A scatter plot where data points are connected by lines in temporal order, revealing how two variables co-evolve over time. Unlike standard time series plots that show one variable against time, this plot encodes time as movement through 2D space, making cyclical patterns, regime changes, and directional trends visible. Popularized by the New York Times, it is a powerful tool for narrative data visualization.
#' anyplot.ai
#' scatter-connected-temporal: Connected Scatter Plot with Temporal Path
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 89/100 | Created: 2026-06-09
library(ggplot2)
library(scales)
library(ragg)
set.seed(42)
# Theme tokens (Imprint palette, theme-adaptive chrome)
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"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
IMPRINT_PALETTE <- c(
"#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314"
)
# Data: unemployment vs inflation (Phillips curve dynamics), 1980-2019
years <- 1980:2019
unemployment <- c(
7.2, 7.6, 9.7, 9.6, 7.5, 7.2, 7.0, 6.2, 5.5, 5.3,
5.6, 6.8, 7.5, 6.9, 6.1, 5.6, 5.4, 4.9, 4.5, 4.2,
4.0, 4.7, 5.8, 6.0, 5.5, 5.1, 4.6, 4.6, 5.8, 9.3,
9.6, 8.9, 8.1, 7.4, 6.2, 5.3, 4.9, 4.4, 3.9, 3.5
)
inflation <- c(
13.5, 10.3, 6.2, 3.2, 4.3, 3.6, 1.9, 3.6, 4.1, 4.8,
5.4, 4.2, 3.0, 3.0, 2.6, 2.8, 2.9, 2.3, 1.6, 2.2,
3.4, 2.8, 1.6, 2.3, 2.7, 3.4, 3.2, 2.9, 3.8, -0.4,
1.6, 3.2, 2.1, 1.5, 1.6, 0.1, 1.3, 2.1, 2.4, 2.3
)
df <- data.frame(
year = years,
unemployment = unemployment,
inflation = inflation
)
key_years <- c(1980, 1990, 2000, 2010, 2019)
df_labels <- df[df$year %in% key_years, ]
# Per-label positions to avoid crowding (2000 nudged left, 2019 nudged higher)
df_labels$lbl_x <- df_labels$unemployment + c( 0.2, 0.2, -0.3, 0.2, 0.2)
df_labels$lbl_y <- df_labels$inflation + c( 0.6, 0.5, 0.5, 0.5, 0.7)
# Arrow segment: from start point toward first step for temporal direction cue
arrow_start <- df[df$year == 1980, ]
arrow_end <- df[df$year == 1981, ]
# Title: scale fontsize for long title (80 chars -> size 10)
plot_title <- "Phillips Curve Dynamics · scatter-connected-temporal · r · ggplot2 · anyplot.ai"
title_size <- max(8L, round(12 * 67 / nchar(plot_title)))
# Plot
p <- ggplot(df, aes(x = unemployment, y = inflation)) +
geom_path(
aes(color = year),
linewidth = 1.0,
alpha = 0.85,
lineend = "round",
linejoin = "round"
) +
# Start-of-path arrow to reinforce temporal direction
geom_segment(
data = data.frame(
x = arrow_start$unemployment,
y = arrow_start$inflation,
xend = arrow_end$unemployment,
yend = arrow_end$inflation
),
aes(x = x, y = y, xend = xend, yend = yend),
color = IMPRINT_PALETTE[1],
linewidth = 1.2,
arrow = arrow(length = unit(0.18, "cm"), type = "closed")
) +
geom_point(
aes(color = year),
size = 3.5,
alpha = 0.95
) +
geom_text(
data = df_labels,
aes(x = lbl_x, y = lbl_y, label = year),
color = INK,
size = 3.2,
fontface = "bold"
) +
scale_color_gradient(
low = IMPRINT_PALETTE[1],
high = IMPRINT_PALETTE[3],
name = "Year",
guide = guide_colorbar(
barwidth = 8,
barheight = 0.5,
title.position = "top",
title.hjust = 0.5
)
) +
scale_x_continuous(
labels = label_number(suffix = "%"),
expand = expansion(mult = c(0.05, 0.08))
) +
scale_y_continuous(
labels = label_number(suffix = "%"),
expand = expansion(mult = c(0.05, 0.15))
) +
labs(
x = "Unemployment Rate",
y = "Inflation Rate",
title = plot_title
) +
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 = INK_MUTED, linewidth = 0.15),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = INK_SOFT, linewidth = 0.4),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = title_size, face = "bold"),
legend.position = "bottom",
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 9),
plot.margin = margin(20, 20, 10, 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/scatter-connected-temporal/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": "scatter-connected-temporal",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/scatter-connected-temporal/r/ggplot2",
"hub": "https://anyplot.ai/scatter-connected-temporal",
"code_json": "https://api.anyplot.ai/specs/scatter-connected-temporal/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/scatter-connected-temporal",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-connected-temporal/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-connected-temporal/r/ggplot2/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Connected Scatter Plot with Temporal Path on anyplot.ai.