Generate a "Table 1" style summary for academic papers (Base R only).

This function creates a comprehensive summary table, often referred to as "Table 1" in scientific publications, providing descriptive statistics (mean and SD for numeric variables, counts and percentages for categorical variables) broken down by a faceting variable. It also includes an overall "Total" column. This version is implemented using only base R functions and the stats package.

table_1(
  df,
  vars,
  facet_var,
  col_names = NULL,
  total_col_name = "Total",
  include_missing = FALSE,
  digits_numeric = 2,
  digits_percent = 2,
  indent_factor_levels = "   "
)

Arguments

df

Data frame containing the data.

vars

Vector of variable names (strings) to include in the summary.

facet_var

Name of the facet variable (string) to group the data by.

col_names

Vector of custom column names for the facet groups (optional).

total_col_name

String for the name of the total column (default: "Total").

include_missing

Logical. If TRUE, a row for 'Missing' counts will be added for each variable (default: FALSE).

digits_numeric

Integer. Number of decimal places for numeric summaries (default: 2).

digits_percent

Integer. Number of decimal places for percentages (default: 2).

indent_factor_levels

String to use for indenting factor levels (default: " ").

Value

A data frame summarizing the data.

Examples

if (FALSE) { # \dontrun{
# Create dummy data
set.seed(123)
df_example <- data.frame(
  group = sample(c("Control", "Treatment A", "Treatment B"), 100, replace = TRUE,
                 prob = c(0.4, 0.3, 0.3)),
  age = rnorm(100, mean = 50, sd = 10),
  sex = sample(c("Male", "Female"), 100, replace = TRUE, prob = c(0.55, 0.45)),
  bmi = rnorm(100, mean = 25, sd = 3),
  disease_status = sample(c("Healthy", "Mild", "Severe", NA), 100, replace = TRUE,
                          prob = c(0.4, 0.3, 0.2, 0.1)),
  smoker = sample(c("Yes", "No"), 100, replace = TRUE, prob = c(0.2, 0.8)),
  stringsAsFactors = FALSE
)
# Convert to factors
df_example$group <- as.factor(df_example$group)
df_example$sex <- as.factor(df_example$sex)
df_example$disease_status <- as.factor(df_example$disease_status)
df_example$smoker <- as.factor(df_example$smoker)

# Basic Table 1
table_1(df_example,
        vars = c("age", "sex", "bmi", "disease_status", "smoker"),
        facet_var = "group")

# Table 1 with custom column names and missing counts
table_1(df_example,
        vars = c("age", "sex", "bmi", "disease_status", "smoker"),
        facet_var = "group",
        col_names = c("Grp A", "Grp B", "Grp C"), # Order must match natural factor levels/alphabetic
        include_missing = TRUE)

# Table 1 with a subset of variables
table_1(df_example,
        vars = c("age", "sex"),
        facet_var = "group")
} # }