Introduction

For the user who needs raw data, PDF is not the right choice [@pdfhammer; @dataversusinformation]. Even if a PDF file is created with a spreadsheet tool such as Microsoft Excel, the resulting file does not contain information about the data structure (i.e., rows and columns). The PDF format was created as digital paper and one of its purposes was to share information, this is, communicating interpretations and conclusions, something different from sharing figures that usually belong in a database [@dataversusinformation; @updatingdollars].

Tabula is a multi-platform tool written in Java for extracting tables in PDF files. For the previous reasons, extracting data provided in PDFs can be challenging and time-consuming, and this tool allows to extract tables into a CSV or Microsoft Excel spreadsheet using a simple, easy-to-use interface.

One notable use case for Tabula is in investigative journalism, and it was used to produced parts of the following stories [@tabula]:

• The New York Times: “What Happens After Calls for New Gun Restrictions? Sales Go Up”
• Foreign Policy: “Money Down the Drain: CRS Report Details U.S. Expenses on Iraq”
• ProPublica: How Industry Dollars Reached Your Doctors

provides R bindings to the Tabula java library, which can be used to computationally extract tables from PDF documents, and allows to directly import tables into R in an automated way or by allowing user to manually select them with a computer mouse thanks to its integration with [@shiny].

Basic usage

We will demonstrate usage by reading tables from a PDF file created with Quarto [@quarto] and with data available in @base. The file, included in the package, contains four tables, with the second and third tables being on the second page to show a frequent use case that posits some challenges for table extraction, and it can be accessed from GitHub.

The main function, extract_tables(), mimics the command-line behavior of Tabula, by extracting tables from a PDF file and, by default, returns those tables as a list of tibbles in R, where the column-types are inferred by using [@tidyverse].

The starting point is to load the package and, optionally, to set the memory allocation for Java:

library(tabulapdf)

# optional: set memory for Java
options(java.parameters = "-Xmx50m")

By default, extract_tables() checks every page for tables using a detection algorithm and returns all of them:

f <- system.file("examples", "mtcars.pdf", package = "tabulapdf")
extract_tables(f)

## [[1]]
## # A tibble: 5 × 12
##   model          mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4     21       6   160   110  3.9   2.62  16.5     0     1     4     4
## 2 Mazda RX4 W…  21       6   160   110  3.9   2.88  17.0     0     1     4     4
## 3 Datsun 710    22.8     4   108    93  3.85  2.32  18.6     1     1     4     1
## 4 Hornet 4 Dr…  21.4     6   258   110  3.08  3.21  19.4     1     0     3     1
## 5 Hornet Spor…  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2
## 
## [[2]]
## # A tibble: 1 × 5
##   Sepal.Length                   Sepal.Width    Petal.Length Petal.Width Species
##   <chr>                          <chr>          <chr>        <chr>       <chr>  
## 1 "5.10\r4.90\r4.70\r4.60\r5.00" "3.50\r3.00\r… "1.40\r1.40… "0.20\r0.2… "setos…
## 
## [[3]]
## # A tibble: 5 × 3
##     len supp   dose
##   <dbl> <chr> <dbl>
## 1   4.2 VC      0.5
## 2  11.5 VC      0.5
## 3   7.3 VC      0.5
## 4   5.8 VC      0.5
## 5   6.4 VC      0.5

As you can see for the second table in the output, the result is not perfect with the default parameters, which is why provides additional functionality to improve the extraction. In some cases the extraction should work without additional arguments.

The pages argument allows to select which pages to attempt to extract tables from:

extract_tables(f, pages = 1)

## [[1]]
## # A tibble: 5 × 12
##   model          mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4     21       6   160   110  3.9   2.62  16.5     0     1     4     4
## 2 Mazda RX4 W…  21       6   160   110  3.9   2.88  17.0     0     1     4     4
## 3 Datsun 710    22.8     4   108    93  3.85  2.32  18.6     1     1     4     1
## 4 Hornet 4 Dr…  21.4     6   258   110  3.08  3.21  19.4     1     0     3     1
## 5 Hornet Spor…  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2

It is possible to specify a remote file, which will be copied to a temporary directory internally handled by R:

f2 <- "https://raw.githubusercontent.com/ropensci/tabulapdf/main/inst/examples/mtcars.pdf"
extract_tables(f2, pages = 1)

Specifying the extraction method

For each page, extract_tables() uses an algorithm to determine whether it contains one consistent table and then extracts it by using a spreadsheet-tailored algorithm with the default parameter method = "lattice".

The correct recognition of a table depends on whether the page contains a table grid. If that is not the case, and the table is a matrix of cells with values without borders, it might not be able to recognise it.

