I was contacted recently by a housing organisation who wanted
an attractive visualisation of their finances, arranged in a circular
form. Because there were two 4 continuous variables to include, all
of which were proportions of each other, the client suggested a plot
similar to a pie chart, but with each segment extending out a different
radius from the segment. I realised later that what I had been asked to
make was a modified coxcomb
plot, invented by
Florence Nightingale
to represent statistics on cause of death during the Crimean War.
In fact, I had been asked to make a “spie chart.”
This post demonstrates, for the first time to my knowledge, how it can be done
using ggplot2. A reproducible example of this, including sample data input, can be
found on the project's github repository: https://github.com/Robinlovelace/lilacPlot . Please fork and attribute as appropriate!

Reading and looking at the data

This is the original dataset I was given:

f <- read.csv("F2.csv")
f[1:10, 1:12]

Without worrying too much about the details, the basics of the dataset are
as follows:

• One observation per row, these will later be bars on the box plot
• Two components of data - captital and revenue
• Different orders of magnitude: some data is in absolute monetary terms, some in percentages

Base on the above points, a prerequisite was to create preliminary plots and manipulate the
data so it would better fit in a coxcomb plot.

The first stage, however, is to demonstrate how the addition of
coord_polar to a barchart can conver it into a pie chart:

(p <- ggplot(f, aes(x = H, y = Allocation)) + geom_bar(color = "black", stat = "identity", 
    width = 1))

p + coord_polar()

The above example works well, but notice that all the bars are of equal widths.
What we want is to be proportional to a value (variable "Value") of each observation.
To do this we use the age-old function cumsum, as described in an
answer to a stackexchange question.

w <- f$Value
pos <- 0.5 * (cumsum(w) + cumsum(c(0, w[-length(w)])))

(p <- ggplot(f, aes(x = pos)) + geom_bar(aes(y = Allocation), width = w, color = "black", 
    stat = "identity"))

p + coord_polar(theta = "x") + scale_x_continuous(labels = f$H, breaks = pos)

Finally a spie chart has been created. After that revelation, it was essentially about adding the 'bells and
whistles', including a 10% line to represent how much more or less than their share each observation was
paying.

Adding the 10 %

f$Deposit/f$Value

##  [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [18] 0.1 0.1 0.1

# add 10% in there
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allocation), width = w, color = "black", stat = "identity") + 
    geom_bar(aes(x = pos, y = 0.1), width = w, color = "black", stat = "identity", 
        fill = "green") + coord_polar()

# make proportional to area
f$Allo <- sqrt(f$Allocation)

p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "black", stat = "identity") + 
    geom_bar(aes(x = pos, y = sqrt(0.1), width = w), color = "black", stat = "identity", 
        fill = "green") + coord_polar()

# add capital
capital <- (f$Captial + f$Deposit)/(f$Value) * f$Allocation
capital <- sqrt(capital)

p + geom_bar(aes(x = pos, y = Allo, width = w), color = "black", stat = "identity") + 
    geom_bar(aes(x = pos, y = capital, width = w), color = "black", stat = "identity", 
        fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1), width = w), color = "black", 
    stat = "identity", fill = "green") + coord_polar() + scale_x_continuous(labels = f$H, 
    breaks = pos)

# add ablines
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity", 
    fill = "lightgrey") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40", 
    stat = "identity", fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1), 
    width = w), color = "grey40", stat = "identity", fill = "green") + geom_abline(intercept = 1, 
    slope = 0, linetype = 2) + geom_abline(intercept = sqrt(1.1), slope = 0, 
    linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3)

# calculate vertical ablines of divisions
v1 <- 0.51 * f$Value[1]
v2 <- cumsum(f$Value)[17] + f$Value[18] * 0.31
v3 <- cumsum(f$Value)[17] + f$Value[18] * 0.64

p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity", 
    fill = "lightgrey") + geom_vline(x = v1, linetype = 5) + geom_vline(x = v2, 
    linetype = 5) + geom_vline(x = v3, linetype = 5) + coord_polar()

# putting it all together
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity", 
    fill = "lightgrey") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40", 
    stat = "identity", fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1), 
    width = w), color = "grey40", stat = "identity", fill = "green") + geom_abline(intercept = 1, 
    slope = 0, linetype = 2) + geom_abline(intercept = sqrt(1.1), slope = 0, 
    linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3) + 
    geom_vline(x = v1, linetype = 5) + geom_vline(x = v2, linetype = 5) + geom_vline(x = v3, 
    linetype = 5) + coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) + 
    theme_classic()

The above looks great, but ideally, for an 'infographic' feel, it would
have no annoying axes clogging up the visuals. This was done by creating an
entirely new ggpot theme.

Create theme with no axes

theme_infog <- theme_classic() + theme(axis.line = element_blank(), axis.title = element_blank(), 
    axis.ticks = element_blank(), axis.text.y = element_blank())
last_plot() + theme_infog

Creating a ring

To add the revenue element to the graph is not a task to be taken likely.
This was how I tackled the problem, by creating a tall, variable-width
bar chart first, and later adding the original spie chart after:

f$Cap.r <- f$Cap/mean(f$Cap) * 0.1 + 1.2
f$Cont.r <- f$Contribution/mean(f$Cap) * 0.1 + 1.2
f$Rep.r <- f$Cont.r + f$Repayments/mean(f$Cap) * 0.1
f$H <- c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", 
    "o", "p", "q", "r", "s", "t")

p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity", 
    fill = "lightgrey")

# we need the axes to be bigger for starters - try 1.3 to 1.5

p + geom_bar(aes(x = pos, y = Cap.r, width = w), color = "grey40", stat = "identity", 
    fill = "white") + geom_bar(aes(x = pos, y = Rep.r, width = w), color = "grey40", 
    stat = "identity", fill = "grey80") + geom_bar(aes(x = pos, y = Cont.r, 
    width = w), color = "grey40", stat = "identity", fill = "grey30") + geom_bar(aes(x = pos, 
    y = 1.196, width = w), color = "white", stat = "identity", fill = "white")

last_plot() + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", 
    stat = "identity", fill = "grey80") + geom_bar(aes(x = pos, y = capital, 
    width = w), color = "grey40", stat = "identity", fill = "grey30") + geom_bar(aes(x = pos, 
    y = sqrt(0.1), width = w), color = "grey40", stat = "identity", fill = "black") + 
    geom_abline(intercept = 1, slope = 0, linetype = 5) + geom_abline(intercept = sqrt(1.1), 
    slope = 0, linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, 
    linetype = 3) + coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) + 
    theme_infog

Just inner

After all that it was decided it looked nicer with only the inner ring anyway.
Here is the finished product:

p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity", 
    fill = "grey80") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40", 
    stat = "identity", fill = "grey30") + geom_bar(aes(x = pos, y = sqrt(0.1), 
    width = w), color = "grey40", stat = "identity", fill = "black") + geom_abline(intercept = 1, 
    slope = 0, linetype = 5) + geom_abline(intercept = sqrt(1.1), slope = 0, 
    linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3) + 
    coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) + theme_infog

ggsave("just-inner.png", width = 7, height = 7, dpi = 800)