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Statistical Visualization Standards

Statistical Visualization Standards

Reference guide for the visualization_agent. Covers APA 7.0 figure guidelines, accessible color palettes, chart type selection, common pitfalls, and code templates.

APA 7.0 Figure Guidelines (Chapter 7 Summary)

General Principles

Every figure must add value — do not visualize data that is better expressed in a sentence or table
Figures are numbered sequentially (Figure 1, Figure 2, ...) in order of first mention
Every figure must be cited in text ("As shown in Figure 1, ...")
Captions appear below the figure (unlike table notes which appear above)
Figures must be interpretable without reading the text — include all necessary context in the caption

Caption Format

Figure [N]                          ← Bold, on its own line
[Descriptive Title]                 ← Italic, sentence case, next line
Note. [Explanation if needed]       ← Plain text, starts with "Note."

Typography Specifications

Element	Size	Style
Figure label ("Figure 1")	10-12 pt	Bold
Figure title	10-12 pt	Italic
Axis labels	8-10 pt	Plain, sentence case
Axis tick labels	8-9 pt	Plain
Legend text	8-9 pt	Plain
Annotations	8 pt	Plain or italic
Note text	8-9 pt	Plain

Required Elements

[ ] Axis labels (both axes, descriptive, with units)
[ ] Tick marks and tick labels
[ ] Legend (for multi-group figures)
[ ] Error bars or confidence intervals (for mean comparisons)
[ ] Scale bars (for images/maps)
[ ] Caption with Figure number + title
[ ] Note (if explanation is needed)

Accessible Color Palettes

Primary: Viridis (Perceptually Uniform)

Best for: continuous/sequential data.

Index	Hex Code	Usage
0	`#440154`	Darkest value
1	`#46327E`
2	`#365C8D`
3	`#277F8E`
4	`#1FA187`
5	`#4AC16D`
6	`#9FDA3A`
7	`#FDE725`	Lightest value

Alternative: Cividis (Deuteranopia/Protanopia Optimized)

Best for: publications where colorblind accessibility is critical.

Index	Hex Code
0	`#00204D`
1	`#00336F`
2	`#39486B`
3	`#5F5D6A`
4	`#7B7463`
5	`#9A8C4F`
6	`#BBA634`
7	`#DEC000`
8	`#FFE945`

Categorical: Tol's Qualitative Palette (Max 8 Categories)

Best for: categorical comparisons, group labels.

Label	Hex Code	Sample Use
Blue	`#0077BB`	Group 1 / Baseline
Cyan	`#33BBEE`	Group 2
Teal	`#009988`	Group 3
Orange	`#EE7733`	Group 4 / Highlight
Red	`#CC3311`	Group 5 / Alert
Magenta	`#EE3377`	Group 6
Grey	`#BBBBBB`	Reference / NA
Black	`#000000`	Outline / Text

Diverging: Blue-Red (For Correlation/Difference Maps)

Negative	Zero	Positive
`#2166AC`	`#F7F7F7`	`#B2182B`
`#4393C3`		`#D6604D`
`#92C5DE`		`#F4A582`

Accessibility Rules

Never rely on color alone — pair with shape, pattern, or label
Minimum contrast ratio: 3:1 (WCAG AA for non-text elements)
Test with a colorblindness simulator before finalizing
When printing in grayscale, patterns or labels must still distinguish groups

Chart Type Decision Tree

Which Chart for Which Data?

Your Data	Your Question	Recommended Chart	Avoid
Categories + values	Compare magnitudes	Bar chart (vertical or horizontal)	Pie chart
Categories + values + groups	Compare across groups	Grouped bar chart or stacked bar	3D bar chart
Continuous variable, 1 group	Show distribution	Histogram + density curve
Continuous variable, 2-5 groups	Compare distributions	Boxplot or violin plot	Bar chart of means only
Two continuous variables	Show relationship	Scatter plot + regression line
Time series (1-5 series)	Show trends	Line chart	Bar chart for time series
Time series (> 5 series)	Show trends	Small multiples (faceted line charts)	Spaghetti plot
Correlation matrix	Show multi-variable relationships	Heatmap	Scatter plot matrix (too dense)
Effect sizes + CIs (meta)	Summarize meta-analysis	Forest plot	Bar chart
Effect sizes + SE (meta)	Check publication bias	Funnel plot
Concepts + relationships	Map theoretical framework	Network graph / concept map
Proportions summing to 100%	Show composition	Stacked bar chart	Pie chart
Geographic data	Show spatial patterns	Choropleth map

