library(tidyverse)
library(janitor)
library(jsonlite)Final Project Testing Feasibility
Introduction
I signed up for the Article Search API from the New York Times. Below is an interface in R to read in the JSON data and transform it into an R data frame. I also attempt to merge this data with data from Letterboxd.
Connect to NYT API
First, I sign up for an API key on the New York Times website. Here, I use the rstudioapi::skForPassword() function to keep my API key private when running the code in R Studio. For running and rendering all code for the purposes of this markdown, I also alternatively save the API key in an R chunk that I elect not to include in this published version.
The base HTTP request URL is defined below as well. I will be using the Article Search API to get New York Times movie reviews, so I define the filter query accordingly.
api_key <- rstudioapi::askForPassword("Authorization Key")Error: RStudio not runningbase_url <-
"https://api.nytimes.com/svc/search/v2/articlesearch.json?"
filter <-
'section_name%3A%22Movies%22%20AND%20type_of_material%3A%22Review%22'Here, I create is a function to pull data based on the filter, page number, and wait time parameters, starting from the newest articles.
get_movies <- function(filter, num_pages, timeout) {
# initialize data frame
df <- tibble()
for (page in seq_len(num_pages)) {
# set url
url <- paste0(base_url,
"fq=",
filter,
"sort=newest&page=",
page,
"&api-key=",
api_key)
# initialize success as false before we get a success
success <- FALSE
while (success == FALSE) {
# while success is false,
tryCatch({
df <- fromJSON(url, flatten = TRUE)$response$docs |>
clean_names() |> rbind(df, .data) # append to df
success <- TRUE # Set success to TRUE if no error occurs
},
# if error,
error = function(e) {
# Add a delay between requests to avoid hitting the rate limit
Sys.sleep({timeout})
})
}
}
# Return the resulting data frame
return(df)
}Load JSON NYT data into R data frame
I call the function, iterating through 20 pages of JSON data, appending them together as the data frame movies_df. Note that I add a delay in the loop to avoid hitting the rate limit and getting a 429 error.
movies_df <- get_movies(filter, 20, 10)Here is the R data frame of NYT JSON movie review data loaded from the NYT API:
head(movies_df) abstract
1 Mark Wahlberg stars as an American soldier in Afghanistan in a film based on Marcus Luttrell’s memoir.
2 “G-Force” manages to be fairly entertaining in that exhausting, rackety, late-summer-kiddie-movie way.
3 “The Son of No One” a police corruption drama, stars Channing Tatum, Ray Liotta, Juliette Binoche and Al Pacino.
4 In this pleasurably intelligent movie, Jafar Panahi explores the divide between fiction and documentary, past and present.
5 Hong Sang-soo’s film, structured as a letter with the pages out of order, is one of his most daring gambits.
6 Young black men headed for trouble. Pat and oversimplified.
web_url
1 https://www.nytimes.com/2013/12/25/movies/mark-wahlberg-stars-in-lone-survivor-by-peter-berg.html
2 https://www.nytimes.com/2009/07/24/movies/24gforce.html
3 https://www.nytimes.com/2011/11/04/movies/the-son-of-no-one-with-channing-tatum-review.html
4 https://www.nytimes.com/2019/03/07/movies/3-faces-review.html
5 https://www.nytimes.com/2020/06/11/movies/hill-of-freedom-review.html
6 https://www.nytimes.com/1995/03/24/movies/film-festival-review-drive-they-said-and-drive-they-did.html
snippet
1 Mark Wahlberg stars as an American soldier in Afghanistan in a film based on Marcus Luttrell’s memoir.
2 “G-Force” manages to be fairly entertaining in that exhausting, rackety, late-summer-kiddie-movie way.
3 “The Son of No One” a police corruption drama, stars Channing Tatum, Ray Liotta, Juliette Binoche and Al Pacino.
4 In this pleasurably intelligent movie, Jafar Panahi explores the divide between fiction and documentary, past and present.
5 Hong Sang-soo’s film, structured as a letter with the pages out of order, is one of his most daring gambits.
6 Young black men headed for trouble. Pat and oversimplified.
lead_paragraph
1 You can tell from the title that “Lone Survivor” is not messing around. Spoiler sensitivity is for sissies and dilettantes: Real men face the inevitable with stoical resolve. So when the movie sends four guys on a dangerous mission, you can be reasonably certain that only one of them is coming back, and you already know (as soon as the credits are over) that it will be the one played by Mark Wahlberg.
