Skip to contents

This module generates descriptive statistics for continuous variables. It provides both a textual summary and a visually appealing summary table. Optionally, you can enable distribution diagnostics to examine normality, skewness, and kurtosis.

Usage

summarydata(
  data,
  vars,
  date_vars = NULL,
  distr = FALSE,
  summary_format = "standard",
  grvar = NULL,
  pivot_layout = "clinical",
  include_confidence = TRUE,
  advanced_metrics = FALSE,
  pivot_export = FALSE,
  summarytools_graphs = TRUE,
  summarytools_round_digits = 2
)

Arguments

data

The data as a data frame.

vars

a string naming the variables from data that contains the continuous values used for the report

date_vars

Variables containing date/time data to be analyzed with date-specific statistics (similar to sumvar's dist_date function)

distr

If TRUE, additional distribution diagnostics (Shapiro-Wilk test, skewness, and kurtosis) will be computed and explained.

summary_format

Choose the format for summary statistics display. New summarytools options provide publication-ready automated EDA summaries with embedded visualizations.

grvar

Optional grouping variable to stratify the summary statistics by categories.

pivot_layout

Layout style for pivottabler enhanced summaries.

include_confidence

Include confidence intervals in pivot summary tables.

advanced_metrics

Include advanced metrics like IQR, MAD, and robust statistics.

pivot_export

Enable enhanced export capabilities for pivot tables.

summarytools_graphs

Include histograms and bar charts in summarytools dfSummary output.

summarytools_round_digits

Number of decimal places for summarytools output.

Value

A results object containing:

results$todoa html
results$texta html
results$text1a html
results$pivot_summarya html
results$pivot_export_infoa html

Examples

# \donttest{
# Example:
# 1. Load your data frame.
# 2. Select one or more continuous variables.
# 3. (Optional) Enable Distribution Diagnostics to view additional tests.
# 4. Run the summarydata module to see descriptive statistics and distribution characteristics.
# }