# Load packages
library(dplyr)
library(forcats)
library(ggplot2)
library(patchwork)Waterfall Plot
The waterfall plot is the standard way to display best tumor response per patient in oncology trials. Each bar is one patient, sorted by the best percentage change in the sum of target-lesion diameters from baseline; bars are typically colored by the RECIST response category (CR, PR, SD, PD) [1]. The plot answers โhow deep are the responses?โ at a glance, complements the Kaplan-Meier survival curves (which answer โhow long did patients live?โ), and is often paired with a spider plot showing each patientโs longitudinal tumor-size trajectory.
Note
Waterfall plots can visually overestimate the response rate because only the best change is displayed and non-measurable progressions may be under-represented [2]. Pair them with spider plots and report the response rate in numbers.
Example
Setup
System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages:
dplyr;forcats;ggplot2;patchwork
sessioninfo::session_info("attached")โ Session info โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
setting value
version R version 4.6.1 (2026-06-24)
os Ubuntu 24.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate C.UTF-8
ctype C.UTF-8
tz UTC
date 2026-10-29
pandoc 3.1.3 @ /usr/bin/ (via rmarkdown)
quarto 1.10.19 @ /usr/local/bin/quarto
โ Packages โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
! package * version date (UTC) lib source
P dplyr * 1.2.1 2026-04-03 [?] RSPM
P forcats * 1.0.1 2025-09-25 [?] RSPM
P ggplot2 * 4.0.3.9000 2026-10-26 [?] Github (tidyverse/ggplot2@b638d65)
P patchwork * 1.3.2 2025-08-25 [?] RSPM
[1] /home/runner/.cache/R/renv/library/Bizard-5140bdc6/linux-ubuntu-noble/R-4.6/x86_64-pc-linux-gnu
[2] /home/runner/work/_temp/Library
[3] /opt/R/4.6.1/lib/R/library
* โโ Packages attached to the search path.
P โโ Loaded and on-disk path mismatch.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโData Preparation
The required input is one row per patient with, at minimum, the best percentage change in target-lesion size and the RECIST category. Because no clinical-trial patient-level data set ships with R, we simulate a realistic phase-II-style cohort: 40 patients whose best change follows the expected pattern of a moderately active regimen (RECIST thresholds: PR requires โฅ30% shrinkage, PD โฅ20% growth) [1]. Replace the simulation with read.csv("your_recist_data.csv") for real data โ the column structure is identical.
set.seed(2026)
n <- 40
response <- sample(c("CR", "PR", "SD", "PD"), n,
prob = c(0.05, 0.40, 0.30, 0.25), replace = TRUE)
best_change <- round(case_when(
response == "CR" ~ -runif(n, 95, 100), # complete response: almost full shrinkage
response == "PR" ~ -runif(n, 30, 92), # partial response: 30-90% shrinkage
response == "SD" ~ runif(n, -25, 18), # stable disease
response == "PD" ~ runif(n, 22, 120) # progressive disease
), 1)
wf <- data.frame(
patient = paste0("P", sprintf("%02d", seq_len(n))),
best_change = best_change,
response = response
)
head(wf) patient best_change response
1 P01 -5.9 SD
2 P02 -19.3 SD
3 P03 -78.5 PR
4 P04 -89.7 PR
5 P05 -6.9 SD
6 P06 -90.6 PRWe also simulate the longitudinal trajectories for the companion spider plot: a straight-line drift from baseline to the best change with small week-to-week noise, with progressors accelerating late.
set.seed(2027)
weeks <- seq(0, 48, 6)
spider <- do.call(rbind, lapply(seq_len(nrow(wf)), function(i) {
k <- length(weeks)
endpoint <- wf$best_change[i]
base <- approx(x = c(1, k), y = c(0, endpoint), xout = 1:k)$y
noise <- runif(k, -4, 4)
if (wf$response[i] == "PD") noise <- noise + seq(0, 12, length.out = k) # acceleration
data.frame(patient = wf$patient[i], week = weeks,
change = round(pmin(base + noise, 130), 1),
response = wf$response[i])
}))
head(spider) patient week change response
1 P01 0 -2.6 SD
2 P01 6 -1.5 SD
3 P01 12 -3.5 SD
4 P01 18 -0.6 SD
5 P01 24 -4.6 SD
6 P01 30 -5.2 SDVisualization
1. Basic Waterfall Plot
Sort patients by best change and flip the coordinates so bars read like the classic publication layout.
p1 <- wf %>%
mutate(patient = fct_reorder(patient, best_change)) %>%
ggplot(aes(patient, best_change)) +
geom_col(fill = "#4393C3", width = 0.75) +
coord_flip() +
labs(x = NULL, y = "Best % change from baseline") +
theme_bw()
p1
2. RECIST-Colored Waterfall with Threshold Lines
Color bars by response category and add the RECIST reference lines: โ30% marks the partial-response boundary and +20% the progression boundary (formally, PD also requires an absolute increase of at least 5 mm).
recist_colors <- c(CR = "#1B7837", PR = "#66BD63", SD = "#FFB300", PD = "#C51B7D")
p2 <- wf %>%
mutate(patient = fct_reorder(patient, best_change)) %>%
ggplot(aes(patient, best_change, fill = response)) +
geom_col(width = 0.75) +
geom_hline(yintercept = c(-30, 20), linetype = "dashed", color = "grey35") +
scale_fill_manual(values = recist_colors, name = "RECIST") +
coord_flip() +
labs(x = NULL, y = "Best % change from baseline") +
theme_bw() +
theme(legend.position = "top")
p2
The two dashed lines instantly reveal the response rate (bars below โ30%) versus the disease-control rate (all bars below +20%).
3. Companion Spider Plot
p3 <- spider %>%
ggplot(aes(week, change, group = patient, color = response)) +
geom_hline(yintercept = c(-30, 20), linetype = "dashed", color = "grey35") +
geom_line(alpha = 0.75, linewidth = 0.6) +
scale_color_manual(values = recist_colors, name = "RECIST") +
labs(x = "Weeks on treatment", y = "% change from baseline") +
theme_bw() +
theme(legend.position = "top")
p3
4. Combined Figure
p2 + p3 + plot_annotation(
title = "Best tumor response and its longitudinal trajectories",
tag_levels = "A"
)
Reading them together: bars tell the depth of response, lines tell its shape โ early shrinkers, late progressors and primary refractory patients are distinguishable in the spider plot only.
Application
Waterfall plots are ubiquitous in clinical oncology papers and FDA/EMA filings to summarize anti-tumor activity of a regimen; the combined waterfall + spider panel below is the typical presentation.
Reference
[1] Eisenhauer EA, Therasse P, Bogaerts J, et al.ย New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228-247. doi:10.1016/j.ejca.2008.10.026
[2] Kim MS, Prasad V. Assessment of accuracy of waterfall plot representations of response rates in cancer treatment published in medical journals. JAMA Netw Open. 2019;2(5):e193981. doi:10.1001/jamanetworkopen.2019.3981