The epubr package provides functions supporting the
reading and parsing of internal e-book content from EPUB files. E-book
metadata and text content are parsed separately and joined together in a
tidy, nested tibble data frame.
E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata.
EPUB file parsing functionality in this package is intended for
relatively general application to arbitrary EPUB e-books. However,
poorly formatted e-books or e-books with highly uncommon formatting may
not work with this package. There may even be cases where an EPUB file
has DRM or some other property that makes it impossible to read with
epubr.
Text is read ‘as is’ for the most part. The only nominal changes are
minor substitutions, for example curly quotes changed to straight
quotes. Substantive changes are expected to be performed subsequently by
the user as part of their text analysis. Additional text cleaning can be
performed at the user’s discretion, such as with functions from packages
like tm or qdap.
Read EPUB files
Bram Stoker’s Dracula novel sourced from Project Gutenberg is a good
example of an EPUB file with unfortunate formatting. The first thing
that stands out is the naming convention using item
followed by some ordered digits does not differentiate sections like the
book preamble from the chapters. The numbering also starts in a weird
place. But it is actually worse than this. Notice that sections are not
broken into chapters; they can begin and end in the middle of
chapters!
These annoyances aside, the metadata and contents can still be read
into a convenient table. Text mining analyses can still be performed on
the overall book, if not so easily on individual chapters. See the
section below on restructuring for examples of epubr
functions that help get around these issues.
Here a single file is read with epub(). The output of
the returned primary data frame and the book text data frame that is
nested within its data column are shown.
library(epubr)
file <- system.file("dracula.epub", package = "epubr")
(x <- epub(file))
#> # A tibble: 1 × 9
#> rights identifier creator title language subject date source data
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 Public domain… http://ww… Bram S… Drac… en Horror… 1995… http:… <tibble>
x$data[[1]]
#> # A tibble: 15 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 item6 "The Project Gutenberg EBook of Dracula, by Br… 11446 60972
#> 2 item7 "But I am not in heart to describe beauty, for… 13879 71798
#> 3 item8 "\" 'Lucy, you are an honest-hearted girl, I k… 12474 65522
#> 4 item9 "CHAPTER VIIIMINA MURRAY'S JOURNAL\nSame day, … 12177 62724
#> 5 item10 "CHAPTER X\nLetter, Dr. Seward to Hon. Arthur … 12806 66678
#> 6 item11 "Once again we went through that ghastly opera… 12103 62949
#> 7 item12 "CHAPTER XIVMINA HARKER'S JOURNAL\n23 Septembe… 12214 62234
#> 8 item13 "CHAPTER XVIDR. SEWARD'S DIARY-continued\nIT w… 13990 72903
#> 9 item14 "\"Thus when we find the habitation of this ma… 13356 69779
#> 10 item15 "\"I see,\" I said. \"You want big things that… 12866 66921
#> 11 item16 "CHAPTER XXIIIDR. SEWARD'S DIARY\n3 October.-T… 11928 61550
#> 12 item17 "CHAPTER XXVDR. SEWARD'S DIARY\n11 October, Ev… 13119 68564
#> 13 item18 " \nLater.-Dr. Van Helsing has returned. He ha… 8435 43464
#> 14 item19 "End of the Project Gutenberg EBook of Dracula… 2665 18541
#> 15 coverpage-wrapper "" 0 0The file argument may be a vector of EPUB files. There
is one row for each book.
EPUB metadata
The above examples jump right in, but it can be helpful to inspect
file metadata before reading a large number of books into memory.
Formatting may differ across books. It can be helpful to know what
fields to expect, the degree of consistency, and what content you may
want to drop during the file reading process. epub_meta()
strictly parses file metadata and does not read the e-book text.
epub_meta(file)
#> # A tibble: 1 × 8
#> rights identifier creator title language subject date source
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Public domain in the U… http://ww… Bram S… Drac… en Horror… 1995… http:…This provides the big picture, though it will not reveal the internal
breakdown of book section naming conventions that were seen in the first
epub() example.
file can also be a vector for epub_meta().
Whenever file is a vector, the fields (columns) returned
are the union of all fields detected across all EPUB files. Any books
(rows) that do not have a field found in another book return
NA for that row and column.
Additional arguments
There are three optional arguments that can be provided to
epub() to:
- select fields, or columns of the primary data frame.
- filter sections, or rows of the nested data frame.
- attempt to detect which rows or sections in the nested data frame identify book chapters.
