edgelist67,
                    edgelist68,
                    edgelist69,
                    edgelist70,
                    edgelist71,
                    edgelist72,
                    edgelist73,
                    edgelist74,
                    edgelist75,
                    edgelist76,
                    edgelist77,
                    edgelist78)


edgelist79  <- subthemes%>%group_by( Change_in_chicken_prices,Value_chain_business_closure)   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Value_chain_business_closure")
edgelist80  <- subthemes%>%group_by( Change_in_chicken_prices,Supply_chain_functioning)        %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Supply_chain_functioning")
edgelist81  <- subthemes%>%group_by( Change_in_chicken_prices,Links_to_other_chains)           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Links_to_other_chains")
edgelist82  <- subthemes%>%group_by( Change_in_chicken_prices,Requests_and_power)              %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Requests_and_power")
edgelist83  <- subthemes%>%group_by( Change_in_chicken_prices,Trade)                           %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Trade")
edgelist84  <- subthemes%>%group_by( Change_in_chicken_prices,Impact_on_labour)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Impact_on_labour")
edgelist85  <- subthemes%>%group_by( Change_in_chicken_prices,Economic_hardship)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Economic_hardship")
edgelist86  <- subthemes%>%group_by( Change_in_chicken_prices,Economic_support)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Economic_support")
edgelist87  <- subthemes%>%group_by( Change_in_chicken_prices,Food_donation)                   %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Food_donation")
edgelist88  <- subthemes%>%group_by( Change_in_chicken_prices,Normalise_market_dynamics)       %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Normalise_market_dynamics")
edgelist89  <- subthemes%>%group_by( Change_in_chicken_prices,Outbreak_management)             %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Outbreak_management")
edgelist90  <- subthemes%>%group_by( Change_in_chicken_prices,Coping_consumers)                %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Coping_consumers")
edgelist91  <- subthemes%>%group_by( Change_in_chicken_prices,Coping_processors)               %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Coping_processors")
edgelist92  <- subthemes%>%group_by( Change_in_chicken_prices,Coping_retailers_and_restaurants) %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Coping_retailers_and_restaurants")
edgelist93  <- subthemes%>%group_by( Change_in_chicken_prices,Coping_farmers)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Coping_farmers")
edgelist94  <- subthemes%>%group_by( Change_in_chicken_prices,Epidemiological_information)     %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Epidemiological_information")
edgelist95  <- subthemes%>%group_by( Change_in_chicken_prices,Animal_welfare)                  %>% summarise( weight = n()) %>% ungroup ()%>% rename ("A" = "Change_in_chicken_prices", "B" = "Animal_welfare")

edgelistE <- rbind( edgelist79,
                    edgelist80,
                    edgelist81,
                    edgelist82,
                    edgelist83,
                    edgelist84,
                    edgelist85,
                    edgelist86,
                    edgelist87,
                    edgelist88,
                    edgelist89,
                    edgelist90,
                    edgelist91,
                    edgelist92,
                    edgelist93,
                    edgelist94,
                    edgelist95)


