GC3 Roundtable 2: Data Visualization for Group Comparison

Author

Jimmy Zhang

Published

October 23, 2025

Setup

This document is a guided workflow for visualizing group comparisons in psychological research. It’s organized as a ladder of evidence:

  • Start with a familiar baseline (bar charts) to establish shared ground.

  • Progressively reveal information that bars hide (distribution, raw data, uncertainty).

  • Match visualization to design (independent vs. paired/within-person; simple vs. factorial).

  • End with model-based estimates when inference requires covariate adjustment or mixed models.

The main goals of this roundtable:

  • Choose a plot that matches your design and research question.

  • Prioritize transparency: show distributions, raw points, and uncertainty.

  • Communicate effect sizes (not just p-values).

# Reproducible seed and core packages
set.seed(42)

pkgs <- c(
  "tidyverse",   # ggplot2, dplyr, etc.
  "gghalves",    # for half-violin rainclouds
  "dabestr",     # estimation plots
  "lme4",        # mixed models
  "emmeans",     # marginal means + CIs
  "ggeffects"    # quick model-based effects plots
)
for (p in pkgs) {
  if (!requireNamespace(p, quietly = TRUE)) {
    install.packages(p, dependencies = TRUE)
  }
}
invisible(lapply(pkgs, library, character.only = TRUE))
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ ggplot2   4.0.0     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Attaching package: 'dabestr'


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Loading required package: Matrix


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Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'
theme_set(theme_classic(base_size = 12))

Data for the examples

We simulate two common scenarios:

  • Independent groups: e.g., Control vs Treatment.

  • Paired/within-subject: e.g., Pre vs Post per participant.

# Independent groups (n ≈ 120)

n_per_group <- 60
df_groups <- tibble(
group = rep(c("Control","Treatment"), each = n_per_group),
score = c(rnorm(n_per_group, 0.00, 1.0),
rnorm(n_per_group, 0.55, 1.0))
)

# Paired data (n subjects, 2 time points)

n_subj <- 60
df_paired <- tibble(
id   = rep(paste0("S", sprintf("%02d", 1:n_subj)), each = 2),
time = rep(c("Pre","Post"), times = n_subj)
) |>
mutate(
subj_eff = rnorm(n_subj, 0, 0.5)[as.integer(as.factor(id))],
score    = 0 + if_else(time == "Post", 0.6, 0) + subj_eff + rnorm(n(), 0, 0.7)
)

Level 1 — Bars with mean ± SE (baseline only)

When to use: very quick preview.

Avoid when: The figure will be used for interpretation or publication, since the usually bars hide the distribution and sample size.

Shows: Group mean; SE.

Common mistakes: Using bars for continuous outcomes as the final figure; ambiguous error bars (SE vs SD vs CI).

ggplot(df_groups, aes(group, score, fill = group)) +
stat_summary(fun = mean, geom = "bar", width = 0.6, alpha = 0.7, show.legend = FALSE) +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.15, linewidth = 0.7) +
labs(x = NULL, y = "Outcome") +
guides(fill = "none")

Figure 1: Bars with mean ± SE. Baseline only.

Level 2 — Boxplots / Violin plots (show the distribution)

When to use: Independent groups; you need medians/quantiles or shape.

Avoid when: Extremely small n (then show points directly). Sometimes also be cautious to use the boxplot for categorical data.

Shows: Median, IQR, outliers (box); shape/density (violin).

Common mistakes: Truncated violins; overplotting points without transparency.

ggplot(df_groups, aes(group, score, fill = group)) +
geom_violin(trim = FALSE, alpha = 0.15, width = 0.9, show.legend = FALSE) +
geom_boxplot(width = 0.15, outlier.shape = NA, fill = "white", show.legend = FALSE) +
labs(x = NULL, y = "Outcome")

Figure 2: Violin + compact boxplot overlay shows spread and shape.

Level 3 — Raw data (dot/strip/beeswarm)

When to use: Small–moderate n; you want to emphasize sample size & variability.

Avoid when: Very large n; then summarize with distributions or hex-binning.

Shows: Every observation.

