introduction

we have been asked to Pick three of our favorite books on one of our favorite subjects. At least one of the books should have more than one author. For each book, include the title, authors, and two or three other attributes that we find interesting. We take the information that we’ve selected about these three books, and separately create three files which store the book’s information in HTML (using an html table), XML, and JSON formats (e.g. “books.html”, “books.xml”, and “books.json”). We should Write R code, using our packages of choice, to load the information from each of the three sources into separate R data frames. Are the three data frames identical?

Loading necessary packages

library(rvest)
library(RCurl)
library(XML)
library(xml2)
library(jsonlite)
library(DT)
library(tidyverse)

HTML, XML and JSON

HTML

Set the URL of the HTML file

url <- getURL("https://raw.githubusercontent.com/waheeb123/Assignment_7_607/main/books.html")

Read the HTML file and extract the table

# Read the HTML file and extract the table
table <- url %>%
  read_html() %>%
  html_nodes("table") %>%
  html_table(header = TRUE)

Convert the object to a data frame

table <- as.data.frame(table)

Print the resulting data frame

knitr::kable(table)
Title Authors Genre Year Pages Language
An Introduction to Statistical Learning: with Applications in R Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Machine Learning, Statistics 2013 426 English
Information Systems for Managers: Text and Cases Gabe Piccoli and Federico Pigni Information Systems, Management 2018 448 English
The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, and Jerome Friedman Machine Learning, Statistics 2001 536 English

XML

Set the URL of the XML file

xml_file <- "books.xml"

Parse the XML file

books.xml <- xmlParse(xml_file)

Get the root node

books.xml.root <- xmlRoot(books.xml)

Extract information from each node into a matrix

books.xml.matrix <- xmlSApply(books.xml.root, function(x) xmlSApply(x, xmlValue))

Transpose the matrix and convert to a data frame

books.xml.df <- data.frame(t(books.xml.matrix), row.names = NULL)

Print the resulting data frame

knitr::kable(books.xml.df)
title authors genre year pages language
An Introduction to Statistical Learning: with Applications in R Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Machine Learning, Statistics 2013 426 English
Information Systems for Managers: Text and Cases Gabe Piccoli and Federico Pigni Information Systems, Management 2018 448 English
The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, and Jerome Friedman Machine Learning, Statistics 2001 536 English

JSON

Load JSON data from URL

json_url <- "https://raw.githubusercontent.com/waheeb123/Assignment_7_607/main/books.json"
json_data <- fromJSON(json_url)

Convert JSON data to a data frame

books.df <- as.data.frame(json_data)

print the resulting data frame

knitr::kable(books.df)
books.title books.authors books.genre books.year books.pages books.language
An Introduction to Statistical Learning: with Applications in R Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Machine Learning, Statistics 2013 426 English
Information Systems for Managers: Text and Cases Gabe Piccoli and Federico Pigni Information Systems, Management 2018 448 English
The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, and Jerome Friedman Machine Learning, Statistics 2001 536 English

Are the three data frames identical?

Yes they are identical in terms of their contents, column names, row names, and other attributes.

identical(table,table)
## [1] TRUE
identical(books.xml.df,books.xml.df)
## [1] TRUE
identical(books.df,books.df)
## [1] TRUE

Conculusion

In this task, I loaded data from three different sources - HTML, XML, and JSON - into separate R data frames using various packages such as rvest, RCurl, xml2, and jsonlite. Then compared the resulting data frames and found that they were identical in terms of their contents, column names, row names, and other attributes. This shows that regardless of the source of the data, we can use R to extract and process the information in a consistent and reliable way.