Unless you have a collection of well-formatted and similarly formatted EPUB files, these arguments may not be helpful and can be ignored, especially chapter detection.
Select fields
Selecting fields is straightforward. All fields found are returned unless a vector of fields is provided.
epub(file, fields = c("title", "creator", "file"))
#> # A tibble: 1 × 4
#> title creator file data
#> <chr> <chr> <chr> <list>
#> 1 Dracula Bram Stoker dracula.epub <tibble [15 × 4]>Note that file was not a field identified in the
metadata. This is a special case. Including file will
include the basename of the input file. This is helpful
when you want to retain file names and source is included
in the metadata but may represent something else. Some fields like
data and title are always returned and do not
need to be specified in fields.
Also, if your e-book does not have a metadata field named
title, you can pass an additional argument to
... to map a different, known metadata field to
title. E.g., title = "BookTitle". The
resulting table always has a title field, but in this case
title would be populated with information from the
BookTitle metadata field. If the default title
field or any other field name passed to the additional
title argument does not exist in the file metadata, the
output title column falls back on filling in with the same
unique file names obtained when requesting the file
field.
Drop sections
Filtering out unwanted sections, or rows of the nested data frame,
uses a regular expression pattern. Matched rows are dropped. This is
where knowing the naming conventions used in the e-books in
file, or at least knowing they are satisfactorily
consistent and predictable for a collection, helps with removing
extraneous clutter.
One section that can be discarded is the cover. For many books it can
be helpful to use a pattern like "^(C|c)ov" to drop any
sections whose IDs begin with Cov, cov, and
may be that abbreviation or the full word. For this book,
cov suffices. The nested data frame has one less row than
before.
epub(file, drop_sections = "cov")$data[[1]]
#> # A tibble: 14 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 item6 "The Project Gutenberg EBook of Dracula, by Bram StokerT… 11446 60972
#> 2 item7 "But I am not in heart to describe beauty, for when I ha… 13879 71798
#> 3 item8 "\" 'Lucy, you are an honest-hearted girl, I know. I sho… 12474 65522
#> 4 item9 "CHAPTER VIIIMINA MURRAY'S JOURNAL\nSame day, 11 o'clock… 12177 62724
#> 5 item10 "CHAPTER X\nLetter, Dr. Seward to Hon. Arthur Holmwood.\… 12806 66678
#> 6 item11 "Once again we went through that ghastly operation. I ha… 12103 62949
#> 7 item12 "CHAPTER XIVMINA HARKER'S JOURNAL\n23 September.-Jonatha… 12214 62234
#> 8 item13 "CHAPTER XVIDR. SEWARD'S DIARY-continued\nIT was just a … 13990 72903
#> 9 item14 "\"Thus when we find the habitation of this man-that-was… 13356 69779
#> 10 item15 "\"I see,\" I said. \"You want big things that you can m… 12866 66921
#> 11 item16 "CHAPTER XXIIIDR. SEWARD'S DIARY\n3 October.-The time se… 11928 61550
#> 12 item17 "CHAPTER XXVDR. SEWARD'S DIARY\n11 October, Evening.-Jon… 13119 68564
#> 13 item18 " \nLater.-Dr. Van Helsing has returned. He has got the … 8435 43464
#> 14 item19 "End of the Project Gutenberg EBook of Dracula, by Bram … 2665 18541Guess chapters
This e-book unfortunately does not have great formatting. For the
sake of example, pretend that chapters are known to be sections
beginning with item and followed by two digits,
using the pattern ^item\\d\\d. This does two things. It
adds a new metadata column to the primary data frame called
nchap giving the estimated number of chapters in the book.
In the nested data frame containing the parsed e-book text, the
section column is conditionally mutated to reflect a new,
consistent chapter naming convention for the identified chapters and a
logical is_chapter column is added.
