A parallel coordinates plot visualizes multivariate data by representing each variable as a vertical axis and each observation as a line connecting values across all axes. This technique is powerful for identifying patterns, clusters, and outliers in high-dimensional datasets where traditional 2D plots fall short. It enables simultaneous comparison of multiple variables for each data point.
#' anyplot.ai
#' parallel-basic: Basic Parallel Coordinates Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 90/100 | Created: 2026-07-24
library(ggplot2)
library(dplyr)
library(tidyr)
library(ragg)
set.seed(42)
# --- Theme tokens -------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
IMPRINT_PALETTE <- c(
"#009E73", # 1 - brand green
"#C475FD", # 2 - lavender
"#4467A3" # 3 - blue
)
# --- Data -----------------------------------------------------------------
# Product feature comparison across 6 metrics for 3 market segments.
n_per_segment <- 30
segment_levels <- c("Budget", "Mid-range", "Premium")
seg_idx <- rep(1:3, each = n_per_segment)
n <- length(seg_idx)
price <- pmax(rnorm(n, c(45, 150, 380)[seg_idx], c(15, 40, 90)[seg_idx]), 10)
rating <- pmin(pmax(rnorm(n, c(3.3, 4.0, 4.6)[seg_idx], c(0.4, 0.3, 0.25)[seg_idx]), 1), 5)
sales_volume <- pmax(rnorm(n, c(9000, 4000, 900)[seg_idx], c(2500, 1500, 400)[seg_idx]), 100)
inventory_turnover <- pmax(rnorm(n, c(14, 8, 3.5)[seg_idx], c(3, 2, 1.2)[seg_idx]), 1)
customer_satisfaction <- pmin(pmax(rnorm(n, c(72, 84, 93)[seg_idx], c(7, 6, 4)[seg_idx]), 40), 100)
market_share <- pmax(rnorm(n, c(15, 8, 3)[seg_idx], c(4, 3, 1.5)[seg_idx]), 0.2)
dimension_cols <- c("Price", "Rating", "Sales Volume", "Inventory Turnover",
"Customer Satisfaction", "Market Share")
products <- tibble::tibble(
id = seq_len(n),
category = factor(segment_levels[seg_idx], levels = segment_levels),
Price = price,
Rating = rating,
`Sales Volume` = sales_volume,
`Inventory Turnover` = inventory_turnover,
`Customer Satisfaction` = customer_satisfaction,
`Market Share` = market_share
)
# Min-max normalize each dimension to [0, 1] so all axes are comparable.
products_norm <- products %>%
mutate(across(all_of(dimension_cols), ~ (. - min(.)) / (max(.) - min(.)), .names = "{.col}_norm"))
products_long <- products_norm %>%
select(id, category, ends_with("_norm")) %>%
pivot_longer(cols = ends_with("_norm"), names_to = "dimension", values_to = "value") %>%
mutate(
dimension = sub("_norm$", "", dimension),
dimension = factor(dimension, levels = dimension_cols)
)
# Original-scale min/max labels shown at each axis endpoint.
axis_fmt <- c(
"Price" = "$%.0f",
"Rating" = "%.1f★",
"Sales Volume" = "%.0f",
"Inventory Turnover" = "%.1f×",
"Customer Satisfaction" = "%.0f%%",
"Market Share" = "%.1f%%"
)
axis_range <- products %>%
summarise(across(all_of(dimension_cols), list(min = min, max = max))) %>%
pivot_longer(everything(), names_to = c("dimension", ".value"), names_pattern = "(.*)_(min|max)") %>%
mutate(
dimension = factor(dimension, levels = dimension_cols),
x = as.numeric(dimension),
min_label = sprintf(axis_fmt[as.character(dimension)], min),
max_label = sprintf(axis_fmt[as.character(dimension)], max)
)
# --- Plot -------------------------------------------------------------------
anyplot_theme <- 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 = element_blank(),
panel.border = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.title = element_blank(),
axis.text.x = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = 12),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 10)
)
p <- ggplot() +
geom_vline(
data = axis_range, aes(xintercept = x),
color = INK_SOFT, alpha = 0.3, linewidth = 0.4
) +
geom_line(
data = products_long,
aes(x = dimension, y = value, group = id, color = category),
alpha = 0.45, linewidth = 0.5
) +
geom_point(
data = products_long,
aes(x = dimension, y = value, color = category),
size = 1.5, alpha = 0.6
) +
geom_text(
data = axis_range, aes(x = x, y = -0.1, label = min_label),
color = INK_SOFT, size = 2.6, vjust = 1
) +
geom_text(
data = axis_range, aes(x = x, y = 1.1, label = max_label),
color = INK_SOFT, size = 2.6, vjust = 0
) +
scale_color_manual(values = IMPRINT_PALETTE, name = "Segment") +
scale_x_discrete(labels = function(x) gsub(" ", "\n", x), expand = expansion(add = 0.6)) +
coord_cartesian(ylim = c(-0.22, 1.22), clip = "off") +
labs(title = "parallel-basic · r · ggplot2 · anyplot.ai") +
anyplot_theme +
theme(plot.margin = margin(t = 20, r = 20, b = 15, l = 20))
# --- 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/parallel-basic/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": "parallel-basic",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/parallel-basic/r/ggplot2",
"hub": "https://anyplot.ai/parallel-basic",
"code_json": "https://api.anyplot.ai/specs/parallel-basic/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/parallel-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/parallel-basic/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/parallel-basic/r/ggplot2/plot-dark.png",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Parallel Coordinates Plot on anyplot.ai.