Example 1: Base Multiple Baseline Design (Gilroy et al., 2015)
The first demonstration uses data from Gilroy et al. (2015), which represents a classic multiple baseline across participants design. This example showcases how to create publication-quality figures with staggered intervention implementation.
Step 1. Load Data and Prepare Base Plot
For convenience, the ggsced package includes sample
datasets from published SCED research. Here, we load the Gilroy et
al. (2015) dataset and prepare the base plot and show the respective
columns.
library(ggsced)
library(tidyverse)
library(ggh4x)
data_set <- Gilroyetal2015
# Illustrate the structure of the dataset
head(data_set)
#> Participant Session Condition Responding PhaseNum LineOff
#> 1 Andrew 1 Baseline 10.81081 1 0
#> 2 Andrew 2 Baseline 13.51351 1 0
#> 3 Andrew 3 Baseline 10.81081 1 0
#> 4 Andrew 4 Baseline 13.51351 1 0
#> 5 Andrew 5 Treatment 29.72973 2 0
#> 6 Andrew 6 Treatment 45.94595 2 0In this dataset, there are columns for Participant,
Session, Condition, and
Responding. Each of these will be used with
ggplot2 to create the base plot.
Step 2. Create Base Plot with ggplot2
The next step is to create the base plot using ggplot2,
plotting Responding over Session for each
Condition, and faceting by Participant. We use
the ggh4x package for enhanced faceting capabilities, in
particular, for retaining the x-axis on each facet while reserving the
tick labels for the bottom figures alone.
# Create condition labels for the plot
data_labels <- data_set %>%
select(Participant, Condition) %>%
filter(Participant == "Andrew") %>%
unique() %>%
mutate(
x = c(2.5, 9, 18.5, 25.5),
y = 100
)
participant_labels <- data_set %>%
select(Participant) %>%
unique() %>%
mutate(
x = rep(27, 3),
y = 0
)
# Create the base ggplot
p <- ggplot(data_set, aes(Session, Responding, group = Condition)) +
geom_line() +
geom_point(size = 3) +
geom_text(data = data_labels,
mapping = aes(x, y, label = Condition)) +
geom_text(data = participant_labels,
mapping = aes(x, y,
label = Participant
),
inherit.aes = FALSE,
hjust = 1,
vjust = 0
) +
facet_grid2(Participant ~ .,
remove_labels = "x",
axes = "x"
)
p
At this point, the figure essentially shows the base theme and
conventions reflected in ggplot2 and
ggh4x.
Step 3. Define Phase Change Lines
The third step involves defining the phase change lines for the
multiple baseline design. In this case, each participant has staggered
intervention start points. The ggsced package allows us to
specify these phase change lines using simple list structure. Note:
keyed names are not imported, they are just named as such for
readability and rely on order.
The phase change lines are specified in vector format and reflex a
point on the x-axis where a vertical line should be drawn. For each
phase change line cross facets (e.g.,
"1" = c(4.5, 11.5, 18.5),), the order of the vector
corresponds with the order of the facets (i.e., as participants would be
drawn).
# Create condition labels for the plot
data_labels <- data_set %>%
select(Participant, Condition) %>%
filter(Participant == "Andrew") %>%
unique() %>%
mutate(
x = c(2.5, 9, 18.5, 25.5),
y = 100
)
participant_labels <- data_set %>%
select(Participant) %>%
unique() %>%
mutate(
x = rep(27, 3),
y = 0
)
# Create the base ggplot
p <- ggplot(data_set, aes(Session, Responding, group = Condition)) +
geom_line() +
geom_point(size = 3) +
geom_text(
data = data_labels,
mapping = aes(x, y, label = Condition)
) +
geom_text(
data = participant_labels,
mapping = aes(x, y,
label = Participant
),
inherit.aes = FALSE,
hjust = 1,
vjust = 0
) +
facet_grid2(Participant ~ .,
remove_labels = "x",
axes = "x"
)
# Define staggered phase change lines for multiple baseline design
staggered_pls <- list(
"1" = c(4.5, 11.5, 18.5),
"2" = c(13.5, 20.5, 23.5),
"3" = c(23.5, 23.5, 23.5)
)
# Add phase change lines using ggsced
ggsced(p, staggered_pls)
This achieves the long-desired effect of clearly demarcating the different phases of the experimental design across multiple participants. However, it warrants noting that this is just one of several conventions expected in SCED figures and other aesthetic features are also critical for publication.
Step 4. Finalize Plot with Styling and Labels
The final step involves applying ggsced styling
functions to ensure the plot adheres to SCED conventions, such as adding
padding to disconnect the x and y axes, and customizing axis labels and
limits. To assist with adding in this space, two variables are specified
(y_mult and x_mult) that define the amount of
padding to add to each axis. The meaning of the ‘mult’ argument is the
proportion of the total axis length to add as padding.
# Create condition labels for the plot
data_labels <- data_set %>%
select(Participant, Condition) %>%
filter(Participant == "Andrew") %>%
unique() %>%
mutate(
x = c(2.5, 9, 18.5, 25.5),
y = 97.5
)
participant_labels <- data_set %>%
select(Participant) %>%
unique() %>%
mutate(
x = rep(27, 3),
y = 0
)
# Set plot scaling parameters
y_mult <- .05
x_mult <- .02
# Create the base ggplot
p <- ggplot(data_set, aes(Session, Responding, group = Condition)) +
geom_line() +
geom_point(size = 3) +
geom_text(
data = data_labels,
mapping = aes(x, y, label = Condition),
hjust = 0.5,
vjust = 0.0625
) +
geom_text(
data = participant_labels,
mapping = aes(x, y,
label = Participant
),
inherit.aes = FALSE,
hjust = 1,
vjust = 0
) +
scale_y_continuous(
name = "Percentage Accuracy",
limits = c(0, 100),
breaks = (0:4) * 25,
expand = expansion(mult = y_mult)
) +
scale_x_continuous(
breaks = c(1:27),
limits = c(1, 27),
expand = expansion(mult = x_mult)
) +
facet_grid2(Participant ~ .,
remove_labels = "x",
axes = "x"
) +
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 = 1) +
ggsced_style_y(y_mult, lwd = 1)
# Define staggered phase change lines for multiple baseline design
staggered_pls <- list(
"1" = c(4.5, 11.5, 18.5),
"2" = c(13.5, 20.5, 23.5),
"3" = c(23.5, 23.5, 23.5)
)
# Add phase change lines using ggsced
final_plot <- ggsced(p, staggered_pls)
As illustrated here, the final plot now adheres to SCED conventions, with clear phase change lines, appropriate axis scaling, and professional styling suitable for publication.
Key Features Demonstrated:
- Multiple Baseline Design: Staggered intervention implementation across three participants
- Phase Change Lines: Clear visual separation between baseline and intervention phases
- Professional Styling: Publication-ready aesthetics with proper fonts and scaling
- Faceted Layout: Separate panels for each participant while maintaining visual coherence