edgelist96  <- subthemes%>%group_by(Value_chain_business_closure,Supply_chain_functioning) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Supply_chain_functioning")
edgelist97  <- subthemes%>%group_by(Value_chain_business_closure,Links_to_other_chains) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Links_to_other_chains")
edgelist98  <- subthemes%>%group_by(Value_chain_business_closure,Requests_and_power) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Requests_and_power")
edgelist99  <- subthemes%>%group_by(Value_chain_business_closure,Trade) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Trade")
edgelist100 <- subthemes%>%group_by(Value_chain_business_closure,Impact_on_labour) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Impact_on_labour")
edgelist101 <- subthemes%>%group_by(Value_chain_business_closure,Economic_hardship) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Economic_hardship")
edgelist102 <- subthemes%>%group_by(Value_chain_business_closure,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Economic_support")
edgelist103 <- subthemes%>%group_by(Value_chain_business_closure,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Food_donation")
edgelist104 <- subthemes%>%group_by(Value_chain_business_closure,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Normalise_market_dynamics")
edgelist105 <- subthemes%>%group_by(Value_chain_business_closure,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Outbreak_management")
edgelist106 <- subthemes%>%group_by(Value_chain_business_closure,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Coping_consumers")
edgelist107 <- subthemes%>%group_by(Value_chain_business_closure,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Coping_processors")
edgelist108 <- subthemes%>%group_by(Value_chain_business_closure,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Coping_retailers_and_restaurants")
edgelist109 <- subthemes%>%group_by(Value_chain_business_closure,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Coping_farmers")
edgelist110 <- subthemes%>%group_by(Value_chain_business_closure,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Epidemiological_information")
edgelist111 <- subthemes%>%group_by(Value_chain_business_closure,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Value_chain_business_closure", "B" = "Animal_welfare")

edgelistF <- rbind( edgelist96,
                    edgelist97,
                    edgelist98,
                    edgelist99,
                    edgelist100,
                    edgelist101,
                    edgelist102,
                    edgelist103,
                    edgelist104,
                    edgelist105,
                    edgelist106,
                    edgelist107,
                    edgelist108,
                    edgelist109,
                    edgelist110,
                    edgelist111)


edgelist112 <- subthemes%>%group_by(Supply_chain_functioning,Links_to_other_chains) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Links_to_other_chains")
edgelist113 <- subthemes%>%group_by(Supply_chain_functioning,Requests_and_power) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Requests_and_power")
edgelist114 <- subthemes%>%group_by(Supply_chain_functioning,Trade) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Trade")
edgelist115 <- subthemes%>%group_by(Supply_chain_functioning,Impact_on_labour) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Impact_on_labour")
edgelist116 <- subthemes%>%group_by(Supply_chain_functioning,Economic_hardship) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Economic_hardship")
edgelist117 <- subthemes%>%group_by(Supply_chain_functioning,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Economic_support")
edgelist118 <- subthemes%>%group_by(Supply_chain_functioning,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Food_donation")
edgelist119 <- subthemes%>%group_by(Supply_chain_functioning,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Normalise_market_dynamics")
edgelist120 <- subthemes%>%group_by(Supply_chain_functioning,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Outbreak_management")
edgelist121 <- subthemes%>%group_by(Supply_chain_functioning,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Coping_consumers")
edgelist122 <- subthemes%>%group_by(Supply_chain_functioning,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Coping_processors")
edgelist123 <- subthemes%>%group_by(Supply_chain_functioning,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Coping_retailers_and_restaurants")
edgelist124 <- subthemes%>%group_by(Supply_chain_functioning,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Coping_farmers")
edgelist125 <- subthemes%>%group_by(Supply_chain_functioning,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Epidemiological_information")
edgelist126 <- subthemes%>%group_by(Supply_chain_functioning,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Supply_chain_functioning", "B" = "Animal_welfare")

edgelistG <- rbind(   edgelist112,
                      edgelist113,
                      edgelist114,
                      edgelist115,
                      edgelist116,
                      edgelist117,
                      edgelist118,
                      edgelist119,
                      edgelist120,
                      edgelist121,
                      edgelist122,
                      edgelist123,
                      edgelist124,
                      edgelist125,
                      edgelist126)


