README.md
In richierocks/yelp: Interface to the Yelp Fusion API
yelp
This R package provides access to the Yelp Fusion API, version 3.
Installation and setup
To use this package, you must install it and get an access token for the API.
Package installation
To install the package, you first need remotes installed.
install.packages("remotes")
remotes::install_github("richierocks/yelp")
Getting the client ID and client secret
To gain access to the API, you have to register with Yelp, log in, and create your own app. This takes 5 to 10 minutes of pointing and clicking. It's reasonably self-explanatory, and there are more details on the API Authentication page.
Once you have registered, make a note of your client ID and client secret.
Getting and storing an access token
You use the client ID and client secret to get an access token.
access_token <- get_access_token(client_id, client_secret)
This access token can be passed into individual calls to the API, or you can store it so the package will automatically find it. store_access_token(access_token) will store it for the R session, or you can use your operating system tools to set a YELP_ACCESS_TOKEN environment variable to store it permanently.
Join the Developer Beta program
Some features such as business match and event search require you to sign up for the Yelp Developer Beta program. You can do this from the Manage App page.
Set your locale
By default, the package assumes that you are in the USA, and uses the locale en_US. You can override this for the session using set_yelp_locale(), or permanently by setting an environment variable named YELP_LOCALE.
Usage
To search for businesses close to a specific location, call business_search() with a string giving a search term and either a string describing a location or numbers giving latitude and longitude. The return value is a tibble (since it has a nicer print method for wider data frames) with one business per row.
library(yelp)
library(dplyr)
salons_in_la <- business_search("beauty salon", "los angeles")
glimpse(salons_in_la)
#> Observations: 20
#> Variables: 25
#> $ business_id <chr> "whbjQu04BvffC11CZfGEzA", "yII2VaCD9MnKmNKqEz...
#> $ alias <chr> "magys-beauty-salon-los-angeles-2", "briannas...
#> $ name <chr> "Magys Beauty Salon", "Brianna's Beauty Salon...
#> $ rating <ord> 4.5, 5, 4, 3.5, 5, 5, 4.5, 4.5, 3.5, 4, 5, 5,...
#> $ review_count <int> 8, 5, 7, 12, 93, 48, 7, 316, 23, 16, 8, 24, 3...
#> $ price <chr> "$$", "$", "$", "$", "$$", "$$", "$$", "$$", ...
#> $ image_url <chr> "https://s3-media4.fl.yelpcdn.com/bphoto/ZHK2...
#> $ is_closed <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
#> $ url <chr> "https://www.yelp.com/biz/magys-beauty-salon-...
#> $ category_aliases <list> [<"hair", "hairremoval", "personalcare">, <"...
#> $ category_titles <list> [<"Hair Salons", "Hair Removal", "Personal C...
#> $ latitude <dbl> 34.21657, 34.08803, 34.09053, 34.03263, 34.06...
#> $ longitude <dbl> -118.3879, -118.3090, -118.3081, -118.1536, -...
#> $ distance_m <dbl> 18310.313, 3156.545, 3443.914, 15757.854, 264...
#> $ transactions <list> [<>, <>, <>, <>, <>, <>, <>, <>, <>, <>, <>,...
#> $ address1 <chr> "8031 Lankershim Blvd", "968 N Western Ave", ...
#> $ address2 <chr> "", "", "", "", "Fl 2", "", "", "", "", "", "...
#> $ address3 <chr> "", "", "", "", "", "", "", "", "", "", "", "...
#> $ city <chr> "Los Angeles", "Los Angeles", "Los Angeles", ...
#> $ zip_code <chr> "91605", "90029", "90029", "90022", "90005", ...
#> $ state <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA...
#> $ country <chr> "US", "US", "US", "US", "US", "US", "US", "US...
#> $ display_address <chr> "8031 Lankershim Blvd, Los Angeles, CA 91605"...
#> $ phone <chr> "+18187436944", "+13238484188", "+13234665829...
#> $ display_phone <chr> "(818) 743-6944", "(323) 848-4188", "(323) 46...
