Generates a comprehensive summary of categorical variables including frequency counts, percentages, missing value information, and optional visual summaries. Supports multiple output formats and sorting options for enhanced data exploration. Automatically handles edge cases like missing values, single categories, and variable conversion.
Usage
reportcat2(
data,
vars,
sumvar_style = FALSE,
show_proportions = TRUE,
sort_by_frequency = FALSE
)Arguments
- data
The data as a data frame containing the categorical variables to be analyzed. The data frame should have at least one row and the specified variables should exist in the dataset.
- vars
A character vector specifying the variable names to summarize. These variables will be automatically converted to factors if they aren't already. Character, numeric, and logical variables are all acceptable and will be treated as categorical.
- sumvar_style
Logical (default: FALSE). If TRUE, provides comprehensive categorical analysis similar to the sumvar package's tab1() function, including enhanced frequency tables, percentage breakdowns, and cumulative statistics for each category.
- show_proportions
Logical (default: TRUE). If TRUE, displays cumulative counts and percentages alongside individual category statistics when using sumvar_style format. Ignored when sumvar_style is FALSE.
- sort_by_frequency
Logical (default: FALSE). If TRUE, categories within each variable are sorted by frequency (most common first) rather than alphabetically. This applies to both output formats.
Examples
# \donttest{
# Example 1: Basic categorical summary
data <- data.frame(
treatment = factor(c("A", "B", "A", "C", "B", "A")),
grade = factor(c("High", "Low", "Medium", "High", "Low", "Medium"))
)
result <- reportcat(data = data, vars = c("treatment", "grade"))
# Example 2: Enhanced summary with cumulative percentages
result_enhanced <- reportcat(
data = data,
vars = "treatment",
sumvar_style = TRUE,
show_proportions = TRUE
)
# Example 3: Sort categories by frequency
result_sorted <- reportcat(
data = data,
vars = "grade",
sort_by_frequency = TRUE
)
# }