2 “G-Force” \u0097 perhaps the most expensive 3-D house-pet action movie ever made and, I’m willing to concede, probably one of the best \u0097 is about an hour shorter than “Transformers: Revenge of the Fallen,” half as pretentious and twice as witty. Call this faint praise, if you like. “G-Force,” as loud and enervating as the words “A Jerry Bruckheimer Production” might lead you to expect, can hardly be accused of modesty, but on the other hand no film full of talking guinea pigs would be so rash as to attempt the kind of lumbering gravitas that Michael Bay has seen fit to bestow on a bunch of toys.
3 The density of surreal urban grit — filthy kitchens, graffiti-stained hallways, broken-down furniture, castoff junk and layers of caked blood and dirt — cannot camouflage the fundamental fatuity of Dito Montiel’s police corruption drama, “The Son of No One.”
4 A road movie that opens into a political allegory, “3 Faces” is filled with unexpected turns. It is the latest from the Iranian filmmaker Jafar Panahi, a master of narrative diversion, who again has taken the twinned roles of director and driver, as he did in the documentary “Taxi” (2015). Here, playing himself, or a version of the filmmaker Jafar Panahi, he spends a large part of the movie behind the wheel of an S.U.V., motoring through the Iranian countryside to help an actress find a missing, possibly dead woman. They succeed but also find other women, including one who’s a ghost in a haunted world.
5 The South Korean director Hong Sang-soo, who began his career completing films at a relatively swift pace, has been on a real tear lately. Because of the vicissitudes of distribution, viewers in the United States who follow his work are catching up with it out of production order. “Yourself and Yours,” reviewed last week, was made in 2016, and this week’s “Hill of Freedom” in 2014.
6 There is a scene at the center of "New Jersey Drive" that captures everything this fast, thoughtful street drama tries to do: a group of black teen-agers in Newark steal a police car from the cop's own driveway and go on a joy ride. Suddenly, the audience is rooting for a bunch of car thieves, young men clearly driving off to destruction. Yet the moment is gleeful. This white cop is corrupt and physically brutal; he deserved it. And the scene takes a comic turn, as the thieves pull over a carload of geeky white guys and discover what it's like to have power. Midget, the tough leader of the thieves, gets on the speaker and in a dead-on white-bread accent scares the preppy types to death. They put their hands up, Midget warns that they have the right to remain stupid, and races on.
print_section print_page source
1 C 16 The New York Times
2 C 6 The New York Times
3 C 10 The New York Times
4 C 8 The New York Times
5 C 4 The New York Times
6 C 8 The New York Times
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mediumThreeByTwo252, mediumThreeByTwo378, threeByTwoLargeAt2X, threeByTwoMediumAt2X, threeByTwoSmallAt2X, articleInline, hpSmall, blogSmallInline, mediumFlexible177, videoSmall, videoHpMedium, videoSixteenByNine600, videoSixteenByNine540, videoSixteenByNine495, videoSixteenByNine390, videoSixteenByNine1050, videoSixteenByNine480, videoSixteenByNine310, videoSixteenByNine225, videoSixteenByNine96, videoSixteenByNine768, videoSixteenByNine150, videoSixteenByNineJumbo1600, miniMoth, windowsTile336H, videoFifteenBySeven1305, facebookJumbo, watch308, watch268, mobileMasterAt3x, images/2020/06/12/arts/hillof1/hillof1-articleLarge.jpg, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 600, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 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6 NULL