edgelist127 <- subthemes%>%group_by(Links_to_other_chains,Requests_and_power) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Requests_and_power")
edgelist128 <- subthemes%>%group_by(Links_to_other_chains,Trade) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Trade")
edgelist129 <- subthemes%>%group_by(Links_to_other_chains,Impact_on_labour) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Impact_on_labour")
edgelist130 <- subthemes%>%group_by(Links_to_other_chains,Economic_hardship) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Economic_hardship")
edgelist131 <- subthemes%>%group_by(Links_to_other_chains,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Economic_support")
edgelist132 <- subthemes%>%group_by(Links_to_other_chains,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Food_donation")
edgelist133 <- subthemes%>%group_by(Links_to_other_chains,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Normalise_market_dynamics")
edgelist134 <- subthemes%>%group_by(Links_to_other_chains,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Outbreak_management")
edgelist135 <- subthemes%>%group_by(Links_to_other_chains,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Coping_consumers")
edgelist136 <- subthemes%>%group_by(Links_to_other_chains,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Coping_processors")
edgelist137 <- subthemes%>%group_by(Links_to_other_chains,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Coping_retailers_and_restaurants")
edgelist138 <- subthemes%>%group_by(Links_to_other_chains,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Coping_farmers")
edgelist139 <- subthemes%>%group_by(Links_to_other_chains,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Epidemiological_information")
edgelist140 <- subthemes%>%group_by(Links_to_other_chains,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Links_to_other_chains", "B" = "Animal_welfare")


edgelistH <- rbind(  edgelist127,
                     edgelist128,
                     edgelist129,
                     edgelist130,
                     edgelist131,
                     edgelist132,
                     edgelist133,
                     edgelist134,
                     edgelist135,
                     edgelist136,
                     edgelist137,
                     edgelist138,
                     edgelist139,
                     edgelist140)  


edgelist141 <- subthemes%>%group_by(Requests_and_power,Trade) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Trade")
edgelist142 <- subthemes%>%group_by(Requests_and_power,Impact_on_labour) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Impact_on_labour")
edgelist143 <- subthemes%>%group_by(Requests_and_power,Economic_hardship) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Economic_hardship")
edgelist144 <- subthemes%>%group_by(Requests_and_power,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Economic_support")
edgelist145 <- subthemes%>%group_by(Requests_and_power,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Food_donation")
edgelist146 <- subthemes%>%group_by(Requests_and_power,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Normalise_market_dynamics")
edgelist147 <- subthemes%>%group_by(Requests_and_power,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Outbreak_management")
edgelist148 <- subthemes%>%group_by(Requests_and_power,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Coping_consumers")
edgelist149 <- subthemes%>%group_by(Requests_and_power,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Coping_processors")
edgelist150 <- subthemes%>%group_by(Requests_and_power,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Coping_retailers_and_restaurants")
edgelist151 <- subthemes%>%group_by(Requests_and_power,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Coping_farmers")
edgelist152 <- subthemes%>%group_by(Requests_and_power,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Epidemiological_information")
edgelist153 <- subthemes%>%group_by(Requests_and_power,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Requests_and_power", "B" = "Animal_welfare")

edgelistI <- rbind(   edgelist141,
                      edgelist142,
                      edgelist143,
                      edgelist144,
                      edgelist145,
                      edgelist146,
                      edgelist147,
                      edgelist148,
                      edgelist149,
                      edgelist150,
                      edgelist151,
                      edgelist152,
                      edgelist153)


edgelist154 <- subthemes%>%group_by(Trade,Impact_on_labour) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Impact_on_labour")
edgelist155 <- subthemes%>%group_by(Trade,Economic_hardship) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Economic_hardship")
edgelist156 <- subthemes%>%group_by(Trade,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Economic_support")
edgelist157 <- subthemes%>%group_by(Trade,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Food_donation")
edgelist158 <- subthemes%>%group_by(Trade,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Normalise_market_dynamics")
edgelist159 <- subthemes%>%group_by(Trade,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Outbreak_management")
edgelist160 <- subthemes%>%group_by(Trade,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Coping_consumers")
edgelist161 <- subthemes%>%group_by(Trade,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Coping_processors")
edgelist162 <- subthemes%>%group_by(Trade,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Coping_retailers_and_restaurants")
edgelist163 <- subthemes%>%group_by(Trade,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Coping_farmers")
edgelist164 <- subthemes%>%group_by(Trade,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Epidemiological_information")
edgelist165 <- subthemes%>%group_by(Trade,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Trade", "B" = "Animal_welfare")

