Скачать или смотреть Mastering R: How to Read a TXT Table from a URL with Free Text Columns

Learn how to efficiently read a TXT table from a URL in R, even with free text columns that contain quotations. This guide simplifies complex functions into clear steps.
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This video is based on the question https://stackoverflow.com/q/63379480/ asked by the user 'Sam S.' ( https://stackoverflow.com/u/6395930/ ) and on the answer https://stackoverflow.com/a/63379704/ provided by the user 'G. Grothendieck' ( https://stackoverflow.com/u/516548/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Mastering R: How to Read a TXT Table from a URL with Free Text Columns

Reading data directly from a URL can sometimes present challenges, especially when dealing with free text columns that include quotes. If you've ever tried to read a table from an online source in R and found that your data is not structured correctly, you’re not alone. A common issue arises when the table's format leads to confusion in column separation. In this guide, we’ll walk through a solution to this problem in a step-by-step manner to help you grasp the concept.

The Problem:

You want to read a text table located at a URL. This table has three columns, and the second column contains character data that is enclosed in quotations. The main issue arises from the special characters within the text which cause standard reading functions, like read_delim(), read.table(), and fread(), to misinterpret the data and mess with column counts.

Here’s a glimpse of how the data looks:

[[See Video to Reveal this Text or Code Snippet]]

The Solution: Using scan for Extraction

Using the scan() function allows you to read the data into a character vector seamlessly, and from there, you can manipulate it into a proper data frame. Let’s break down the steps:

Step 1: Reading the Data

First, we’ll use the scan() function to read in the data. This function fetches the whole text at once.

[[See Video to Reveal this Text or Code Snippet]]

Here, Lines is a string representation of the text data, which we will define later.

Step 2: Structuring the Data

Next, we can convert the character vector into a data frame. We do this by using the first three elements of our vector as column names.

[[See Video to Reveal this Text or Code Snippet]]

The tail(s, -3) function omits the first three elements (the header), creating a matrix with the remaining data, and converting it into a data frame.

Step 3: Converting Column Types

Lastly, we will convert the types of each column to suit our needs.

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Example Output

Running the entire process will yield a data frame that looks like this:

[[See Video to Reveal this Text or Code Snippet]]

Note

To reproduce the input setup, you can use the following:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Reading complex tables from a URL requires a little finesse, but by leveraging the scan() function in R, you can effectively navigate and extract the necessary data without needing to download or transform the file manually. This method is straightforward and efficient – and once you get comfortable with it, you'll find it a handy tool in your R programming toolkit!

If you've faced challenges while trying to read text tables from URLs in R, give this method a try. You just might find it to be the best solution for your data extraction needs!