The Federalist Papers: corpus metadata, marker-word counts, and text

Three linked datasets built from the 85 essays of The Federalist (1787-1788), published under the pseudonym "Publius" by Alexander Hamilton, James Madison, and John Jay to promote ratification of the U.S. Constitution. Twelve of the essays (49-58, 62, 63) have long-disputed authorship between Hamilton and Madison.

Format

Federalist_corpus: A data frame with 85 observations (one per essay) on the following 10 variables.

essay: integer: essay number, 1-85
title: character: essay heading as printed
venue: character: original publication venue (NA for essay 58)
date: Date: original publication date (NA for 35 essays; see Details)
byline: factor: author as originally printed, with levels Hamilton, Madison, Jay, Hamilton and Madison, Hamilton or Madison
author: factor: traditional/Mosteller & Wallace-consensus attribution, with levels Hamilton, Madison, Jay, Disputed
n_words: integer: essay body word count
word_length: numeric: mean word length in characters
word_length_sd: numeric: standard deviation of word length
n_para: integer: number of paragraphs in the essay body

Federalist_markers: A data frame with 1020 observations (85 essays x 12 words, long format) on the following 3 variables.

essay: integer: essay number, 1-85, joins to Federalist_corpus$essay
word: factor: one of upon, enough, whilst, while, by, on, to, this, there, any, an, also
n: integer: raw count of word in that essay

Federalist_text: A data frame with 1091 observations (one row per essay paragraph, long format) on the following 3 variables.

essay: integer: essay number, 1-85, joins to Federalist_corpus$essay
paragraph: integer: paragraph number within the essay, starting at 1
text: character: cleaned paragraph text

Source

Project Gutenberg eBook #18, https://www.gutenberg.org/ebooks/18.

Details

The papers are historically important in the development of statistical inference in their own right: Mosteller & Wallace's 1963/1964 Bayesian analysis of function-word frequencies, resolving the disputed authorship in Madison's favor, is a landmark early application of applied Bayesian methods to a real inference problem.

Federalist_corpus carries two distinct author variables that are worth keeping separate rather than collapsing into one: byline, the author as originally printed on the page (which is itself sometimes "Hamilton or Madison" or "Hamilton and Madison" – the original ambiguity), and author, the traditional/Mosteller & Wallace-consensus grouping conventionally used for analysis (Hamilton/Madison/Jay/Disputed). These genuinely disagree for essay 58: printed flatly as "Madison" here, but traditionally still grouped with the eleven other "disputed" essays. date is NA for 35 essays – all essays originally published in the Independent Journal, all published directly in McLean's bound edition rather than serialized, and No. 58 – because this transcription simply doesn't carry a dateline for them, not because the dates are unknown to history.

Federalist_markers gives raw counts for twelve function words with documented Hamilton/Madison discriminating power. All twelve are confirmed members of Mosteller & Wallace's own original word sets (see References below). Rates (e.g. per 1,000 words) are not stored here – join on essay against Federalist_corpus$n_words to compute them, rather than storing a derived value that could go stale.

Federalist_text gives cleaned prose – footnote markers and line-wraps removed, original casing and punctuation kept – for general text-mining use (tokenization, n-grams, or discriminator words beyond the twelve curated here).

Essay No. 70 appears twice in the source Gutenberg transcription – two genuine historical textual variants, the first flagged with an editorial note in place of a title. The second (clean) version is used here; the first is dropped.

Prior work

Two existing packages carry Federalist Papers data, both with real gaps this fills:

DiscreteDatasets::federalist (CRAN, active): a 77 x 1984 wide matrix of doc_no, doc_author, and raw counts of the 1,500 most frequent words, built from the syllogi package's Gutenberg text. It excludes Jay's essays and one co-authored essay entirely, and deduplicates No. 70 (as done here too). No title/venue/date metadata, no curated marker-word set, no paragraph-level text.
corpus::federalist: an 85-row raw-text corpus (title, author, text), used e.g. in Evangeline Reynolds' worked example (https://evangelinereynolds.netlify.app/post/federalist-papers/) via quanteda document-feature matrices. The corpus package itself was archived and removed from CRAN on 2023-05-20, so it is no longer install.packages()-able. Essay-level only; no paragraph granularity.

Ruoyu Zhang's clustering-analysis writeup (https://rpubs.com/ruoyuzhang426/federalist_papers) independently reproduces Mosteller (1963)'s 70-word function-word list as per-essay word proportions (rather than raw counts) with an Author variable distinguishing collaborative ("COL") and disputed ("DIS") essays – a third independent confirmation of the same underlying 70-word list used here and in DiscreteDatasets::federalist.

References

Mosteller, F. and Wallace, D. L. (1963). Inference in an Authorship Problem. Journal of the American Statistical Association, 58(302), 275-309.

Mosteller, F. and Wallace, D. L. (1964). Inference and Disputed Authorship: The Federalist. Addison-Wesley.

Adair, D. (1944). The Authorship of the Disputed Federalist Papers. William and Mary Quarterly, 1(2), 97-122; 1(3), 235-264. (Origin of the "while"/"whilst" marker, twenty years before Mosteller & Wallace.)

Jeong, S. W. and Ročková, V. (2025). From Small to Large Language Models: Revisiting the Federalist Papers. arXiv:2503.01869. (Appendix D reproduces Mosteller (1963)'s original function-word tables; independent modern replication via LASSO recovers "whilst" and "upon" as the strongest discriminators.)

Examples


data(Federalist_corpus)
#> Warning: data set 'Federalist_corpus' not found
str(Federalist_corpus)
#> Error: object 'Federalist_corpus' not found
table(Federalist_corpus$author)
#> Error: object 'Federalist_corpus' not found

# byline vs. author disagree only for essay 58
subset(Federalist_corpus, byline != "Hamilton or Madison" &
                           byline != "Hamilton and Madison" &
                           author == "Disputed")
#> Error: object 'Federalist_corpus' not found

data(Federalist_markers)
#> Warning: data set 'Federalist_markers' not found
str(Federalist_markers)
#> Error: object 'Federalist_markers' not found

# reproduce the classic "upon" rate-per-1000-words comparison
upon <- subset(Federalist_markers, word == "upon")
#> Error: object 'Federalist_markers' not found
upon <- merge(upon, Federalist_corpus[c("essay", "author", "n_words")])
#> Error: object 'upon' not found
upon$rate_1000 <- 1000 * upon$n / upon$n_words
#> Error: object 'upon' not found
aggregate(rate_1000 ~ author, data = subset(upon, author != "Jay"), mean)
#> Error in eval(m$data, parent.frame()): object 'upon' not found

data(Federalist_text)
#> Warning: data set 'Federalist_text' not found
str(Federalist_text)
#> Error: object 'Federalist_text' not found
subset(Federalist_text, essay == 1 & paragraph == 1)
#> Error: object 'Federalist_text' not found