tabulapdf provides R bindings to the
Tabula java library, which can be
used to computationally extract tables from PDF documents. The main function
extract_tables()
mimics the command-line behavior of the Tabula, by extracting
all tables from a PDF file and, by default, returns those tables as a list of
character tibbles in R.
library("tabulapdf") # set Java memory limit to 600 MB (optional) options(java.parameters = "-Xmx600m") f <- system.file("examples", "data.pdf", package = "tabulapdf") # extract table from first page of example PDF tab <- extract_tables(f, pages = 1) head(tab[[1]])
The pages
argument allows you to select which pages to attempt to extract
tables from. By default, Tabula (and thus tabulapdf) checks every page for
tables using a detection algorithm and returns all of them. pages
can be an
integer vector of any length; pages are indexed from 1.
It is possible to specify a remote file, which will be copied to R's temporary directory before processing:
f2 <- "https://raw.githubusercontent.com/ropensci/tabulapdf/main/inst/examples/data.pdf" extract_tables(f2, pages = 2)
The default method used by extract_tables()
mimics the behaviour of Tabula.
For each page the algorithm decides whether it contains one consistent table
and then extracts it by using spreadsheet-tailored algorithm
method = "lattice"
. The correct recognition of a table depends on whether the
page contains a table grid. If it doesn't and the table is a matrix of cells
with values without borders, it might not be able to recognise it. This also
happens when multiple tables with different number of columns are present on the
same page. In those cases another, more general, algorithm method = "stream"
is used, which relies on the distances between text characters on the page.
# Extract tables by deciding for each page individually extract_tables(f2, method = "decide")
It is possible to specify the preferred algorithm which might be a better option for more difficult cases.
# Extract tables by using "lattice" method extract_tables(f2, pages = 2, method = "lattice")
# Extract tables by using "stream" method extract_tables(f2, pages = 2, method = "stream")
By default, extract_tables()
returns a list of character tibbles. This is
because many tables might be malformed or irregular and thus not be easily
coerced to an R data.frame. This can easily be changed by specifying the
output
argument:
# attempt to coerce tables to data.frames extract_tables(f, pages = 2)
Tabula itself implements three "writer" methods that write extracted tables to
disk as CSV, TSV, or JSON files. These can be specified by output = "csv"
,
output = "tsv"
, and output = "json"
, respectively. For CSV and TSV, one file
is written to disk for each table and R session's temporary directory
tempdir()
is used by default (alternatively, the directory can be specified
through output
argument). For JSON, one file is written containing information
about all tables. For these methods, extract_tables()
returns a path to the
directory containing the output files.
# extract tables to CSVs extract_tables(f, output = "csv")
If none of the standard methods works well, you can specify output = "asis"
to
return an rJava "jobjRef" object, which is a pointer to a Java ArrayList of
Tabula Table objects. Working with that object might be quite awkward as it
requires knowledge of Java and Tabula's internals, but might be useful to
advanced users for debugging purposes.
By default, tabulapdf uses Tabula's table detection algorithm to automatically
identify tables within each page of a PDF. This automatic detection can be
toggled off by setting guess = FALSE
and specifying an "area" within each PDF
page to extract the table from. Here is a comparison of the default settings,
versus extracting from two alternative areas within a page:
# this does not return the desired tables on page 2 extract_tables(f, pages = 2, guess = TRUE)
The area
argument should be a list either of length 1 (to use the same area
for each specified page) or equal to the number of pages specified. This also
means that you can extract multiple areas from one page, but specifying the page
twice and indicating the two areas separately:
# this returns the desired tables on page 2 extract_tables( f, pages = c(2, 2), area = list(c(58, 125, 182, 488), c(387, 125, 513, 492)), guess = FALSE )
In addition to the programmatic extraction offered by extract_tables()
, it is
also possible to work interactively with PDFs via the extract_areas()
function. This function triggers a process by which each (specified) page of a
PDF is converted to a PNG image file and then loaded as an R graphic. From
there, you can use your mouse to specify upper-left and lower-right bounds of an
area on each page. Pages are cycled through automatically and, after selecting
areas for each page, those areas are extracted auto-magically (and the return
value is the same as for extract_tables()
).
locate_areas()
handles the area identification process without performing the
extraction, which may be useful as a debugger, or simply to define areas to be
used in a programmatic extraction.
# same as the previous example # use locate_areas(f, pages = 2) to select the area in the web app # don't forget to click "done" when you're finished selecting areas # first_table <- locate_areas(f, pages = 2) # second_table <- locate_areas(f, pages = 2) first_table <- c(58.15032, 125.26869, 182.02355, 488.12966) second_table <- c(387.7791, 125.2687, 513.7519, 492.3246) extract_tables(f, pages = 2, area = list(first_table), guess = FALSE) extract_tables(f, pages = 2, area = list(second_table), guess = FALSE) # alternatively, use extract_areas(f, pages = 2) to do the same in less steps
Tabula is built on top of the Java PDFBox library), which provides low-level functionality for working with PDFs. A few of these tools are exposed through tabulapdf, as they might be useful for debugging or generally for working with PDFs. These functions include:
extract_text()
converts the text of an entire file or specified pages into
an R character vector.split_pdf()
and merge_pdfs()
split and merge PDF documents, respectively.extract_metadata()
extracts PDF metadata as a list.get_n_pages()
determines the number of pages in a document.get_page_dims()
determines the width and height of each page in pt (the
unit used by area
and columns
arguments).make_thumbnails()
converts specified pages of a PDF file to image files.Add the following code to your website.
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