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Created three files which store the books’ information in HTML(using an html table), XML, and JSON formats.(Uploaded on Github). For XML file had to work with local as pulling from git is giving issues.

library(XML)
library(RJSONIO)
library(rvest)
## Warning: package 'rvest' was built under R version 4.0.4
library(RCurl)
 
library(jsonlite)
## 
## Attaching package: 'jsonlite'
## The following objects are masked from 'package:RJSONIO':
## 
##     fromJSON, toJSON
library(xml2)
library(plyr)

Conclusion : While the HTML, JSON and XML files have different structures, after manipulation process, the three data frames created are very similar especially for the ones from HTML and XML. xml data is little difficult to manipulate as it has been demostrated.For all other cases except html and json I loaded 2 authors under same but for xml created multilist for authors.

# 1. HTML - read and loaded data to data frame

htmlURL <- "https://raw.githubusercontent.com/yathdeep/data607_labs/main/books.html"

readHtml <- read_html(htmlURL)
tables <- html_nodes(readHtml,"table")
tables_ls <- html_table(tables, fill = TRUE)
booksHTML.df <- as.data.frame(tables_ls)

booksHTML.df
##                                                        title              ISBN
## 1                                   Python for Data Analysis 978-1-449-31979-3
## 2 Hands-On Machine Learning with Scikit-Learn and TensorFlow 978-1-491-96229-9
## 3                                         R for Data Science 978-1-491-91039-9
##                          authors price
## 1 Julie Steele;Meghan Blanchette 39.99
## 2                   Nicole Tache 49.99
## 3 Marie Beaugureau;Mike Loukides 39.99
#2. XML - loaded xml on local machine - as loading from git was giving issues.

# Convert the input xml file to a data frame.
#first way of showing data
xmldataframe <- xmlToDataFrame("books.xml")
xmldataframe
##                                                        title              isbn
## 1                                   Python for Data Analysis 978-1-449-31979-3
## 2 Hands-On Machine Learning with Scikit-Learn and TensorFlow 978-1-491-96229-9
## 3                                         R for Data Science 978-1-491-91039-9
##        Authors price
## 1       DS1DS2 39.99
## 2 Nicole Tache 49.99
## 3       AJ1AJ2 39.99
#Second way of showing data
ldply(xmlToList("books.xml"), data.frame)
##    .id                                                      title
## 1 book                                   Python for Data Analysis
## 2 book Hands-On Machine Learning with Scikit-Learn and TensorFlow
## 3 book                                         R for Data Science
##                isbn Authors.Author Authors.Author.1 price       Author
## 1 978-1-449-31979-3            DS1              DS2 39.99         <NA>
## 2 978-1-491-96229-9           <NA>             <NA> 49.99 Nicole Tache
## 3 978-1-491-91039-9            AJ1              AJ2 39.99         <NA>
# 3. JSON - read and loaded data to data frame
jsonURL <- "https://raw.githubusercontent.com/yathdeep/data607_labs/main/books.json"

#Convert data in JSON into a data frame
book.json.dataframe <- flatten(as.data.frame(fromJSON(jsonURL)))

book.json.dataframe
##                                                        title              ISBN
## 1                                   Python for Data Analysis 978-1-449-31979-3
## 2 Hands-On Machine Learning with Scikit-Learn and TensorFlow 978-1-491-96229-9
## 3                                         R for Data Science 978-1-491-91039-9
##                          authors price
## 1 Julie Steele;Meghan Blanchette 39.99
## 2                   Nicole Tache 49.99
## 3 Marie Beaugureau;Mike Loukides 39.99