A tiny dataset containing estimates of global coffee in thousands of 60 kg bags produced by country. Purpose: teaching **not** research.
coffee_dataA 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 International Coffee Organization (ICO). See http://www.ico.org/ and http://www.ico.org/prices/m1-exports.pdf
The examples section shows how this can be joined with spatial data to create a simple map.
head(coffee_data)
#> # A tibble: 6 × 3
#> name_long coffee_production_2016 coffee_production_2017
#> <chr> <int> <int>
#> 1 Angola NA NA
#> 2 Bolivia 3 4
#> 3 Brazil 3277 2786
#> 4 Burundi 37 38
#> 5 Cameroon 8 6
#> 6 Central African Republic NA NA
if (FALSE) { # \dontrun{
if (requireNamespace("dplyr")) {
library(dplyr)
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")
if (requireNamespace("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
}}
} # }