Serpentine Plots for Long Timeline, Sequential and Survey Data

Visualize long timelines, extended sequences and temporally chained survey responses and experience sampling data using intuitive serpentine (snake) plots. Supports distribution bars, tick-mark plots, inter-item correlation arcs, faceted multi-construct panels, and daily time-of-day positioning for ecological momentary assessment data.

snakeplot

Serpentine (snake) plots for survey responses, experience sampling (EMA/ESM) data, and activity timelines using base R graphics. Zero external dependencies.

Installation

# From GitHub.
devtools::install_github("mohsaqr/snakeplot")

# Once on CRAN
install.packages("snakeplot")

Overview

Snake plots arrange data as horizontal bands in a serpentine layout — each row reverses direction and connects to the next through a U-turn arc. The package provides six plotting functions and 10 built-in color palettes:

Bundled datasets

Three datasets from Neubauer & Schmiedek (2024) are included:

Examples

library(snakeplot)

labs7 <- c("1" = "Not at all", "2" = "Slightly", "3" = "Somewhat",
           "4" = "Moderate",   "5" = "Quite",    "6" = "Very",
           "7" = "Extremely")

survey_snake() — daily value distribution ticks

survey_snake(ema_beeps, var = "angry", day = "day",
             colors = snake_palettes$ocean, level_labels = labs7,
             title = "Anger — 14 days, value distribution")

survey_snake() — correlation arcs

survey_snake(ema_emotions, tick_shape = "line",
             arc_fill = "correlation", sort_by = "mean",
             colors = snake_palettes$ocean, level_labels = labs7,
             title = "Emotions — correlations at U-turns")

survey_snake() — dot plot with dark bands

survey_snake(ema_emotions, tick_shape = "dot", sort_by = "mean",
             colors = snake_palettes$ocean, level_labels = labs7,
             band_palette = c("#1a1228", "#1a2a42"),
             title = "Emotions — dots on dark bands")

survey_snake() — faceted multi-construct

survey_snake(student_survey, facet = TRUE, facet_ncol = 2L,
             tick_shape = "bar", sort_by = "mean",
             colors = snake_palettes$ocean, level_labels = labs7)

survey_snake() — daily distribution bars

survey_snake(ema_beeps, var = "happy", day = "day",
             tick_shape = "bar", bar_reverse = TRUE,
             colors = snake_palettes$ocean, level_labels = labs7,
             title = "Happiness — 14 days, distribution bars")

activity_snake() — rug ticks

set.seed(42)
days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
d <- data.frame(
  day      = rep(days, each = 40),
  start    = round(runif(280, 360, 1400)),
  duration = 0
)
activity_snake(d)

activity_snake() — duration blocks

d2 <- data.frame(
  day      = rep(days, each = 8),
  start    = round(runif(56, 360, 1200)),
  duration = round(runif(56, 15, 120))
)
activity_snake(d2, event_color = "#e09480", band_color = "#3d2518")

sequence_snake() — state sequence

set.seed(42)
verbs <- c("Read", "Write", "Discuss", "Listen",
           "Search", "Plan", "Code", "Review")
seq75 <- character(0)
while (length(seq75) < 75) {
  seq75 <- c(seq75, rep(sample(verbs, 1), sample(1:4, 1)))
}
seq75 <- seq75[seq_len(75)]
sequence_snake(seq75, title = "75-step learning sequence")

timeline_snake() — career/event timeline

career <- data.frame(
  role  = c("Intern", "Junior Dev", "Mid Dev",
            "Senior Dev", "Tech Lead", "Architect"),
  start = c("2015-01", "2015-07", "2017-01",
            "2019-07", "2022-07", "2024-01"),
  end   = c("2015-06", "2016-12", "2019-06",
            "2022-06", "2023-12", "2024-12")
)
timeline_snake(career,
               title = "Software Engineer — Career Path (2015-2024)")

survey_sequence() — stacked bars

survey_sequence(ema_emotions, colors = snake_palettes$ocean)

line_snake() — continuous intensity

set.seed(42)
hours <- seq(0, 1440, by = 10)
d_line <- data.frame(
  day   = rep(c("Mon", "Tue", "Wed", "Thu", "Fri"), each = length(hours)),
  time  = rep(hours, 5),
  value = sin(rep(hours, 5) / 1440 * 4 * pi) * 50 + 50 +
          rnorm(5 * length(hours), 0, 8)
)
line_snake(d_line, fill_color = "#e74c3c")

Built-in palettes

10 palettes ship with the package — 5 diverging, 5 sequential:

names(snake_palettes)
#> "classic" "earth" "ocean" "sunset" "berry" "blues" "greens" "grays" "warm" "viridis"

# Use directly
survey_snake(ema_emotions, colors = snake_palettes$earth, tick_shape = "bar")

# Interpolate to any length
snake_palette("sunset", n = 5)

Key parameters for survey_snake()

Data source

The bundled datasets are derived from:

Neubauer, A. B., & Schmiedek, F. (2024). Approaching academic adjustment on multiple time scales. Zeitschrift fuer Erziehungswissenschaft, 27(1), 147-168. https://doi.org/10.1007/s11618-023-01182-8

• Original data
• Codebook
• Code
• License: CC-BY 4.0

License

MIT