ggsced

License: MIT R package Codecov test coverage

Utilities and helpers for Single-Case Experimental Design (SCED) using ggplot2

Overview

The ggsced package extends the powerful ggplot2 visualization framework to provide specialized tools for creating high-quality graphics for Single-Case Experimental Design (SCED) research. SCED studies are a crucial methodology in behavioral and educational research, where individual participants serve as their own controls through carefully designed experimental phases. This approaches rests on careful visual inspection of data presented in graphs that clearly delineate phase changes and patterns.

Purpose

Single-case experimental designs require specific visualization conventions that are not easily achieved with standard plotting approaches. The ggsced package bridges the gap between the flexibility of ggplot2 and the specific visualization needs of single-case researchers by providing:

Key Features

Core Visualization Functions

Design Pattern Support

Professional Styling

Installation

# Install from GitHub (development version)
# install.packages("devtools")
devtools::install_github("miyamot0/ggsced")

# Load the package
library(ggsced)

Quick Start

Example Multiple Baseline: Gilroy et al. (2015)

library(ggsced)
library(tidyverse)
library(ggh4x)

data <- Gilroyetal2021

y_mult = .05
x_mult = .02

p = ggplot(data, aes(Session, Responding,
                     group = Condition)) +

  geom_line() +
  geom_point(size = 2.5,
             pch = 21,
             fill = 'black') +

  geom_line(mapping = aes(Session, Reinforcers),
            lty = 2) +
  geom_point(mapping = aes(Session, Reinforcers),
             size = 2.5,
             pch = 24,
             fill = 'white') +
  scale_x_continuous(breaks = c(1:25),
                     limits = c(1, 25),
                     expand = expansion(mult = x_mult)) +
  facet_grid2(Participant ~ .,
              scales = "free_y",
              remove_labels = "x",
              axes = "x")  +
  facetted_pos_scales(
    y = list(
      scale_y_continuous(name = "Frequency",
                         breaks = c(0, 10, 20),
                         limits = c(0, 20),
                         expand = expansion(mult = y_mult)),
      scale_y_continuous(name = "Frequency",
                         breaks = c(0, 5, 10),
                         limits = c(0, 10),
                         expand = expansion(mult = y_mult)),
      scale_y_continuous(name = "Frequency",
                         breaks = c(0, 10, 20),
                         limits = c(0, 20),
                         expand = expansion(mult = y_mult))
    )
  ) +
  theme(
    text = element_text(size = 14,
                        color = 'black'),
    panel.background = element_blank(),
    strip.background = element_blank(),
    strip.text = element_blank()
  ) +
  ggsced_style_x(x_mult, lwd = 2) +
  ggsced_style_y(y_mult, lwd = 2)

simple_facet_labels_df = ggsced_facet_labels(p, y = 20)
simple_facet_labels_df[2, "Responding"] <- 10
simple_facet_labels_df[3, "Responding"] <- 8

p <- p + geom_text(data = simple_facet_labels_df,
                   hjust = 1,
                   vjust = 0.5,
                   mapping = aes(label = label))

simple_condition_labels_df = ggsced_condition_labels(p)
simple_condition_labels_df$label = gsub("2", "", simple_condition_labels_df$label)

p <- p + geom_text(data = simple_condition_labels_df,
                   mapping = aes(label = label),
                   hjust = 0.5,
                   vjust = 0.5)

# Create extra rows for Bx Labels
extra_labels_df <- simple_condition_labels_df[1:2,]
extra_labels_df$Session <- 21.25
extra_labels_df$x0 <- 21
extra_labels_df$x1 <- 19.5
extra_labels_df$y <- 15

extra_labels_df[1, "label"] <- 'Responding'
extra_labels_df[1, "Responding"] <- 15

extra_labels_df[2, "label"] <- 'Reinforcers'
extra_labels_df[2, "Responding"] <- 5
extra_labels_df[2, "y"] <- 5

p <- p + geom_text(data = extra_labels_df,
                   mapping = aes(label = label),
                   hjust = 0,
                   vjust = 0.5)

p <- p + geom_segment(data = extra_labels_df,
                      mapping = aes(x = x0,
                                    y,
                                    xend = x1,
                                    yend = y),
                      arrow = arrow(length = unit(0.25, "cm")))

staggered_pls = list(
  '1' = c(3.5,   3.5,   3.5),
  '2' = c(6.5,   6.5,   8.5),
  '3' = c(9.5,   9.5,  11.5),
  '4' = c(12.5,  16.5,  16.5),
  '5' = c(15.5,  22.5,  19.5)
)

offsets_pls = list(
  '1' = c(F, F, F),
  '2' = c(F, F, F),
  '3' = c(F, F, F),
  '4' = c(F, F, F),
  '5' = c(T, F, F)
)

ggsced(p, legs = staggered_pls, offs = offsets_pls)
Gilroy et al. (2015) Figure

Example Augmented Multiple Baseline: Gilroy et al. (2021)


