The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Introduction

This vignette of package DemographicTable (CRAN, Github) presents an idiot-proof interface to create a summary table of simple statistics, often known as a demographic table.

Package DemographicTable Imports packages

• cli (Csárdi 2025, v3.6.5), for attractive command line interfaces
• 🗝 flextable (Gohel and Skintzos 2025, v0.9.9) and officer (Gohel, Moog, and Heckmann 2025, v0.6.10), key dependencies, for flexible table
• scales (Wickham, Pedersen, and Seidel 2025, v1.4.0), for colour pallete

Prerequisite

Packages DemographicTable requires R version 4.5.0 (released 2025-04-11) or higher (macOS, Windows). An Integrated Development Environment (IDE), e.g., RStudio or Positron, is not required, but highly recommended. This vignette is created under R version 4.5.1 (2025-06-13) using packages knitr (Xie 2025, v1.50), quarto (Allaire and Dervieux 2025, v1.5.0 with Quarto v1.7.33) and rmarkdown (Allaire et al. 2024, v2.29).

Sys.info()[c('sysname', 'release', 'machine')]
#  sysname  release  machine 
# "Darwin" "24.6.0"  "arm64"
R.version
#                _                           
# platform       aarch64-apple-darwin20      
# arch           aarch64                     
# os             darwin20                    
# system         aarch64, darwin20           
# status                                     
# major          4                           
# minor          5.1                         
# year           2025                        
# month          06                          
# day            13                          
# svn rev        88306                       
# language       R                           
# version.string R version 4.5.1 (2025-06-13)
# nickname       Great Square Root

Experimental (and maybe unstable) features are released extremely frequently to Github. Stable releases to CRAN are typically updated every 2 to 3 months.

remotes::install_github('tingtingzhan/DemographicTable')

utils::install.packages('DemographicTable')

Getting Started

Examples in this vignette require that the search path has

library(DemographicTable)
library(flextable)

Users may remove the last pipe |> as_flextable() from all examples. This is required in the vignette to make quarto rendering work.

Demographic Table

Data example penguins from package datasets

Summary of all subjects

datasets::penguins |>
  DemographicTable(include = c('species', 'island', 'bill_len')) |> 
  as_flextable()

Summary by one group

Color of each individual group is determined by scales::pal_hue(), which is the default color pallete used in package ggplot2.

datasets::penguins |>
  DemographicTable(groups = 'sex', include = c('species', 'bill_dep')) |> 
  as_flextable()

User may choose to hide the p-values with option compare = FALSE.

datasets::penguins |>
  DemographicTable(groups = 'sex', include = c('species', 'bill_dep'), compare = FALSE) |>
  as_flextable()

Summary by multiple groups

datasets::penguins |>
  DemographicTable(groups = c('sex', 'island'), include = c('species', 'bill_dep'), compare = FALSE) |> 
  as_flextable()

Combine multiple DemographicTables

tb1 = datasets::penguins |>
  subset(subset = (sex == 'male')) |>
  DemographicTable(groups = 'island', include = c('species', 'bill_dep'), data.name = 'Male Penguins', compare = FALSE)

tb2 = datasets::penguins |>
  subset(subset = (sex == 'female')) |>
  DemographicTable(groups = 'island', include = c('species', 'bill_dep'), data.name = 'Female Penguins', compare = FALSE)

c(tb1, tb2) |> as_flextable()

Exception Handling

Missing value in groups

datasets::penguins |>
  DemographicTable(groups = c('sex'), include = c('body_mass', 'species')) |>
  as_flextable()

Use of logical values

Using logical values is discouraged, as this practice is proved confusing to scientists without a strong data background.

mtc = datasets::mtcars |>
  within.data.frame(expr = {
    vs_straight = as.logical(vs)
    am_manual = as.logical(am)
  })

A warning message will be printed if logical variables are used in groups and/or include.

tryCatch(DemographicTable(mtc, groups = 'am_manual', include = c('drat', 'vs_straight')), warning = identity)
# <simpleWarning in DemographicTable(mtc, groups = "am_manual", include = c("drat",     "vs_straight")): 
# Some scientists do not understand logical value (e.g., arm_intervention being TRUE/FALSE)
# Consider using 2-level factor (e.g., arm being intervention/control)>

Instead of using logical variables

mtc |>
  DemographicTable(groups = 'am_manual', include = c('drat', 'vs_straight')) |>
  as_flextable()

We recommend using 2-level factors.

mtcars |>
  within.data.frame(expr = {
    vs = ifelse(vs, yes = 'Straight', no = 'V-shaped')
    am = ifelse(am, yes = 'manual', no = 'automatic')
  }) |> 
  DemographicTable(groups = 'am', include = c('drat', 'vs'), data.name = 'mtcars') |>
  as_flextable()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.