When NOT to Visualize

Fewer than 3 data points (use text or a table)
A single percentage or mean (state in text)
Data that requires more than 2 sentences to explain the chart (use a table)
Redundant visualization of data already in a table

Common Pitfalls

Critical Errors (Never Do)

Pitfall	Problem	Fix
Pie charts	Human perception is poor at comparing angles/areas	Use bar chart
3D charts	Distorts values through perspective projection	Use 2D
Dual y-axes	Implies false correlation; scale choice is arbitrary	Two separate panels
Rainbow colormap	Not perceptually uniform; not colorblind-safe	Use viridis/cividis
Truncated y-axis (without marking)	Exaggerates small differences	Start at 0 or mark break clearly
Missing error bars	Hides uncertainty; readers cannot assess significance	Add SE, SD, or 95% CI bars
Chartjunk (decorative elements)	Reduces data-ink ratio; distracts from data	Remove unnecessary elements

Subtle Errors (Easy to Miss)

Pitfall	Problem	Fix
Unequal bin widths in histogram	Distorts frequency perception	Use equal bins
Overlapping labels	Unreadable at print size	Rotate, abbreviate, or reduce categories
Too many colors (> 8)	Indistinguishable at print size	Group categories or use facets
Legend far from data	Reader must scan back and forth	Place legend inside plot area or use direct labels
Aspect ratio distortion	Exaggerates or minimizes trends	Use 4:3 default; 16:9 for time series

Python matplotlib Code Templates

Template 1: Bar Chart

import matplotlib.pyplot as plt
import numpy as np

# APA 7.0 settings
plt.rcParams.update({
    'font.family': 'sans-serif',
    'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
    'font.size': 9, 'axes.titlesize': 11, 'axes.labelsize': 10,
    'xtick.labelsize': 8, 'ytick.labelsize': 8, 'legend.fontsize': 8,
    'figure.dpi': 300, 'savefig.dpi': 300, 'savefig.bbox': 'tight',
    'axes.spines.top': False, 'axes.spines.right': False,
})
CB = ['#0077BB', '#33BBEE', '#009988', '#EE7733', '#CC3311', '#EE3377']

# Data
categories = ['Group A', 'Group B', 'Group C', 'Group D']
values = [4.2, 3.8, 5.1, 4.5]
errors = [0.3, 0.4, 0.2, 0.5]

fig, ax = plt.subplots(figsize=(6.9, 4.5))
bars = ax.bar(categories, values, yerr=errors, capsize=4,
              color=CB[:len(categories)], edgecolor='black', linewidth=0.5)
ax.set_ylabel('Score (points)')
ax.set_xlabel('Group')
ax.set_ylim(0, max(values) * 1.3)

plt.tight_layout()
plt.savefig('figure_01.pdf', format='pdf')
plt.savefig('figure_01.png', format='png')
plt.show()

Template 2: Boxplot

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9, 'axes.labelsize': 10,
    'figure.dpi': 300, 'axes.spines.top': False, 'axes.spines.right': False,
})
CB = ['#0077BB', '#33BBEE', '#009988', '#EE7733']

# Data (replace with actual data)
np.random.seed(42)
data = [np.random.normal(loc, 1, 50) for loc in [3.5, 4.0, 3.8, 4.5]]
labels = ['Public', 'Private', 'Technical', 'National']

fig, ax = plt.subplots(figsize=(6.9, 4.5))
bp = ax.boxplot(data, labels=labels, patch_artist=True, widths=0.6,
                medianprops=dict(color='black', linewidth=1.5))
for patch, color in zip(bp['boxes'], CB):
    patch.set_facecolor(color)
    patch.set_alpha(0.7)

ax.set_ylabel('Satisfaction Score (1-5)')
ax.set_xlabel('Institution Type')
plt.tight_layout()
plt.savefig('figure_02.pdf', format='pdf')
plt.show()

Template 3: Line Chart (Trend)

