The goal of outscrapr is to implement a simplified subset of the Outscraper API in R.
Installation
You can install the development version of outscrapr like so:
devtools::install_github("CorradoLanera/outscrapr")Places
Single query
library(outscrapr)
# Get your API key from https://outscraper.com/ and set it as the
# environment variable `OUTSCRAPER_API_KEY`
query <- "restaurants, Brooklyn 11203"
restaurants <- google_maps_search(query = query, limit = 2)
restaurants |>
tibble::as_tibble()
#> # A tibble: 2 × 58
#> query name name_for_emails place_id google_id full_address borough street
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 restaura… LILL… Lilli Restaura… ChIJU4x… 0x89c25d… 1344 Utica … East F… 1344 …
#> 2 restaura… Irie… Irie Bliss Res… ChIJJ8K… 0x89c25b… 3916 Church… Little… 3916 …
#> # ℹ 50 more variables: postal_code <chr>, area_service <lgl>,
#> # country_code <chr>, country <chr>, city <chr>, us_state <chr>, state <chr>,
#> # plus_code <lgl>, latitude <dbl>, longitude <dbl>, h3 <chr>,
#> # time_zone <chr>, popular_times <lgl>, site <chr>, phone <chr>, type <chr>,
#> # logo <chr>, description <lgl>, typical_time_spent <lgl>, located_in <lgl>,
#> # located_google_id <lgl>, category <chr>, subtypes <chr>, posts <lgl>,
#> # reviews_tags <lgl>, rating <dbl>, reviews <int>, photos_count <int>, …
restaurants$working_hours
#> Monday Tuesday Wednesday Thursday Friday Saturday Sunday
#> 1 4-10PM 4-10PM 4-10PM 4-10PM 4PM-1AM 12PM-1AM 12-10PM
#> 2 Closed 10AM-10PM 10AM-10PM 10AM-10PM 8AM-10PM 10AM-10PM 10AM-6PM
restaurants$about |>
tibble::as_tibble()
#> # A tibble: 2 × 12
#> `Service options`$`Curbside pickup` Highlights$Fast serv…¹ `Popular for`$Lunch
#> <lgl> <lgl> <lgl>
#> 1 TRUE TRUE TRUE
#> 2 TRUE NA TRUE
#> # ℹ abbreviated name: ¹Highlights$`Fast service`
#> # ℹ 16 more variables: `Service options`$`No-contact delivery` <lgl>,
#> # $Takeout <lgl>, $`Dine-in` <lgl>, $Delivery <lgl>,
#> # `Popular for`$Dinner <lgl>, $`Solo dining` <lgl>, $Breakfast <lgl>,
#> # Accessibility <df[,1]>, Offerings <df[,11]>, `Dining options` <df[,8]>,
#> # Amenities <df[,2]>, Atmosphere <df[,3]>, Crowd <df[,1]>, Planning <df[,1]>,
#> # Payments <df[,3]>, Parking <df[,2]>
restaurants$about$`Popular for`
#> Lunch Dinner Solo dining Breakfast
#> 1 TRUE TRUE TRUE NA
#> 2 TRUE TRUE TRUE TRUE
restaurants$reviews_per_score
#> 1 2 3 4 5
#> 1 10 6 6 20 215
#> 2 0 0 0 1 28Multiple queries
You can retrieve max 500 places per one query search on Google Maps. For densely populated areas you might want to split your query into subqueries in order to get all the places inside. (e.g., c("restaurants, Brooklyn 11211", "restaurants, Brooklyn 11215")).
