Loading the HTML, JSON, & XML files into R

###HTML

##This will load the table as a list containing the head and body. To fix this, I will need a handy bit of code that can reconvert this list into a table

books_html <- read_html("https://raw.githubusercontent.com/Mattr5541/DATA-607/main/Week%206/books.html")

##Now the lists will be condensed into a singular list containing the table laid out in a more organized manner. Of course, I still need to convert this into an R dataframe for completeness

books_html <- html_table(books_html)



books_html <- as.data.frame(books_html[[1]])
kable(books_html)
Title Author Genre Release Sales (estimate) Adapted into Movie Format (y/n)
DUNE Frank Herbert Sci-Fi 1965 20,000,000 y
The Three Body Problem Liu Cixin Sci-Fi 2008 8,000,000 n
The Long Earth Terry Pratchett and Stephen Baxter Sci-Fi 2012 100,000,000 n 
###JSON

##THis code will load the json table into R. However, it seems to convert the sales values into scientific notation. Let's fix that
books_json <- fromJSON("https://raw.githubusercontent.com/Mattr5541/DATA-607/main/Week%206/books_new.json")

books_json$`Sales (estimate)` <- format(books_json$`Sales (estimate)`, scientific = F)

kable(books_json)
Title Author Genre Release Sales (estimate) Adapted into Movie Format (y/n)
DUNE Frank Herbert Sci-Fi 1965 20000000 y
The Three Body Problem Liu Cixin Sci-Fi 2008 8000000 n
The Long Earth Terry Pratchett and Stephen Baxter Sci-Fi 2012 100000000 n 
###XML

##And finally, let's load in an XML table

books_xml <- read_xml("https://raw.githubusercontent.com/Mattr5541/DATA-607/main/Week%206/books.xml")

##But it saved every element in the schema as a list, so I'll have to do something that's a little less straightforward than the html table, but it should be manageable

Title <- xml_text(xml_find_all(books_xml, "//Title"))
Author <- xml_text(xml_find_all(books_xml, "//Author"))
Genre <- xml_text(xml_find_all(books_xml, "//Genre"))
Release <- xml_text(xml_find_all(books_xml, "//Release"))
`Sales (estimate)` <- xml_text(xml_find_all(books_xml, "//Sales"))
Adapted_Into_Movie_Format <- xml_text(xml_find_all(books_xml, "//Adapted_Into_Movie_Format"))

books_xml <- data.frame(Title = Title, 
                Author = Author, 
                Genre = Genre, 
                Release = as.numeric(Release),
                `Sales (estimate)` = `Sales (estimate)`, Adapted_Into_Movie_Format = Adapted_Into_Movie_Format)

kable(books_xml)
Title Author Genre Release Sales..estimate. Adapted_Into_Movie_Format
DUNE Frank Herbert Sci-Fi 1965 20,000,000 y
The Three Body Problem Liu Cixin Sci-Fi 2008 8,000,000 n
The Long Earth Terry Pratchett and Stephen Baxter Sci-Fi 2012 100,000,000 n

So, to summarize the results of this exercise, the three dataframes were not entirely identical, and required some cleaning for standardization purposes. However, they were all rather similar.