Analysis Of Categorical Data With R May 2026

: Used for analyzing associations in multi-way contingency tables. Recommended Packages

: Cross-tabulating two or more variables can be done with table() or the crosstab() function from the descr package . Data Visualization

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

: Useful for visualizing contingency tables, showing the relative proportion of each combination of categories. : Used for analyzing associations in multi-way contingency

Inferential methods allow researchers to test hypotheses about categorical relationships in a population.

Analysis of categorical data in R involves specialized techniques for variables that represent qualitative characteristics, such as gender, region, or recovery status. Unlike continuous numerical data, categorical data—referred to as in R—is divided into discrete groups or "levels". Data Representation and Handling : For binary outcomes (e

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