coffee_data {spData} | R Documentation |
A tiny dataset containing estimates of global coffee in thousands of 60 kg bags produced by country. Purpose: teaching **not** research.
coffee_data
A data frame (tibble) with 58 for the following 12 variables:
name_long name of country or coffee variety
coffee_production_2016 production in 2016
coffee_production_2017 production in 2017
The examples section shows how this can be joined with spatial data to create a simple map.
The International Coffee Organization (ICO). See http://www.ico.org/ and http://www.ico.org/prices/m1-exports.pdf
head(coffee_data) ## Not run: library(tidyverse) library(sf) # found by searching for "global coffee data" u = "http://www.ico.org/prices/m1-exports.pdf" download.file(u, "data.pdf", mode = "wb") install.packages("pdftables") # requires api key pdftables::convert_pdf(input_file = "data.pdf", output_file = "coffee-data-messy.csv") d = read_csv("coffee-data-messy.csv") file.remove("coffee-data-messy.csv") file.remove("data.pdf") coffee_data = slice(d, -c(1:9)) %>% select(name_long = 1, coffee_production_2016 = 2, coffee_production_2017 = 3) %>% filter(!is.na(coffee_production_2016)) %>% mutate_at(2:3, str_replace, " ", "") %>% mutate_at(2:3, as.integer) world_coffee = left_join(world, coffee_data) plot(world_coffee[c("coffee_production_2016", "coffee_production_2017")]) b = c(0, 500, 1000, 2000, 3000) library(tmap) tm_shape(world_coffee) + tm_fill("coffee_production_2017", title = "Thousand 60kg bags", breaks = b, textNA = "No data", colorNA = NULL) tmap_mode("view") # for an interactive version ## End(Not run)