Common mistakes: No jitter/transparency; points obscuring each other.

ggplot(df_groups, aes(group, score, color = group)) +
geom_jitter(width = 0.15, alpha = 0.6, size = 1.8, show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "black") +
labs(x = NULL, y = "Outcome")

Figure 3: Jittered raw data emphasizes sample size and variability.

Level 4 — Rainclouds (distribution + raw + summary)

When to use: Great default for continuous independent-group outcomes.

Avoid when: Discrete outcomes; tiny n (then dot + intervals may suffice).

Shows: Density, raw points, mean.

Common mistakes: Overcrowded aesthetics; inconsistent scales.

ggplot(df_groups, aes(x = group, y = score, fill = group)) +
  # left half violin for density
  gghalves::geom_half_violin(
    side = "l", alpha = 0.2, width = 0.9, show.legend = FALSE
  ) +
  # right side jittered raw points
  geom_jitter(
    aes(color = group),
    width = 0.1, alpha = 0.6, size = 1.6, show.legend = FALSE
  ) +
  # mean points
  stat_summary(fun = mean, geom = "point", size = 3, color = "black") +
  labs(x = NULL, y = "Outcome") +
  theme_classic(base_size = 12)

Figure 4: Raincloud plot via gghalves: density + raw + summary.

Level 5 — Model-based estimates (EMMs with 95% CI)

When to use: You adjusted for covariates or fit a mixed model; you want adjusted group means and interaction summaries.

Avoid when: You’re only describing raw data and no model is fit.

Shows: Estimated marginal means with CIs; simple effects/contrasts.

Common mistakes: Plotting raw means after fitting an adjusted model; unlabeled CIs.

# Synthetic group factor layered onto paired data

set.seed(7)
df_paired2 <- df_paired |>
mutate(group = rep(rep(c("Control","Treatment"), each = n_subj/2), each = 2),
score = score + if_else(group=="Treatment" & time=="Post", 0.4, 0))

m2 <- lme4::lmer(score ~ group * time + (1|id), data = df_paired2)
emm_gt <- emmeans::emmeans(m2, ~ group * time)
emm_df <- as.data.frame(emm_gt)

ggplot(emm_df, aes(x = time, y = emmean, group = group, shape = group, linetype = group)) +
geom_line() +
geom_point(size = 2.5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), width = 0.07) +
labs(x = NULL, y = "Estimated mean (±95% CI)", shape = "Group", linetype = "Group")

A more advanced example of violin plot for 3-group comparison

# ---- Packages ----
# install.packages(c("tidyverse","ggdist","cowplot","emmeans","effectsize","ggpubr","ggtext"))
library(tidyverse)
library(ggdist)      # density slabs (for violin-like "slab" with shaded quantiles)
library(cowplot)     # clean theme

Attaching package: 'cowplot'
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library(emmeans)     # EMMs and pairwise contrasts
library(effectsize)  # effect sizes (eta2, Cohen's d)

Attaching package: 'effectsize'
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library(ggpubr)      # brackets + labels

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library(ggtext)      # markdown in subtitles

set.seed(101)

# ==========================================================
# 1) SIMULATE DATA (3 "groups", imbalanced n to feel realistic)
# ==========================================================
n <- c(70, 55, 90)                         # group sizes
grp <- rep(c("Group A","Group B","Group C"), times = n)
# true means and sd
mu  <- c(0.0, 0.45, 1.00)
sdv <- c(1.0,  1.0,  1.0)

y <- c(rnorm(n[1], mu[1], sdv[1]),
       rnorm(n[2], mu[2], sdv[2]),
       rnorm(n[3], mu[3], sdv[3]))

df <- tibble(group = factor(grp, levels = c("Group A","Group B","Group C")),
             score = y)

# optional second factor for later extensions:
# df$cond <- sample(c("Pre","Post"), nrow(df), replace = TRUE)

# ==========================================================
# 2) SUMMARY TABLE (mean, SE, CI, quartiles, etc.)
# ==========================================================
summary_df <- df %>%
  group_by(group) %>%
  summarise(
    mean   = mean(score),
    sd     = sd(score),
    se     = sd/sqrt(n()),
    n      = dplyr::n(),
    q25    = quantile(score, .25),
    q50    = median(score),
    q75    = quantile(score, .75),
    loci   = mean - 1.96*se,
    upci   = mean + 1.96*se,
    .groups = "drop"
  )

