I’ll do that by subsetting the first element and the transforming list into a character vector using unlist(). For now, I’ll focus on changing the column names. There are two issues here: 1.) there are three elements that are named ‘avg’ 2.) there is only one element named ‘Player,’ but each player’s name is split between two columns (I’ll fix that later). Let’s focus now the first element, which will be the column names of our data frame. The structure of our new all_stats_lines object is a list. I will use strsplt() to split the elements of each string into substrings. I’ll use str_replace_all() to remove the comma.Īfter the whitespace and the commas have been removed, I can focus on separating each element. I also need to remove the comma between each player’s first and last name. The str_squish() function reduces the repeated whitespace between each string. The first problem to tackle is the whitespace between the different elements in each line of text.
CLEAN TEXT FUNCTION IN R SERIES
In the next series of steps, I will use functions in the stringr package to manipulate the lines of text into a desirable form. I am going to call this new object season_stats. Line 9 consists of the column names of our resulting data frame. I want to focus on the season statistics of the players, which makes up lines 9 through 24 of our new file. The read_lines() function reads the lines of our new file. I am going to call my new object ‘UC_text’ and I am going to use the pdf_text command to read the text of my file.
CLEAN TEXT FUNCTION IN R PDF
The next step is to load your PDF into your Datazar project.
I use this book almost every day - it goes where I go. It is a great book for beginners as well as a pocket reference for more advanced programmers. I highly recommend purchasing R for Data Science by Hadley Wickham and Garrett Grolemund. The packages in therein are designed to make data science easy. The stringr package is a member of the tidyverse collection of R packages (more on that here if you are not familiar). The first step is to load the packages that are needed using library(). In the end, I will create a tibble showing season statistics for minutes played, field goal percentage, total points, and average points per game for each player. In anticipation of March Madness and being a University of Cincinnati alumnus along with some other my other Datazar constituents, I have chosen to extract season statistics from the UC men’s basketball team. In this post, you will learn how to: use pdftools to extract text from a PDF, use the stringr package to manipulate strings of text, and create a tidy data set. If you have ever found yourself in this dilemma, fret not - pdftools has you covered.
Yet, sometimes, the data we need is locked away in a file format that is less accessible such as a PDF. Many of the more common file types like CSV, XLSX, and plain text (TXT) are easy to access and manage. In the digital age of today, data comes in many forms.
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