The assignment this week we are supposed to choose one of the New York Times APIs,construct an interface in R to read in the JSON data, and to transform it into an R DataFrame.
Here I have imported the httr library to read the url,the tidyverse so I can use dyplr to use the pipes and jsonlite to read the json data.
library(httr)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(jsonlite)## Warning: package 'jsonlite' was built under R version 4.1.3##
## Attaching package: 'jsonlite'## The following object is masked from 'package:purrr':
##
## flattenThe API I had chosen was the New York Times movie reviews API,it had contained many information on nyt critics that reviewed various kinds of movies. I had to tweak the link a few times to make sure the connection was working.Checking the status_code I got 200 which means the connection was successful.
##reading the url from the API I had to tweak it a few times and even then I didn't understand how to hide the api key from the users
r <- GET("https://api.nytimes.com/svc/movies/v2/reviews/picks.json?api-key=cmTGJ9nWOxo50ATlttCSZaOaXI3CNYEe")
r$status_code## [1] 200I used r$content to take a glance at the raw bytes inside the url and then I extract the content of the url using the content function using the text argument.
head(r$content)## [1] 7b 22 73 74 61 74r_data <- content(r, as = "text")From here I just simply used the dplyr function and I piped the r_data into the Json data and then I converted it into a data_frame which I displayed below.
df <- r_data %>%
fromJSON() %>%
as.data.frame()clean_df <-df %>%
select(-c(1:4,7,14:15))
head(clean_df)## results.display_title results.mpaa_rating results.byline
## 1 Everything Everywhere All at Once R A.O. Scott
## 2 You Are Not My Mother Jeannette Catsoulis
## 3 Wood and Water Beatrice Loayza
## 4 Superior Amy Nicholson
## 5 Glenn Kenny
## 6 Întregalde Manohla Dargis
## results.headline
## 1 ‘Everything Everywhere All at Once’ Review: It’s Messy, and Glorious
## 2 ‘You Are Not My Mother’ Review: Parental Misguidance
## 3 ‘Wood and Water’ Review: The Distances Between Us
## 4 ‘Superior’ Review: Double Fantasy
## 5 ‘The Torch’ Review: The Blues Legend Buddy Guy Sows His Legacy
## 6 ‘Intregalde’ Review: Oh, Be Good!
## results.summary_short
## 1 Michelle Yeoh stars as a stressed-out laundromat owner dragged into cosmic battle and genre chaos.
## 2 A lonely teenager is traumatized by her mother’s volatile behavior in this impressive horror debut.
## 3 In this elegant feature debut about modern alienation, the German writer-director Jonas Bak casts his real-life mother as a retired secretary who travels to Hong Kong to visit her estranged son.
## 4 Two identical sisters reunite under mysterious circumstances in a compelling debut feature from Erin Vassilopoulos.
## 5 A new documentary by Jim Farrell focuses on the musician’s sense of mission.
## 6 A Romanian satire charts what happens when some humanitarian aid workers set out to save others (and need to be saved themselves).
## results.publication_date results.opening_date results.date_updated
## 1 2022-03-24 2022-03-25 2022-03-24 16:58:03
## 2 2022-03-24 <NA> 2022-03-24 15:32:03
## 3 2022-03-24 <NA> 2022-03-24 14:09:03
## 4 2022-03-24 <NA> 2022-03-24 11:01:03
## 5 2022-03-17 <NA> 0001-01-01 00:00:00
## 6 2022-03-17 2022-03-18 2022-03-17 15:59:03I had some difficulties understanding the task mostly this is my first time exposed to APIs and reading data from the web.But overall I had fun :).Looking through the data I see there are a bunch of reviews for movies I have never heard of.. There were also a lot of unecessary columns in which I had removed to make it easier for the reader to understand.