# ==========================================================
# 3) ONE-WAY ANOVA + EFFECT SIZE (eta^2) + EMMs
# ==========================================================
fit <- aov(score ~ group, data = df)
anova_tbl <- broom::tidy(fit)

# partial eta^2 (for one-way, equals eta^2)
eta <- effectsize::eta_squared(fit, partial = FALSE, ci = 0.95)
# extract eta for text
eta_txt <- sprintf("&#951;<sup>2</sup> = %.2f, 95%% CI [%.2f, %.2f]",
                   eta$Eta2[1], eta$CI_low[1], eta$CI_high[1])

# EMMs and pairwise contrasts
emm    <- emmeans::emmeans(fit, ~ group)
pairs  <- pairs(emm, adjust = "tukey")              # or "none"/"bonferroni" as you like
pairs_df <- as.data.frame(pairs)

# Cohen's d for each pair (pooled SD; uses model sigma by default)
d_pairs <- as.data.frame(emmeans::eff_size(emm, sigma = sigma(fit), edf = df.residual(fit),
                                           method = "pairwise"))
# merge p values & d
pairs_df <- pairs_df %>%
  left_join(
    d_pairs %>%
      select(contrast, effect.size, lower.CL, upper.CL, SE = any_of("SE.d")),
    by = "contrast"
  ) %>%
  rename(
    d    = effect.size,
    d_lo = lower.CL,
    d_hi = upper.CL
  )

# quick helper to format p nicely
fmt_p <- function(p) ifelse(p < .001, "< .001",
                     ifelse(p < .01, paste0("= ", sprintf("%.3f", p)),
                                        paste0("= ", sprintf("%.2f", p))))

# pick two brackets to annotate (edit as you wish)
# contrasts come out in alphabetical order; check with `pairs_df$contrast`
pairs_df$contrast
[1] "Group A - Group B" "Group A - Group C" "Group B - Group C"
# e.g., "Group B - Group A", "Group C - Group A", "Group C - Group B"
lab_BvsA <- paste0("d = ", sprintf("%.2f", pairs_df$d[pairs_df$contrast=="Group B - Group A"]),
                   ", *p* ", fmt_p(pairs_df$p.value[pairs_df$contrast=="Group B - Group A"]))
lab_CvsB <- paste0("d = ", sprintf("%.2f", pairs_df$d[pairs_df$contrast=="Group C - Group B"]),
                   ", *p* ", fmt_p(pairs_df$p.value[pairs_df$contrast=="Group C - Group B"]))

# ==========================================================
# 4) ADVANCED VIOLIN (DENSITY SLAB) + EMM SUMMARY + BRACKETS
# ==========================================================
# Palette
pal <- c("#78AAA9", "#FFDB6E", "#E09CB4")

p <- ggplot(df, aes(x = group, y = score)) +
  cowplot::theme_half_open() +
  ggdist::stat_slab(
    side = "both",
    aes(fill_ramp = after_stat(level)),  # updated syntax
    fill = pal[1],
    .width = c(.50, 1),
    scale = .4
  ) +
  geom_pointrange(
    data = summary_df,
    aes(x = group, y = mean, ymin = loci, ymax = upci),
    inherit.aes = FALSE
  ) +
  geom_text(
    data = summary_df,
    aes(x = group, y = mean, label = sprintf("%.2f", mean)),
    vjust = 2.8, size = 3, color = "black"
  ) +
  geom_text(
    data = summary_df,
    aes(
      label = paste0("n = ", n),
      y = min(mean) - 3*mean(sd)
    ),
    size = 2.7, color = "grey40"
  ) +
  guides(fill_ramp = "none") +
  labs(
    x = "Group",
    y = "Outcome (simulated units)",
    subtitle = glue::glue("{eta_txt}  &nbsp;&nbsp; <span style='color:#666;'></span>")
  ) +
  theme(
    plot.subtitle = ggtext::element_markdown(),
    axis.title = element_text(face = "bold")
  ) +
  # significance/effect-size brackets (you can adjust y.position to fit your scale)
  ggpubr::geom_bracket(xmin = 1, xmax = 2, y.position = max(df$score) + 0.6,
                       label = lab_BvsA, label.size = 3, tip.length = 0.02, vjust = 0) +
  ggpubr::geom_bracket(xmin = 2, xmax = 3, y.position = max(df$score) + 1.2,
                       label = lab_CvsB, label.size = 3, tip.length = 0.02, vjust = 0)

p

  • Distributional patterns:

    • All three groups show roughly normal-like spreads, but Group C’s distribution is slightly shifted upward, consistent with a higher mean.