The results include the longitude and latitude of each business, so you can use ggmap to plot their locations.
library(ggmap)
library(magrittr)
la <- salons_in_la %$%
c(median(longitude), median(latitude))
la_map <- get_map(la, zoom = 11)
#> Source : https://maps.googleapis.com/maps/api/staticmap?center=34.057281,-118.309928&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN
(map_plot_of_salons_in_la <- ggmap(la_map) +
geom_point(aes(longitude, latitude), data = salons_in_la)
)
You can use standard dplyr tools to filter for businesses that are interesting to you.
five_stars_and_cheap <- salons_in_la %>%
filter(rating == 5, price == "$")
business_lookup() gives you more detailed information about businesses, including opening hours. It costs 1 API call per business, so do your filtering before you call it.
five_stars_and_cheap_details <- business_lookup(five_stars_and_cheap)
glimpse(five_stars_and_cheap_details)
#> Observations: 3
#> Variables: 29
#> $ business_id <chr> "yII2VaCD9MnKmNKqEzOY0w", "rCxfVI-UQGzgU...
#> $ alias <chr> "briannas-beauty-salon-los-angeles-2", "...
#> $ name <chr> "Brianna's Beauty Salon", "Yuritzi's Bea...
#> $ rating <ord> 5, 5, 5
#> $ review_count <int> 5, 24, 8
#> $ price <chr> "$", "$", "$"
#> $ image_url <chr> "https://s3-media4.fl.yelpcdn.com/bphoto...
#> $ is_closed <lgl> FALSE, FALSE, FALSE
#> $ url <chr> "https://www.yelp.com/biz/briannas-beaut...
#> $ category_aliases <list> [<"hair", "waxing", "eyelashservice">, ...
#> $ category_titles <list> [<"Hair Salons", "Waxing", "Eyelash Ser...
#> $ latitude <dbl> 34.08803, 34.11381, 34.03223
#> $ longitude <dbl> -118.3090, -118.2543, -118.2997
#> $ distance_m <dbl> NA, NA, NA
#> $ transactions <list> [<>, <>, <>]
#> $ address1 <chr> "968 N Western Ave", "3254 Atwater Ave",...
#> $ address2 <chr> "", "", ""
#> $ address3 <chr> "", "", ""
#> $ city <chr> "Los Angeles", "Los Angeles", "Los Angeles"
#> $ zip_code <chr> "90029", "90039", "90007"
#> $ state <chr> "CA", "CA", "CA"
#> $ country <chr> "US", "US", "US"
#> $ display_address <chr> "968 N Western Ave, Los Angeles, CA 9002...
#> $ phone <chr> "+13238484188", "+13236615084", "+132373...
#> $ display_phone <chr> "(323) 848-4188", "(323) 661-5084", "(32...
#> $ photos <list> [["https://s3-media4.fl.yelpcdn.com/bph...
#> $ is_claimed <lgl> TRUE, TRUE, TRUE
#> $ is_permanently_closed <lgl> FALSE, FALSE, FALSE
#> $ opening_hours <list> [<# A tibble: 7 x 4, start_day start_...
five_stars_and_cheap_details$opening_hours
#> [[1]]
#> # A tibble: 7 x 4
#> start_day start_time end_day end_time
#> <chr> <chr> <chr> <chr>
#> 1 Monday 0900 Monday 2100
#> 2 Tuesday 0900 Tuesday 2100
#> 3 Wednesday 0900 Wednesday 2100
#> 4 Thursday 0900 Thursday 2100
#> 5 Friday 0900 Friday 2100
#> 6 Saturday 0900 Saturday 2100
#> 7 Sunday 0900 Sunday 2100
#>
#> [[2]]
#> # A tibble: 6 x 4
#> start_day start_time end_day end_time
#> <chr> <chr> <chr> <chr>
#> 1 Monday 0930 Monday 1830
#> 2 Tuesday 0930 Tuesday 1830
#> 3 Wednesday 0930 Wednesday 1830
#> 4 Thursday 0930 Thursday 1830
#> 5 Friday 0930 Friday 1830
#> 6 Saturday 0830 Saturday 1830
#>
#> [[3]]
#> # A tibble: 7 x 4
#> start_day start_time end_day end_time
#> <chr> <chr> <chr> <chr>
#> 1 Monday 1000 Monday 1900
#> 2 Tuesday 1000 Tuesday 1900
#> 3 Wednesday 1000 Wednesday 1900
#> 4 Thursday 1000 Thursday 1900
#> 5 Friday 1000 Friday 1900
#> 6 Saturday 0900 Saturday 1900
#> 7 Sunday 1000 Sunday 1800
You can also use reviews() to get reviews of the businesses. Again, it costs 1 API call per business. Also, you are limited to 3 reviews per business, and the text of each is truncated. This is an API limitation, not a problem with this R package.