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9, 'axes.labelsize': 10,
    'figure.dpi': 300, 'axes.spines.top': False, 'axes.spines.right': False,
})
CB = ['#0077BB', '#CC3311', '#009988']

years = [2018, 2019, 2020, 2021, 2022, 2023]
series_a = [72, 75, 68, 71, 78, 82]
series_b = [65, 63, 60, 58, 55, 52]

fig, ax = plt.subplots(figsize=(6.9, 4.5))
ax.plot(years, series_a, 'o-', color=CB[0], label='Public universities', linewidth=1.5, markersize=5)
ax.plot(years, series_b, 's--', color=CB[1], label='Private universities', linewidth=1.5, markersize=5)
ax.set_xlabel('Year')
ax.set_ylabel('Enrollment (thousands)')
ax.legend(frameon=False)
ax.set_xlim(min(years) - 0.5, max(years) + 0.5)

plt.tight_layout()
plt.savefig('figure_03.pdf', format='pdf')
plt.show()

Template 4: Scatter Plot + Regression

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9, 'axes.labelsize': 10,
    'figure.dpi': 300, 'axes.spines.top': False, 'axes.spines.right': False,
})

# Data
np.random.seed(42)
x = np.random.uniform(10, 50, 80)
y = 0.6 * x + np.random.normal(0, 5, 80) + 10

slope, intercept, r, p, se = stats.linregress(x, y)
x_line = np.linspace(min(x), max(x), 100)
y_line = slope * x_line + intercept

fig, ax = plt.subplots(figsize=(6.9, 5.5))
ax.scatter(x, y, color='#0077BB', alpha=0.6, edgecolors='black', linewidth=0.3, s=30)
ax.plot(x_line, y_line, color='#CC3311', linewidth=1.5,
        label=f'y = {slope:.2f}x + {intercept:.2f}, r = {r:.2f}, p < .001')
ax.set_xlabel('Faculty-Student Ratio')
ax.set_ylabel('Student Satisfaction Score')
ax.legend(frameon=False, loc='lower right')

plt.tight_layout()
plt.savefig('figure_04.pdf', format='pdf')
plt.show()

Template 5: Forest Plot (Meta-Analysis)

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9, 'axes.labelsize': 10,
    'figure.dpi': 300,
})

studies = ['Smith (2018)', 'Chen (2019)', 'Johnson (2020)',
           'Lee (2021)', 'Garcia (2022)', 'Overall']
effects = [0.35, 0.42, 0.28, 0.51, 0.38, 0.39]
ci_lower = [0.15, 0.22, 0.08, 0.31, 0.18, 0.27]
ci_upper = [0.55, 0.62, 0.48, 0.71, 0.58, 0.51]
weights = [18, 22, 15, 25, 20, None]

fig, ax = plt.subplots(figsize=(6.9, 4.0))
y_pos = np.arange(len(studies))

for i, study in enumerate(studies):
    color = '#CC3311' if study == 'Overall' else '#0077BB'
    marker = 'D' if study == 'Overall' else 'o'
    size = 8 if study == 'Overall' else 6
    ax.errorbar(effects[i], i, xerr=[[effects[i]-ci_lower[i]], [ci_upper[i]-effects[i]]],
                fmt=marker, color=color, markersize=size, capsize=3, linewidth=1.2)

ax.axvline(x=0, color='grey', linestyle='--', linewidth=0.8)
ax.set_yticks(y_pos)
ax.set_yticklabels(studies)
ax.set_xlabel("Effect Size (Cohen's d)")
ax.invert_yaxis()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('figure_05.pdf', format='pdf')
plt.show()

Template 6: Correlation Heatmap

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9,
    'figure.dpi': 300,
})

# Correlation matrix
labels = ['Teaching', 'Research', 'Service', 'Satisfaction', 'Retention']
corr = np.array([
    [1.00, 0.45, 0.32, 0.67, 0.55],
    [0.45, 1.00, 0.28, 0.38, 0.30],
    [0.32, 0.28, 1.00, 0.41, 0.35],
    [0.67, 0.38, 0.41, 1.00, 0.72],
    [0.55, 0.30, 0.35, 0.72, 1.00],
])

fig, ax = plt.subplots(figsize=(5.5, 5.0))
mask = np.triu(np.ones_like(corr, dtype=bool), k=1)
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f', cmap='RdBu_r',
            center=0, vmin=-1, vmax=1, square=True,
            xticklabels=labels, yticklabels=labels,
            linewidths=0.5, cbar_kws={'shrink': 0.8, 'label': 'r'}, ax=ax)

plt.tight_layout()
plt.savefig('figure_06.pdf', format='pdf')
plt.show()