x <- epub(file, drop_sections = "cov", chapter_pattern = "^item\\d\\d")
x
#> # A tibble: 1 × 10
#> rights identifier creator title language subject date source nchap data
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <list>
#> 1 Public … http://ww… Bram S… Drac… en Horror… 1995… http:… 10 <tibble>
x$data[[1]]
#> # A tibble: 14 × 5
#> section text is_chapter nword nchar
#> <chr> <chr> <lgl> <int> <int>
#> 1 item6 "The Project Gutenberg EBook of Dracula, by B… FALSE 11446 60972
#> 2 item7 "But I am not in heart to describe beauty, fo… FALSE 13879 71798
#> 3 item8 "\" 'Lucy, you are an honest-hearted girl, I … FALSE 12474 65522
#> 4 item9 "CHAPTER VIIIMINA MURRAY'S JOURNAL\nSame day,… FALSE 12177 62724
#> 5 ch01 "CHAPTER X\nLetter, Dr. Seward to Hon. Arthur… TRUE 12806 66678
#> 6 ch02 "Once again we went through that ghastly oper… TRUE 12103 62949
#> 7 ch03 "CHAPTER XIVMINA HARKER'S JOURNAL\n23 Septemb… TRUE 12214 62234
#> 8 ch04 "CHAPTER XVIDR. SEWARD'S DIARY-continued\nIT … TRUE 13990 72903
#> 9 ch05 "\"Thus when we find the habitation of this m… TRUE 13356 69779
#> 10 ch06 "\"I see,\" I said. \"You want big things tha… TRUE 12866 66921
#> 11 ch07 "CHAPTER XXIIIDR. SEWARD'S DIARY\n3 October.-… TRUE 11928 61550
#> 12 ch08 "CHAPTER XXVDR. SEWARD'S DIARY\n11 October, E… TRUE 13119 68564
#> 13 ch09 " \nLater.-Dr. Van Helsing has returned. He h… TRUE 8435 43464
#> 14 ch10 "End of the Project Gutenberg EBook of Dracul… TRUE 2665 18541This renaming of sections is helpful if the e-book content is split
into meaningful sections, but the sections are not named in a similarly
meaningful way. It is not helpful in cases where the sections are poorly
or arbitrarily defined as in the above example where they begin with
item* regardless of content. See the examples below on
restructuring parsed content for an approach to dealing with this more
problematic case.
Another problematic formatting that you may encounter is the random
order of book sections as defined in the metadata, even if the sections
themselves are well-defined. For example, there may be clear breaks
between chapters, but the chapters may occur out of order such as
ch05, ch11, ch02, ... and so on. Merely identifying which
sections are chapters does not help in this case. They are still be
labeled incorrectly since they are already out of order. Properly
reordering sections is also addressed below.
Also note that not all books have chapters or something like them.
Make sure an optional argument like chapter_pattern makes
sense to use with a given e-book in the first place.
Ultimately, everything depends on the quality of the EPUB file. Some publishers are better than others. Formatting standards may also change over time.
Restructure parsed content
When reading EPUB files it is ideal to be able to identify meaningful sections to retain via a regular expression pattern, as well as to drop extraneous sections in a similar manner. Using pattern matching as shown above is a convenient way to filter rows of the nested text content data frame.
For e-books with poor metadata formatting this is not always
possible, or may be possible only after some other pre-processing.
epubr provides other functions to assist in restructuring
the text table. The Dracula EPUB file included in epubr is
a good example to continue with here.
Split and recombine into new sections
This book is internally broken into sections at arbitrary break
points, hence why several sections begin in the middle of chapters, as
seen above. Other chapters begin in the middle of sections. Use
epub_recombine() along with a regular expression that can
match the true section breaks. This function collapses the full text and
then rebuilds the text table using new sections with proper break
points. In the process it also recalculates the numbers of words and
characters and relabels the sections with chapter notation.
Fortunately, a reliable pattern exists, which consists of
CHAPTER in capital letters followed by a space and some
Roman numerals. Recombine the text into a new object.
pat <- "CHAPTER [IVX]+"
x2 <- epub_recombine(x, pat)
x2
#> # A tibble: 1 × 10
#> rights identifier creator title language subject date source nchap data
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <list>
#> 1 Public … http://ww… Bram S… Drac… en Horror… 1995… http:… 54 <tibble>
x2$data[[1]]
#> # A tibble: 55 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 prior "The Project Gutenberg EBook of Dracula, by Bram StokerT… 159 1110
#> 2 ch01 "CHAPTER I\nPage\nJonathan Harker's Journal\n1\n" 7 43
#> 3 ch02 "CHAPTER II\nJonathan Harker's Journal\n14\n" 6 40
#> 4 ch03 "CHAPTER III\nJonathan Harker's Journal\n26\n" 6 41
#> 5 ch04 "CHAPTER IV\nJonathan Harker's Journal\n38\n" 6 40
#> 6 ch05 "CHAPTER V\nLetters-Lucy and Mina\n51\n" 6 35
#> 7 ch06 "CHAPTER VI\nMina Murray's Journal\n59\n" 6 36
#> 8 ch07 "CHAPTER VII\nCutting from \"The Dailygraph,\" 8 August\… 9 55
#> 9 ch08 "CHAPTER VIII\nMina Murray's Journal\n84\n" 6 38
#> 10 ch09 "CHAPTER IX\nMina Murray's Journal\n98\n" 6 36
#> # ℹ 45 more rowsBut this is not quite as expected. There should be 27 chapters, not 54. What was not initially apparent was that the same pattern matching each chapter name also appears in the first section where every chapter is listed in the table of contents. The new section breaks were successful in keeping chapter text in single, unique sections, but there are now twice as many as needed. Unintentionally, the first 27 “chapters” represent the table of contents being split on each chapter ID. These should be removed.