five_stars_and_cheap_reviews <- reviews(five_stars_and_cheap)
glimpse(five_stars_and_cheap_reviews)
#> Observations: 9
#> Variables: 8
#> $ business_id <chr> "yII2VaCD9MnKmNKqEzOY0w", "yII2VaCD9MnKmNKqEzOY...
#> $ review_id <chr> "XhjsOuc2tAJ21Lh3ypsJIQ", "eSPWyeP6Tv5Whj0hvdNv...
#> $ rating <ord> 4, 5, 5, 5, 5, 5, 5, 5, 5
#> $ text <chr> "Fast & quick. If you are coming for a touch up...
#> $ time_created <chr> "2018-09-03 17:53:08", "2017-10-20 13:10:42", "...
#> $ url <chr> "https://www.yelp.com/biz/briannas-beauty-salon...
#> $ user_image_url <chr> "https://s3-media3.fl.yelpcdn.com/photo/BPZueyJ...
#> $ user_name <chr> "Rida Q.", "Sidney T.", "Roy S.", "Patrick B.",...
In theory, these can be used with the tidytext package to perform a sentiment analysis, though the limited amount of text is a problem.
library(tidytext)
(five_stars_and_cheap_sentiments <- five_stars_and_cheap_reviews %>%
select(business_id, review_id, rating, text) %>%
unnest_tokens(word, text) %>%
anti_join(get_stopwords(), by = "word") %>%
inner_join(get_sentiments("bing"))
)
#> Joining, by = "word"
#> # A tibble: 30 x 5
#> business_id review_id rating word sentiment
#> <chr> <chr> <ord> <chr> <chr>
#> 1 yII2VaCD9MnKmNKqEzOY… XhjsOuc2tAJ21Lh3ypsJ… 4 fast positive
#> 2 yII2VaCD9MnKmNKqEzOY… XhjsOuc2tAJ21Lh3ypsJ… 4 clean positive
#> 3 yII2VaCD9MnKmNKqEzOY… XhjsOuc2tAJ21Lh3ypsJ… 4 affordable positive
#> 4 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 recommend… positive
#> 5 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 impressed positive
#> 6 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 amazing positive
#> 7 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 work positive
#> 8 yII2VaCD9MnKmNKqEzOY… I31QLakCUD999rTygBTF… 5 work positive
#> 9 yII2VaCD9MnKmNKqEzOY… I31QLakCUD999rTygBTF… 5 like positive
#> 10 rCxfVI-UQGzgUPkJDiC5… xIWyHJXOPRmDW29pYA68… 5 nice positive
#> # ... with 20 more rows
Yelp also provide information on events. Call event_search() to see them.
events_in_la <- event_search("los angeles")
glimpse(events_in_la)
#> Observations: 50
#> Variables: 28
#> $ event_id <chr> "los-angeles-3rd-annual-say-no-bullying-festi...
#> $ name <chr> "3rd Annual Say NO Bullying Festival", "A Tas...
#> $ category <chr> "festivals-fairs", "food-and-drink", "food-an...
#> $ description <chr> "Come one! Come ALL! 3rd Annual Say NO Bullyi...
#> $ is_free <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS...
#> $ cost <dbl> NA, 55, 75, NA, 3, 6, NA, 20, 26, 21, 26, 30,...
#> $ cost_max <dbl> NA, NA, 100, NA, 12, 14, NA, 50, 1895, 35, 40...
#> $ business_id <chr> "griffith-park-los-angeles-3", "old-town-monr...
#> $ event_site_url <chr> "https://www.yelp.com/events/los-angeles-3rd-...
#> $ image_url <chr> "https://s3-media2.fl.yelpcdn.com/ephoto/1WEI...
