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# functions.R
# Bespoke functions I've created to help produce the final output
# This function takes the raw egg data and cleans it ready for plotting
clean_data <- function(raw_data) {
clean_data <- raw_data %>%
clean_names() %>%
select(year, enriched, barn, free_range, organic) %>%
slice(1:23) %>%
mutate(year = as.numeric(year)) %>%
gather(key = "production_method", value = "thousand_cases", -year) %>%
as_tibble()
return(clean_data)
}
# This function takes the cleaned data and creates a line chart
create_plot <- function(data) {
plot <- data %>%
mutate(label = if_else(year == max(year), production_method, NA_character_)) %>%
ggplot(aes(x = year, y = thousand_cases, color = production_method)) +
geom_line(show.legend = FALSE, size = 1.5, na.rm = TRUE) +
geom_text(aes(x = 2010, y = 16000, label = "Enriched (intensive)", color = "enriched"), check_overlap = TRUE, show.legend = FALSE) +
geom_text(aes(x = 2013, y = 10000, label = "Free range", color = "free_range"), check_overlap = TRUE, show.legend = FALSE) +
geom_text(aes(x = 2004, y = 3000, label = "Barn", color = "barn"), check_overlap = TRUE, show.legend = FALSE) +
geom_text(aes(x = 2012, y = 3000, label = "Organic", color = "organic"), check_overlap = TRUE, show.legend = FALSE) +
labs(
title = "UK egg throughput by production method",
subtitle = "Free range has overtaken enriched throughput",
x = "",
y = "Thousand cases"
) +
theme_minimal()
print(plot)
}
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