Pick three of your favorite books on one of your 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 you find interesting. Take the information that you’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”).
library(xml2)
library(RJSONIO)
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## guess_encodingSince the data is stored in a table in the HTML file, we can read the table into a data frame using the xml2 library. Once, the data frame is created, we can make edits using the tidyr library.
#load data
HTML_df <- as.data.frame(read_html('https://raw.githubusercontent.com/yinaS1234/data-607/main/data%20607%20lab%207/book.html')
%>% html_table(fill = TRUE))
#separate the authors into two columns
HTML_df <- HTML_df %>%
mutate(Author1 = str_remove(str_match(Author.s., '.*,'),','),
Author2 = str_remove(str_match(Author.s., ',.*'),', ')) %>%
select(Title, Author1, Author2, Publisher, Publication.Year)
HTML_df## Title Author1 Author2 Publisher
## 1 R for Data Science Garret Grolemund Hadley Wickham O'Reilly
## 2 Text Mining with R: A Tidy Approach Julia Silge David Robinson O'Reilly
## 3 Data Science for Business Tom Fawcett Foster Provost O'Reilly
## Publication.Year
## 1 2017
## 2 2017
## 3 2013The XML file requires more attention to it as there are two authors for each book, and are saved as attributes in the authors element.
My approach is to search for elements and attributes separately and combine them together.
#load data
XML_doc <- read_xml('https://raw.githubusercontent.com/yinaS1234/data-607/main/data%20607%20lab%207/book.xml')
#search xml file for title elements
XML_title <- XML_doc %>%
xml_find_all('//title') %>%
xml_text() %>%
trimws()
#search xml file for the first attribute in the authors element
XML_author1 <- XML_doc %>%
xml_find_all('//authors') %>%
xml_attr('first')
#search xml file for the second attribute in the authors element
XML_author2 <- XML_doc %>%
xml_find_all('//authors') %>%
xml_attr('second')
#search xml file for publisher element
XML_publisher <- XML_doc %>%
xml_find_all('//publisher') %>%
xml_text() %>%
trimws()
#search xml file for publication year element
XML_publication_year <- XML_doc %>%
xml_find_all('//publication_year') %>%
xml_text() %>%
trimws()
#combining all the vectors into a dataframe
XML_df <- data.frame(cbind(Title=XML_title, Author1=XML_author1, Author2=XML_author2,
Publisher=XML_publisher, Publication_year=XML_publication_year))
XML_df## Title Author1 Author2 Publisher
## 1 R for Data Science Garret Grolemund Hadley Wickham O'Reilly
## 2 Text Mining with R: A Tidy Approach Julia Silge David Robinson O'Reilly
## 3 Data Science for Business Tom Fawcett Foster Provost O'Reilly
## Publication_year
## 1 2017
## 2 2017
## 3 2013My approach is similar to parsing the XML file, that is to save values separately by their keys, then to combine together into a dataframe.
#load data
jbooks <- fromJSON(content = 'https://raw.githubusercontent.com/yinaS1234/data-607/main/data%20607%20lab%207/book.json')
#unlisting the data to search by their keys
jvec <- unlist(jbooks, recursive = TRUE, use.names = TRUE)
#search for title values
js_title <- data.frame(jvec[str_detect(names(jvec), 'title')])
#search for first values
js_author1 <- data.frame(jvec[str_detect(names(jvec), 'first')])
#search for second values
js_author2 <- data.frame(jvec[str_detect(names(jvec), 'second')])
#search for publisher values
js_publisher <- data.frame(jvec[str_detect(names(jvec), 'publisher')])
#search for publication_year values
js_publication_year <- data.frame(jvec[str_detect(names(jvec), 'publication_year')])
#combine the data
js_df <- cbind(js_title, js_author1, js_author2, js_publisher, js_publication_year)
colnames(js_df) <- c('Title', 'Author1', 'Author2', 'Publisher', 'Publication_Year')
js_df## Title Author1 Author2 Publisher
## 1 R for Data Science Garret Grolemund Hadley Wickham O'Reilly
## 2 Text Mining with R: A Tidy Approach Julia Silge David Robinson O'Reilly
## 3 Data Science for Business Tom Fawcett Foster Provost O'Reilly
## Publication_Year
## 1 2017
## 2 2017
## 3 2013The data was stored differently in each file and my approach to parse the data changed accordingly. However,the final data frames are identical.