jjstatsplot Plot Gallery

This gallery provides a quick visual reference for the main plots you can create with the ClinicoPath jamovi module. Each entry includes a brief description of when to use the plot and an example using clinical data.

Histogram

When to use it: To explore the distribution of a single continuous variable (e.g., age, lab value).

data("breast_cancer_data", package = "ClinicoPath")
jjhistostats(
  data = breast_cancer_data,
  dep = "age",
  title = "Distribution of Patient Age",
  xlab = "Age (years)"
)

jamovi Location: JJStatsPlot -> Continuous -> Histogram

Box-Violin Plot (Between Groups)

When to use it: To compare a continuous variable between two or more independent groups.

data("breast_cancer_data", package = "ClinicoPath")
jjbetweenstats(
  data = breast_cancer_data,
  dep = "age",
  group = "cancer_status",
  title = "Age Distribution by Cancer Status",
  xlab = "Cancer Status",
  ylab = "Age (years)"
)

jamovi Location: JJStatsPlot -> Categorical vs Continuous -> Box-Violin Plots (Between Groups)

Bar Chart

When to use it: To show the relationship between two categorical variables.

data("breast_cancer_data", package = "ClinicoPath")
jjbarstats(
  data = breast_cancer_data,
  dep = "mammography",
  group = "cancer_status",
  title = "Mammography Results by Cancer Status",
  xlab = "Cancer Status"
)

jamovi Location: JJStatsPlot -> Categorical vs Categorical -> Bar Charts

Scatter Plot

When to use it: To explore the relationship and correlation between two continuous variables.

data("melanoma", package = "boot")
jjscatterstats(
  data = melanoma,
  x = "age",
  y = "thickness",
  title = "Correlation between Age and Tumor Thickness",
  xlab = "Age (years)",
  ylab = "Tumor Thickness (mm)"
)

jamovi Location: JJStatsPlot -> Continuous vs Continuous -> Scatter Plot

Correlation Matrix

When to use it: To visualize the correlations between multiple continuous variables at once.

data("melanoma", package = "boot")
jjcorrmat(
  data = melanoma,
  dep = c("age", "thickness", "time"),
  title = "Correlation Matrix of Melanoma Variables"
)

jamovi Location: JJStatsPlot -> Continuous vs Continuous -> Correlation Matrix

Within-Subject Plot (Paired Data)

When to use it: To compare a continuous variable in the same subjects at two or more time points (e.g., before and after treatment).

# Create simulated paired data
set.seed(123)
long_data <- data.frame(
  patient_id = rep(1:20, 2),
  timepoint = rep(c("Before", "After"), each = 20),
  biomarker = c(rnorm(20, 100, 15), rnorm(20, 80, 15))
)

jjwithinstats(
  data = long_data,
  x = "timepoint",
  y = "biomarker",
  id = "patient_id",
  paired = TRUE,
  title = "Biomarker Levels Before and After Treatment",
  ylab = "Biomarker Level"
)

jamovi Location: JJStatsPlot -> Continuous -> Within Subject

Raincloud Plot

When to use it: To show the distribution, individual data points, and summary statistics all in one plot. It is an enhanced version of a violin or box plot.

# Load raincloud data
data("advancedraincloud_data", package = "ClinicoPath")

# Ensure the grouping variable is a factor
advancedraincloud_data$group <- factor(advancedraincloud_data$group)

advancedraincloud(
  data = advancedraincloud_data,
  dep = "score",
  group = "group",
  title = "Score Distribution by Group with Raincloud Plot"
)

jamovi Location: JJStatsPlot -> Advanced -> Raincloud Plot