Visual tools help identify patterns and relationships between categories.
: The table() function generates counts for each category.
: Use prop.table() on a frequency table to find proportions. Multiplying by 100 provides percentages. Analysis of categorical data with R
: Standard bar plots can be created with base R's barplot() or the ggplot2 package using geom_bar() .
Descriptive analysis focuses on summarizing frequency and distribution. 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. Analysis of categorical data with R
: Use chisq.test() to determine if there is a significant association between two categorical variables.