Dataviz caveat

The issue with pie chart

Bad by definition

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. In the adjacent pie chart, try to figure out which group is the biggest one and try to order them by value. You will probably struggle to do so and this is why pie charts must be avoided.

Show code

# Libraries
library(tidyverse)
library(hrbrthemes)
library(viridis)
library(patchwork)

# create 3 data frame:
data1 <- data.frame( name=letters[1:5], value=c(17,18,20,22,24) )
data2 <- data.frame( name=letters[1:5], value=c(20,18,21,20,20) )
data3 <- data.frame( name=letters[1:5], value=c(24,23,21,19,18) )

# Plot
plot_pie <- function(data, vec){

ggplot(data, aes(x="name", y=value, fill=name)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start=0, direction = -1) +
  scale_fill_viridis(discrete = TRUE,  direction=-1) +
  geom_text(aes(y = vec, label = rev(name), size=4, color=c( "white", rep("black", 4)))) +
  scale_color_manual(values=c("black", "white")) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=14),
    panel.grid = element_blank(),
    axis.text = element_blank(),
    legend.margin=unit(0, "null")
  ) +
  xlab("") +
  ylab("")

}

plot_pie(data1, c(10,35,55,75,93))

The issue with pie chart – chart example

If you’re still not convinced, let’s try to compare several pie plots. Once more, try to understand which group has the highest value in these 3 graphics. Also, try to figure out what is the evolution of the value among groups.

Show code

a <- plot_pie(data1, c(10,35,55,75,93))
b <- plot_pie(data2, c(10,35,53,75,93))
c <- plot_pie(data3, c(10,29,50,75,93))
a + b + c

Now, let’s represent exactly the same data using a barplot:

Show code

# A function to make barplots
plot_bar <- function(data){
  ggplot(data, aes(x=name, y=value, fill=name)) +
    geom_bar( stat = "identity") +
    scale_fill_viridis(discrete = TRUE, direction=-1) +
    scale_color_manual(values=c("black", "white")) +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14),
      panel.grid = element_blank(),
    ) +
    ylim(0,25) +
    xlab("") +
    ylab("")
}

# Make 3 barplots
a <- plot_bar(data1)
b <- plot_bar(data2)
c <- plot_bar(data3)

# Put them together with patchwork
a + b + c

As you can see on this barplot, there is a heavy difference between the three pie plots with a hidden pattern that you definitely don’t want to miss when you tell your story.

And often made even worse

Even if pie charts are bad by definition, it is still possible to make them even worse by adding other bad features:

3d
legend aside
percentages that do not sum to 100
too many items
exploded pie charts

Alternatives

The barplot is the best alternative to pie plots. If you have many values to display, you can also consider a lollipop plot that is a bit more elegant in my opinion. Here is an example based on the amount of weapons sold by a few countries in the world:

Show code

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/7_OneCatOneNum.csv", header=TRUE, sep=",")

# plot
data %>%
  filter(!is.na(Value)) %>%
  arrange(Value) %>%
  mutate(Country=factor(Country, Country)) %>%
  ggplot( aes(x=Country, y=Value) ) +
    geom_segment( aes(x=Country ,xend=Country, y=0, yend=Value), color="grey") +
    geom_point(size=3, color="#69b3a2") +
    coord_flip() +
    theme_ipsum() +
    theme(
      panel.grid.minor.y = element_blank(),
      panel.grid.major.y = element_blank(),
      legend.position="none"
    ) +
    xlab("")

Another possibility would be to create a treemap if your goal is to describe what the whole is composed of.

Show code

# Package
library(treemap)

# Plot

data %>%
  drop_na() %>%
  treemap(  # data
            index="Country",
            vSize="Value",
            type="index",

            # Main
            title="",
            palette="Dark2",

            # Borders:
            border.col=c("black"),
            border.lwds=1,

            #Labels
            fontsize.labels=0.5,
            fontcolor.labels="white",
            fontface.labels=1,
            bg.labels=c("transparent"),
            align.labels=c("left", "top"),
            overlap.labels=0.5,
            inflate.labels=T                        # If true, labels are bigger when rectangle is bigger.
            )

Going further

Who sells more weapons: see this story that gives several alternatives to pie plot.
Four problems with Pie Charts (And what to do instead) by iCharts.net
The worst chart in the world by Business insider
doing pie plots in R and Python

Bad by definition

And often made even worse

Alternatives

Going further

Dataviz decision tree