HTML and JSON: Book Comparison

Author

Lakeranie Boodram

Introduction

The assignment consists of learning how to use HTML and JSON files in R. The books chosen for this assignment are It Ends with Us, The Silent Patient, and The Wife Between Us, as these works share themes of relationships, secrets, and struggles.

I created the HTML file and the JSON file in the same way using the same book data including title, authors, year, publisher, and genre information.

In R, I will import both files in two separate data frames and compare them with each other.

Load Required Packages

library(rvest)
library(jsonlite)
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Import HTML Data

In this section, I will import the HTML file that I manually created with information about my three selected books. I will use the rvest package to read the HTML file and extract the table into an R data frame.

# Read the HTML file from GitHub
html_url <- "https://raw.githubusercontent.com/LBoodram26/Week7_Book-Comparison-/main/books.html"

html_file <- read_html(html_url)

# Extract the HTML table
books_html <- html_file |>
  html_element("table") |>
  html_table()

# Convert to a data frame
books_html <- as.data.frame(books_html)

# Display the HTML data
print(books_html)

                Title                         Authors Year          Publisher
1     It Ends with Us                  Colleen Hoover 2016        Atria Books
2  The Silent Patient                Alex Michaelides 2019      Celadon Books
3 The Wife Between Us Greer Hendricks; Sarah Pekkanen 2018 St. Martin's Press
                   Genre
1   Contemporary Romance
2 Psychological Thriller
3 Psychological Thriller

Import JSON Data

I will use the jsonlite package to import my JSON file directly from GitHub. The book records will be converted into a separate R data frame.

# Read the JSON file from GitHub
json_url <- "https://raw.githubusercontent.com/LBoodram26/Week7_Book-Comparison-/main/books.json"

json_file <- fromJSON(json_url)

# Convert to a data frame
books_json <- as.data.frame(json_file$books)

# Display the JSON data
print(books_json)

                Title                         Authors Year          Publisher
1     It Ends with Us                  Colleen Hoover 2016        Atria Books
2  The Silent Patient                Alex Michaelides 2019      Celadon Books
3 The Wife Between Us Greer Hendricks; Sarah Pekkanen 2018 St. Martin's Press
                   Genre
1   Contemporary Romance
2 Psychological Thriller
3 Psychological Thriller

Compare HTML and JSON Data Frames

In this section, I will compare the two data frames to determine whether they contain identical information. I will use the identical() function in R to check if both data frames have the same values, column names, data types, and structure.

# Compare the two data frames
comparison_result <- identical(books_html, books_json)

# Display the comparison result
print(comparison_result)

[1] TRUE

# Check the structure of each data frame
str(books_html)

'data.frame':   3 obs. of  5 variables:
 $ Title    : chr  "It Ends with Us" "The Silent Patient" "The Wife Between Us"
 $ Authors  : chr  "Colleen Hoover" "Alex Michaelides" "Greer Hendricks; Sarah Pekkanen"
 $ Year     : int  2016 2019 2018
 $ Publisher: chr  "Atria Books" "Celadon Books" "St. Martin's Press"
 $ Genre    : chr  "Contemporary Romance" "Psychological Thriller" "Psychological Thriller"

str(books_json)

'data.frame':   3 obs. of  5 variables:
 $ Title    : chr  "It Ends with Us" "The Silent Patient" "The Wife Between Us"
 $ Authors  : chr  "Colleen Hoover" "Alex Michaelides" "Greer Hendricks; Sarah Pekkanen"
 $ Year     : int  2016 2019 2018
 $ Publisher: chr  "Atria Books" "Celadon Books" "St. Martin's Press"
 $ Genre    : chr  "Contemporary Romance" "Psychological Thriller" "Psychological Thriller"

Comparison Results

The comparison returned TRUE, which means that both data frames are identical. Each data frame contains three rows and five columns, and the information matches across both files.

This shows that even though HTML and JSON organize data differently, they can produce the same results when imported into R.

Conclusion

Two files were created manually with the use of HTML and JSON for recording information about three books for this project. Both files were uploaded on GitHub, and then R was used to transfer the data into separate data frames.

The data frames were compared through the identical() function, and the outcome indicated TRUE, which dealt with both files having the same data, from the title of the books to their authors and publishing information.

Working on this task provided me with insight into the issue of various file formats storing the same data as well as how R packages can be helpful for data manipulation and comparison.

References

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2nd ed.). O’Reilly Media. https://r4ds.hadley.nz/

R Core Team. (2026). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/

Ooms, J. (n.d.). jsonlite: A robust, high performance JSON parser and generator for R. https://cran.r-project.org/package=jsonlite

Wickham, H. (n.d.). rvest: Easily harvest (scrape) web pages. https://cran.r-project.org/package=rvest

Data Sources

The HTML and JSON files used in this assignment are available in my public GitHub repository:

https://github.com/LBoodram26/Week7_Book-Comparison-