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"Breakdown of the average per capita intake of cereals by specific cereal-based commodity types, measured in kilocalories per person per day. This figure measures the primary equivalent of all food products derived from a given commodity (e.g. "wheat" represents the primary equivalent of its derived
products). Data refers to cereal food supply at the consumer level but does not account for consumer wastage."
cereals <- read.csv("./DATA/per-capita-consumption-of-cereals-by-commodity-type-daily-kilocalories.csv")
colnames(cereals) <- c("Country","Code","Year","Wheat","Rice","Barley","Maize","Rye","Oats","Sorghum")
dlookr::diagnose_numeric(cereals)
cereals_longer <- cereals %>% filter(Country !="World") %>% select(-Code) %>%
pivot_longer(cols = Wheat:Sorghum)
cereals_by_years <- cereals_longer %>% filter(value >0) %>% group_by(name,Year) %>%
summarise(Count = n(),
Total = sum(value),
Mean = mean(value)) %>% ungroup()
ggplot(cereals_by_years) + geom_line(aes(x=Year,y=Total,col=name),lwd=1) +
scale_y_log10(labels = comma) +
labs(title="Total kilocalories Per Year by Cereal Type")
Total kilocalories Per Year by Cereal Type
ggplot(cereals_by_years) + geom_line(aes(x=Year,y=Total),lwd=1) + facet_wrap(~name,scale = "free_y",ncol=2) + scale_y_log10(labels = comma) + labs(title="Total kilocalories Consumed Per Year by Cereal Type")
Total kilocalories Consumed Per Year by Cereal Type