keywords
1 creative_works, persons, persons, subject, Lone Survivor (Movie), Wahlberg, Mark, Berg, Peter, Movies, 1, 2, 3, 4, N, N, N, N
2 subject, persons, Movies, Bruckheimer, Jerry, 1, 2, N, N
3 subject, persons, persons, persons, persons, persons, Movies, Montiel, Dito, Tatum, Channing, Holmes, Katie, Morgan, Tracy, Binoche, Juliette, 1, 2, 3, 4, 5, 6, N, N, N, N, N, N
4 subject, creative_works, persons, persons, Movies, 3 Faces (Movie), Panahi, Jafar, JAFARI, BEHNAZ, 1, 2, 3, 4, N, N, N, N
5 subject, persons, creative_works, Movies, Hong Sang-soo, Hill of Freedom (Movie), 1, 2, 3, N, N, N
6 subject, subject, creative_works, Reviews, MOTION PICTURES, New Jersey Drive (Movie), 1, 2, 3, N, N, N
pub_date document_type news_desk section_name
1 2013-12-24T21:55:30+0000 article Culture Movies
2 2009-07-23T22:10:04+0000 article Weekend Movies
3 2011-11-03T23:41:33+0000 article Weekend Movies
4 2019-03-07T16:16:04+0000 article Weekend Movies
5 2020-06-11T11:00:04+0000 article Weekend Movies
6 1995-03-24T05:00:00+0000 article Weekend Desk Movies
type_of_material id
1 Review nyt://article/01b9319e-11f7-5dc9-8ac5-b01b02ff262c
2 Review nyt://article/01ba7bc7-cc7d-5145-831e-a576e078fcdb
3 Review nyt://article/01bb395f-06fb-5ee2-9378-e30873980b51
4 Review nyt://article/01bc1603-7e0e-5c3d-b2e0-534bb42d56be
5 Review nyt://article/01bc8e90-4f37-57e0-8b79-15a29ba81486
6 Review nyt://article/01bf335d-5790-54cd-aeda-18ef9dc4d4b6
word_count uri
1 653 nyt://article/01b9319e-11f7-5dc9-8ac5-b01b02ff262c
2 768 nyt://article/01ba7bc7-cc7d-5145-831e-a576e078fcdb
3 608 nyt://article/01bb395f-06fb-5ee2-9378-e30873980b51
4 887 nyt://article/01bc1603-7e0e-5c3d-b2e0-534bb42d56be
5 397 nyt://article/01bc8e90-4f37-57e0-8b79-15a29ba81486
6 738 nyt://article/01bf335d-5790-54cd-aeda-18ef9dc4d4b6
headline_main
1 Soldiers Fighting, but Coping Most of All
2 Keeping the World Safe for Rodents
3 A Cop From the Projects and a Boy’s Guilty Secret
4 ‘3 Faces’ Review: On the Road in Iran (and Off It, Too)
5 ‘Hill of Freedom’ Review: A Lover’s Frustrated Quest
6 Drive, They Said, And Drive They Did
headline_kicker headline_content_kicker
1 Movie Review NA
2 Movie Review | 'G-Force' NA
3 Movie Review | 'The Son of No One' NA
4 Critic’s Pick NA
5 Critic’s Pick NA
6 FILM FESTIVAL REVIEW NA
headline_print_headline headline_name
1 Soldiers Fighting, But Coping Most of All NA
2 Keeping the World Safe for Rodents NA
3 A Cop From the Projects And a Boy’s Guilty Secret NA
4 On the Road in Iran (and Off It, Too) NA
5 Frustrated Hearts Beat Out of Time NA
6 FILM FESTIVAL REVIEW; Drive, They Said, And Drive They Did NA
headline_seo headline_sub byline_original
1 NA NA By A.O. Scott
2 NA NA By A.O. Scott
3 NA NA By Stephen Holden
4 NA NA By Manohla Dargis
5 NA NA By Glenn Kenny
6 NA NA By Caryn James
byline_person byline_organization
1 A., O., Scott, NA, NA, reported, , 1 <NA>
2 A., O., Scott, NA, NA, reported, , 1 <NA>
3 Stephen, NA, Holden, NA, NA, reported, , 1 <NA>
4 Manohla, NA, Dargis, NA, NA, reported, , 1 <NA>
5 Glenn, NA, Kenny, NA, NA, reported, , 1 <NA>
6 Caryn, NA, James, NA, NA, reported, , 1 <NA>Cleaning NYT data
Before I finalize this data set, I subset the data frame to several variables of interest and do some variable cleaning.
movies_clean <- movies_df |>
select(
headline_main,
headline_kicker,
headline_print_headline,
byline_original,
abstract,
lead_paragraph,
keywords,
pub_date
) |>
mutate(key = row_number(),
pub_date = as_datetime(pub_date)) |>
arrange(desc(pub_date))I also unnest the data by creating two unique tables–a movie table and a nyt_movies table–since the keywords variable in the original data frame was a list-column.