Template 7: Funnel Plot

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9, 'axes.labelsize': 10,
    'figure.dpi': 300, 'axes.spines.top': False, 'axes.spines.right': False,
})

# Data
np.random.seed(42)
effects = np.random.normal(0.4, 0.15, 20)
se = np.random.uniform(0.05, 0.25, 20)
mean_effect = np.mean(effects)

fig, ax = plt.subplots(figsize=(6.9, 5.0))
ax.scatter(effects, se, color='#0077BB', edgecolors='black', linewidth=0.3, s=40, alpha=0.7)
ax.axvline(x=mean_effect, color='#CC3311', linestyle='--', linewidth=1, label=f'Mean = {mean_effect:.2f}')

# Funnel boundaries (95% CI)
se_range = np.linspace(0.01, max(se) * 1.1, 100)
ax.fill_betweenx(se_range, mean_effect - 1.96 * se_range, mean_effect + 1.96 * se_range,
                 alpha=0.1, color='grey', label='95% CI')

ax.set_xlabel("Effect Size (Cohen's d)")
ax.set_ylabel('Standard Error')
ax.invert_yaxis()
ax.legend(frameon=False)

plt.tight_layout()
plt.savefig('figure_07.pdf', format='pdf')
plt.show()

Template 8: Grouped Bar Chart

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    'font.family': 'sans-serif', 'font.size': 9, 'axes.labelsize': 10,
    'figure.dpi': 300, 'axes.spines.top': False, 'axes.spines.right': False,
})
CB = ['#0077BB', '#EE7733', '#009988']

categories = ['Teaching', 'Research', 'Service', 'Admin']
group_a = [4.1, 3.5, 3.8, 3.2]
group_b = [3.8, 4.2, 3.5, 3.0]
group_c = [4.3, 3.9, 4.0, 3.5]

x = np.arange(len(categories))
width = 0.25

fig, ax = plt.subplots(figsize=(6.9, 4.5))
ax.bar(x - width, group_a, width, label='Public', color=CB[0], edgecolor='black', linewidth=0.5)
ax.bar(x, group_b, width, label='Private', color=CB[1], edgecolor='black', linewidth=0.5)
ax.bar(x + width, group_c, width, label='National', color=CB[2], edgecolor='black', linewidth=0.5)

ax.set_xlabel('Domain')
ax.set_ylabel('Mean Score (1-5)')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.set_ylim(0, 5.5)
ax.legend(frameon=False)

plt.tight_layout()
plt.savefig('figure_08.pdf', format='pdf')
plt.show()

R ggplot2 Code Templates

Template 1: Bar Chart

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    plot.title = element_text(size = 11, face = "bold", hjust = 0),
    axis.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    legend.title = element_text(size = 9),
    legend.text = element_text(size = 8),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    strip.text = element_text(size = 9, face = "bold")
  )
cb_palette <- c("#0077BB", "#33BBEE", "#009988", "#EE7733", "#CC3311", "#EE3377")

df <- data.frame(
  group = c("Group A", "Group B", "Group C", "Group D"),
  value = c(4.2, 3.8, 5.1, 4.5),
  se = c(0.3, 0.4, 0.2, 0.5)
)

ggplot(df, aes(x = group, y = value, fill = group)) +
  geom_col(color = "black", linewidth = 0.3, width = 0.7) +
  geom_errorbar(aes(ymin = value - se, ymax = value + se), width = 0.2) +
  scale_fill_manual(values = cb_palette) +
  labs(x = "Group", y = "Score (points)") +
  theme_apa +
  theme(legend.position = "none") +
  coord_cartesian(ylim = c(0, NA))

ggsave("figure_01.pdf", width = 6.9, height = 4.5, units = "in", dpi = 300)

Template 2: Boxplot

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    axis.title = element_text(size = 10), axis.text = element_text(size = 8),
    panel.grid.minor = element_blank(), panel.grid.major.x = element_blank()
  )
cb_palette <- c("#0077BB", "#33BBEE", "#009988", "#EE7733")

set.seed(42)
df <- data.frame(
  type = rep(c("Public", "Private", "Technical", "National"), each = 50),
  score = c(rnorm(50, 3.5, 1), rnorm(50, 4.0, 1), rnorm(50, 3.8, 1), rnorm(50, 4.5, 1))
)

ggplot(df, aes(x = type, y = score, fill = type)) +
  geom_boxplot(alpha = 0.7, outlier.shape = 21, outlier.size = 1.5) +
  scale_fill_manual(values = cb_palette) +
  labs(x = "Institution Type", y = "Satisfaction Score (1-5)") +
  theme_apa +
  theme(legend.position = "none")

ggsave("figure_02.pdf", width = 6.9, height = 4.5, units = "in", dpi = 300)

