Description
Utilities and Helpers for Single Case Experimental Design (SCED) using 'ggplot2'.
Description
Provides specialized visualization tools for Single-Case Experimental Design (SCED) research using 'ggplot2'. 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 package extends 'ggplot2' to create publication-ready graphics with professional phase change lines, support for multiple baseline designs, and styling functions that follow SCED visualization conventions. Key functions include adding phase change demarcation lines to existing plots and formatting axes with broken axis appearance commonly used in single-case research.
README.md
ggsced
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:
- Professional Phase Change Lines: Clear visual demarcation between experimental phases that meet publication standards
- Multiple Baseline Design Support: Staggered intervention implementation across participants with precise phase change timing
- Complex Data Pattern Visualization: Support for multiple dependent variables plotted simultaneously
- Publication-Ready Graphics: APA and journal-compliant figures with consistent styling
Key Features
Core Visualization Functions
ggsced(): Primary function for adding phase change lines to existing ggplot objectsggsced_style_x()andggsced_style_y(): Styling functions for axes that follow SCED conventions with broken axis appearance
Design Pattern Support
- Multiple Baseline Designs: Staggered intervention implementation across participants
- Alternating Treatment Designs: Rapid alternation between different intervention conditions
- Functional Analysis Designs: Multiple dependent variables with distinct visual markers
- Complex Phase Patterns: Support for multiple intervention phases within studies
Professional Styling
- Consistent Visual Standards: Publication-quality aesthetics with proper fonts and scaling
- Broken Axis Convention: SCED-specific axis styling with disconnected x/y axes
- Flexible Customization: Modular design allows adaptation to diverse research needs
- Multi-panel Support: Seamless integration with
ggplot2faceting for participant comparisons
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)
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)
Other Example Datasets
The package includes several real research datasets for learning and demonstration:
Gilroyetal2015: Multiple baseline design dataGilroyetal2021: Cross-lagged Alternating Treatment Design
Documentation
- Comprehensive vignette: Detailed examples with real research data
- Function documentation: Complete help files for all exported functions
- Demo files: Executable examples in the
demo/directory - Test suite: Extensive testing to ensure reliability
Dependencies
ggplot2: Core plotting functionalitygrid: Low-level graphics operationsgtable: Plot layout managementggh4x: Extended ggplot2 functionality
Author and Contact
Shawn P. Gilroy, Ph.D.
Louisiana State University
📧 [email protected]
🆔 ORCID: 0000-0002-1097-8366
Bug Reports and Feature Requests
If you encounter any issues or have suggestions for improvements, please:
- Check existing issues: Browse the GitHub Issues to see if your issue has already been reported
- Create a new issue: If your issue is new, please open a new issue with:
- A clear, descriptive title
- Detailed description of the problem or feature request
- Minimal reproducible example (if reporting a bug)
- Your session info (
sessionInfo()) - Expected vs. actual behavior
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.