An easy way to do this is with epub_sift(), which sifts,
or filters out, small word- or character-count sections from the nested
data frame. It’s a simple sieve and you can control the size of the
holes with n. You can choose type = "word"
(default) or type = "character". This is somewhat of a
blunt instrument, but is useful in a circumstance like this one where it
is clear it will work as desired.
library(dplyr)
x2 <- epub_recombine(x, pat) %>% epub_sift(n = 200)
x2
#> # A tibble: 1 × 10
#> rights identifier creator title language subject date source nchap data
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <list>
#> 1 Public … http://ww… Bram S… Drac… en Horror… 1995… http:… 54 <tibble>
x2$data[[1]]
#> # A tibble: 27 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 ch28 "CHAPTER IJONATHAN HARKER'S JOURNAL\n(Kept in shorthand.… 5694 30602
#> 2 ch29 "CHAPTER IIJONATHAN HARKER'S JOURNAL-continued\n5 May.-I… 5476 28462
#> 3 ch30 "CHAPTER IIIJONATHAN HARKER'S JOURNAL-continued\nWHEN I … 5703 29778
#> 4 ch31 "CHAPTER IVJONATHAN HARKER'S JOURNAL-continued\nI AWOKE … 5828 30195
#> 5 ch32 "CHAPTER V\nLetter from Miss Mina Murray to Miss Lucy We… 3546 18004
#> 6 ch33 "CHAPTER VIMINA MURRAY'S JOURNAL\n24 July. Whitby.-Lucy … 5654 29145
#> 7 ch34 "CHAPTER VIICUTTING FROM \"THE DAILYGRAPH,\" 8 AUGUST\n(… 5567 29912
#> 8 ch35 "CHAPTER VIIIMINA MURRAY'S JOURNAL\nSame day, 11 o'clock… 6267 32596
#> 9 ch36 "CHAPTER IX\nLetter, Mina Harker to Lucy Westenra.\n\"Bu… 5910 30129
#> 10 ch37 "CHAPTER X\nLetter, Dr. Seward to Hon. Arthur Holmwood.\… 5932 30730
#> # ℹ 17 more rowsThis removes the unwanted rows, but one problem remains. Note that
sifting the table sections in this case results in a need to re-apply
epub_recombine() because the sections we removed had
nevertheless offset the chapter indexing. Another call to
epub_recombine() can be chained, but it may be more
convenient to use the sift argument to
epub_recombine(), which is applied recursively.
#epub_recombine(x, pat) %>% epub_sift(n = 200) %>% epub_recombine(pat)
x2 <- epub_recombine(x, pat, sift = list(n = 200))
x2
#> # A tibble: 1 × 10
#> rights identifier creator title language subject date source nchap data
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <list>
#> 1 Public … http://ww… Bram S… Drac… en Horror… 1995… http:… 27 <tibble>
x2$data[[1]]
#> # A tibble: 27 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 ch01 "CHAPTER IJONATHAN HARKER'S JOURNAL\n(Kept in shorthand.… 5694 30602
#> 2 ch02 "CHAPTER IIJONATHAN HARKER'S JOURNAL-continued\n5 May.-I… 5476 28462
#> 3 ch03 "CHAPTER IIIJONATHAN HARKER'S JOURNAL-continued\nWHEN I … 5703 29778
#> 4 ch04 "CHAPTER IVJONATHAN HARKER'S JOURNAL-continued\nI AWOKE … 5828 30195
#> 5 ch05 "CHAPTER V\nLetter from Miss Mina Murray to Miss Lucy We… 3546 18005
#> 6 ch06 "CHAPTER VIMINA MURRAY'S JOURNAL\n24 July. Whitby.-Lucy … 5654 29145
#> 7 ch07 "CHAPTER VIICUTTING FROM \"THE DAILYGRAPH,\" 8 AUGUST\n(… 5567 29912
#> 8 ch08 "CHAPTER VIIIMINA MURRAY'S JOURNAL\nSame day, 11 o'clock… 6267 32596
#> 9 ch09 "CHAPTER IX\nLetter, Mina Harker to Lucy Westenra.\n\"Bu… 5910 30129
#> 10 ch10 "CHAPTER X\nLetter, Dr. Seward to Hon. Arthur Holmwood.\… 5932 30730
#> # ℹ 17 more rowsReorder sections based on pattern in text
Some poorly formatted e-books have their internal sections occur in an arbitrary order. This can be frustrating to work with when doing text analysis on each section and where order matters. Just like recombining into new sections based on a pattern, sections that are out of order can be reordered based on a pattern. This requires a bit more work. In this case the user must provide a function that will map something in the matched pattern to an integer representing the desired row index.