Template 3: Line Chart (Trend)

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    axis.title = element_text(size = 10), axis.text = element_text(size = 8),
    legend.text = element_text(size = 8), panel.grid.minor = element_blank()
  )

df <- data.frame(
  year = rep(2018:2023, 2),
  enrollment = c(72, 75, 68, 71, 78, 82, 65, 63, 60, 58, 55, 52),
  type = rep(c("Public", "Private"), each = 6)
)

ggplot(df, aes(x = year, y = enrollment, color = type, shape = type)) +
  geom_line(linewidth = 1) +
  geom_point(size = 2.5) +
  scale_color_manual(values = c("#0077BB", "#CC3311")) +
  labs(x = "Year", y = "Enrollment (thousands)", color = NULL, shape = NULL) +
  theme_apa

ggsave("figure_03.pdf", width = 6.9, height = 4.5, units = "in", dpi = 300)

Template 4: Scatter Plot + Regression

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    axis.title = element_text(size = 10), axis.text = element_text(size = 8),
    panel.grid.minor = element_blank()
  )

set.seed(42)
df <- data.frame(ratio = runif(80, 10, 50))
df$satisfaction <- 0.6 * df$ratio + rnorm(80, 0, 5) + 10

ggplot(df, aes(x = ratio, y = satisfaction)) +
  geom_point(color = "#0077BB", alpha = 0.6, size = 1.5) +
  geom_smooth(method = "lm", color = "#CC3311", se = TRUE, linewidth = 1, fill = "#CC3311", alpha = 0.1) +
  labs(x = "Faculty-Student Ratio", y = "Student Satisfaction Score") +
  theme_apa

ggsave("figure_04.pdf", width = 6.9, height = 5.5, units = "in", dpi = 300)

Template 5: Forest Plot

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    axis.title = element_text(size = 10), axis.text = element_text(size = 8),
    panel.grid.minor = element_blank(), panel.grid.major.y = element_blank()
  )

df <- data.frame(
  study = c("Smith (2018)", "Chen (2019)", "Johnson (2020)", "Lee (2021)", "Garcia (2022)", "Overall"),
  effect = c(0.35, 0.42, 0.28, 0.51, 0.38, 0.39),
  ci_lower = c(0.15, 0.22, 0.08, 0.31, 0.18, 0.27),
  ci_upper = c(0.55, 0.62, 0.48, 0.71, 0.58, 0.51),
  is_overall = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE)
)
df$study <- factor(df$study, levels = rev(df$study))

ggplot(df, aes(x = effect, y = study, xmin = ci_lower, xmax = ci_upper)) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "grey50") +
  geom_errorbarh(height = 0.2, linewidth = 0.8) +
  geom_point(aes(shape = is_overall, color = is_overall), size = 3) +
  scale_color_manual(values = c("FALSE" = "#0077BB", "TRUE" = "#CC3311")) +
  scale_shape_manual(values = c("FALSE" = 16, "TRUE" = 18)) +
  labs(x = "Effect Size (Cohen's d)", y = NULL) +
  theme_apa +
  theme(legend.position = "none")

ggsave("figure_05.pdf", width = 6.9, height = 4.0, units = "in", dpi = 300)

Template 6: Correlation Heatmap

library(ggplot2)
library(reshape2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(axis.title = element_blank(), axis.text = element_text(size = 8))

labels <- c("Teaching", "Research", "Service", "Satisfaction", "Retention")
corr_matrix <- matrix(c(
  1.00, 0.45, 0.32, 0.67, 0.55,
  0.45, 1.00, 0.28, 0.38, 0.30,
  0.32, 0.28, 1.00, 0.41, 0.35,
  0.67, 0.38, 0.41, 1.00, 0.72,
  0.55, 0.30, 0.35, 0.72, 1.00
), nrow = 5, dimnames = list(labels, labels))

