Introduction
Data on annual carbon dioxide (\(CO_2\)) emissions per person for 130 nations worldwide was published by the Food and Agriculture Organization of the United Nations (FAO) on the nu3 website, and a subset of the data used here was scraped and shared by Kasia Kulm (2020).
Data Visualization
Code
########## Read in the data
food_consumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-18/food_consumption.csv')
########## Clean the data
food_consumption <- food_consumption %>%
#Recode values for country
mutate(country = recode(country,
"USA" = "United States"),
country = recode(country,
"Hong Kong SAR. China" = "Hong Kong (China)"))
#Rename variables to give units
food_consumption_subset <- food_consumption %>%
#Select only co2 emissions data
select(-consumption) %>%
#Change to tidy format: long to wide
pivot_wider(names_from = food_category,
values_from = co2_emmission) %>%
#Recode values for country
mutate(country = recode(country,
"USA" = "United States"),
country = recode(country,
"Hong Kong SAR. China" = "Hong Kong (China)")) %>%
#Rename last food category name
rename(nuts_inc_peanut_butter = "Nuts inc. Peanut Butter")
########## Top 15 countries by CO2 emissions
#Categorize CO2 emissions by food consumption and country
top15_total_co2_by_country <- food_consumption %>%
#Group by country
group_by(country) %>%
#Total annual CO2 emissions per capita across food categories
summarize(total_annual_co2_kg_person_year = sum(co2_emmission)) %>%
#Top 15 countries with total annual CO2 emissions per year
slice_max(total_annual_co2_kg_person_year, n = 15) %>%
#Reorder country based on total_annual_co2_kg
mutate(country = reorder(country, total_annual_co2_kg_person_year))
########## Top 15 countries by CO2 emissions and by food group
#Categorize CO2 emissions by food consumption and country
annual_co2_country_food <- food_consumption_subset %>%
#Group by country
group_by(country) %>%
#Sum kg CO2/person/year across foods in new column
rowwise() %>%
#Total annual CO2 emissions per capita across food categories
mutate(total_annual_co2_kg_person_year = sum(c_across(Pork:nuts_inc_peanut_butter))) %>%
#Ungroup
ungroup() %>%
#Arrange in descending order
arrange(desc(total_annual_co2_kg_person_year)) %>%
#Select top 15 countries with highest total annual CO2 emissions per capita
slice_max(total_annual_co2_kg_person_year, n = 15) %>%
#Reorder country based on total_annual_co2_kg
mutate(country = reorder(country,
total_annual_co2_kg_person_year))
########## Data reordering (long format)
#Convert back to long format
annual_co2_country_food_long <- annual_co2_country_food %>%
pivot_longer(cols = Pork:nuts_inc_peanut_butter,
names_to = "food_category",
values_to = "co2_emission_kg_person_year") %>%
#Recode values for food category
mutate(food_category = recode(food_category,
"Lamb & Goat" = "Lamb and Goat",
"Milk - inc. cheese" = "Milk including Cheese",
"nuts_inc_peanut_butter" = "Nuts including Peanut Butter"))
########## Final figure
#Bar plot of top 15 countries with total annual CO2 emissions from consumption with food
ggplot(data = annual_co2_country_food_long,
aes(x = co2_emission_kg_person_year,
y = country,
#Color by food category
fill = food_category)) +
#Define stacked column/bar plot
#Values for each food summed up within each bar
#Bars colored based off how much of that total sum is in each food
geom_col(stat = "identity") +
#Add text for x-values
#Adjust horizontal text placement
#Adjust text size
geom_text(data = top15_total_co2_by_country,
aes(x = total_annual_co2_kg_person_year,
y = country,
label = total_annual_co2_kg_person_year,
fill = NULL),
hjust = -0.1, size = 2.7, color = "darkgrey") +
#Change colors
scale_fill_paletteer_d(palette = "unikn::pal_unikn_pair") +
#Change x-axis and y-axis labels
labs(x = expression(atop(paste(bold("Total Annual "),
bold(CO[2]),
bold(" Emissions from Food Consumption")),
paste(bold("(kg/person/year)")))),
y = "Country",
fill = "Food Category") +
#Change theme
theme_minimal() +
#Change x-axis scale
scale_x_continuous(breaks = seq(0, 2500, by = 500)) +
#Focus plot area on limits and allow text outside margins
coord_cartesian(xlim = c(0, 2500), clip = "off") +
#Customize plot further
#Bold and increase font of ]y-axis label
theme(axis.title.y = element_text(size = 12, face = "bold"),
#Move x-label down from plot
axis.title.x = element_text(vjust = -3),
#Bold and change size of legend title
#Change size of legend text
legend.title = element_text(face = "bold", size = 9),
legend.text = element_text(size = 8.5),
#Increase x-tick and y-tick text
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
#Increase margins: theme_minimal()$plot.margin
#Top, right, bottom, left
plot.margin = unit(c(0.3, 0.3, 0.6, 0.3), "cm"))
Figure 1. Top 15 countries with the highest total annual carbon dioxide (\(CO_2\)) emissions (kg/person/year) based on diet or food consumption from the year 2018. Grey text to the right of the bars in the plot report the value for the total annual \(CO_2\) emissions (kg/person/year) for each country. The foods that generate the highest \(CO_2\) emissions are animal products that include beef, lamb and goat, and milk and cheese (dairy products). Foods that generate lower \(CO_2\) emissions are non-animal products, such as nuts, soybeans, and rice. Data source: nu3 (2018) & Kulma (2020).
References
Data and Literature
nu3. Food carbon footprint index 2018. https://www.nu3.de/blogs/nutrition/food-carbon-footprint-index-2018.
Kulma, K. (2020). Webscraping with R - from messy & unstructured to blisfully tidy. https://r-tastic.co.uk/post/from-messy-to-tidy/.
R Libraries
Firke, S. (2021). janitor: Simple Tools for Examining and Cleaning Dirty Data. R package version 2.1.0. https://CRAN.R-project.org/package=janitor.
Hvitfeldt, E. (2021). paletteer: Comprehensive Collection of Color Palettes. version 1.3.0. https://github.com/EmilHvitfeldt/paletteer.
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686.