queries <- c(
"restaurants, Brooklyn 11211",
"restaurants, Brooklyn 11215"
)
# Results' limit refers to the number of places to retrieve for each
# query. The results of all queries are combined into a single tibble.
multi_restaurants <- google_maps_search(query = queries, limit = 2)
multi_restaurants |>
tibble::as_tibble()
#> # A tibble: 4 × 58
#> query name name_for_emails place_id google_id full_address borough street
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 restaura… The … The Commodore ChIJpwY… 0x89c259… 366 Metropo… Willia… 366 M…
#> 2 restaura… Lilia Lilia ChIJJXC… 0x89c259… 567 Union A… Willia… 567 U…
#> 3 restaura… Chela Chela ChIJ3Vk… 0x89c25a… 408 5th Ave… Park S… 408 5…
#> 4 restaura… The … The Common Par… ChIJR-z… 0x89c25b… 548 4th Ave… Gowanus 548 4…
#> # ℹ 50 more variables: postal_code <chr>, area_service <lgl>,
#> # country_code <chr>, country <chr>, city <chr>, us_state <chr>, state <chr>,
#> # plus_code <lgl>, latitude <dbl>, longitude <dbl>, h3 <chr>,
#> # time_zone <chr>, popular_times <lgl>, site <chr>, phone <chr>, type <chr>,
#> # logo <chr>, description <chr>, typical_time_spent <lgl>, located_in <lgl>,
#> # located_google_id <lgl>, category <chr>, subtypes <chr>, posts <lgl>,
#> # reviews_tags <lgl>, rating <dbl>, reviews <int>, photos_count <int>, …
multi_restaurants$working_hours
#> Sunday Monday Tuesday Wednesday
#> 1 11AM-4AM 4PM-4AM 4PM-4AM 4PM-4AM
#> 2 4-9:30PM 5-9:30PM 5-9:30PM 5-9:30PM
#> 3 11AM-10PM 12-10PM 12-10PM 12-10PM
#> 4 8:30AM-3:30PM 8:30AM-3:30PM 8:30AM-3:30PM,5-9:30PM 8:30AM-3:30PM,5-9:30PM
#> Thursday Friday Saturday
#> 1 4PM-4AM 4PM-4AM 11AM-4AM
#> 2 5-9:30PM 4-9:30PM 4-9:30PM
#> 3 12-10PM 12-11PM 11AM-11PM
#> 4 8:30AM-3:30PM 8:30AM-3:30PM,5-9:30PM 8:30AM-3:30PM
multi_restaurants$about |>
tibble::as_tibble()
#> # A tibble: 4 × 15
#> Service options$Outdoor…¹ $Delivery Highlights$Fast serv…² `Popular for`$Lunch
#> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE TRUE TRUE TRUE
#> 2 TRUE FALSE NA TRUE
#> 3 TRUE TRUE TRUE TRUE
#> 4 NA TRUE TRUE TRUE
#> # ℹ abbreviated names: ¹`Service options`$`Outdoor seating`,
#> # ²Highlights$`Fast service`
#> # ℹ 22 more variables: `Service options`$Takeout <lgl>, $`Dine-in` <lgl>,
#> # $`Curbside pickup` <lgl>, $`No-contact delivery` <lgl>,
#> # Highlights$`Great coffee` <lgl>, $`Great dessert` <lgl>,
#> # $`Live music` <lgl>, `Popular for`$Dinner <lgl>, $`Solo dining` <lgl>,
#> # $Breakfast <lgl>, Accessibility <df[,4]>, Offerings <df[,14]>, …
multi_restaurants$about$Highlights
#> Fast service Great coffee Great dessert Live music
#> 1 TRUE NA NA NA
#> 2 NA TRUE TRUE NA
#> 3 TRUE NA NA TRUE
#> 4 TRUE TRUE NA NA
multi_restaurants$reviews_per_score
#> 1 2 3 4 5
#> 1 67 35 57 292 807
#> 2 117 62 161 274 1228
#> 3 30 32 67 171 857
#> 4 1 0 4 5 112Reviews
library(outscrapr)
# Get your API key from https://outscraper.com/ and set it as the
# environment variable `OUTSCRAPER_API_KEY`
query <- "restaurants, Brooklyn 11203"
restaurant <- google_maps_search(query = query, limit = 1)
restaurant |>
tibble::as_tibble()
#> # A tibble: 1 × 58
#> query name name_for_emails place_id google_id full_address borough street
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 restaura… LILL… Lilli Restaura… ChIJU4x… 0x89c25d… 1344 Utica … East F… 1344 …
#> # ℹ 50 more variables: postal_code <chr>, area_service <lgl>,
#> # country_code <chr>, country <chr>, city <chr>, us_state <chr>, state <chr>,
#> # plus_code <lgl>, latitude <dbl>, longitude <dbl>, h3 <chr>,
#> # time_zone <chr>, popular_times <lgl>, site <chr>, phone <chr>, type <chr>,
