About

This chart, and its step-by-step guide, has been realised by Benjamin Nowak. Thanks to him for sharing this content!

As a teaser, here is the chart we are going to build:

Waffle Chart

Load packages and data

In R, waffle charts may be created with the {waffle} package. In addition to this library, we will load several packages that we will need for this tutorial:

library(tidyverse)
library(waffle)   
library(ggtext)
library(rnaturalearth)
library(sf)

But what data are we going to represent? End of suspense, the graph shows average national wheat and corn yields for the period 2010 to 2020. These data have been downloaded from FAOSTAT.

data <- read_csv('https://raw.githubusercontent.com/BjnNowak/book/main/data/wheat_maize_2010_2020.csv')

Compute mean yields for the period

The great advantage of FAOSTAT data tables is that they are always formatted in the same way. So, once you’re used to them, it’s easy to find your way around. What’s more, these tables are in long format, making them easy to process with the tidyverse.

About the columns we’ll be using here:

the country code is stored in the Area Code (M49) column
the Item column indicates the type of crop (wheat or maize here)
the Value column gives the value of the variable of interest (here, yield)

The average yield over the period is therefore calculated as follows:

clean<-data%>%
  dplyr::rename(M49 = 'Area Code (M49)')%>%
  drop_na(Value)%>%
  group_by(M49, Item)%>%
  summarize(yield=mean(Value)/10000)%>%
  ungroup()

We’re now going to assign a continent to each country. To do this, we’ll use the world map available in the {rnaturalearth} package, keeping only the attribute table and ignoring the geometry.

The join will be based on the United Nations codes (column M49 in our data table, column un_a3 in the {rnaturalearth} table).

wrld <- ne_countries(type = "countries", scale = "small", returnclass = "sf")%>%
  st_drop_geometry()

clean <- clean%>%
  left_join(wrld,by=c('M49'='un_a3'))%>%
  select(M49,Item,yield,continent)%>%
  drop_na(continent)

Make waffle chart for wheat

To create our waffle charts, we need to (i) create yield classes and (ii) count the number of countries in each class (with a different count for each continent).

This is done as follows:

clean <- clean %>%
  mutate(
    rd=floor(yield),
    ct=1
  ) %>%
  group_by(rd, Item, continent)%>%
  summarize(
    n=sum(ct)
  ) %>%
  ungroup()

We’re now ready to create our first distribution curve plotted as a waffle chart! But to make things easier, we’ll start with wheat only.

With {waffle}, waffle charts are created with geom_waffle().

With this function, there’s one important thing to know: the yield classes here must be added as successive graphs (facets), not simply as classes on the x-axis.

ggplot(
    # Keep only data for wheat
    clean%>%filter(Item=="Wheat"),
    aes(values=n, fill=continent)
  )+
  waffle::geom_waffle(
    n_rows = 4,        # Number of squares in each row
    color = "white",   # Border color
    flip = TRUE, na.rm=TRUE
  )+
  facet_grid(~rd)+
  coord_equal()

Note that the x-axis and y-axis values don’t correspond to anything significant here (we’ll remove them later). Only the facet titles are important (0 for countries with yields from 0 to 1 ton, etc.).

Make waffle chart for both crops

We’re now going to present the values for the two crops, wheat and maize, simultaneously but keeping only the yields below 10 tons.

ggplot(
    clean%>%filter(rd<10),
    aes(values=n, fill=continent)
  )+
  waffle::geom_waffle(
    n_rows = 4,        # Number of squares in each row
    color = "white",   # Border color
    flip = TRUE, na.rm=TRUE
  )+
  # Add Item in facet_grid
  facet_grid(Item~rd)+
  coord_equal()

If you try to remove the filter on the yield, you’ll see that R returns an error message. What’s the problem? Because, unlike corn, wheat doesn’t have a country with a mean yield above 10 tons. The corresponding facets cannot be represented with ggplot().

The workaround? Create an imaginary continent that can be present for all combinations, but which we’ll then make disappear in the color palettes!