# Lower triangle only
corr_matrix[upper.tri(corr_matrix)] <- NA
melted <- melt(corr_matrix, na.rm = TRUE)

ggplot(melted, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white") +
  geom_text(aes(label = sprintf("%.2f", value)), size = 3) +
  scale_fill_gradient2(low = "#2166AC", mid = "#F7F7F7", high = "#B2182B",
                       midpoint = 0, limit = c(-1, 1), name = "r") +
  coord_fixed() +
  theme_apa

ggsave("figure_06.pdf", width = 5.5, height = 5.0, units = "in", dpi = 300)

Template 7: Funnel Plot

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    axis.title = element_text(size = 10), axis.text = element_text(size = 8),
    panel.grid.minor = element_blank()
  )

set.seed(42)
df <- data.frame(
  effect = rnorm(20, 0.4, 0.15),
  se = runif(20, 0.05, 0.25)
)
mean_effect <- mean(df$effect)

ggplot(df, aes(x = effect, y = se)) +
  geom_point(color = "#0077BB", size = 2, alpha = 0.7) +
  geom_vline(xintercept = mean_effect, linetype = "dashed", color = "#CC3311") +
  geom_ribbon(data = data.frame(
    se = seq(0.01, max(df$se) * 1.1, length.out = 100),
    xmin = mean_effect - 1.96 * seq(0.01, max(df$se) * 1.1, length.out = 100),
    xmax = mean_effect + 1.96 * seq(0.01, max(df$se) * 1.1, length.out = 100)
  ), aes(x = NULL, y = se, xmin = xmin, xmax = xmax), alpha = 0.1, fill = "grey") +
  scale_y_reverse() +
  labs(x = "Effect Size (Cohen's d)", y = "Standard Error") +
  theme_apa

ggsave("figure_07.pdf", width = 6.9, height = 5.0, units = "in", dpi = 300)

Template 8: Grouped Bar Chart

library(ggplot2)

theme_apa <- theme_minimal(base_size = 10, base_family = "Arial") +
  theme(
    axis.title = element_text(size = 10), axis.text = element_text(size = 8),
    legend.text = element_text(size = 8), panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()
  )
cb_palette <- c("#0077BB", "#EE7733", "#009988")

df <- data.frame(
  domain = rep(c("Teaching", "Research", "Service", "Admin"), 3),
  type = rep(c("Public", "Private", "National"), each = 4),
  score = c(4.1, 3.5, 3.8, 3.2, 3.8, 4.2, 3.5, 3.0, 4.3, 3.9, 4.0, 3.5)
)

ggplot(df, aes(x = domain, y = score, fill = type)) +
  geom_col(position = position_dodge(0.8), width = 0.7, color = "black", linewidth = 0.3) +
  scale_fill_manual(values = cb_palette) +
  labs(x = "Domain", y = "Mean Score (1-5)", fill = NULL) +
  coord_cartesian(ylim = c(0, 5.5)) +
  theme_apa

ggsave("figure_08.pdf", width = 6.9, height = 4.5, units = "in", dpi = 300)

LaTeX Figure Inclusion Template

Single Figure

\begin{figure}[htbp]
    \centering
    \includegraphics[width=\columnwidth]{figures/figure_01.pdf}
    \caption{\textit{Comparison of Student Satisfaction Scores Across Three Institution Types.}
    Error bars represent 95\% confidence intervals. $N = 1{,}247$.}
    \label{fig:satisfaction}
\end{figure}

Multi-Panel Figure

\usepackage{subcaption}  % Required in preamble

\begin{figure}[htbp]
    \centering
    \begin{subfigure}[b]{0.48\textwidth}
        \centering
        \includegraphics[width=\textwidth]{figures/figure_02a.pdf}
        \caption{Public universities}
        \label{fig:dist-public}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.48\textwidth}
        \centering
        \includegraphics[width=\textwidth]{figures/figure_02b.pdf}
        \caption{Private universities}
        \label{fig:dist-private}
    \end{subfigure}
    \caption{\textit{Distribution of Faculty-Student Ratios by Institution Type.}
    Box plots show median, interquartile range, and outliers (circles beyond whiskers).}
    \label{fig:ratio-distribution}
\end{figure}

Full-Page Landscape Figure

\usepackage{pdflscape}  % Required in preamble

\begin{landscape}
\begin{figure}[htbp]
    \centering
    \includegraphics[width=\linewidth]{figures/figure_wide.pdf}
    \caption{\textit{Comprehensive Correlation Matrix of All Study Variables.}}
    \label{fig:correlation-full}
\end{figure}
\end{landscape}