R for PDF Scraping
R for PDF Scraping

Nikita Parab

I had a chance to work for a project which required me to scrape a lot of PDFs. The problem I was facing was the structure of some of the tables. It made it very difficult to get correct values in Alteryx and Tableau Prep. So, I decided to use R script instead. And it worked wonders. If you have a PDF which is really tough to scrape in Alteryx and Tableau Prep, give this code a try!

Some Prerequisite

You will need to download R Studio Desktop which is free and Java. Once you have both downloaded and installed, open R Studio and let’s get started!

The first step is to install all the packages we need to scrape our PDF. Packages are groups of multiple functions which are already written. We need to install and call packages that we need for scraping PDF. The packages are listed below. Just copy and paste it.

Install Packages


If you have an error while installing the packages, make sure you have the latest versions of Java as well as R studio.

Call Packages


Once all the packages are ready we can start with the code. The first thing to do is select the area of table. Copy and paste the code below in R Studio. Edit the file location and the page number. PS: The file location should be entered with ‘\\’ instead of ‘\’.

Select Table to get coordinates

area <- locate_areas(‘File Location’,
pages =page number, copy = FALSE)

Once you run the above statement, you will get a pop up where it will ask you to select the area. Select the area and click on done. After that it is time to extract the table. Copy and paste the code below to get the table by editing the file location and page number. You can enter multiple page numbers separated by commas. You need to enter the same page number twice is you need to scrape two tables from the same page.

Get Table as a list

table <- extract_tables(‘File Location‘,
output = ‘data.frame’,
pages = c(page number),
area= list(area[[1]]),
guess = FALSE)

The last step is to save the extracted table as a dataframe. Do that by using the code below. You can then clean it up a bit and save it using write.csv.

Save as a dataframe

data <- table[[1]]

If you have any questions or feedback feel free to reach me at LinkedInTwitter.

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