edgelistJ <- rbind(edgelist154,
                   edgelist155,
                   edgelist156,
                   edgelist157,
                   edgelist158,
                   edgelist159,
                   edgelist160,
                   edgelist161,
                   edgelist162,
                   edgelist163,
                   edgelist164,
                   edgelist165)   

edgelist166 <- subthemes%>%group_by(Impact_on_labour,Economic_hardship) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Economic_hardship")
edgelist167 <- subthemes%>%group_by(Impact_on_labour,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Economic_support")
edgelist168 <- subthemes%>%group_by(Impact_on_labour,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Food_donation")
edgelist169 <- subthemes%>%group_by(Impact_on_labour,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Normalise_market_dynamics")
edgelist170 <- subthemes%>%group_by(Impact_on_labour,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Outbreak_management")
edgelist171 <- subthemes%>%group_by(Impact_on_labour,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Coping_consumers")
edgelist172 <- subthemes%>%group_by(Impact_on_labour,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Coping_processors")
edgelist173 <- subthemes%>%group_by(Impact_on_labour,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Coping_retailers_and_restaurants")
edgelist174 <- subthemes%>%group_by(Impact_on_labour,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Coping_farmers")
edgelist175 <- subthemes%>%group_by(Impact_on_labour,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Epidemiological_information")
edgelist176 <- subthemes%>%group_by(Impact_on_labour,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Impact_on_labour", "B" = "Animal_welfare")

edgelistK <- rbind(edgelist166,
                   edgelist167,
                   edgelist168,
                   edgelist169,
                   edgelist170,
                   edgelist171,
                   edgelist172,
                   edgelist173,
                   edgelist174,
                   edgelist175,
                   edgelist176)


edgelist177 <- subthemes%>%group_by(Economic_hardship,Economic_support) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Economic_support")
edgelist178 <- subthemes%>%group_by(Economic_hardship,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Food_donation")
edgelist179 <- subthemes%>%group_by(Economic_hardship,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Normalise_market_dynamics")
edgelist180 <- subthemes%>%group_by(Economic_hardship,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Outbreak_management")
edgelist181 <- subthemes%>%group_by(Economic_hardship,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Coping_consumers")
edgelist182 <- subthemes%>%group_by(Economic_hardship,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Coping_processors")
edgelist183 <- subthemes%>%group_by(Economic_hardship,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Coping_retailers_and_restaurants")
edgelist184 <- subthemes%>%group_by(Economic_hardship,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Coping_farmers")
edgelist185 <- subthemes%>%group_by(Economic_hardship,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Epidemiological_information")
edgelist186 <- subthemes%>%group_by(Economic_hardship,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_hardship", "B" = "Animal_welfare")

edgelistL <- rbind(edgelist177,
                   edgelist178,
                   edgelist179,
                   edgelist180,
                   edgelist181,
                   edgelist182,
                   edgelist183,
                   edgelist184,
                   edgelist185,
                   edgelist186)


edgelist187 <- subthemes%>%group_by(Economic_support,Food_donation) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Food_donation")
edgelist188 <- subthemes%>%group_by(Economic_support,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Normalise_market_dynamics")
edgelist189 <- subthemes%>%group_by(Economic_support,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Outbreak_management")
edgelist190 <- subthemes%>%group_by(Economic_support,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Coping_consumers")
edgelist191 <- subthemes%>%group_by(Economic_support,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Coping_processors")
edgelist192 <- subthemes%>%group_by(Economic_support,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Coping_retailers_and_restaurants")
edgelist193 <- subthemes%>%group_by(Economic_support,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Coping_farmers")
edgelist194 <- subthemes%>%group_by(Economic_support,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Epidemiological_information")
edgelist195 <- subthemes%>%group_by(Economic_support,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Economic_support", "B" = "Animal_welfare")

