Introduction

When it comes to conveying information effectively, data visualization is a powerful tool that can make complex data more accessible and understandable. One captivating type of data visualization is the lollipop chart. Lollipop charts are a great way to showcase and compare data points while adding a touch of elegance to your presentations. In this blog post, we will dive into what lollipop charts are, why they are useful, and how you can create your own stunning lollipop charts using the ggplot2 package in R.

What are Lollipop Charts?

Lollipop charts, also known as dot plot charts, combine elements of bar charts and scatter plots to visualize data in a unique and engaging manner. They consist of a set of data points represented by circles (or any chosen shape) positioned at the end of a vertical line. These lines serve as the reference point for each data value, making it easy to compare values across categories or groups.

The primary advantage of lollipop charts is their simplicity and efficiency in highlighting individual data points. This makes them particularly useful when you want to emphasize specific data values in your dataset or when you have a relatively small number of data points to display.

Creating Lollipop Charts using ggplot2

Now, let’s roll up our sleeves and create our own lollipop charts using the popular ggplot2 package in R. But first, make sure you have ggplot2 installed by running:

install.packages("ggplot2")

Once you have ggplot2 installed, you can create a custom function, lollipop_chart(), to generate lollipop charts with ease. Here’s how you can define the function:

library(ggplot2)
library(dplyr)

lollipop_chart <- function(.data, x, y, title) {
  x_var <- rlang::enquo(x)
  y_var <- rlang::enquo(y)
  
  .data |>
  ggplot(aes(x = {{ x_var }}, y = {{ y_var }})) +
    geom_segment(aes(x = {{ x_var }}, xend = {{ x_var }}, 
                     y = 0, yend = {{ y_var }}), 
                 color = "gray50") +
    geom_point(size = 3, color = "steelblue") +
    labs(title = title, x = "", y = "") +
    theme_minimal() +
    theme(panel.grid.major.y = element_blank())
}

Let’s break down the components of the lollipop_chart() function: – data: The dataset containing the data points. – x: The variable on the x-axis (categorical or ordinal). – y: The variable on the y-axis (numeric). – title: The title of the chart.

movies <- tibble(
  Movie = c("The Shawshank Redemption", "The Godfather", "The Dark Knight", "Pulp Fiction") |> factor(),
  Rating = c(9.3, 9.2, 9.0, 8.9)
)

lollipop_chart(movies, Movie, Rating, "Top Movies' IMDb Ratings")

exam_scores <- data.frame(
  Class = rep(c("Class A", "Class B"), each = 5),
  Student = c("Alice", "Bob", "Carol", "David", "Emma", "Frank", "Grace", "Hannah", "Ivan", "Jack") |> factor(),
  Score = c(85, 78, 92, 67, 75, 88, 82, 95, 70, 79)
)

lollipop_chart(exam_scores, Student, Score, "Exam Scores Comparison")