This is how we will proceed:

cpl<-clean%>%
  # Removing some countries with unrealistic yield for maize
  filter(rd<14)%>%
  # Complete all combinations for real continents, but with 0 value 
  # (this prevents cases from being created during the 2nd application of the function)
  complete(
    rd,Item,continent, 
    fill=list(n=0)
  )%>%
  # Add imaginary 'z' continent
  add_row(continent='z',Item='Wheat',n=0,rd=1)%>%
  # Complete all combination for the 'z' continente
  complete(
    rd,Item,continent, 
    fill=list(n=1)
  )

We can now plot the data without filter.

ggplot(
    cpl, # Use cpl (complete) tibble 
    aes(values=n, fill=continent)
  )+
  waffle::geom_waffle(
    n_rows = 4,              # Number of squares in each row
    color = "white",         # Border color
    flip = TRUE, na.rm=TRUE
  )+
  # Add Item in facet_grid
  facet_grid(Item~rd)+
  coord_equal()

Plot customization

We now have some work to do to customize the graph ! First thing: create two color palette (one for fill, one for borders) to hide the imaginary z continent.

Here’s the palette color I use for each continent in my book. Using the same color for a given continent throughout the book makes it easier to read.

pal_fill <- c(
  "Africa" = "#FFC43D", "Asia" = "#F0426B", "Europe" = "#5A4FCF",
  "South America" = "#06D6A0", "North America" = "#059E75",
  "Oceania" = "#F68EA6",
  # Set alpha to 0 to hide 'z'
  'z'=alpha('white',0)
)

pal_color <- c(
  "Africa" = "white", "Asia" = "white", "Europe" = "white",
  "South America" = "white", "North America" = "white",
  "Oceania" = "white",
  # Set alpha to 0 to hide 'z'
  'z'=alpha('white',0)
)

It also seems more natural to place corn at the bottom (as it has more yield classes). We’ll do this by reordering the factors:

# Order crop names
cpl$Item <- as.factor(cpl$Item)
cpl$Item <- fct_relevel(cpl$Item, "Wheat","Maize (corn)")

Let’s go for a new version of the plot:

p1<-ggplot(
  cpl,
  aes(values=n,fill=continent,color=continent)
) +
  waffle::geom_waffle(
    n_rows = 3, 
    flip = TRUE, na.rm=TRUE
  ) +
  facet_grid(
    Item~rd, 
    switch="both"
  ) +
  scale_x_discrete()+
  scale_fill_manual(values=pal_fill)+
  scale_color_manual(values=pal_color)+
  coord_equal()

p1

We are now going to place the legend in the subtitle (with each continent name colored accordingly), using {ggtext} syntax:

title<-"<b>The Yield Gap</b>"
sub<-"Each square is a country, colored according to continent:<br><b><span style='color:#FFC43D;'>Africa</span></b>, <b><span style='color:#F0426B;'>Asia</span></b>, <b><span style='color:#5A4FCF;'>Europe</span></b>, <b><span style='color:#059E75;'>Central and North America</span></b>, <b><span style='color:#06D6A0;'>South America</span></b> or <b><span style='color:#F68EA6;'>Oceania</span>. "

We may now add these labels to our plot:

p2<-p1+
  # Hide legend
  guides(fill='none',color='none')+
  # Add title and subtitle
  labs(title=title,subtitle=sub)+
  theme(
    # Enable markdown for title and subtitle
    plot.title=element_markdown(),
    plot.subtitle=element_markdown(),
    # "Clean" facets 
    panel.background=element_rect(fill="white"),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    strip.background.x = element_rect(fill="white"),
    strip.background.y = element_rect(fill="dimgrey"),
    strip.text.y = element_text(color="white")
  )

ggsave("img/graph/web-waffle-chart-for-distribution.png",p2)
p2

Here’s a relatively clean version of the chart!

What can we see here?

For both crops, the distribution curves are centered on low yields
A majority of African countries show low to very low yields

In this way, major improvements in food production could be achieved by improving yields in the least efficient countries (by moving towards a “Gaussian” distribution) rather than by gaining a few percentages of production in already efficient countries. This is all the more true given that the higher the yields, the greater the quantity of inputs needed to further increase productivity (and therefore the lower the efficiency of these inputs).

Going further

This post explains how to create a waffle chart as a distribution curve for two crops.

If you want to learn more, you can check the waffle section of the gallery and how to play with subgroups and colors in waffle charts.

Visualizing a distribution with Waffle Charts in R