A cycle plot, also known as a seasonal subseries plot (introduced by William Cleveland), separates the seasonal pattern of a time series from the trend within each season. The series is split by seasonal period (e.g. one group per month or weekday); within each group the values are drawn in chronological order as a small line, and a horizontal reference line marks that group's mean. Comparing the mean lines across groups reveals the seasonal effect, while the slope of each subseries reveals the trend within that season — two patterns that are entangled in a standard time series plot.
#' anyplot.ai
#' line-cycle-seasonal: Cycle Plot (Seasonal Subseries)
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 88/100 | Created: 2026-06-15
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"
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"
RULE_COLOR <- if (THEME == "light") "#1A1A1726" else "#F0EFE826"
IMPRINT_PALETTE <- c(
"#009E73", # 1 brand green — first series
"#C475FD", # 2 lavender
"#4467A3", # 3 blue
"#BD8233", # 4 ochre
"#AE3030", # 5 matte red
"#2ABCCD", # 6 cyan
"#954477", # 7 rose
"#99B314" # 8 lime
)
# Data: synthetic monthly average temperature (degC) 2000-2024
# Scenario: temperate Northern Hemisphere city showing annual cycle + warming trend
n_years <- 25
start_year <- 2000
years <- start_year:(start_year + n_years - 1)
month_nums <- 1:12
month_labels <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
# Seasonal baseline (degC per month)
seasonal_base <- c(-1.8, 0.6, 5.4, 11.2, 16.5, 20.8, 23.1, 22.4, 17.3, 10.9, 4.5, -0.5)
# Build observations: expand.grid cycles year fastest within each month
raw_df <- expand.grid(year = years, month = month_nums)
noise <- rnorm(nrow(raw_df), 0, 0.85)
df <- raw_df |>
mutate(
year_idx = year - start_year, # 0 to 24
temp = seasonal_base[month] + year_idx * 0.05 + noise,
# Horizontal position: month integer + year offset in [-0.36, +0.36]
x_pos = month + (year_idx / (n_years - 1) - 0.5) * 0.72
)
# Seasonal group means for horizontal reference bars
group_means <- df |>
group_by(month) |>
summarise(mean_temp = mean(temp), .groups = "drop")
HALF_W <- 0.36 # half-width of each mean segment (matches x_pos spread)
# Scale title font size proportionally when title > 67 chars
plot_title <- paste0(
"Monthly Temperature Trends · line-cycle-seasonal · ",
"r · ggplot2 · anyplot.ai"
)
title_size <- max(8L, round(12L * 67L / nchar(plot_title)))
p <- ggplot() +
# Subtle vertical dividers between month groups
geom_vline(
xintercept = seq(1.5, 11.5, by = 1),
color = INK_MUTED,
linewidth = 0.25,
alpha = 0.45
) +
# Within-month chronological trend lines (Imprint green — first series)
geom_line(
data = df,
aes(x = x_pos, y = temp, group = month, color = "Within-month trend"),
linewidth = 1.05
) +
# Seasonal mean reference segments (Imprint blue — third series)
geom_segment(
data = group_means,
aes(
x = month - HALF_W,
xend = month + HALF_W,
y = mean_temp,
yend = mean_temp,
color = "Seasonal mean"
),
linewidth = 1.6
) +
# Annotate warming trend on July (month 7) — warmest month, most visible slope
annotate(
"text",
x = 7,
y = max(df$temp[df$month == 7]) + 0.9,
label = "+1.25 °C over 25 yrs",
color = INK_SOFT,
size = 2.5,
hjust = 0.5
) +
annotate(
"segment",
x = 6.63,
xend = 7.37,
y = max(df$temp[df$month == 7]) + 0.55,
yend = max(df$temp[df$month == 7]) + 0.55,
color = INK_MUTED,
linewidth = 0.3
) +
scale_x_continuous(
breaks = month_nums,
labels = month_labels,
expand = expansion(add = 0.55)
) +
scale_color_manual(
values = c(
"Within-month trend" = IMPRINT_PALETTE[1], # #009E73 brand green
"Seasonal mean" = IMPRINT_PALETTE[3] # #4467A3 blue
),
name = NULL
) +
labs(
x = NULL,
y = "Avg. temperature (°C)",
title = plot_title,
subtitle = paste0(
"2000–2024 | Each line traces one month’s values across 25 years; ",
"horizontal bar = that month’s mean"
),
caption = "Synthetic data — warming trend ≈0.05 °C / yr"
) +
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.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = RULE_COLOR, linewidth = 0.4),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.35),
axis.ticks = element_blank(),
axis.title.y = element_text(color = INK, size = 10),
axis.text.x = element_text(color = INK_SOFT, size = 9),
axis.text.y = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = title_size, face = "bold"),
plot.subtitle = element_text(color = INK_SOFT, size = 8, margin = margin(b = 6)),
plot.caption = element_text(color = INK_MUTED, size = 7, hjust = 0),
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.position = "top",
legend.justification = "right",
legend.key.width = unit(20, "pt"),
plot.margin = margin(16, 20, 12, 16, "pt")
)
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Part of Cycle Plot (Seasonal Subseries) on anyplot.ai.