library(ggsced)
library(tidyverse)
library(ggh4x)

data <- Gilroyetal2021

y_mult = .05
x_mult = .02

p = ggplot(data, aes(Session, Responding,
                     group = Condition)) +

  geom_line() +
  geom_point(size = 2.5,
             pch = 21,
             fill = 'black') +

  geom_line(mapping = aes(Session, Reinforcers),
            lty = 2) +
  geom_point(mapping = aes(Session, Reinforcers),
             size = 2.5,
             pch = 24,
             fill = 'white') +
  scale_x_continuous(breaks = c(1:25),
                     limits = c(1, 25),
                     expand = expansion(mult = x_mult)) +
  facet_grid2(Participant ~ .,
              scales = "free_y",
              remove_labels = "x",
              axes = "x")  +
  facetted_pos_scales(
    y = list(
      scale_y_continuous(name = "Frequency",
                         breaks = c(0, 10, 20),
                         limits = c(0, 20),
                         expand = expansion(mult = y_mult)),
      scale_y_continuous(name = "Frequency",
                         breaks = c(0, 5, 10),
                         limits = c(0, 10),
                         expand = expansion(mult = y_mult)),
      scale_y_continuous(name = "Frequency",
                         breaks = c(0, 10, 20),
                         limits = c(0, 20),
                         expand = expansion(mult = y_mult))
    )
  ) +
  theme(
    text = element_text(size = 14,
                        color = 'black'),
    panel.background = element_blank(),
    strip.background = element_blank(),
    strip.text = element_blank()
  ) +
  ggsced_style_x(x_mult, lwd = 2) +
  ggsced_style_y(y_mult, lwd = 2)

simple_facet_labels_df = ggsced_facet_labels(p, y = 20)
simple_facet_labels_df[2, "Responding"] <- 10
simple_facet_labels_df[3, "Responding"] <- 8

p <- p + geom_text(data = simple_facet_labels_df,
                   hjust = 1,
                   vjust = 0.5,
                   mapping = aes(label = label))

simple_condition_labels_df = ggsced_condition_labels(p)
simple_condition_labels_df$label = gsub("2", "", simple_condition_labels_df$label)

p <- p + geom_text(data = simple_condition_labels_df,
                   mapping = aes(label = label),
                   hjust = 0.5,
                   vjust = 0.5)

# Create extra rows for Bx Labels
extra_labels_df <- simple_condition_labels_df[1:2,]
extra_labels_df$Session <- 21.25
extra_labels_df$x0 <- 21
extra_labels_df$x1 <- 19.5
extra_labels_df$y <- 15

extra_labels_df[1, "label"] <- 'Responding'
extra_labels_df[1, "Responding"] <- 15

extra_labels_df[2, "label"] <- 'Reinforcers'
extra_labels_df[2, "Responding"] <- 5
extra_labels_df[2, "y"] <- 5

p <- p + geom_text(data = extra_labels_df,
                   mapping = aes(label = label),
                   hjust = 0,
                   vjust = 0.5)

p <- p + geom_segment(data = extra_labels_df,
                      mapping = aes(x = x0,
                                    y,
                                    xend = x1,
                                    yend = y),
                      arrow = arrow(length = unit(0.25, "cm")))

staggered_pls = list(
  '1' = c(3.5,   3.5,   3.5),
  '2' = c(6.5,   6.5,   8.5),
  '3' = c(9.5,   9.5,  11.5),
  '4' = c(12.5,  16.5,  16.5),
  '5' = c(15.5,  22.5,  19.5)
)

offsets_pls = list(
  '1' = c(F, F, F),
  '2' = c(F, F, F),
  '3' = c(F, F, F),
  '4' = c(F, F, F),
  '5' = c(T, F, F)
)

ggsced(p, legs = staggered_pls, offs = offsets_pls)
Gilroy et al. (2021) Figure

Other Example Datasets

The package includes several real research datasets for learning and demonstration:

Documentation

Dependencies

Author and Contact

Shawn P. Gilroy, Ph.D.
Louisiana State University
📧 sgilroy1@lsu.edu
🆔 ORCID: 0000-0002-1097-8366

Bug Reports and Feature Requests

If you encounter any issues or have suggestions for improvements, please:

  1. Check existing issues: Browse the GitHub Issues to see if your issue has already been reported
  2. Create a new issue: If your issue is new, please open a new issue with:

License

This package is licensed under the GPL License (V2+).

Citation

If you use ggsced in your research, please cite it appropriately:

Gilroy, S. P. (2026). ggsced: Utilities and helpers for Single-Case
Experimental Design using ggplot2. R package version 0.1.0.
https://github.com/miyamot0/ggsced

Acknowledgments

This package was developed to support the single-case research community with publication-quality visualization tools. Special thanks to the researchers who provided data for demonstration examples and to the broader R community for the foundation provided by ggplot2 and related packages.

Keywords: single-case design, SCED, behavioral research, data visualization, ggplot2, R package