Extracting Useful Information from HTML Data in R: A Step-by-Step Guide

Extracting Useful Information from HTML Data in R

Introduction

As data analysts and scientists, we often encounter data that comes in the form of HTML tags. The question of how to clean and split these tags to extract useful information is a common one. In this article, we will explore how to accomplish this task using R.

Background

HTML (Hypertext Markup Language) is a standard markup language used for creating web pages. It consists of elements such as p, span, a, and many others, which are used to define the structure and content of a web page. When working with HTML data in R, we often encounter problems with extracting specific information from these tags.

Problem Statement

Consider the following example:

<div>
<p>My parser create a data frame, which looks like:</p>
<pre><code>    name          html
 1  John         &lt;span class="incident-icon" data-minute="68" data-second="37" data-id="8028"&gt;&lt;/span&gt;&lt;span class="name-meta-data"&gt;68&lt;/span&gt;
 2 Steve         &lt;span class="incident-icon" data-minute="69" data-second="4" data-id="132205"&gt;&lt;/span&gt;&lt;span class="name-meta-data"&gt;69&lt;/span&gt;
</code></pre>
<p>So how I can extract usefull information from HTML? For example, I want to use some HTML attributes as features:</p>
<pre><code>   name minute second     id
1  John     68     37   8028
2 Steve     69      4 132205
</code></pre>
</div>

In this example, we have a data frame with the name column and an html column. The html column contains HTML tags that we want to extract useful information from. Specifically, we want to use the attributes of these HTML tags (e.g., data-minute, data-second, data-id) as features.

Solution

To accomplish this task, we can use a combination of R packages and functions.

Step 1: Load necessary libraries

We will need to load the following libraries:

stringi for string manipulation
dplyr for data manipulation
tidyr is not used in this solution but if needed, can be loaded as well

library(stringi)
library(dplyr)

Step 2: Extract numbers from HTML tags

We will use the stri_extract_all_regex() function to extract all numbers from the HTML tags.

mydf$url <- stri_extract_all_regex(str = mydf$url, pattern = "[0-9]+")

This will create a new column called url in the mydf data frame that contains all the numbers extracted from the HTML tags.

Step 3: Unlist and convert to matrix

We will use the unlist() function to convert the vector of numbers into a list, and then use the matrix() function to convert it into a matrix.

mydf$url <- unlist(mydf$url)
matrix(mydf$url, ncol = 4, byrow = T)

This will create a new data frame called mydf.url that contains all the numbers extracted from the HTML tags in a matrix format.

Step 4: Convert to data frame and bind with original data

We will use the data.frame() function to convert the matrix into a data frame, and then use the bind_cols() function to bind it with the original data frame called mydf.

mydf.url <- data.frame(mydf.url)
setNames(mydf.url, c("minute", "second", "ID", "data"))
mydf.url <- bind_cols(mydf.url, mydf["name"])

This will create a new data frame called mydf.url that contains all the numbers extracted from the HTML tags in a data frame format and bound with the original name column.

Step 5: Final result

The final data frame mydf.url now contains all the useful information we need, which is the attributes of the HTML tags (e.g., data-minute, data-second, data-id) as features.

#   name minute second     ID data
#1  John     68     37   8028   68
#2 Steve     69      4 132205   69

Conclusion

In this article, we have explored how to clean and split HTML tags in R. We used a combination of the stringi, dplyr libraries, and some custom code to extract all numbers from the HTML tags and convert them into a data frame with the desired format.

This is just one possible way to accomplish this task, and there may be other approaches depending on the specific requirements and constraints of your project. However, by using these techniques, you should be able to effectively clean and split HTML tags in R and extract useful information from them.

References

“stringi: Fast and convenient string manipulation.” www.stringi.r-project.org.
“dplyr: A Grammar of Data Manipulation.” dplyr.tidyverse.org.

Code

Here is the full code that we used in this article:

library(stringi)
library(dplyr)

mydf$url <- stri_extract_all_regex(str = mydf.url, pattern = "[0-9]+")
mydf.url <- unlist(mydf.url)
matrix(mydf.url, ncol = 4, byrow = T)
mydf.url <- data.frame(mydf.url)
setNames(mydf.url, c("minute", "second", "ID", "data"))
mydf.url <- bind_cols(mydf.url, mydf["name"])

Future Work

In the future, we may want to explore other ways of accomplishing this task. For example, we could use machine learning algorithms to extract features from the HTML tags. We could also consider using other data manipulation libraries such as tidyr or readr.

Last modified on 2023-10-28