Pies and Fans

So let us start with “eating humble pie”: discussing a Pie chart first.

A pie chart is a circle divided into sectors that each represent a proportion of the whole. It is often used to show percentage, where the sum of the sectors equals 100%.

The problem is that humans are pretty bad at reading angles. This ubiquitous chart is much vilified in the industry and bar charts that we have seen earlier, are viewed as better options. However do read this spirited defense of pie charts here. https://speakingppt.com/why-tufte-is-flat-out-wrong-about-pie-charts/

On the other hand, pie charts are ubiquitous in business circles, and are very much accepted ! So there is an attractive, and similar-looking alternative, called a fan chart which we will explore here.

(Base) R has a simple pie command that does the job.

pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)
labels <- c("Blueberry", "Cherry",
    "Apple", "Boston Cream", "Other", "Vanilla Cream")
pie(x = pie.sales, labels = labels,col =  grDevices::hcl.colors(palette= "Plasma", n = 6)) # default colours

The fan plot displays numerical values as arcs of overlapping sectors. This allows for more effective comparison:

plotrix::fan.plot(x = pie.sales, labels = labels,
                  col = grDevices::hcl.colors(palette= "Plasma", n = 6),
                  shrink = 0.03, # How much to shrink each successive sector
                  label.radius = 1.15,
                  main = "Fan Plot of Ice Cream Flavours",
                  ticks = 360,
                  max.span = pi)

The donut chart suffers from the same defects as the pie, so should be used with discretion. The donut chart is essentially a geom_rect from ggplot, plotted on a polar coordinate set of of axes:

# Data
df <- data.frame(group = LETTERS[1:3],
                 value = c(25, 20, 35))

df <-
  df %>% dplyr::mutate(
    fraction = value / sum(value), # percentages
    ymax = cumsum(fraction), # cumulative percentages
    ymin = lag(ymax, 1, default = 0),
    # bottom edge of each
    label = paste0(group, "\n value: ", value),
    labelPosition = (ymax + ymin) / 2 # labels midway on arcs
  )

df

##   group value fraction   ymax   ymin         label labelPosition
## 1     A    25   0.3125 0.3125 0.0000 A\n value: 25       0.15625
## 2     B    20   0.2500 0.5625 0.3125 B\n value: 20       0.43750
## 3     C    35   0.4375 1.0000 0.5625 C\n value: 35       0.78125

ggplot(df) + 
# `geom_rect()` requires aesthetics: xmin, xmax, ymin, and ymax
  geom_rect(aes(xmin = 2, xmax = 4, ymin = ymin, ymax = ymax, fill = group),colour = "black") + 
  geom_label( x=3.5, aes(y=labelPosition, label= label), size=4) +
  coord_polar(theta = "y",direction = 1) + # Upto here will give us a pie chart

# When switching to polar coords:
# x maps to r
# y maps to theta
# so we create a "hole" in the radius, in in 
xlim(c(0,4)) + # try to play with the "0"
theme_void() +
theme(legend.position = "none")

Parliament and Waffle Charts

Waffle charts are often called “square pie charts” !

# install.packages("waffle", repos = "https://cinc.rud.is")
library(waffle)

# Data
df <- data.frame(group = LETTERS[1:3],
                 value = c(25, 20, 35))

# Waffle plot
ggplot(df, aes(fill = group, values = value)) +
  geom_waffle(n_rows = 8, size = 0.33, colour = "white") +
  scale_fill_manual(name = NULL,
                    values = c("#BA182A", "#FF8288", "#FFDBDD"),
                    labels = c("A", "B", "C")) +
  coord_equal() +
  theme_void() 

The package ggpol offers an interesting visualization in the shape of a array of “seats” in a parliament. ( There is also a package called ggparliament which in my opinion is a bit cumbersome, having a two step procedure to convert data into “parliament form” etc. )

df <- data.frame(group = LETTERS[1:3],
                 value = c(25, 20, 35))

# Parliament Plot
ggplot(df) +
  ggpol::geom_parliament(aes(seats = value, 
                             fill = group),
                         r0 = 2, # inner radius
                         r1 = 4 # Outer radius
  ) + 
  scale_fill_manual(name = NULL,
                    values = c("#BA182A", "#FF8288", "#FFDBDD"),
                    labels = c("A", "B", "C")) +
  coord_equal() +
  theme_void() 

## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Trees, Dendrograms, and Circle Packings

There are still more esoteric plots to explore, if you are hell-bent on startling people ! There is an R package called ggraph, that can do these charts, and many more:

ggraph is an extension of ggplot2 aimed at supporting relational data structures such as networks, graphs, and trees. While it builds upon the foundation of ggplot2 and its API it comes with its own self-contained set of geoms, facets, etc., as well as adding the concept of layouts to the grammar.

We will explore these charts when we examine network diagrams. For now, we can quickly see what these diagrams look like. Although the R-code is visible to you, it may not make sense at the moment!

# create an edge list data frame giving the hierarchical structure of your individuals
d1 <- data.frame(from="origin", to=paste("group", seq(1,5), sep=""))
d2 <- data.frame(from=rep(d1$to, each=5), to=paste("subgroup", seq(1,25), sep="_"))
edges <- rbind(d1, d2)
 
# Create a graph object 
mygraph1 <- tidygraph::as_tbl_graph( edges )
 
# Basic tree
p1 <- ggraph(mygraph1, layout = 'dendrogram', circular = TRUE) + 
  geom_edge_diagonal() +
  geom_node_point() +
  theme_void()


# create a data frame 
data <- data.frame(
  level1="CEO",
  level2=c( rep("boss1",4), rep("boss2",4)),
  level3=paste0("mister_", letters[1:8])
)
 
# transform it to a edge list!
edges_level1_2 <- data %>% select(level1, level2) %>% unique %>% rename(from=level1, to=level2)
edges_level2_3 <- data %>% select(level2, level3) %>% unique %>% rename(from=level2, to=level3)
edge_list=rbind(edges_level1_2, edges_level2_3)
 
# Now we can plot that
mygraph2 <- as_tbl_graph( edge_list )
p2 <- ggraph(mygraph2, layout = 'dendrogram', circular = FALSE) + 
  geom_edge_diagonal() +
  geom_node_point() +
  theme_void()

p1 + p2+ theme(aspect.ratio = 1)

library(tidygraph)
library(ggraph)
graph <- tbl_graph(flare$vertices, flare$edges)
set.seed(1)
ggraph(graph, 'circlepack', weight = size) + 
  geom_node_circle(aes(fill = as_factor(depth)), size = 0.25, n = 50) + 
  coord_fixed() +
  scale_fill_discrete(name = "Depth") +
  theme_void()