edgelistM <- rbind(edgelist187,
                   edgelist188,
                   edgelist189,
                   edgelist190,
                   edgelist191,
                   edgelist192,
                   edgelist193,
                   edgelist194,
                   edgelist195)

edgelist196 <- subthemes%>%group_by(Food_donation,Normalise_market_dynamics) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Normalise_market_dynamics")
edgelist197 <- subthemes%>%group_by(Food_donation,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Outbreak_management")
edgelist198 <- subthemes%>%group_by(Food_donation,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Coping_consumers")
edgelist199 <- subthemes%>%group_by(Food_donation,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Coping_processors")
edgelist200 <- subthemes%>%group_by(Food_donation,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Coping_retailers_and_restaurants")
edgelist201 <- subthemes%>%group_by(Food_donation,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Coping_farmers")
edgelist202 <- subthemes%>%group_by(Food_donation,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Epidemiological_information")
edgelist203 <- subthemes%>%group_by(Food_donation,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Food_donation", "B" = "Animal_welfare")

edgelistN <- rbind(edgelist196,
                   edgelist197,
                   edgelist198,
                   edgelist199,
                   edgelist200,
                   edgelist201,
                   edgelist202,
                   edgelist203)


edgelist204 <- subthemes%>%group_by(Normalise_market_dynamics,Outbreak_management) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Outbreak_management")
edgelist205 <- subthemes%>%group_by(Normalise_market_dynamics,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Coping_consumers")
edgelist206 <- subthemes%>%group_by(Normalise_market_dynamics,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Coping_processors")
edgelist207 <- subthemes%>%group_by(Normalise_market_dynamics,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Coping_retailers_and_restaurants")
edgelist208 <- subthemes%>%group_by(Normalise_market_dynamics,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Coping_farmers")
edgelist209 <- subthemes%>%group_by(Normalise_market_dynamics,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Epidemiological_information")
edgelist210 <- subthemes%>%group_by(Normalise_market_dynamics,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Normalise_market_dynamics", "B" = "Animal_welfare")

edgelistO <- rbind(edgelist204,
                   edgelist205,
                   edgelist206,
                   edgelist207,
                   edgelist208,
                   edgelist209,
                   edgelist210)


edgelist211 <- subthemes%>%group_by(Outbreak_management,Coping_consumers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Outbreak_management", "B" = "Coping_consumers")
edgelist212 <- subthemes%>%group_by(Outbreak_management,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Outbreak_management", "B" = "Coping_processors")
edgelist213 <- subthemes%>%group_by(Outbreak_management,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Outbreak_management", "B" = "Coping_retailers_and_restaurants")
edgelist214 <- subthemes%>%group_by(Outbreak_management,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Outbreak_management", "B" = "Coping_farmers")
edgelist215 <- subthemes%>%group_by(Outbreak_management,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Outbreak_management", "B" = "Epidemiological_information")
edgelist216 <- subthemes%>%group_by(Outbreak_management,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Outbreak_management", "B" = "Animal_welfare")

edgelistP <- rbind( edgelist211,
                    edgelist212,
                    edgelist213,
                    edgelist214,
                    edgelist215,
                    edgelist216)

edgelist217 <- subthemes%>%group_by(Coping_consumers,Coping_processors) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_consumers", "B" = "Coping_processors")
edgelist218 <- subthemes%>%group_by(Coping_consumers,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_consumers", "B" = "Coping_retailers_and_restaurants")
edgelist219 <- subthemes%>%group_by(Coping_consumers,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_consumers", "B" = "Coping_farmers")
edgelist220 <- subthemes%>%group_by(Coping_consumers,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_consumers", "B" = "Epidemiological_information")
edgelist221 <- subthemes%>%group_by(Coping_consumers,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_consumers", "B" = "Animal_welfare")