# create normal movies table
movies <- movies_clean |>
select(-keywords)
head(movies) headline_main
1 ‘Artie Shaw: Time Is All You’ve Got’ Review: A Lens on a Jazz Luminary
2 ‘Dancing in the Dust’ Review: Marriage, Money and Morals
3 ‘Sublime’ Review: Two Boys, One in Love
4 ‘Cadejo Blanco’ Review: Negotiating Guatemalan Street Life
5 ‘Showing Up’ Review: Making Art in All Its Everyday Glory
6 ‘The First Fallen’ Review: A Remembrance of Those Passed
headline_kicker headline_print_headline
1 Critic’s Pick Artie Shaw: Time Is All You’ve Got
2 <NA> Dancing in the Dust
3 <NA> Sublime
4 <NA> Cadejo Blanco
5 Critic’s Pick Sculptures Can Be Shaped, But Life Isn’t Quite So Easy
6 <NA> The First Fallen
byline_original
1 By Glenn Kenny
2 By Ben Kenigsberg
3 By Erik Piepenburg
4 By Concepción de León
5 By Manohla Dargis
6 By Lisa Kennedy
abstract
1 Brigitte Berman’s dazzling 1985 look at the self-taught virtuoso clarinetist and bandleader is showing after a restoration.
2 The first feature from Asghar Farhadi (“A Separation”) arrives in a remastered version on streaming services.
3 A teenager dreams of pop songs, and his best friend, in Mariano Biasin’s tender gay coming-of-age drama.
4 In this film a young woman searching for her missing sister infiltrates a gang, but the focus is diffuse and the promised thriller never materializes.
5 In their latest movie together, Michelle Williams and Kelly Reichardt paint a portrait of an artist who’s a real and wonderful piece of work.
6 A director crafts an elegiac tribute set during the dawn of the AIDS crisis in Brazil.
lead_paragraph
1 The documentarian Brigitte Berman has made two spectacular pictures about American jazz pioneers. The first, “Bix: ‘Ain’t None of Them Play Like Him Yet’” (1981), chronicled the life of the brilliant and tragically short-lived cornetist and composer Bix Beiderbecke. It screened in a restoration at Film Forum a couple of years back. Now, her follow-up to that movie, “Artie Shaw: Time Is All You’ve Got” (1985), is similarly restored and booked at Film Forum.
2 The debut feature from the Iranian director Asghar Farhadi, “Dancing in the Dust” never received a proper release in the United States. Now, 20 years after it was first shown, it is surfacing in a remastered version on streaming platforms. Two of Farhadi’s subsequent features (“A Separation” and “The Salesman”) won the Oscar for what was then called the best foreign language film, and he has become a reliable, methodical specialist in a certain brand of domestic drama. In Farhadi films, right and wrong are never clear-cut, and the suspense is intertwined with mounting moral complexities.
3 In high school I had a crush on my best friend. I sat with him when he played piano in church. I was furious inside when he told me he liked a girl. When I came out to him in college he said “so what?” and I cried. Tweak some details and switch the setting from suburban America to a coastal town in Argentina and it’s also the entire plot of Mariano Biasin’s familiar but muted coming-out drama “Sublime.”
4 The description of “Cadejo Blanco,” directed by Justin Lerner, reads like a thriller: After her sister disappears, a young Guatemalan woman infiltrates a gang to try to find her. But there are few thrills in the film, which moves slowly and with too much ease through the world of Guatemalan gangs. It’s beautifully shot and gives an authentic view of street life there, but the characters’ journeys are not sufficiently developed, and the resolutions feel unearned.
5 The stubbornly independent filmmaker Kelly Reichardt makes small-scale movies rooted in specific worlds, both inner and outer; nearly all take place in Oregon, where she’s long lived and worked. She traveled back in time for her last movie, “First Cow,” a moving chronicle of love, land and capitalism set in the Oregon Territory in the 19th century. Reichardt is back on more familiar ground in her latest, “Showing Up,” a wonderful slice of life that’s set in present-day Portland and is about something that she knows intimately: making art.