    • Group B is intermediate between A and C.

    • Group A’s mean is near zero or slightly below.

  • Mean differences:

    • The numeric means are approximately:

      • Group A ≈ −0.13

      • Group B ≈ 0.52

      • Group C ≈ 0.99

    • These suggest a steady upward trend across the three groups.

  • Effect size (η² = 0.20):

    • This is a medium-sized omnibus effect — about 20% of variance in the simulated outcome is accounted for by group membership.

    • The 95% CI [0.12, 1.00] is wide, meaning uncertainty about the exact magnitude, but it confirms a nontrivial difference.

  • Pairwise contrasts:

    • The brackets show which pairs were compared (A vs B, B vs C).

    • The “p” placeholders indicate both comparisons reached statistical significance.

    • The positive d values mean the higher-numbered group (B or C) scored higher than the comparison group.

    • So, both Group B > A and Group C > B.

An example of multiple group comparison (many groups)

library(tidyverse)
library(scales)

Attaching package: 'scales'
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set.seed(2025)

# --- simulate (same idea, simpler) ---
topics <- c("Health","Education","Economy","Elections","Foreign affairs","Energy",
            "Environment","Crime","Transportation","Housing","Labor","Culture",
            "Technology","Sports","Tourism","Religion","Regional conflict","Misc")
countries <- c("USA","UK","China")

topic_center <- runif(length(topics), 0.08, 0.30)

df <- expand.grid(topic = topics, country = countries) |>
  as_tibble() |>
  mutate(
    weight = sample(c(0.2, 0.4, 0.6), n(), replace = TRUE),
    base   = topic_center[match(topic, topics)],
    prop   = pmin(pmax(rnorm(n(), mean = base, sd = 0.045), 0.01), 0.45)
  ) |>
  select(-base)

# order topics by overall mean proportion (desc)
topic_levels <- df |>
  group_by(topic) |>
  summarise(mu = mean(prop), .groups = "drop") |>
  arrange(desc(mu)) |>
  pull(topic)

df <- df |>
  mutate(topic = factor(topic, levels = topic_levels))

# --- plot: crisper styling, borders, smaller bubbles, cleaner x axis ---
pos <- position_dodge(width = 0.6)  # stronger separation within topic
# if still too tight, try: pos <- position_dodge(width = 0.7)

ggplot(df, aes(x = prop, y = topic)) +
  # bubble outlines via shape 21 (fill = country, color = border)
  geom_point(aes(fill = country, size = weight),
             shape = 21, color = "grey25", stroke = 0.5,
             alpha = 0.9, position = pos) +
  scale_size_area(max_size = 3, breaks = c(0.2, 0.4, 0.6), name = "Weight") +
  scale_x_continuous(
    labels = percent_format(accuracy = 1),
    breaks = c(0, 0.20, 0.40),
    limits = c(0, 0.45)
  ) +
  labs(x = NULL, y = NULL, fill = NULL) +
  theme_classic(base_size = 12) +
  theme(
    panel.grid.major.x = element_line(color = "grey88"),
    panel.grid.major.y = element_line(color = "grey93"),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(color = "grey60", fill = NA, linewidth = 0.6),
    axis.text.y = element_text(margin = margin(r = 6))
  )

  • Compare horizontally within each topic:

    • The farther a bubble is to the right, the higher that country’s relative proportion for that topic.

    • Example: if China’s blue bubble is farthest right for Economy, it emphasizes that topic more than the USA or UK.

  • Compare vertically across topics:

    • Topics near the top (like Sports or Crime) tend to have lower overall proportions (< 10–15%), whereas those near the bottom (like Culture or Health) show higher proportions (~30–40%).

  • Interpret bubble size:

    • A large bubble means that estimate carries more weight—perhaps a more confident or frequent observation.

    • A smaller bubble means the underlying estimate is less certain or based on fewer observations.