Serpentine Plots for Long Timeline, Sequential and Survey 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.