Splitting Long Strings from PDF Text Rows

In this article, we’ll delve into the process of extracting specific data from a long string generated by pdf_text() when working with tables in PDF files.

Introduction to PDF Text Extraction

PDFs have become an essential format for storing and exchanging information across various platforms. However, unlike other formats like CSV or Excel, PDFs don’t inherently support easy text extraction or manipulation without some additional processing steps.

The popular pdftools package in R is a powerful tool that can extract the text from PDF files using the poppler library. It allows for the extraction of structured data in tables within PDFs, making it easier to work with this format.

The Problem at Hand

In our scenario, we have a PDF file containing a table spanning three pages. After running pdf_text(), we receive a character vector where each element is a long string representing all text from each page of the PDF. Our goal is to isolate the row corresponding to “Below one or more housing standards” and then extract the value in the 8th column.

Initial Steps: Understanding pdf_text() Output

Let’s first understand what data we’re working with when we use pdf_text(). The function returns a character vector where each element represents the text from a page in the PDF. For our specific case, this results in three elements corresponding to each of the three pages.

library(pdftools)
df <- pdf_text("file.pdf")
str(df)

Breaking Down Long Strings into Lines

The first step is to break down these long strings into individual lines based on the line break character “\n”. This can be achieved using readLines() from the stringr package, which requires creating a text connection.

library(stringr)
lines <- lapply(df, function(t) readLines(textConnection(t)))

Identifying the Relevant Line and Extracting the 8th Column Value

Next, we need to identify the line corresponding to “Below one or more housing standards” and extract the value in the 8th column. This involves searching for our target string within each line’s vector.

target <- lines[[2]][grep("Below one or more housing standards", lines[[2]])]
sub("(Below one or more housing standards)([ ]*\\d*[,]*\\d*){6}[ ]*(\\d*[.]*\\d*)(.*)", "\\3", target)

Handling Variations in Numeric Specifications

Notice that our regular expression may not work perfectly with all numeric columns since only the first six are allowed to have commas and not decimals.

To make it more general, we could use a character class like [.,] to allow both commas and decimal points. Alternatively, you might consider using a package designed for handling tabular PDF formatting in a more principled manner.

Conclusion

In this article, we’ve covered the process of extracting specific data from long strings generated by pdf_text(), including splitting lines, identifying relevant rows, and accessing values within those rows. While working with tables in PDFs can be challenging, packages like pdftools make it easier to extract structured data.

By following these steps, you should be able to isolate the row of interest and extract its specific value. Remember that handling variations in numeric specifications may require some adjustments to your approach.

Last modified on 2024-10-13