#> $ tickets_url <chr> "http://www.saynobullying.org", "https://www....
#> $ interested_count <int> 3, 13, 4, 2, 16, 5, 17, 11, 81, 3, 1, 7, 35, ...
#> $ attending_count <int> 3, 6, 3, 2, 17, 1, 20, 2, 63, 3, 2, 5, 24, 33...
#> $ time_start <chr> "2018-10-14 20:00", "2018-10-22 00:00", "2018...
#> $ time_end <chr> "2018-10-14 23:00", "2018-10-22 03:00", "", "...
#> $ is_canceled <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
#> $ is_official <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
#> $ latitude <dbl> 34.13662, 34.14807, 33.98179, 33.84865, 34.04...
#> $ longitude <dbl> -118.2944, -118.0010, -118.3924, -118.0079, -...
#> $ address1 <chr> "4730 Crystal Springs Dr", "400 S Myrtle Ave"...
#> $ address2 <chr> "", "", "", "", "", "", "", "", "", "", "", "...
#> $ address3 <chr> "", "", "", "", "", "", "", "", "", "", "", "...
#> $ city <chr> "Los Angeles", "Monrovia", "Culver City", "Bu...
#> $ zip_code <chr> "90027", "91016", "90230", "90620", "91768", ...
#> $ state <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA...
#> $ country <chr> "US", "US", "US", "US", "US", "US", "US", "US...
#> $ display_address <chr> "4730 Crystal Springs Dr, Los Angeles, CA 900...
#> $ cross_streets <chr> "", "", "", "", "", "", "", "", "", "", "", "...
There are some other functions with more niche usage.
autocomplete() takes a search term, and returns some related search terms that Yelp understands.business_match() provides a more targetted search for a business, when you know its name and rough address.event_lookup() returns details for a specific event given its Yelp ID.featured_event() returns the Yelp-curated featured event for a location.food_delivery_search() lets you find businesses that will deliver food to a given location.categories() provides more information on the search category aliases.phone_search() lets you search for a business by phone number.
richierocks/yelp documentation built on May 3, 2019, 4:08 p.m.
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yelp
This R package provides access to the Yelp Fusion API, version 3.
Installation and setup
To use this package, you must install it and get an access token for the API.
Package installation
To install the package, you first need remotes installed.
install.packages("remotes")
remotes::install_github("richierocks/yelp")
Getting the client ID and client secret
To gain access to the API, you have to register with Yelp, log in, and create your own app. This takes 5 to 10 minutes of pointing and clicking. It's reasonably self-explanatory, and there are more details on the API Authentication page.
Once you have registered, make a note of your client ID and client secret.
Getting and storing an access token
You use the client ID and client secret to get an access token.
access_token <- get_access_token(client_id, client_secret)
This access token can be passed into individual calls to the API, or you can store it so the package will automatically find it. store_access_token(access_token) will store it for the R session, or you can use your operating system tools to set a YELP_ACCESS_TOKEN environment variable to store it permanently.
Join the Developer Beta program
Some features such as business match and event search require you to sign up for the Yelp Developer Beta program. You can do this from the Manage App page.
Set your locale
By default, the package assumes that you are in the USA, and uses the locale en_US. You can override this for the session using set_yelp_locale(), or permanently by setting an environment variable named YELP_LOCALE.
Usage
To search for businesses close to a specific location, call business_search() with a string giving a search term and either a string describing a location or numbers giving latitude and longitude. The return value is a tibble (since it has a nicer print method for wider data frames) with one business per row.
library(yelp)
library(dplyr)
salons_in_la <- business_search("beauty salon", "los angeles")
glimpse(salons_in_la)
#> Observations: 20
#> Variables: 25
#> $ business_id <chr> "whbjQu04BvffC11CZfGEzA", "yII2VaCD9MnKmNKqEz...
#> $ alias <chr> "magys-beauty-salon-los-angeles-2", "briannas...
#> $ name <chr> "Magys Beauty Salon", "Brianna's Beauty Salon...
#> $ rating <ord> 4.5, 5, 4, 3.5, 5, 5, 4.5, 4.5, 3.5, 4, 5, 5,...