#> # logo <chr>, description <lgl>, typical_time_spent <lgl>, located_in <lgl>,
#> # located_google_id <lgl>, category <chr>, subtypes <chr>, posts <lgl>,
#> # reviews_tags <lgl>, rating <dbl>, reviews <int>, photos_count <int>, …
reviews <- restaurant[["place_id"]] |>
google_maps_reviews(reviews_limit = 2)
reviews |>
tibble::as_tibble()
#> # A tibble: 1 × 64
#> query name name_for_emails place_id google_id full_address borough street
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ChIJU4xg… LILL… Lilli Restaura… ChIJU4x… 0x89c25d… 1344 Utica … East F… 1344 …
#> # ℹ 56 more variables: postal_code <chr>, area_service <lgl>,
#> # country_code <chr>, country <chr>, city <chr>, us_state <chr>, state <chr>,
#> # plus_code <chr>, latitude <dbl>, longitude <dbl>, h3 <chr>,
#> # time_zone <chr>, popular_times <list>, site <chr>, phone <chr>, type <chr>,
#> # logo <chr>, description <lgl>, typical_time_spent <chr>, located_in <lgl>,
#> # located_google_id <lgl>, category <chr>, subtypes <chr>, posts <lgl>,
#> # reviews_tags <list>, rating <dbl>, reviews <int>, reviews_data <list>, …
identical(reviews$place_id, restaurant$place_id)
#> [1] TRUE
reviews$reviews |>
tibble::as_tibble()
#> # A tibble: 1 × 1
#> value
#> <int>
#> 1 257
reviews$reviews_data[[1]] |>
tibble::as_tibble()
#> # A tibble: 2 × 23
#> google_id review_id review_pagination_id author_link author_title author_id
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 0x89c25db3d… ChdDSUhN… CAESY0NBRVFBUnBFUTJ… https://ww… samuel lyght 11508292…
#> 2 0x89c25db3d… ChZDSUhN… CAESY0NBRVFBaHBFUTJ… https://ww… Najuma Burke 10067722…
#> # ℹ 17 more variables: author_image <chr>, author_reviews_count <int>,
#> # author_ratings_count <int>, review_text <chr>, review_img_urls <list>,
#> # review_img_url <chr>, review_questions <df[,6]>, review_photo_ids <list>,
#> # owner_answer <chr>, owner_answer_timestamp <int>,
#> # owner_answer_timestamp_datetime_utc <chr>, review_link <chr>,
#> # review_rating <int>, review_timestamp <int>, review_datetime_utc <chr>,
#> # review_likes <int>, reviews_id <chr>
reviews$reviews_data[[1]]$review_text
#> [1] "The appetizers were delicious, the ambiance was pleasant, and the drinks were excellent. However, it was a little on the expensive side. The new waitress was outstanding. Despite her slow service and lack of menu knowledge, I ordered the snapper, which was well-seasoned but not particularly tasty. The fried rice was slightly salty."
#> [2] "From the time we entered things were pleasant. We were met with a smile and greetings. The staff were accommodating and work well together. The drinks made by Simone were 10/10. They were smooth and the flavors well balanced. Our server Mimi was amazing and attentive and and overall sweetheart. I’ll be requesting her again. Big up to the chef because the food was finger licking good. Chef Omar knows what he is doing with his flavors… 10/10. We had no complaints we. We will be on repeat."
reviews$reviews_data[[1]]$review_questions
#> Service Meal type Price per person Food Atmosphere Recommended dishes
#> 1 3 Brunch $50–100 4 4 NA
#> 2 5 Brunch $50–100 5 5 NA
reviews$reviews_data[[1]]$owner_answer
#> [1] "Hi Samuel \n\nThank you so much for the great feedback! Guest experience is very important to us and we strive to make this restaurant a place that everyone can enjoy! We are happy you had a good time and we hope to see you again. \n\n"
#> [2] NACode of Conduct
Please note that the outscrapr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.