edgelistQ <- rbind( edgelist217,
                    edgelist218,
                    edgelist219,
                    edgelist220,
                    edgelist221)


edgelist222 <- subthemes%>%group_by(Coping_processors,Coping_retailers_and_restaurants) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_processors", "B" = "Coping_retailers_and_restaurants")
edgelist223 <- subthemes%>%group_by(Coping_processors,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_processors", "B" = "Coping_farmers")
edgelist224 <- subthemes%>%group_by(Coping_processors,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_processors", "B" = "Epidemiological_information")
edgelist225 <- subthemes%>%group_by(Coping_processors,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_processors", "B" = "Animal_welfare")

edgelistR <- rbind( edgelist222,
                    edgelist223,
                    edgelist224,
                    edgelist225)

edgelist226 <- subthemes%>%group_by(Coping_retailers_and_restaurants,Coping_farmers) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_retailers_and_restaurants", "B" = "Coping_farmers")
edgelist227 <- subthemes%>%group_by(Coping_retailers_and_restaurants,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_retailers_and_restaurants", "B" = "Epidemiological_information")
edgelist228 <- subthemes%>%group_by(Coping_retailers_and_restaurants,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_retailers_and_restaurants", "B" = "Animal_welfare")

edgelistS <- rbind(edgelist226,
                   edgelist227,
                   edgelist228)

edgelist229 <- subthemes%>%group_by(Coping_farmers,Epidemiological_information) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_farmers", "B" = "Epidemiological_information")
edgelist230 <- subthemes%>%group_by(Coping_farmers,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Coping_farmers", "B" = "Animal_welfare")

edgelistT <- rbind(edgelist229,
                   edgelist230)

edgelistU<- subthemes%>%group_by(Epidemiological_information,Animal_welfare) %>% summarise(weight = n()) %>% ungroup ()%>% rename ("A" = "Epidemiological_information", "B" = "Animal_welfare")

edgelist <- rbind(edgelistA,
                   edgelistB,
                   edgelistC,
                   edgelistD,
                   edgelistE,
                   edgelistF,
                   edgelistG,
                   edgelistH,
                   edgelistI,
                   edgelistJ,
                   edgelistK,
                   edgelistL,
                   edgelistM,
                   edgelistN,
                   edgelistO,
                   edgelistP,
                   edgelistQ,
                   edgelistR,
                   edgelistS,
                   edgelistT, 
                   edgelistU)
                   
   

edges<- edgelist %>% left_join (nodes, by = c("A" = "label")) %>% rename (from = id)
edges<-edges %>% left_join (nodes, by = c("B" = "label")) %>% rename (to = id)

edges<- edges %>% na.omit
edges

edges <- edges[c("from", "to", "weight")]

####add columns to nodes####

nodes$theme.type<- NA

nodes$theme.type[nodes$label == "Consumer demand changes"   |                                     
                    nodes$label =="Reasons for demand change"  |     
                    nodes$label =="Communication(s)"            |         
                    nodes$label =="Change in chicken sales"   |    
                    nodes$label == "Change in chicken prices" ]      <-  1   #"Demand and market effects"


nodes$theme.type[nodes$label == "Value chain business closure"   |                                     
                    nodes$label =="Supply chain functioning"  |     
                    nodes$label =="Links to other chains"    |         
                    nodes$label =="Requests and power"    ]          <-  2    #Value chain effects 



nodes$theme.type[nodes$label == "Trade"    ]                        <-  3    #Trade


nodes$theme.type[nodes$label == "Impact on labour"   |                                     
                    nodes$label =="Economic hardship"    ]          <-  4     #Labour and livelihoods 


nodes$theme.type[nodes$label == "Economic support"   |                                     
                    nodes$label =="Food donation"  |
                    nodes$label =="Normalise market dynamics"  |     
                    nodes$label =="Outbreak management"       ]      <-  5     #Mitigation and interventions


