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)
str(Federalist_corpus)
#> 'data.frame':	85 obs. of  10 variables:
#>  $ essay         : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ title         : chr  "General Introduction" "Concerning Dangers from Foreign Force and Influence" "The Same Subject Continued (Concerning Dangers From Foreign Force and Influence)" "The Same Subject Continued (Concerning Dangers From Foreign Force and Influence)" ...
#>  $ venue         : chr  "Independent Journal" "Independent Journal" "Independent Journal" "Independent Journal" ...
#>  $ date          : Date, format: NA NA ...
#>  $ byline        : Factor w/ 5 levels "Hamilton","Madison",..: 1 3 3 3 3 1 1 1 1 2 ...
#>  $ n_words       : int  1597 1672 1448 1644 1346 2144 2264 1998 1985 3007 ...
#>  $ word_length   : num  4.74 4.9 4.89 4.77 5 ...
#>  $ word_length_sd: num  2.82 2.77 2.75 2.64 2.67 ...
#>  $ n_para        : int  12 15 19 18 13 21 11 14 18 24 ...
#>  $ author        : Factor w/ 4 levels "Hamilton","Madison",..: 1 3 3 3 3 1 1 1 1 2 ...
table(Federalist_corpus$author)
#> 
#> Hamilton  Madison      Jay Disputed 
#>       51       17        5       12 

# byline vs. author disagree only for essay 58
subset(Federalist_corpus, byline != "Hamilton or Madison" &
                           byline != "Hamilton and Madison" &
                           author == "Disputed")
#>    essay
#> 58    58
#>                                                                                                          title
#> 58 Objection That The Number of Members Will Not Be Augmented as the Progress of Population Demands Considered
#>    venue date  byline n_words word_length word_length_sd n_para   author
#> 58  <NA> <NA> Madison    2089       4.938          3.027      3 Disputed

data(Federalist_markers)
str(Federalist_markers)
#> 'data.frame':	1020 obs. of  3 variables:
#>  $ essay: int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ word : Factor w/ 12 levels "upon","enough",..: 1 2 3 4 5 6 7 8 9 10 ...
#>  $ n    : int  6 1 0 0 14 9 71 14 2 6 ...

# reproduce the classic "upon" rate-per-1000-words comparison
upon <- subset(Federalist_markers, word == "upon")
upon <- merge(upon, Federalist_corpus[c("essay", "author", "n_words")])
upon$rate_1000 <- 1000 * upon$n / upon$n_words
aggregate(rate_1000 ~ author, data = subset(upon, author != "Jay"), mean)
#>     author rate_1000
#> 1 Hamilton 3.2783802
#> 2  Madison 0.2012173
#> 3 Disputed 0.1582006

data(Federalist_text)
str(Federalist_text)
#> 'data.frame':	1091 obs. of  3 variables:
#>  $ essay    : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ paragraph: int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ text     : chr  "To the People of the State of New York:" "After an unequivocal experience of the inefficacy of the subsisting federal government, you are called upon to "| __truncated__ "This idea will add the inducements of philanthropy to those of patriotism, to heighten the solicitude which all"| __truncated__ "Among the most formidable of the obstacles which the new Constitution will have to encounter may readily be dis"| __truncated__ ...
subset(Federalist_text, essay == 1 & paragraph == 1)
#>   essay paragraph                                    text
#> 1     1         1 To the People of the State of New York: