Visualizing Rank Over Time: Creating Bump Charts in R

Overview

A bump chart is a data visualization tool specifically designed to display how the rankings of different categories or entities change over time or across another sequential dimension. Unlike standard line charts that focus on the magnitude of values, bump charts emphasize the relative position (rank) of each item at each point in time, making them ideal for tracking and comparing changes in rank rather than actual values

Example of a Bump Chart

Replicate the visualization: Link to code

Step-by-Step Guide

Load the required libraries.

# load libraries

library(tidyverse)

library(paletteer)

Load dataset.

# load data

superstore<-read.csv("https://raw.githubusercontent.com/bernardkilonzo-rigor/dataviz/main/data/Sample%20-%20Superstore.csv")

Compute rankings.

# computing rankings

Rank_data<-superstore%>%

  mutate(mon = month(Order.Date, label = TRUE))%>%

  group_by(mon,Region)%>%

  summarise(sales =round(sum(Sales),0))%>%

  mutate(rank =dense_rank(desc(sales)))

Prepare labels.

# Adding labels

Rank_data$label<-paste0(Rank_data$rank,"-",Rank_data$Region,"~",Rank_data$sales)

Create a basic bump chart.

# creating a basic plot

Rank_data%>%ggplot(aes(x = mon, y = rank, group =Region, color =Region))+

  geom_line(linewidth = 1.0)+

  geom_point(size =5)+

  scale_y_reverse()

See the resulting view.

Formatting and customizing the plot.

# formatting & customizing the bump chart

Rank_data%>%ggplot(aes(x = mon, y = rank, group =Region, color =Region))+

  geom_line(linewidth = 1.0)+

  geom_point(size =5)+

  scale_y_reverse()+

  geom_text(data = Rank_data%>%filter(mon=="Jan"), aes(label = label), hjust=0.1,vjust=-1, size =3)+

  geom_text(data = Rank_data%>%filter(mon=="Dec"), aes(label = label), hjust=0.7,vjust=-1, size =3)+

  scale_color_paletteer_d("PrettyCols::Autumn")+

  labs(title = "Ranking Sales Performance by Region",

       caption = "Viz by: Bernard Kilonzo",

       x = "Months", y = "Rank")+

  theme(panel.background = element_blank(),

        axis.text.y = element_blank(),

        axis.ticks = element_blank(),

        axis.title = element_blank(),

        axis.title.y = element_blank(),

        axis.title.x = element_text(family = "serif", size = 9, color = "gray35"),

        axis.text.x = element_text(family = "serif", size = 9, color = "gray35"),

        legend.position = "none",

        plot.title = element_text(family = "serif", face = "bold", size = 12, colour = "gray25"),

        plot.caption = element_text(family = "serif", face = "italic", size = 9, color = "gray35"))

See the resulting view.

Build similar viz in Tableau: Explore how to create a bump chart in Tableau

Conclusion

Bump charts offer a visually compelling way to explore changes in rankings over time - an ideal solution for datasets where position matters more than raw values. Whether you're analyzing sports team standings, brand popularity, or product performance, bump charts highlight the dynamic interplay between competing entities in a way that's both intuitive and engaging.

By using R, particularly the flexibility of ggplot2, you gain precise control over design, customization, and interactivity. With just a few lines of code, you can transform a simple dataset into a meaningful visual story of how ranks evolve - complete with line crossovers, labelled paths, and contextual annotations.

Mastering bump charts not only enhances your data storytelling toolkit but also deepens your understanding of relative patterns and temporal shifts. As the world becomes increasingly data-driven, tools like these empower analysts, educators, and decision-makers to reveal trends that traditional charts might miss.

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