# gutenbergr: Search and download public domain texts from Project Gutenberg

#### David Robinson, Myfanwy Johnston

The gutenbergr package helps you download and process public domain works from the Project Gutenberg collection. This includes both tools for downloading books (and stripping header/footer information), and a complete dataset of Project Gutenberg metadata that can be used to find words of interest. Includes:

- A function `gutenberg_download()` that downloads one or more works from Project Gutenberg by ID: e.g., `gutenberg_download(84)` downloads the text of Frankenstein.
- Metadata for all Project Gutenberg works as R datasets, so that they can be searched and filtered:
  - `gutenberg_metadata` contains information about each work, pairing Gutenberg ID with title, author, language, etc
  - `gutenberg_authors` contains information about each author, such as aliases and birth/death year
  - `gutenberg_subjects` contains pairings of works with Library of Congress subjects and topics

### Project Gutenberg Metadata

This package contains metadata for all Project Gutenberg works as R datasets, so that you can search and filter for particular works before downloading.

The dataset `gutenberg_metadata` contains information about each work, pairing Gutenberg ID with title, author, language, etc:

    library(gutenbergr)
    library(dplyr)
    gutenberg_metadata
    #> # A tibble: 72,569 × 8
    #>    gutenberg_id title    author gutenberg_author_id language gutenberg_bookshelf
    #>           <int> <chr>    <chr>                <int> <chr>    <chr>
    #>  1            1 "The De… Jeffe…                1638 en       "Politics/American…
    #>  2            2 "The Un… Unite…                   1 en       "Politics/American…
    #>  3            3 "John F… Kenne…                1666 en       ""
    #>  4            4 "Lincol… Linco…                   3 en       "US Civil War"
    #>  5            5 "The Un… Unite…                   1 en       "United States/Pol…
    #>  6            6 "Give M… Henry…                   4 en       "American Revoluti…
    #>  7            7 "The Ma… <NA>                    NA en       ""
    #>  8            8 "Abraha… Linco…                   3 en       "US Civil War"
    #>  9            9 "Abraha… Linco…                   3 en       "US Civil War"
    #> 10           10 "The Ki… <NA>                    NA en       "Banned Books List…
    #> # ℹ 72,559 more rows
    #> # ℹ 2 more variables: rights <chr>, has_text <lgl>

For example, you could find the Gutenberg ID(s) of Jane Austen’s Persuasion by doing:

    gutenberg_metadata %>%
      filter(title == "Persuasion")
    #> # A tibble: 3 × 8
    #>   gutenberg_id title     author gutenberg_author_id language gutenberg_bookshelf
    #>          <int> <chr>     <chr>                <int> <chr>    <chr>
    #> 1          105 Persuasi… Auste…                  68 en       ""
    #> 2        22963 Persuasi… Auste…                  68 en       ""
    #> 3        36777 Persuasi… Auste…                  68 fr       "FR Littérature"
    #> # ℹ 2 more variables: rights <chr>, has_text <lgl>

In many analyses, you may want to filter just for English works, avoid duplicates, and include only books that have text that can be downloaded. The `gutenberg_works()` function does this pre-filtering:

    gutenberg_works()
    #> # A tibble: 44,042 × 8
    #>    gutenberg_id title    author gutenberg_author_id language gutenberg_bookshelf
    #>           <int> <chr>    <chr>                <int> <chr>    <chr>
    #>  1            1 "The De… Jeffe…                1638 en       "Politics/American…
    #>  2            2 "The Un… Unite…                   1 en       "Politics/American…
    #>  3            3 "John F… Kenne…                1666 en       ""
    #>  4            4 "Lincol… Linco…                   3 en       "US Civil War"
    #>  5            5 "The Un… Unite…                   1 en       "United States/Pol…
    #>  6            6 "Give M… Henry…                   4 en       "American Revoluti…
    #>  7            7 "The Ma… <NA>                    NA en       ""
    #>  8            8 "Abraha… Linco…                   3 en       "US Civil War"
    #>  9            9 "Abraha… Linco…                   3 en       "US Civil War"
    #> 10           10 "The Ki… <NA>                    NA en       "Banned Books List…
    #> # ℹ 44,032 more rows
    #> # ℹ 2 more variables: rights <chr>, has_text <lgl>

It also allows you to perform filtering as an argument:

    gutenberg_works(author == "Austen, Jane")
    #> # A tibble: 11 × 8
    #>    gutenberg_id title    author gutenberg_author_id language gutenberg_bookshelf
    #>           <int> <chr>    <chr>                <int> <chr>    <chr>
    #>  1          105 "Persua… Auste…                  68 en       ""
    #>  2          121 "Northa… Auste…                  68 en       "Gothic Fiction"
    #>  3          141 "Mansfi… Auste…                  68 en       ""
    #>  4          158 "Emma"   Auste…                  68 en       ""
    #>  5          946 "Lady S… Auste…                  68 en       ""
    #>  6         1212 "Love a… Auste…                  68 en       ""
    #>  7         1342 "Pride … Auste…                  68 en       "Best Books Ever L…
    #>  8        21839 "Sense … Auste…                  68 en       ""
    #>  9        31100 "The Co… Auste…                  68 en       ""
    #> 10        37431 "Pride … Auste…                  68 en       ""
    #> 11        42078 "The Le… Auste…                  68 en       ""
    #> # ℹ 2 more variables: rights <chr>, has_text <lgl>