The same issue appears when multiple tables with different number of columns are present on the same page. In that case, the parameter method = "stream" can be a better option as it will use the distances between text characters on the page:

# incorrect
extract_tables(f, pages = 2, method = "lattice")[[1]]

## # A tibble: 1 × 5
##   Sepal.Length                   Sepal.Width    Petal.Length Petal.Width Species
##   <chr>                          <chr>          <chr>        <chr>       <chr>  
## 1 "5.10\r4.90\r4.70\r4.60\r5.00" "3.50\r3.00\r… "1.40\r1.40… "0.20\r0.2… "setos…

# correct
extract_tables(f, pages = 2, method = "stream")[[1]]

## # A tibble: 5 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa

Extracting areas

uses a table detection algorithm to automatically identify tables within each page of a PDF. This automatic detection can be disables with the parameter guess = FALSE and specifying an area within each PDF page to extract the table from.

The area argument should be a list either of length equal to the number of pages specified, allowing the extraction of multiple areas from one page if the page is specified twice and with two areas separately:

extract_tables(
  f,
  pages = c(2, 2),
  area = list(c(58, 125, 182, 488), c(387, 125, 513, 492)),
  guess = FALSE
)

## [[1]]
## # A tibble: 5 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa 
## 
## [[2]]
## # A tibble: 5 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  
##          <dbl>       <dbl>        <dbl>       <dbl> <chr>    
## 1          6.7         3            5.2         2.3 virginica
## 2          6.3         2.5          5           1.9 virginica
## 3          6.5         3            5.2         2   virginica
## 4          6.2         3.4          5.4         2.3 virginica
## 5          5.9         3            5.1         1.8 virginica

Interactive table extraction

In addition to the programmatic extraction offered by extract_tables(), it is also possible to work interactively with PDFs. The locate_areas() function allows to use a computer mouse to select areas on each page of a PDF, which can then be used to extract tables:

Selecting areas for table extraction.

The selection from Figure 1 can be used to extract the tables as follows:

# manual selection, result transcribed below
# first_table <- locate_areas(f, pages = 2)[[1]]
# second_table <- locate_areas(f, pages = 2)[[1]]

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)

## [[1]]
## # A tibble: 5 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa

extract_tables(f, pages = 2, area = list(second_table), guess = FALSE)

## [[1]]
## # A tibble: 5 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  
##          <dbl>       <dbl>        <dbl>       <dbl> <chr>    
## 1          6.7         3            5.2         2.3 virginica
## 2          6.3         2.5          5           1.9 virginica
## 3          6.5         3            5.2         2   virginica
## 4          6.2         3.4          5.4         2.3 virginica
## 5          5.9         3            5.1         1.8 virginica

Use case: COVID-19 treatments in Italy

It is possible to extract the tables containing the number of pharmaceutical treatments for hospitalized patients from the Monitoraggio Antivirali per COVID-19 (Antiviral Monitoring for COVID-19) [@agenzia].

It is not the case for this data, but combined cells in the header can difficult the extraction process. It is possible to select the figures only and set the col_names argument to FALSE to avoid this issue.

The following code downloads and tidies the table for the first report:

f <- system.file("examples", "covid.pdf", package = "tabulapdf")

# this corresponds to page four in the original document
# locate_areas(f, pages = 1)

covid <- extract_tables(f,
  pages = 1, guess = FALSE, col_names = FALSE,
  area = list(c(140.75, 88.14, 374.17, 318.93))
)

covid <- covid[[1]]

colnames(covid) <- c("region", "treatments", "pct_increase")
covid$treatments <- as.numeric(gsub("\\.", "", covid$treatments))
covid$pct_increase <- as.numeric(
  gsub(",", ".", gsub("%", "", covid$pct_increase))
) / 100

covid

## # A tibble: 22 × 3
##    region                treatments pct_increase
##    <chr>                      <dbl>        <dbl>
##  1 Abruzzo                     2343        0.03 
##  2 Basilicata                   927        0.012
##  3 Calabria                    1797        0.023
##  4 Campania                    3289        0.041
##  5 Emilia Romagna              7945        0.1  
##  6 Friuli Venezia Giulia       1063        0.013
##  7 Lazio                      11206        0.141
##  8 Liguria                     5332        0.067
##  9 Lombardia                  12089        0.153
## 10 Marche                      3739        0.047
## # ℹ 12 more rows

Miscellaneous functionality

Tabula is built on top of the Java PDFBox library [@apachepdfbox], which provides low-level functionality for working with PDFs. A few of these tools are exposed through , 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.
• 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.
• split_pdf() and merge_pdfs() split and merge PDF documents, respectively.