Exploring Meat toy model

NETLOGO TOY MODEL

TODO

• changer le script scala pour passer des parametres directement depuis le notebook !
• prendre en sorte le fait qu’on a 3 données par pas de temps dans le graphique

Producing csv outputs

You could produce csv outputs of Netlogo MeatModel using Netlogo interface by opening the ./toy/PicSaintLoup/MeatModel-V5 model, running some experiments, and when you’re donne, clic on the button save-csv.

If you have sbt build engine installed on your computer you could also run this headless script by running this chunk directly from R/RStudio.

./toy/PicSaintLoup/MeatModel-V5 folder.

Producing explorations

TO LONG DATA AGE

gdfFinalAge <- merge(gdFinalByAge_valueOpinion, gdFinalByAge_nbVoisin, by=c("age","time"), type="inner")
gdfFinalAge <- merge(gdfFinalAge, gdFinalByAge_apparie, by=c("age","time"), type="inner")
gDfToLongAge <- gather(gdfFinalAge, facteur, value , by=c(ValueOpinion,nb,nbVoisins))
p <- ggplot(data=gDfToLongAge, aes(x=time, y=value, group=age, fill=age, colour=as.character(age)))
p <- p + geom_line(size=1) + facet_grid(facteur ~ .) #show.legend = FALSE
p

TO LONG DATA EDU

gdfFinalEdu <- merge(gdFinalByEdu_valueOpinion, gdFinalByEdu_nbVoisin, by=c("edu", "time"), type="inner")
gdfFinalEdu <- merge(gdfFinalEdu, gdFinalByEdu_apparie, by=c("edu","time"), type="inner")
gDfToLongEdu <- gather(gdfFinalEdu, facteur, value , by=c(ValueOpinion,nb,nbVoisins))
p <- ggplot(data=gDfToLongEdu, aes(x=time, y=value, group=edu, fill=edu, colour=as.character(edu)))
p <- p + geom_line(size=1) + facet_grid(facteur ~ .) #show.legend = FALSE
p

TO LONG DATA SEX

gdfFinalSex <- merge(gdFinalBySex_valueOpinion, gdFinalBySex_nbVoisin, by=c("sex", "time"), type="inner")
gdfFinalSex <- merge(gdfFinalSex, gdFinalBySex_apparie, by=c("sex","time"), type="inner")
gDfToLongSex <- gather(gdfFinalSex, facteur, value , by=c(ValueOpinion,nb,nbVoisins))
p <- ggplot(data=gDfToLongSex, aes(x=time, y=value, group=sex, fill=sex, colour=as.character(sex)))
p <- p + geom_line(size=1) + facet_grid(facteur ~ .) #show.legend = FALSE
p

FAT MODEL

specialRead <- function(x) {
  read_csv(file=x, col_names = TRUE, col_types= "nnnnnnnnnn")
} 
concatFiles <- function (base, folder){
   fullPath = paste(base,folder,sep="")
   list.files(path = fullPath, full.names = TRUE, pattern = "(\\d_\\d_\\d)") %>% lapply(specialRead) %>% bind_rows
}

Exploring scenarii

random residential scenario 1

maxProbaToSwitch	constraintsStrength	inertiaCoefficient	healthyDietReward	seed
0.8	0.05	0.5	0.4	42

RANDOM POP NO MOVE SEED 42

sortie_sc1 <- concatFiles(base,"results_ose_Scenario1_RandomPop_NoMove_42")
sortie_sc1$propHealthy <- sortie_sc1$healthy / sortie_sc1$effective 
  
sortieLong_sc1 <- gather(sortie_sc1, facteur, valeur, by=c(propHealthy,avgOpinion) ) 
sortieLong_sc1 <- mutate(sortieLong_sc1, "ds" = gsub(" ", "", paste(paste("[",paste(sortieLong_sc1$day, sortieLong_sc1$slice)),"]")))
p <- ggplot(data=sortieLong_sc1, aes(x=ds, y=valeur, group=educ, fill=educ, colour=as.character(educ)))
p <- p + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p <- p + geom_line(size=0.5) + facet_grid(facteur ~ sex ~ age, labeller = label_both) #show.legend = FALSE
p

ggsave("sc1_randomPop_noMove.pdf")