    # or with a regular expression

    library(stringr)
    gutenberg_works(str_detect(author, "Austen"))
    #> # A tibble: 16 × 8
    #>    gutenberg_id title    author gutenberg_author_id language gutenberg_bookshelf
    #>           <int> <chr>    <chr>                <int> <chr>    <chr>
    #>  1          105 "Persua… Auste…                  68 en       ""
    #>  2          121 "Northa… Auste…                  68 en       "Gothic Fiction"
    #>  3          141 "Mansfi… Auste…                  68 en       ""
    #>  4          158 "Emma"   Auste…                  68 en       ""
    #>  5          946 "Lady S… Auste…                  68 en       ""
    #>  6         1212 "Love a… Auste…                  68 en       ""
    #>  7         1342 "Pride … Auste…                  68 en       "Best Books Ever L…
    #>  8        17797 "Memoir… Auste…                7603 en       ""
    #>  9        21839 "Sense … Auste…                  68 en       ""
    #> 10        22536 "Jane A… Auste…               25392 en       ""
    #> 11        22536 "Jane A… Auste…               25393 en       ""
    #> 12        31100 "The Co… Auste…                  68 en       ""
    #> 13        33513 "The Fr… Auste…               36446 en       ""
    #> 14        37431 "Pride … Auste…                  68 en       ""
    #> 15        39897 "Discov… Layar…               40288 en       ""
    #> 16        42078 "The Le… Auste…                  68 en       ""
    #> # ℹ 2 more variables: rights <chr>, has_text <lgl>

The meta-data currently in the package was last updated on 19 December 2022.

### Downloading books by ID

The function `gutenberg_download()` downloads one or more works from Project Gutenberg based on their ID. For example, we earlier saw that one version of Persuasion has ID 105 (see the URL here), so `gutenberg_download(105)` downloads this text.

    persuasion <- gutenberg_download(105)

    persuasion
    #> # A tibble: 8,328 × 2
    #>    gutenberg_id text
    #>           <int> <chr>
    #>  1          105 "Persuasion"
    #>  2          105 ""
    #>  3          105 ""
    #>  4          105 "by"
    #>  5          105 ""
    #>  6          105 "Jane Austen"
    #>  7          105 ""
    #>  8          105 "(1818)"
    #>  9          105 ""
    #> 10          105 ""
    #> # ℹ 8,318 more rows

Notice it is returned as a tbl_df (a type of data frame) including two variables: `gutenberg_id` (useful if multiple books are returned), and a character vector of the text, one row per line.

You can also provide `gutenberg_download()` a vector of IDs to download multiple books. For example, to download Renascence, and Other Poems (book 109) along with Persuasion, do:

    books <- gutenberg_download(c(109, 105), meta_fields = "title")

    books
    #> # A tibble: 9,550 × 3
    #>    gutenberg_id text          title
    #>           <int> <chr>         <chr>
    #>  1          105 "Persuasion"  Persuasion
    #>  2          105 ""            Persuasion
    #>  3          105 ""            Persuasion
    #>  4          105 "by"          Persuasion
    #>  5          105 ""            Persuasion
    #>  6          105 "Jane Austen" Persuasion
    #>  7          105 ""            Persuasion
    #>  8          105 "(1818)"      Persuasion
    #>  9          105 ""            Persuasion
    #> 10          105 ""            Persuasion
    #> # ℹ 9,540 more rows

Notice that the `meta_fields` argument allows us to add one or more additional fields from the `gutenberg_metadata` to the downloaded text, such as title or author.

    books %>%
      count(title)
    #> # A tibble: 2 × 2
    #>   title                           n
    #>   <chr>                       <int>
    #> 1 Persuasion                   8328
    #> 2 Renascence, and Other Poems  1222

### Other meta-datasets

You may want to select books based on information other than their title or author, such as their genre or topic. `gutenberg_subjects` contains pairings of works with Library of Congress subjects and topics. “lcc” means Library of Congress Classification, while “lcsh” means Library of Congress subject headings:

    gutenberg_subjects
    #> # A tibble: 231,741 × 3
    #>    gutenberg_id subject_type subject
    #>           <int> <chr>        <chr>
    #>  1            1 lcsh         United States -- History -- Revolution, 1775-1783 …
    #>  2            1 lcsh         United States. Declaration of Independence
    #>  3            1 lcc          E201
    #>  4            1 lcc          JK
    #>  5            2 lcsh         Civil rights -- United States -- Sources
    #>  6            2 lcsh         United States. Constitution. 1st-10th Amendments
    #>  7            2 lcc          JK
    #>  8            2 lcc          KF
    #>  9            3 lcsh         United States -- Foreign relations -- 1961-1963
    #> 10            3 lcsh         Presidents -- United States -- Inaugural addresses
    #> # ℹ 231,731 more rows

