library(rvest)
library(jsonlite)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionWeek7
Approach
Introduction
For this assignment, My goal is to practice working with HTML and JSON data formats. These formats are mostly used when collecting data from the web, so understanding structure is important before learning web scraping and APIs. For this assignment, I selected three books on the subject of entrepreneurship and business. For each book, I saved some basic information such as the title, authors, publication year, publisher, and ISBN.
After collecting this information, I created two files:
HTML filethat contains a table with the book information.JSON filethat contains the same data using JSON structure
Both files store exactly the same dataset but in different formats.
Data Selection
The books I selected are related to entrepreneurship and starting a business, which is a topic I am interested in. I chose three books and made sure that at least one book has multiple authors, which is requiremenst of assignment.
For each book, I recorded the following attributes:
- Title
- Authors
- Publication Year
- Publisher
- ISBN
These attributes were organized into a small dataset containing three books.
File Creation
After that, I manually created two data files to store the dataset.
The first file is an HTML file (books.html) that contains a table with rows and columns including the book information.
The second file is a JSON file (books.json) that stores the same book data using JSON structure, where each book is represented as an object inside a list.
Both files were uploaded to GitHub so they can be accessed publicly.
HTML file:
https://raw.githubusercontent.com/sinemkilicdere/Data607/refs/heads/main/Week7/books.html
JSON file:
https://raw.githubusercontent.com/sinemkilicdere/Data607/refs/heads/main/Week7/books.json
Plan for Analysis
After creating the HTML and JSON files, I will use R to load both datasets.First, I will read the HTML table using an R package that can extract tables from HTML documents. Next, I will read the JSON file and convert the JSON structure into an R data frame.
Finally, I will compare the two data frames to determine whether they contain the same information. This will confirm that both files represent the same dataset even though they are stored in different formats.
Book Sources
Books were collected from Google Books:
Entrepreneurship and Small Business
https://www.google.com/books/edition/Entrepreneurship_and_Small_Business/QG1NEAAAQBAJ
Diversity and Entrepreneurship
https://www.google.com/books/edition/Diversity_and_Entrepreneurship/cD_tAAAAMAAJ
Start Your Own Business
https://www.google.com/books/edition/Start_Your_Own_Business/bQpDEAAAQBAJ
Code Base
In this section, I use R to load both the HTML and JSON files and convert them into data frames. Then I compare the two datasets to check if they are identical.
First, I load the HTML file from GitHub and extract the table.
html_url <- "https://raw.githubusercontent.com/sinemkilicdere/Data607/refs/heads/main/Week7/books.html"
html_page <- read_html(html_url)
html_table <- html_table(html_page)[[1]]
html_table# A tibble: 3 × 5
Title Authors Publication_Year Publisher ISBN
<chr> <chr> <int> <chr> <dbl>
1 Entrepreneurship and Small Business Paul B… 2022 Bloomsbu… 9.78e12
2 Diversity and Entrepreneurship Vaness… 2006 Edward E… 9.78e12
3 Start Your Own Business The St… 2021 Entrepre… 9.78e12Next, I load the JSON file from GitHub.
json_url <- "https://raw.githubusercontent.com/sinemkilicdere/Data607/refs/heads/main/Week7/books.json"
json_data <- fromJSON(json_url)
json_table <- json_data$books
json_table Title
1 Entrepreneurship and Small Business
2 Diversity and Entrepreneurship
3 Start Your Own Business
Authors Publication_Year
1 Paul Burns 2022
2 Vanessa Ratten 2006
3 The Staff of Entrepreneur Media, Cheryl Kimball 2021
Publisher ISBN
1 Bloomsbury Publishing 9781352012364
2 Edward Elgar Publishing 9781845422892
3 Entrepreneur Press 9781599186613I make both data frames use the same data types so they can be compared correctly
html_table <- as.data.frame(html_table)
json_table <- as.data.frame(json_table)
html_table$Title <- as.character(html_table$Title)
html_table$Authors <- as.character(html_table$Authors)
html_table$Publication_Year <- as.numeric(html_table$Publication_Year)
html_table$Publisher <- as.character(html_table$Publisher)
html_table$ISBN <- as.character(html_table$ISBN)
json_table$Title <- as.character(json_table$Title)
json_table$Authors <- as.character(json_table$Authors)
json_table$Publication_Year <- as.numeric(json_table$Publication_Year)
json_table$Publisher <- as.character(json_table$Publisher)
json_table$ISBN <- as.character(json_table$ISBN)Comparing 2 datasets
identical(html_table, json_table)[1] TRUEComparison Result
After loading both the HTML and JSON files into R, I compared the two data frames using the identical() function.
Before comparing them, I made sure that both datasets used the same structure and data types. Sometimes, numeric values or character values may not match exactly unless they are converted to the same type. After standardizing the data types, I used the identical() function to compare the two datasets. The result returned TRUE, which means both data frames contain the same information even they are in different format.
Conclusion
I created two files containing the same book dataset: one an HTML table and another JSON format. Then I used R to load both files, convert them into data frames and compare them. This assignment helped me understand how different data formats can represent the same dataset and how R can be used to read and analyze these formats.