Assignment- extract data using NYT API

Can College Graduation Rate be used as precdictor for number of jobs in county (James Topor’s )

1. Load the packages

library(stringr)
library(httr)

## Warning: package 'httr' was built under R version 3.2.4

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.2.3

## 
## 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

2. Extract data using API

y<-NULL
book_final<-NULL

x<-c("2015-01-01","2015-02-02","2015-03-02","2015-04-02","2015-05-02","2015-06-02","2015-07-02","2015-07-02","2015-08-02","2015-09-02","2015-10-02","2015-11-02","2015-12-02")

for (i in 1:12)
{
y[i]<-paste("http://api.nytimes.com/svc/books/v3/lists/",x[i],"/hardcover-fiction.json?callback=books&api-key=d812579c567895749c504107780ae56d:14:74860761")
y<-str_replace_all(y," ","")
}

 
for (i in 1:length(y))
  
{
url<-y[i]  
list<-GET(url)
content<-content(list)
book<-content$results$books
book_list<-as.data.frame(do.call("rbind",book),stringsAsFactors = FALSE)
book_final<-rbind(book_list,book_final)
  
}

colnames(book_final)

##  [1] "rank"                 "rank_last_week"       "weeks_on_list"       
##  [4] "asterisk"             "dagger"               "primary_isbn10"      
##  [7] "primary_isbn13"       "publisher"            "description"         
## [10] "price"                "title"                "author"              
## [13] "contributor"          "contributor_note"     "book_image"          
## [16] "amazon_product_url"   "age_group"            "book_review_link"    
## [19] "first_chapter_link"   "sunday_review_link"   "article_chapter_link"
## [22] "isbns"

book_final<-select(book_final,title,author,description,rank,rank_last_week,weeks_on_list,publisher)
book_final$title<-as.character(book_final$title)
book_final$author<-as.character(book_final$author)  
book_final$description<-as.character(book_final$description) 
book_final$publisher<-as.character(book_final$publisher) 
book_final$rank<-as.numeric(book_final$rank) 
book_final$rank_last_week<-as.numeric(book_final$rank_last_week)
book_final$weeks_on_list<-as.numeric(book_final$weeks_on_list)

3. Analysis of top book list

# top books on the list for more than 10 weeks 
par(mfrow = c(2, 2)) 

barplot(table(book_final[book_final$weeks_on_list>10,]$title),las=2,cex.names = 1)
barplot(table(book_final[book_final$weeks_on_list>10,]$author),las=2,cex.names = 1)

book_final$title[which.max(book_final$weeks_on_list)]

## [1] "ALL THE LIGHT WE CANNOT SEE"

book_final$author[which.max(book_final$weeks_on_list)]

## [1] "Anthony Doerr"