zipf_plot: Create a Zipf's Law Plot

In AnimalSequences: Analyse Animal Sequential Behaviour and Communication

Create a Zipf's Law Plot

Description

This function creates a log-log plot to visualize Zipf's law, which states that the frequency of a word is inversely proportional to its rank in the frequency table. The plot compares the observed frequency distribution of elements with the expected distribution if Zipf's law were true.

Usage

zipf_plot(sequences_long)

Arguments

sequences_long

A data frame containing at least one column named ‘element' which represents the elements of sequences. Each element’s frequency is used to create the plot.

Details

- **Observed Frequencies**: Calculated from the provided 'sequences_long' data frame. - **Expected Frequencies**: Calculated using Zipf's law formula, where the frequency of the element is inversely proportional to its rank. - **Plotting**: Both observed and expected frequencies are plotted on a log-log scale to compare against Zipf's law.

Value

A ‘ggplot' object that visualizes the observed and expected frequencies of elements according to Zipf’s law. The plot includes:

Rank	The rank of each element based on its frequency, plotted on a log scale.
Count	The observed frequency of each element, plotted on a log scale.
Expected	The expected frequency of each element if Zipf's law were true, shown as a grey dashed line.

Examples

# Example data frame
sequences_long <- data.frame(element = c('a', 'b', 'a', 'c', 'b', 'a', 'd', 'c', 'b', 'a'))

# Generate the Zipf's law plot
zipf_plot(sequences_long)

AnimalSequences documentation built on Sept. 30, 2024, 9:18 a.m.