nodes$theme.type[nodes$label == "Coping consumers"   |                                     
                    nodes$label =="Coping processors"  |
                    nodes$label =="Coping retailers and restaurants"  |     
                    nodes$label =="Coping farmers"       ]           <-  6     #Coping

nodes$theme.type[nodes$label == "Epidemiological information"    ]   <-  7    #Epidemiological factors  


nodes$theme.type[nodes$label == "Animal welfare"    ]                <-  8    #Animal welfare



nodes$theme.name<- NA

nodes$theme.name[nodes$label == "Consumer demand changes"   |                                     
                     nodes$label =="Reasons for demand change"  |     
                     nodes$label =="Communication(s)"            |         
                     nodes$label =="Change in chicken sales"   |    
                     nodes$label == "Change in chicken prices" ]      <-  "Demand and market effects"


nodes$theme.name[nodes$label == "Value chain business closure"   |                                     
                     nodes$label =="Supply chain functioning"  |     
                     nodes$label =="Links to other chains"    |         
                     nodes$label =="Requests and power"    ]          <-  "Value chain effects" 



nodes$theme.name[nodes$label == "Trade"    ]                        <- "Trade"


nodes$theme.name[nodes$label == "Impact on labour"   |                                     
                     nodes$label =="Economic hardship"    ]          <-  "Labour and livelihoods"


nodes$theme.name[nodes$label == "Economic support"   |                                     
                     nodes$label =="Food donation"  |
                     nodes$label =="Normalise market dynamics"  |     
                     nodes$label =="Outbreak management"       ]      <-  "Mitigation and interventions"


nodes$theme.name[nodes$label == "Coping consumers"   |                                     
                     nodes$label =="Coping processors"  |
                     nodes$label =="Coping retailers and restaurants"  |     
                     nodes$label =="Coping farmers"       ]           <-  "Coping"

nodes$theme.name[nodes$label == "Epidemiological information"    ]   <-  "Epidemiological factors"  


nodes$theme.name[nodes$label == "Animal welfare"    ]                <-  "Animal welfare"

####create graph####
graph<- graph_from_data_frame(d = edges, vertices = nodes, directed = F)


colrs<- brewer.pal(8, "Dark2")
V(graph)$color <- colrs[V(graph)$theme.type]

V(graph)$label <- NA


summary(nodes$theme.name)
aa<- unique(nodes[c("theme.name")])


plot(graph,   
     layout = layout_in_circle, 
       edge.width = edges$weight/10  )
legend(x=-1.8, y=1.2, c(aa$theme.name), pch=21,
       col="#777777", pt.bg=colrs, pt.cex=2.5, cex=1, bty="n", ncol=1)

locator <- function(...) {
   assign('.last.plot', recordPlot(), envir=.GlobalEnv)
   graphics::locator(...)
}

undo <- function()
   if (exists('.last.plot')) replayPlot(.last.plot)


text(locator(1),"Trade")
text(locator(1),"Impact \non labour")
text(locator(1),"Economic \nhardship")
text(locator(1),"Economic \nsupport")
text(locator(1),"Food \ndonation")
text(locator(1),"Normalise \nmarket dynamics")
text(locator(1),"Outbreak \nmanagement")
text(locator(1),"Coping \nconsumers")
text(locator(1),"Coping \nprocessors")
text(locator(1),"Coping retailers \nand restaurants")
text(locator(1),"Coping \nfarmers")
text(locator(1),"Epidemiological \ninformation")
text(locator(1),"Animal \nwelfare")
text(locator(1),"Consumer \ndemand changes")
text(locator(1),"Reasons for \ndemand changes")
text(locator(1),"Communication(s)")
text(locator(1),"Change in \nchicken sales")
text(locator(1),"Change in \nchicken prices")
text(locator(1),"Value chain \nbusiness closure")
text(locator(1),"Supply chain \nfunctioning")
text(locator(1),"Links to \nother chains")
text(locator(1),"Requests \nand power")

undo()