: Standard bar plots can be created with base R's barplot() or the ggplot2 package using geom_bar() .
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 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
: 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. Data Representation and Handling : Provides functions for
: Used for analyzing associations in multi-way contingency tables. Recommended Packages
: Use prop.table() on a frequency table to find proportions. Multiplying by 100 provides percentages.