This is useful for extracting texts from a particular topic or genre, such as detective stories, or a particular character, such as Sherlock Holmes. The `gutenberg_id` column can then be used to download these texts or to link with other metadata.

    gutenberg_subjects %>%
      filter(subject == "Detective and mystery stories")
    #> # A tibble: 811 × 3
    #>    gutenberg_id subject_type subject
    #>           <int> <chr>        <chr>
    #>  1          170 lcsh         Detective and mystery stories
    #>  2          173 lcsh         Detective and mystery stories
    #>  3          244 lcsh         Detective and mystery stories
    #>  4          305 lcsh         Detective and mystery stories
    #>  5          330 lcsh         Detective and mystery stories
    #>  6          481 lcsh         Detective and mystery stories
    #>  7          547 lcsh         Detective and mystery stories
    #>  8          863 lcsh         Detective and mystery stories
    #>  9          905 lcsh         Detective and mystery stories
    #> 10         1155 lcsh         Detective and mystery stories
    #> # ℹ 801 more rows

    gutenberg_subjects %>%
      filter(grepl("Holmes, Sherlock", subject))
    #> # A tibble: 54 × 3
    #>    gutenberg_id subject_type subject
    #>           <int> <chr>        <chr>
    #>  1          108 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  2          221 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  3          244 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  4          834 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  5         1661 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  6         2097 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  7         2343 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  8         2344 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #>  9         2345 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #> 10         2346 lcsh         Holmes, Sherlock (Fictitious character) -- Fiction
    #> # ℹ 44 more rows

`gutenberg_authors` contains information about each author, such as aliases and birth/death year:

    gutenberg_authors
    #> # A tibble: 23,980 × 7
    #>    gutenberg_author_id author        alias birthdate deathdate wikipedia aliases
    #>                  <int> <chr>         <chr>     <int>     <int> <chr>     <chr>
    #>  1                   1 United States U.S.…        NA        NA https://… U.S.A.
    #>  2                   3 Lincoln, Abr… <NA>       1809      1865 https://… United…
    #>  3                   4 Henry, Patri… <NA>       1736      1799 https://… <NA>
    #>  4                   5 Adam, Paul    <NA>       1849      1931 https://… <NA>
    #>  5                   7 Carroll, Lew… Dodg…      1832      1898 https://… Dodgso…
    #>  6                   8 United State… <NA>         NA        NA https://… Agency…
    #>  7                   9 Melville, He… Melv…      1819      1891 https://… Melvil…
    #>  8                  10 Barrie, J. M… <NA>       1860      1937 https://… Barrie…
    #>  9                  11 Church of Je… <NA>         NA        NA https://… <NA>
    #> 10                  12 Smith, Josep… Smit…      1805      1844 https://… Smith,…
    #> # ℹ 23,970 more rows

### Analysis

What’s next after retrieving a book’s text? Well, having the book as a data frame is especially useful for working with the tidytext package for text analysis.

    library(tidytext)

    words <- books %>%
      unnest_tokens(word, text)

    words
    #> # A tibble: 90,532 × 3
    #>    gutenberg_id title      word
    #>           <int> <chr>      <chr>
    #>  1          105 Persuasion persuasion
    #>  2          105 Persuasion by
    #>  3          105 Persuasion jane
    #>  4          105 Persuasion austen
    #>  5          105 Persuasion 1818
    #>  6          105 Persuasion chapter
    #>  7          105 Persuasion 1
    #>  8          105 Persuasion sir
    #>  9          105 Persuasion walter
    #> 10          105 Persuasion elliot
    #> # ℹ 90,522 more rows

    word_counts <- words %>%
      anti_join(stop_words, by = "word") %>%
      count(title, word, sort = TRUE)

    word_counts
    #> # A tibble: 6,620 × 3
    #>    title      word          n
    #>    <chr>      <chr>     <int>
    #>  1 Persuasion anne        447
    #>  2 Persuasion captain     303
    #>  3 Persuasion elliot      254
    #>  4 Persuasion lady        214
    #>  5 Persuasion wentworth   191
    #>  6 Persuasion charles     155
    #>  7 Persuasion time        152
    #>  8 Persuasion sir         149
    #>  9 Persuasion miss        125
    #> 10 Persuasion walter      123
    #> # ℹ 6,610 more rows

You may also find these resources useful:

- The Natural Language Processing CRAN View suggests many R packages related to text mining, especially around the tm package
- You could match the `wikipedia` column in `gutenberg_author` to Wikipedia content with the WikipediR package or to pageview statistics with the wikipediatrend package
- If you’re considering an analysis based on author name, you may find the humaniformat (for extraction of first names) and gender (prediction of gender from first names) packages useful. (Note that humaniformat has a `format_reverse` function for reversing “Last, First” names).