Visualise Changes in Rankings with 'ggplot2'

Calculates, inspects, tabulates, and visualises changes in rankings across two to four ordered states. Creates 'ggplot2'-based rank-transition charts that retain categories entering or leaving a selected top-rank boundary. Supports ranks calculated from numeric values as well as authoritative ranks supplied without values.

ggrank

Visualise how rankings change across time, groups, and scenarios using ggplot2.

ggrank is an independent open-source project and is not affiliated with or endorsed by the Institute for Health Metrics and Evaluation (IHME). All data included with the package are synthetic teaching data and do not contain Global Burden of Disease estimates.

Installation

Once released on CRAN, install the stable version with:

install.packages("ggrank")

Install the development version from GitHub:

pak::pak("ThinkDenominator/ggrank")

library(ggrank)

ggrank(
  ggrank_causes,
  category = cause,
  period = year,
  value = rate,
  rank = rank,
  label = display_value,
  group = cause_group,
  periods = c(1990, 2021),
  top_n = 10,
  value_header = "Rate (95% interval)"
)

Two complementary views

ggrank() answers How did the ranking evolve? It shows the complete rank structure, values, trajectories, risers, fallers, entrants, and exits. The default boundary view retains categories that enter or leave the selected top ranks, so important transitions are not silently discarded.

Use ggrank_table() when you need the readable analytical result behind a figure—for example, exact before/after ranks, value changes, entrants, and exits.

ggrank_change() answers Who moved the most? It highlights the largest increases and decreases in rank. It accepts raw data directly:

ggrank_change(ggrank_products, product, year, sales, top = 5)

By default this displays the latest comparison, excludes unchanged categories, and selects the largest absolute changes. Use comparison = "all" for every adjacent transition. Both functions return ordinary ggplot objects.

See the movement, status, and legend guide for status definitions, detailed rank labels, colours, legend labels, and comparison selection.

For a point-and-click workflow, launch the local Shiny interface:

ggrank_app()

The app uses the same package functions. It can open the synthetic teaching data or an uploaded CSV, create the ranking, display both charts, and show the rank-change table.

How ranks are calculated

Normally, supply one numeric value for each category and period; you do not need to create a rank column. ggrank() ranks the exact values within each period before applying top_n.

ggrank_data(ggrank_products, product, year, sales)

Equal values share a competition rank by default (1, 2, 3, 3, 5). They are placed on separate rows and all categories tied at the top_n boundary are retained. value controls ranking, while an optional label controls only the printed formatting. Do not pre-filter data to the top N: supply all relevant categories so entrants and exits can be identified.

Already have ranks but no values?

A value, mark, score, or rate is not required when authoritative ranks already exist. Supply category, period, and rank:

student_ranks <- data.frame(
  year = rep(c(2024, 2025), each = 4),
  student = rep(c("Asha", "Ben", "Chen", "Dina"), 2),
  rank = c(1, 2, 3, 4, 3, 1, 2, 4)
)

ggrank(
  student_ranks,
  category = student,
  period = year,
  rank = rank,
  top_n = 4
)

The chart uses a compact category-and-rank layout and does not draw an empty value column. Ranks must be positive whole numbers. If several institutions have separate rankings in the same period, analyse each institution separately or create a distinct period/list identifier; group controls colour but does not create independent ranking populations.

Exporting results

ggrank() returns an ordinary ggplot object, so use standard R tools:

p <- ggrank(ggrank_products, product, year, sales, top_n = 5)

ggplot2::ggsave(
  "rank-chart.png", p,
  width = 12, height = 7, units = "in", dpi = 300
)

changes <- ggrank_table(
  ggrank_products, product, year, sales,
  periods = c(2022, 2024), top_n = 5
)

write.csv(changes, "rank-changes.csv", row.names = FALSE)