Analysis Of Categorical Data With R May 2026

For more advanced categorical analysis, these packages are widely used:

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

: Cross-tabulating two or more variables can be done with table() or the crosstab() function from the descr package . Data Visualization For more advanced categorical analysis, these packages are

Visual tools help identify patterns and relationships between categories. Data Visualization Visual tools help identify patterns and

: Functions like factor() or as.factor() convert character vectors into categorical variables.

: By default, R orders levels alphabetically. For ordinal data (e.g., "Low", "Medium", "High"), you can define a specific order using the levels argument in factor() or functions in the forcats package . Descriptive Statistics

: Specialized for working with factors and reordering levels.