#> $ review_count <int> 8, 5, 7, 12, 93, 48, 7, 316, 23, 16, 8, 24, 3...
#> $ price <chr> "$$", "$", "$", "$", "$$", "$$", "$$", "$$", ...
#> $ image_url <chr> "https://s3-media4.fl.yelpcdn.com/bphoto/ZHK2...
#> $ is_closed <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
#> $ url <chr> "https://www.yelp.com/biz/magys-beauty-salon-...
#> $ category_aliases <list> [<"hair", "hairremoval", "personalcare">, <"...
#> $ category_titles <list> [<"Hair Salons", "Hair Removal", "Personal C...
#> $ latitude <dbl> 34.21657, 34.08803, 34.09053, 34.03263, 34.06...
#> $ longitude <dbl> -118.3879, -118.3090, -118.3081, -118.1536, -...
#> $ distance_m <dbl> 18310.313, 3156.545, 3443.914, 15757.854, 264...
#> $ transactions <list> [<>, <>, <>, <>, <>, <>, <>, <>, <>, <>, <>,...
#> $ address1 <chr> "8031 Lankershim Blvd", "968 N Western Ave", ...
#> $ address2 <chr> "", "", "", "", "Fl 2", "", "", "", "", "", "...
#> $ address3 <chr> "", "", "", "", "", "", "", "", "", "", "", "...
#> $ city <chr> "Los Angeles", "Los Angeles", "Los Angeles", ...
#> $ zip_code <chr> "91605", "90029", "90029", "90022", "90005", ...
#> $ state <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA...
#> $ country <chr> "US", "US", "US", "US", "US", "US", "US", "US...
#> $ display_address <chr> "8031 Lankershim Blvd, Los Angeles, CA 91605"...
#> $ phone <chr> "+18187436944", "+13238484188", "+13234665829...
#> $ display_phone <chr> "(818) 743-6944", "(323) 848-4188", "(323) 46...
The results include the longitude and latitude of each business, so you can use ggmap to plot their locations.
library(ggmap)
library(magrittr)
la <- salons_in_la %$%
c(median(longitude), median(latitude))
la_map <- get_map(la, zoom = 11)
#> Source : https://maps.googleapis.com/maps/api/staticmap?center=34.057281,-118.309928&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN
(map_plot_of_salons_in_la <- ggmap(la_map) +
geom_point(aes(longitude, latitude), data = salons_in_la)
)
You can use standard dplyr tools to filter for businesses that are interesting to you.
five_stars_and_cheap <- salons_in_la %>%
filter(rating == 5, price == "$")
business_lookup() gives you more detailed information about businesses, including opening hours. It costs 1 API call per business, so do your filtering before you call it.
five_stars_and_cheap_details <- business_lookup(five_stars_and_cheap)
glimpse(five_stars_and_cheap_details)
#> Observations: 3
#> Variables: 29
#> $ business_id <chr> "yII2VaCD9MnKmNKqEzOY0w", "rCxfVI-UQGzgU...
#> $ alias <chr> "briannas-beauty-salon-los-angeles-2", "...
#> $ name <chr> "Brianna's Beauty Salon", "Yuritzi's Bea...
#> $ rating <ord> 5, 5, 5
#> $ review_count <int> 5, 24, 8
#> $ price <chr> "$", "$", "$"
#> $ image_url <chr> "https://s3-media4.fl.yelpcdn.com/bphoto...
#> $ is_closed <lgl> FALSE, FALSE, FALSE
#> $ url <chr> "https://www.yelp.com/biz/briannas-beaut...
#> $ category_aliases <list> [<"hair", "waxing", "eyelashservice">, ...
#> $ category_titles <list> [<"Hair Salons", "Waxing", "Eyelash Ser...
#> $ latitude <dbl> 34.08803, 34.11381, 34.03223
#> $ longitude <dbl> -118.3090, -118.2543, -118.2997
#> $ distance_m <dbl> NA, NA, NA
#> $ transactions <list> [<>, <>, <>]
#> $ address1 <chr> "968 N Western Ave", "3254 Atwater Ave",...