Saving 20 x 12.4 in image

survey residential scenario 2

maxProbaToSwitch	constraintsStrength	inertiaCoefficient	healthyDietReward	seed
0.8	0.05	0.5	0.4	42

sortie_sc2 <- concatFiles(base,"results_ose_Scenario2_RandomPop_RandomMove_42")
sortie_sc2$propHealthy <- sortie_sc2$healthy / sortie_sc2$effective 
  
sortieLong_sc2 <- gather(sortie_sc2, facteur, valeur, by=c(propHealthy,avgOpinion) ) 
sortieLong_sc2 <- mutate(sortieLong_sc2, "ds" = gsub(" ", "", paste(paste("[",paste(sortieLong_sc2$day, sortieLong_sc2$slice)),"]")))
p <- ggplot(data=sortieLong_sc2, aes(x=ds, y=valeur, group=educ, fill=educ, colour=as.character(educ)))
p <- p + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p <- p + geom_line(size=0.5) + facet_grid(facteur ~ sex ~ age, labeller = label_both) #show.legend = FALSE
p

ggsave("sc2_randomPop_randomMove.pdf")

Saving 20 x 12.4 in image

random activity scenario 3

maxProbaToSwitch	constraintsStrength	inertiaCoefficient	healthyDietReward	seed
0.8	0.5	0.5	0.4	42

sortie_sc3 <- concatFiles(base,"results_ose_Scenario3_ObservedPop_NoMove_42")
sortie_sc3$propHealthy <- sortie_sc3$healthy / sortie_sc3$effective 
  
sortieLong_sc3 <- gather(sortie_sc3, facteur, valeur, by=c(propHealthy,avgOpinion) ) 
sortieLong_sc3 <- mutate(sortieLong_sc3, "ds" = gsub(" ", "", paste(paste("[",paste(sortieLong_sc3$day, sortieLong_sc3$slice)),"]")))
p <- ggplot(data=sortieLong_sc3, aes(x=ds, y=valeur, group=educ, fill=educ, colour=as.character(educ)))
p <- p + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p <- p + geom_line(size=0.5) + facet_grid(facteur ~ sex ~ age, labeller = label_both) #show.legend = FALSE
p

ggsave("sc3_obsPop_noMove.pdf")

Saving 20 x 12.4 in image

survey activity scenario 4

maxProbaToSwitch	constraintsStrength	inertiaCoefficient	healthyDietReward	seed
0.8	0.05	0.5	0.4	42

sortie_sc4 <- concatFiles(base,"results_ose_Scenario4_ObservedPop_RandomMove_42")
sortie_sc4$propHealthy <- sortie_sc4$healthy / sortie_sc4$effective 
  
sortieLong_sc4 <- gather(sortie_sc4, facteur, valeur, by=c(propHealthy,avgOpinion) ) 
sortieLong_sc4 <- mutate(sortieLong_sc4, "ds" = gsub(" ", "", paste(paste("[",paste(sortieLong_sc4$day, sortieLong_sc4$slice)),"]")))
p <- ggplot(data=sortieLong_sc4, aes(x=ds, y=valeur, group=educ, fill=educ, colour=as.character(educ)))
p <- p + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p <- p + geom_line(size=0.5) + facet_grid(facteur ~ sex ~ age, labeller = label_both) #show.legend = FALSE
p

ggsave("sc4_obsPop_randomMove.pdf")

Saving 20 x 12.4 in image

survey activity scenario 4

maxProbaToSwitch	constraintsStrength	inertiaCoefficient	healthyDietReward	seed
0.8	0.05	0.5	0.4	42

sortie_sc5 <- concatFiles(base,"results_ose_Scenario5_ObservedPop_ObservedMove_42")
sortie_sc5$propHealthy <- sortie_sc5$healthy / sortie_sc5$effective 
  
sortieLong_sc5 <- gather(sortie_sc5, facteur, valeur, by=c(propHealthy,avgOpinion) ) 
sortieLong_sc5 <- mutate(sortieLong_sc5, "ds" = gsub(" ", "", paste(paste("[",paste(sortieLong_sc5$day, sortieLong_sc5$slice)),"]")))
p <- ggplot(data=sortieLong_sc5, aes(x=ds, y=valeur, group=educ, fill=educ, colour=as.character(educ)))
p <- p + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p <- p + geom_line(size=0.5) + facet_grid(facteur ~ sex ~ age, labeller = label_both) #show.legend = FALSE
p

ggsave("sc5_obsPop_obsMove.pdf")

Saving 20 x 12.4 in image