Analysis Of Categorical Data With R May 2026

: Use prop.table() on a frequency table to find proportions. Multiplying by 100 provides percentages.

For more advanced categorical analysis, these packages are widely used: Analysis of categorical data with R

: For binary outcomes (e.g., "Success/Failure"), the glm() function with family = binomial is the standard for modeling how predictors influence the probability of an outcome. : Use prop

: Provides advanced tools for visualizing categorical data, including mosaic and association plots. confreq : Designed for Configural Frequency Analysis (CFA). Analysis of categorical data with R

: Provides functions for multivariate categorical data analysis using the Akaike Information Criterion (AIC). Categorical Data Descriptive Statistics

: Standard bar plots can be created with base R's barplot() or the ggplot2 package using geom_bar() .