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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.

```f\$Deposit/f\$Value

##   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
##  0.1 0.1 0.1

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
v2 <- cumsum(f\$Value) + f\$Value * 0.31
v3 <- cumsum(f\$Value) + f\$Value * 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)
```