6 At the start of his pandemic film, “The First Fallen,” the Brazilian director Rodrigo de Oliveira stakes a claim to the elegiac: A soldier steps through a jungle ruminating in voice-over about those who die early in battle. The significance of this scene — which cuts to Suzano (Johnny Massaro) watching his nephew, Muriel (Alex Bonin), in the surf — is made clearer later. But the meaning of the blood trickling from Suzano’s nose is a given from the beginning.
pub_date key
1 2024-01-04 12:00:09 196
2 2023-09-28 11:00:02 144
3 2023-06-20 17:48:07 195
4 2023-06-15 11:00:12 26
5 2023-04-06 17:44:17 78
6 2023-02-21 20:48:29 158# Regular expression to get text within final parenthetical
pattern_type <- ".*\\(([^\\(\\)]+)\\)$"
pattern_title <- "(.*) \\(([^\\(\\)]+)\\)$"
# create normal keywords table
nyt_movies <- movies_clean |>
unnest(keywords, keep_empty = TRUE) |>
filter(name == "creative_works") |>
mutate(creative_work_type = str_match(value, pattern_type)[,2] |>
tolower(),
name = str_match(value, pattern_title)[,2] |>
tolower()) |>
filter(creative_work_type == "movie") |>
select(-c(value:major, creative_work_type))
head(nyt_movies)# A tibble: 6 × 9
headline_main headline_kicker headline_print_headl…¹ byline_original abstract
<chr> <chr> <chr> <chr> <chr>
1 ‘Artie Shaw: … Critic’s Pick Artie Shaw: Time Is … By Glenn Kenny Brigitt…
2 ‘Dancing in t… <NA> Dancing in the Dust By Ben Kenigsb… The fir…
3 ‘Sublime’ Rev… <NA> Sublime By Erik Piepen… A teena…
4 ‘Cadejo Blanc… <NA> Cadejo Blanco By Concepción … In this…
5 ‘Showing Up’ … Critic’s Pick Sculptures Can Be Sha… By Manohla Dar… In thei…
6 ‘The First Fa… <NA> The First Fallen By Lisa Kennedy A direc…
# ℹ abbreviated name: ¹headline_print_headline
# ℹ 4 more variables: lead_paragraph <chr>, name <chr>, pub_date <dttm>,
# key <int>Attempt a Merge w/ Letterboxd Data
Try merging this data with the Letterboxd data:
# load letterboxd data
letterboxd_movies <-
read_csv(
"https://raw.githubusercontent.com/naomibuell/DATA607_FinalProject/main/movies_trimmed.csv"
) |>
drop_na(minute) |>
mutate(name = name |> tolower()) # switching to lower case Rows: 85614 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): name, tagline, description
dbl (4): id, date, minute, rating
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(letterboxd_movies)# A tibble: 6 × 7
id name date tagline description minute rating
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl>
1 1000001 barbie 2023 She's … "Barbie an… 114 3.91
2 1000002 parasite 2019 Act li… "All unemp… 133 4.57
3 1000003 everything everywhere all at … 2022 The un… "An aging … 140 4.32
4 1000004 fight club 1999 Mischi… "A ticking… 139 4.27
5 1000005 interstellar 2014 Mankin… "The adven… 169 4.32
6 1000006 joker 2019 Put on… "During th… 122 3.83merged <- inner_join(nyt_movies, letterboxd_movies)Joining with `by = join_by(name)`perc_merged <- nrow(merged)/nrow(nyt_movies)
head(merged)# A tibble: 6 × 15
headline_main headline_kicker headline_print_headl…¹ byline_original abstract
<chr> <chr> <chr> <chr> <chr>
1 ‘Dancing in t… <NA> Dancing in the Dust By Ben Kenigsb… The fir…
2 ‘Sublime’ Rev… <NA> Sublime By Erik Piepen… A teena…
3 ‘Sublime’ Rev… <NA> Sublime By Erik Piepen… A teena…
4 ‘Cadejo Blanc… <NA> Cadejo Blanco By Concepción … In this…
5 ‘Showing Up’ … Critic’s Pick Sculptures Can Be Sha… By Manohla Dar… In thei…
6 ‘The First Fa… <NA> The First Fallen By Lisa Kennedy A direc…
# ℹ abbreviated name: ¹headline_print_headline
# ℹ 10 more variables: lead_paragraph <chr>, name <chr>, pub_date <dttm>,
# key <int>, id <dbl>, date <dbl>, tagline <chr>, description <chr>,
# minute <dbl>, rating <dbl>84 percent of data from NYT was able to be merged with a Letterboxd rating!