Generate Statistical Tables
Create publication-ready statistical tables for reports and manuscripts.
When to Use
- Creating descriptive statistics tables
- Formatting regression or ANOVA output
- Building correlation matrices
- Producing APA-style tables for academic papers
- Generating tables for Quarto/R Markdown documents
Inputs
- Required: Statistical analysis results (model objects, summary data)
- Required: Output format (HTML, PDF, Word)
- Optional: Style guide (APA, journal-specific)
- Optional: Table numbering scheme
Procedure
Step 1: Choose 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 |
Expected: A table package selected based on the output format and use case. The chosen package is installed and loadable.
On failure: If the required package is not installed, run install.packages("gt") (or the appropriate package). For gtsummary, both gt and gtsummary must be 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*")
)
Expected: A gt table object with formatted means, SDs, and counts grouped by category. Column headers use proper statistical notation (italicized M, SD, n).
On failure: If group_by() produces unexpected results, verify the grouping variable exists and has the expected levels. If fmt_number() throws an error, ensure the target columns contain numeric data.
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")
Expected: A gtsummary regression table with bolded p-values, model fit statistics (R-squared, N) in a source note, and a descriptive caption.
On failure: If tbl_regression() fails, verify the input is a model object (e.g., lm, glm). If add_glance_source_note() errors, check that broom can tidy the model: 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")
Expected: A lower-triangle correlation matrix rendered as a gt table with blanked upper triangle, two decimal places, and a clear caption.
On failure: If sub_missing() does not blank the upper triangle, verify that NA values were set correctly with cor_matrix[upper.tri(cor_matrix)] <- NA. If variables are non-numeric, cor() will fail; 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")
Expected: A formatted ANOVA table with Source, df, SS, MS, F, and p columns. Interaction terms are clearly labeled and p-values are formatted to three decimal places.
On failure: If broom::tidy(aov_result) produces unexpected columns, verify the model is an aov object. For Type III sums of squares, use car::Anova(model, type = 3) instead of 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")
Expected: Table saved to the specified file format (HTML, Word, PNG, or PDF). The output file opens correctly in the appropriate application.
On failure: If gtsave() fails for Word format, ensure the webshot2 package is installed. For PDF output via kableExtra, ensure a LaTeX distribution (TinyTeX or MiKTeX) is installed.
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.
Expected: The table renders inline in the Quarto document with a cross-referenceable label (@tbl-*) and a proper caption. The table adapts to the document's output format automatically.
On failure: If the table does not render, verify the chunk label starts with tbl- for Quarto cross-referencing. If formatting is lost in PDF, switch from gt to kableExtra for LaTeX-based output.
Validation
Common Pitfalls
- gt in PDF: gt has limited PDF support. Use kableExtra for LaTeX-heavy documents.
- Rounding inconsistency: Always use
fmt_number() (gt) or format() rather than round() for display
- Missing values display: Configure with
sub_missing() in gt or options(knitr.kable.NA = "")
- Wide tables in PDF: Tables exceeding page width need
landscape() or font size reduction
- APA number formatting: No leading zero for values bounded by 1 (p-values, correlations): ".03" not "0.03"
Related Skills
format-apa-report - tables within APA manuscriptscreate-quarto-report - embedding tables in reportsbuild-parameterized-report - tables that adapt to parameters