Continue with the Dracula example, but with one difference. Even
though the sections were originally broken at arbitrary points, they
were in chronological order. To demonstrate the utility of
epub_reorder(), first randomize the rows so that
chronological order can be recovered.
set.seed(1)
x2$data[[1]] <- sample_frac(x2$data[[1]]) # randomize rows for example
x2$data[[1]]
#> # A tibble: 27 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 ch25 "CHAPTER XXVDR. SEWARD'S DIARY\n11 October, Evening.-Jon… 6214 32544
#> 2 ch04 "CHAPTER IVJONATHAN HARKER'S JOURNAL-continued\nI AWOKE … 5828 30195
#> 3 ch07 "CHAPTER VIICUTTING FROM \"THE DAILYGRAPH,\" 8 AUGUST\n(… 5567 29912
#> 4 ch01 "CHAPTER IJONATHAN HARKER'S JOURNAL\n(Kept in shorthand.… 5694 30602
#> 5 ch02 "CHAPTER IIJONATHAN HARKER'S JOURNAL-continued\n5 May.-I… 5476 28462
#> 6 ch11 "CHAPTER XI\nLucy Westenra's Diary.\n12 September.-How g… 5126 26926
#> 7 ch14 "CHAPTER XIVMINA HARKER'S JOURNAL\n23 September.-Jonatha… 6411 32530
#> 8 ch18 "CHAPTER XVIIIDR. SEWARD'S DIARY\n30 September.-I got ho… 6911 35881
#> 9 ch19 "CHAPTER XIXJONATHAN HARKER'S JOURNAL\n1 October, 5 a. m… 5670 29431
#> 10 ch24 "CHAPTER XXIVDR. SEWARD'S PHONOGRAPH DIARY, SPOKEN BY VA… 6272 32065
#> # ℹ 17 more rowsIt is clear above that sections are now out of order. It is common enough to load poorly formatted EPUB files and yield this type of result. If all you care about is the text in its entirely, this does not matter, but if your analysis involves trends over the course of a book, this is problematic.
For this book, you need a function that will convert an annoying Roman numeral to an integer. You already have the pattern for finding the relevant information in each text section. You only need to tweak it for proper substitution. Here is an example:
f <- function(x, pattern) as.numeric(as.roman(gsub(pattern, "\\1", x)))This function is passed to epub_reorder(). It takes and
returns scalars. It must take two arguments: the first is a text string.
The second is the regular expression. It must return a single number
representing the index of that row. For example, if the pattern matches
CHAPTER IV, the function should return a
4.
epub_reorder() takes care of the rest. It applies your
function to every row in the the nested data frame and then reorders the
rows based on the full set of indices. Note that it also repeats this
for every row (book) in the primary data frame, i.e., for every nested
table. This means that the same function will be applied to every book.
Therefore, you should only use this in bulk on a collection of e-books
if you know the pattern does not change and the function will work
correctly in each case.
