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descriptive_stats_plots.R
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descriptive_stats_plots.R
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# plot data and produce descriptive statistics for SSA
y_ssa <- y %>% filter(grepl("Africa", continent) & !grepl("North Africa and Middle East", continent))
pop = wb(indicator = "SP.POP.TOTL", startdate = 1960, enddate = 2019)
y_ssa <- merge(y_ssa, pop, by.x=c("Code", "Year"), by.y=c("iso3c", "date"), all.x=T)
y_ssa <- y_ssa %>% group_by(Year) %>% dplyr::summarise(Beef.and.buffalo..kg.=stats::weighted.mean(Beef.and.buffalo..kg., value, na.rm=T), Pigmeat..kg.=stats::weighted.mean(Pigmeat..kg., value, na.rm=T), Poultry..kg.=stats::weighted.mean(Poultry..kg., value, na.rm=T), Mutton...goat..kg.=stats::weighted.mean(Mutton...goat..kg., value, na.rm=T))
y_ssa <- melt(y_ssa, id.vars = "Year")
y_ssa$variable <- as.character(y_ssa$variable)
y_ssa$variable[y_ssa$variable=="Beef.and.buffalo..kg."] <- "Beef and buffalo"
y_ssa$variable[y_ssa$variable=="Pigmeat..kg."] <- "Pigmeat"
y_ssa$variable[y_ssa$variable=="Poultry..kg."] <- "Poultry"
y_ssa$variable[y_ssa$variable=="Mutton...goat..kg."] <- "Mutton and goat"
a <- ggplot(y_ssa)+
theme_classic()+
geom_line(aes(x=Year, y=value, colour=variable, group=variable))+
ggtitle("Evolution of per-capita meat consumption in sub-Saharan Africa")+
ylab("kg/person/year")+
scale_colour_discrete(name="Meat type")+
theme(legend.position = "none")
#
# plot data and produce descriptive statistics for SSA
y_ssa <- y %>% filter(grepl("Africa", continent) & !grepl("North Africa and Middle East", continent))
pop = wb(indicator = "SP.POP.TOTL", startdate = 1960, enddate = 2019)
y_ssa <- merge(y_ssa, pop, by.x=c("Code", "Year"), by.y=c("iso3c", "date"), all.x=T)
y_ssa <- y_ssa %>% group_by(Year) %>% dplyr::summarise(Beef.and.buffalo..kg.= sum(Beef.and.buffalo..kg.*value, na.rm=T), Pigmeat..kg.=sum(Pigmeat..kg.*value, na.rm=T), Poultry..kg.=sum(Poultry..kg.*value, na.rm=T), Mutton...goat..kg.=sum(Mutton...goat..kg.*value, na.rm=T))
y_ssa <- melt(y_ssa, id.vars = "Year")
y_ssa$variable <- as.character(y_ssa$variable)
y_ssa$variable[y_ssa$variable=="Beef.and.buffalo..kg."] <- "Beef and buffalo"
y_ssa$variable[y_ssa$variable=="Pigmeat..kg."] <- "Pigmeat"
y_ssa$variable[y_ssa$variable=="Poultry..kg."] <- "Poultry"
y_ssa$variable[y_ssa$variable=="Mutton...goat..kg."] <- "Mutton and goat"
b <- ggplot(y_ssa)+
theme_classic()+
geom_line(aes(x=Year, y=value/1000000, colour=variable, group=variable))+
ggtitle("Evolution of total meat consumption in sub-Saharan Africa")+
ylab("Kt/year")+
scale_colour_discrete(name="Meat type")+
theme(legend.position = "bottom", legend.direction = "horizontal")
y_ssa <- y %>% filter(grepl("Africa", continent) & !grepl("North Africa and Middle East", continent))
pop <- filter(pop, iso3c %in% unique(y_ssa$Code))
pop <- group_by(pop, date) %>% summarise(value=sum(value, na.rm = T)) %>% ungroup()
c <- ggplot(data=pop, aes(x=as.numeric(date), y=value/1000000))+
theme_classic()+
geom_line()+
ggtitle("Evolution of population in sub-Saharan Africa")+
ylab("Million people")+
xlab("Year")
d <- plot_grid(a, c, b, rel_heights = c(1, 1, 1.15), labels="AUTO", ncol = 1)
ggsave(plot=d, device = "png", filename = "fig1.png", scale=2, width = 3)
###
write.csv(y, "data_historical.csv")
write.csv(ssps, "data_future.csv")