The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Residual Plot in Python, R, Julia and JavaScript.
A residual plot displays the difference between observed and predicted values (residuals) against fitted values in regression analysis. This diagnostic visualization helps identify violations of regression assumptions including non-linearity, heteroscedasticity (non-constant variance), and outliers. A well-fitting model shows residuals randomly scattered around zero with no discernible pattern.
#' anyplot.ai
#' residual-plot: Residual Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 91/100 | Created: 2026-09-05
library(ggplot2)
library(dplyr)
library(ragg)
set.seed(42)
# --- Theme tokens -----------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
RULE <- scales::alpha(INK, 0.15)
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314")
# --- Data --------------------------------------------------------------------
# House price prediction: a simple linear model fit to a mildly non-linear,
# heteroscedastic relationship between square footage and sale price. The
# residual plot exposes both the curvature the linear fit misses and the
# fanning variance that grows with home size.
n <- 220
square_footage <- runif(n, 800, 4200)
noise_scale <- 6000 + square_footage * 12
sale_price <- 45000 + 95 * square_footage + 0.018 * square_footage^2 +
rnorm(n, mean = 0, sd = noise_scale)
homes <- tibble::tibble(square_footage, sale_price)
model <- lm(sale_price ~ square_footage, data = homes)
homes <- homes %>%
mutate(
fitted = fitted(model),
residual = resid(model),
band = 2 * sd(residual),
is_outlier = abs(residual) > band,
status = factor(if_else(is_outlier, "Outlier (>2σ)", "Normal"),
levels = c("Normal", "Outlier (>2σ)"))
)
band_width <- unique(homes$band)
# --- Plot ---------------------------------------------------------------
p <- ggplot(homes, aes(x = fitted, y = residual)) +
geom_hline(yintercept = c(-band_width, band_width),
linetype = "dashed", linewidth = 0.5, color = INK_SOFT) +
geom_hline(yintercept = 0, linewidth = 0.8, color = INK) +
geom_smooth(method = "loess", formula = y ~ x, se = FALSE,
color = IMPRINT_PALETTE[3], linewidth = 1.0) +
geom_point(aes(color = status, shape = status), size = 2.5, alpha = 0.75) +
scale_color_manual(values = c("Normal" = IMPRINT_PALETTE[1],
"Outlier (>2σ)" = IMPRINT_PALETTE[5])) +
scale_shape_manual(values = c("Normal" = 16, "Outlier (>2σ)" = 17)) +
labs(
title = "residual-plot · r · ggplot2 · anyplot.ai",
x = "Fitted Sale Price ($)",
y = "Residual ($)",
color = NULL,
shape = NULL
) +
scale_x_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "k")) +
scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "k")) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid.major = element_line(color = RULE, linewidth = 0.3),
panel.grid.minor = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.line = element_line(color = INK_SOFT),
plot.title = element_text(color = INK, size = 12, face = "bold"),
legend.position = "top",
legend.text = element_text(color = INK_SOFT, size = 8),
legend.key = element_blank()
)
# --- Save -----------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/residual-plot/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "residual-plot",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/residual-plot/r/ggplot2",
"hub": "https://anyplot.ai/residual-plot",
"code_json": "https://api.anyplot.ai/specs/residual-plot/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/residual-plot",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/r/ggplot2/plot-dark.png",
"quality_score": 91.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Residual Plot on anyplot.ai.