Skip to contents

Getting started

aesopR.Rmd

What is aesopR?

aesopR provides a tidy, public-domain corpus of Aesop’s Fables sourced from the Library of Congress.

It is designed for teaching, exploration, and reproducible text analysis workflows—without relying on copyrighted text.

The package ships with:

  • aesops_fables: one row per fable (text + metadata)

  • aesops_tokens: one row per word token (analysis-ready)

  • aesops_bing: tokens joined with Bing sentiment labels

  • aesops_afinn: tokens joined with AFINN sentiment scores

In this vignette, we’ll use one fable as a running example: “The Fox and the Grapes.”

library(aesopR)

The core datasets

aesops_fables

full texts and metadata

aesops_fables contains the fables as complete narratives, along with a moral and a source URL.

aesops_fables

aesops_tokens

tidy word-level data

aesops_tokens is derived from the fable texts and is ready for word frequency, n-grams, and sentiment workflows.

aesops_tokens

Find “The Fox and the Grapes” 

fox_text <- aesops_fables |> filter(fable_id == "005")
fox_text

Token-based exploration

Once you have a fable_id, you can pull its word tokens.

fox_tokens <-  aesops_tokens |> filter(fable_id == "005")
head(fox_tokens)

Most common words in this fable 

fox_tokens |> count(word, sort = TRUE)

This is intentionally simple. In a methods class, this is a great moment to discuss:

  • stop words

  • lemmatization/stemming

  • how preprocessing choices influence results

Sentiment datasets (no external downloads)

To avoid interactive prompts and downloads during checks, aesopR ships with two sentiment-joined datasets:

  • aesops_bing (positive/negative labels)

  • aesops_afinn (numeric sentiment scores)

Bing sentiment: positive vs negative counts 

fox_bing <- aesops_bing |> filter(fable_id == "005")
table(fox_bing$sentiment)

AFINN sentiment: summary of scores 

fox_afinn <- aesops_afinn |> filter(fable_id == "005")
summary(fox_afinn$value)

A nice discussion prompt:

  • Do the sentiment signals match the moral of the story?

  • What words are driving the sentiment?

  • How might results change with a different lexicon?

Where to go next

If you’re teaching or learning research methods, aesopR works well for:

  • operational definitions (what counts as a “theme”?)

  • measurement decisions (lexicon choice, preprocessing choices)

  • replication and transparency (same corpus, different pipelines)

  • quick classroom activities (frequency, sentiment, moral inference)