name: generate-statistical-tables
locale: caveman
source_locale: en
source_commit: 82c77053
fence_basis_commit: 82c77053
translator: "Julius Brussee homage — caveman"
translation_date: "2026-04-24"
description: >
Generate publication-ready statistical tables using gt, kableExtra,
or flextable. Covers descriptive statistics, regression results,
ANOVA tables, correlation matrices, and APA formatting. Use when
creating descriptive statistics tables, formatting regression or
ANOVA output, building correlation matrices, producing APA-style
tables for academic papers, or generating tables for Quarto and
R Markdown documents.
license: MIT
allowed-tools: Read Write Edit Bash Grep Glob
metadata:
author: Philipp Thoss
version: "1.0"
domain: reporting
complexity: intermediate
language: R
tags: r, tables, gt, statistics, publication
Generate Statistical Tables
Make publication-ready stats tables for reports + manuscripts.
When Use
- Make descriptive stats tables
- Format regression or ANOVA output
- Build correlation matrices
- Make APA-style tables for academic papers
- Make tables for Quarto/R Markdown docs
Inputs
- Required: Stats analysis results (model objects, summary data)
- Required: Output format (HTML, PDF, Word)
- Optional: Style guide (APA, journal-specific)
- Optional: Table numbering scheme
Steps
Step 1: Pick Table Package
| Package | Best for | Formats |
|---|
gt | HTML, general-purpose | HTML, PDF, Word |
kableExtra | LaTeX/PDF documents | PDF, HTML |
flextable | Word documents | Word, PDF, HTML |
gtsummary | Clinical/statistical summaries | All via gt/flextable |
Got: Table package picked by output format + use case. Package installed + loadable.
If fail: Package not installed? Run install.packages("gt") (or right one). gtsummary needs both gt + gtsummary installed.
Step 2: Descriptive Statistics Table
library(gt)
descriptives <- data |>
group_by(group) |>
summarise(
n = n(),
M = mean(score, na.rm = TRUE),
SD = sd(score, na.rm = TRUE),
Min = min(score, na.rm = TRUE),
Max = max(score, na.rm = TRUE)
)
gt(descriptives) |>
tab_header(
title = "Table 1",
subtitle = "Descriptive Statistics by Group"
) |>
fmt_number(columns = c(M, SD), decimals = 2) |>
fmt_number(columns = c(Min, Max), decimals = 1) |>
cols_label(
group = "Group",
n = md("*n*"),
M = md("*M*"),
SD = md("*SD*")
)
Got: gt table object with formatted means, SDs, counts by category. Column headers use proper stats notation (italic M, SD, n).
If fail: group_by() unexpected? Verify grouping variable exists + has expected levels. fmt_number() errors? Target columns must be numeric.
Step 3: Regression Results Table
model <- lm(outcome ~ predictor1 + predictor2 + predictor3, data = data)
library(gtsummary)
tbl_regression(model) |>
bold_p() |>
add_glance_source_note(
include = c(r.squared, adj.r.squared, nobs)
) |>
modify_header(label = "**Predictor**") |>
modify_caption("Table 2: Regression Results")
Got: gtsummary regression table with bold p-values, model fit stats (R-squared, N) in source note, descriptive caption.
If fail: tbl_regression() fails? Verify input is model object (lm, glm). add_glance_source_note() errors? Check broom can tidy: broom::glance(model).
Step 4: Correlation Matrix
library(gt)
cor_matrix <- cor(data[, c("var1", "var2", "var3", "var4")],
use = "pairwise.complete.obs")
# Format lower triangle
cor_matrix[upper.tri(cor_matrix)] <- NA
as.data.frame(cor_matrix) |>
tibble::rownames_to_column("Variable") |>
gt() |>
fmt_number(decimals = 2) |>
sub_missing(missing_text = "") |>
tab_header(title = "Table 3", subtitle = "Correlation Matrix")
Got: Lower-triangle correlation matrix as gt table. Upper triangle blank, two decimal places, clear caption.
If fail: sub_missing() won't blank upper triangle? Verify NA set via cor_matrix[upper.tri(cor_matrix)] <- NA. Non-numeric variables → cor() fails; filter to numeric columns first.
Step 5: ANOVA Table
aov_result <- aov(score ~ group * condition, data = data)
library(gtsummary)
tbl_anova <- broom::tidy(aov_result) |>
gt() |>
fmt_number(columns = c(sumsq, meansq, statistic), decimals = 2) |>
fmt_number(columns = p.value, decimals = 3) |>
cols_label(
term = "Source",
df = md("*df*"),
sumsq = md("*SS*"),
meansq = md("*MS*"),
statistic = md("*F*"),
p.value = md("*p*")
) |>
tab_header(title = "Table 4", subtitle = "ANOVA Results")
Got: Formatted ANOVA table with Source, df, SS, MS, F, p columns. Interaction terms labeled, p-values to three decimals.
If fail: broom::tidy(aov_result) unexpected columns? Verify model = aov object. Type III sums of squares → use car::Anova(model, type = 3) not base aov().
Step 6: Save Tables
# Save as HTML
gtsave(my_table, "table1.html")
# Save as Word
gtsave(my_table, "table1.docx")
# Save as PNG image
gtsave(my_table, "table1.png")
# For LaTeX/PDF (kableExtra)
kableExtra::save_kable(kable_table, "table1.pdf")
Got: Table saved to specified format (HTML, Word, PNG, PDF). Output file opens in right application.
If fail: gtsave() fails for Word? webshot2 package needed. PDF output via kableExtra → needs LaTeX distribution (TinyTeX or MiKTeX).
Step 7: Embed in Quarto Document
```{r}
#| label: tbl-descriptives
#| tbl-cap: "Descriptive Statistics by Group"
gt(descriptives) |>
fmt_number(columns = c(M, SD), decimals = 2)
```
See @tbl-descriptives for summary statistics.
Got: Table renders inline in Quarto doc, cross-reference label (@tbl-*), proper caption. Table adapts to document output format automatically.
If fail: Table won't render? Chunk label must start with tbl- for Quarto cross-ref. Formatting lost in PDF → switch from gt to kableExtra for LaTeX output.
Checks
Pitfalls
- gt in PDF: gt has limited PDF support. Use kableExtra for LaTeX-heavy docs.
- Rounding inconsistency: Always use
fmt_number() (gt) or format() not round() for display
- Missing values display: Set with
sub_missing() in gt or options(knitr.kable.NA = "")
- Wide tables in PDF: Tables over page width need
landscape() or smaller font
- APA number formatting: No leading zero for values bounded by 1 (p-values, correlations): ".03" not "0.03"
See Also
format-apa-report - tables in APA manuscripts
create-quarto-report - embed tables in reports
build-parameterized-report - tables that adapt to parameters