A friend who doesn’t use the Tidyverse send me this very nice plot:
My first intuition to obtain the data for this unidentified plot was to go to FAO, and it was there!
I went to FAO Stat, filtered the countries and years seen in the plot and I got the required inputs to re-express the information.
Now it’s time to use the Tidyverse, or at least parts of it. The resulting datasets from the in-browser filters is here.
library(ggplot2) library(dplyr) library(forcats) bananas <- readr::read_csv("../../data/2022-12-21-banana-exports-in-tonnes-1994-2005/FAOSTAT_data_en_12-21-2022.csv") %>% mutate( Year2 = fct_relevel( substr(Year, 3, 4), c(94:99, paste0("0", 0:5))), Area = case_when( Area %in% c("Belgium","Luxembourg") ~ "Belgium-Luxembourg", TRUE ~ Area ) ) %>% group_by(Year2, Area) %>% summarise(Value = sum(Value, na.rm = T)) ggplot(bananas) + geom_col(aes(x = Year2, y = Value), fill = "#f5e41a") + facet_wrap(~ Area, ncol = 3) + labs( x = "Year", y = "Value (tonnes)", title = "Export in Bananen in Tonnen von 1994-2005\n(Banana exports in tonnes from 1994-2005)", subtitle = "Source: Unidentified" ) + theme_minimal(base_size = 13) + scale_y_continuous(labels = scales::label_number(suffix = " M", scale = 1e-6))
The challenges were:
- Combine Belgium and Luxembourg data into a single Area
- Express the axis in millions of tonnes
- Find a right banana yellow for the plot
I hope it’s less cluttered than the original plot!