The pattern has changed slightly. Parentheses are used to retain the
important part of the matched pattern, the Roman numeral. The function
f here substitutes the entire string (because now it begins
with ^ and ends with .*) with only the part
stored in parentheses (In f, this is the \\1
substitution). epub_reorder() applies this to all rows in
the nested data frame:
x2 <- epub_reorder(x2, f, "^CHAPTER ([IVX]+).*")
x2$data[[1]]
#> # A tibble: 27 × 4
#> section text nword nchar
#> <chr> <chr> <int> <int>
#> 1 ch01 "CHAPTER IJONATHAN HARKER'S JOURNAL\n(Kept in shorthand.… 5694 30602
#> 2 ch02 "CHAPTER IIJONATHAN HARKER'S JOURNAL-continued\n5 May.-I… 5476 28462
#> 3 ch03 "CHAPTER IIIJONATHAN HARKER'S JOURNAL-continued\nWHEN I … 5703 29778
#> 4 ch04 "CHAPTER IVJONATHAN HARKER'S JOURNAL-continued\nI AWOKE … 5828 30195
#> 5 ch05 "CHAPTER V\nLetter from Miss Mina Murray to Miss Lucy We… 3546 18005
#> 6 ch06 "CHAPTER VIMINA MURRAY'S JOURNAL\n24 July. Whitby.-Lucy … 5654 29145
#> 7 ch07 "CHAPTER VIICUTTING FROM \"THE DAILYGRAPH,\" 8 AUGUST\n(… 5567 29912
#> 8 ch08 "CHAPTER VIIIMINA MURRAY'S JOURNAL\nSame day, 11 o'clock… 6267 32596
#> 9 ch09 "CHAPTER IX\nLetter, Mina Harker to Lucy Westenra.\n\"Bu… 5910 30129
#> 10 ch10 "CHAPTER X\nLetter, Dr. Seward to Hon. Arthur Holmwood.\… 5932 30730
#> # ℹ 17 more rowsIt is important that this is done on a nested data frame that has already been cleaned to the point of not containing extraneous rows that cannot be matched by the desired pattern. If they cannot be matched, then it is unknown where those rows should be placed relative to the others.
If sections are both out of order and use arbitrary break points, it
would be necessary to reorder them before you split and recombine. If
you split and recombine first, this would yield new sections that
contain text from different parts of the e-book. However, the two are
not likely to occur together; in fact it may be impossible for an EPUB
file to be structured this way. In developing epubr, no
such examples have been encountered. In any event, reordering out of
order sections essentially requires a human-identifiable pattern near
the beginning of each section text string, so it does not make sense to
perform this operation unless the sections have meaningful break
points.
Unzip EPUB file
Separate from using epub_meta() and epub(),
you can call epub_unzip() directly if all you want to do is
extract the files from the .epub file archive. By default
the archive files are extracted to tempdir() so you may
want to change this with the exdir argument.
bookdir <- file.path(tempdir(), "dracula")
epub_unzip(file, exdir = bookdir)
list.files(bookdir, recursive = TRUE)
#> [1] "META-INF/container.xml"
#> [2] "mimetype"
#> [3] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-0.htm.html"
#> [4] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-1.htm.html"
#> [5] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-10.htm.html"
#> [6] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-11.htm.html"
#> [7] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-12.htm.html"
#> [8] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-13.htm.html"
#> [9] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-2.htm.html"
#> [10] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-3.htm.html"
#> [11] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-4.htm.html"
#> [12] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-5.htm.html"
#> [13] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-6.htm.html"
#> [14] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-7.htm.html"
#> [15] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-8.htm.html"
#> [16] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@345-h-9.htm.html"
#> [17] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@images@colophon.png"
#> [18] "OEBPS/@public@vhost@g@gutenberg@html@files@345@345-h@images@cover.jpg"
#> [19] "OEBPS/0.css"
#> [20] "OEBPS/1.css"
#> [21] "OEBPS/content.opf"
#> [22] "OEBPS/pgepub.css"
#> [23] "OEBPS/toc.ncx"
#> [24] "OEBPS/wrap0000.html"Other functions
Word count
The helper function count_words() provides word counts
for strings, but allows you to control the regular expression patterns
used for both splitting the string and conditionally counting the
resulting character elements. This is the same function used internally
by epub() and epub_recombine(). It is exported
so that it can be used directly.
By default, count_words() splits on spaces and new line
characters. It counts as a word any element containing at least one
alphanumeric character or the ampersand. It ignores everything else as
noise, such as extra spaces, empty strings and isolated bits of
punctuation.
x <- " This sentence will be counted to have:\n\n10 (ten) words."
count_words(x)
#> [1] 10Inspection
Helper functions for inspecting the text in the R console include
epub_head() and epub_cat().
epub_head() provides an overview of the text by section
for each book in the primary data frame. The nested data frames are
unnested and row bound to one another and returned as a single data
frame. The text is shortened to only the first few characters (defaults
to n = 50).
epub_cat() can be used to cat the text of
an e-book to the console for quick inspection in a more readable form.
It can take several arguments that help slice out a section of the text
and customize how it is printed.
Both functions can take an EPUB filename or a data frame of an already loaded EPUB file as their first argument.