#> $ address2 <chr> "", "", ""
#> $ address3 <chr> "", "", ""
#> $ city <chr> "Los Angeles", "Los Angeles", "Los Angeles"
#> $ zip_code <chr> "90029", "90039", "90007"
#> $ state <chr> "CA", "CA", "CA"
#> $ country <chr> "US", "US", "US"
#> $ display_address <chr> "968 N Western Ave, Los Angeles, CA 9002...
#> $ phone <chr> "+13238484188", "+13236615084", "+132373...
#> $ display_phone <chr> "(323) 848-4188", "(323) 661-5084", "(32...
#> $ photos <list> [["https://s3-media4.fl.yelpcdn.com/bph...
#> $ is_claimed <lgl> TRUE, TRUE, TRUE
#> $ is_permanently_closed <lgl> FALSE, FALSE, FALSE
#> $ opening_hours <list> [<# A tibble: 7 x 4, start_day start_...
five_stars_and_cheap_details$opening_hours
#> [[1]]
#> # A tibble: 7 x 4
#> start_day start_time end_day end_time
#> <chr> <chr> <chr> <chr>
#> 1 Monday 0900 Monday 2100
#> 2 Tuesday 0900 Tuesday 2100
#> 3 Wednesday 0900 Wednesday 2100
#> 4 Thursday 0900 Thursday 2100
#> 5 Friday 0900 Friday 2100
#> 6 Saturday 0900 Saturday 2100
#> 7 Sunday 0900 Sunday 2100
#>
#> [[2]]
#> # A tibble: 6 x 4
#> start_day start_time end_day end_time
#> <chr> <chr> <chr> <chr>
#> 1 Monday 0930 Monday 1830
#> 2 Tuesday 0930 Tuesday 1830
#> 3 Wednesday 0930 Wednesday 1830
#> 4 Thursday 0930 Thursday 1830
#> 5 Friday 0930 Friday 1830
#> 6 Saturday 0830 Saturday 1830
#>
#> [[3]]
#> # A tibble: 7 x 4
#> start_day start_time end_day end_time
#> <chr> <chr> <chr> <chr>
#> 1 Monday 1000 Monday 1900
#> 2 Tuesday 1000 Tuesday 1900
#> 3 Wednesday 1000 Wednesday 1900
#> 4 Thursday 1000 Thursday 1900
#> 5 Friday 1000 Friday 1900
#> 6 Saturday 0900 Saturday 1900
#> 7 Sunday 1000 Sunday 1800
You can also use reviews() to get reviews of the businesses. Again, it costs 1 API call per business. Also, you are limited to 3 reviews per business, and the text of each is truncated. This is an API limitation, not a problem with this R package.
five_stars_and_cheap_reviews <- reviews(five_stars_and_cheap)
glimpse(five_stars_and_cheap_reviews)
#> Observations: 9
#> Variables: 8
#> $ business_id <chr> "yII2VaCD9MnKmNKqEzOY0w", "yII2VaCD9MnKmNKqEzOY...
#> $ review_id <chr> "XhjsOuc2tAJ21Lh3ypsJIQ", "eSPWyeP6Tv5Whj0hvdNv...
#> $ rating <ord> 4, 5, 5, 5, 5, 5, 5, 5, 5
#> $ text <chr> "Fast & quick. If you are coming for a touch up...
#> $ time_created <chr> "2018-09-03 17:53:08", "2017-10-20 13:10:42", "...
#> $ url <chr> "https://www.yelp.com/biz/briannas-beauty-salon...
#> $ user_image_url <chr> "https://s3-media3.fl.yelpcdn.com/photo/BPZueyJ...
#> $ user_name <chr> "Rida Q.", "Sidney T.", "Roy S.", "Patrick B.",...
In theory, these can be used with the tidytext package to perform a sentiment analysis, though the limited amount of text is a problem.
library(tidytext)
(five_stars_and_cheap_sentiments <- five_stars_and_cheap_reviews %>%
select(business_id, review_id, rating, text) %>%
unnest_tokens(word, text) %>%
anti_join(get_stopwords(), by = "word") %>%
inner_join(get_sentiments("bing"))
)
#> Joining, by = "word"
#> # A tibble: 30 x 5
#> business_id review_id rating word sentiment
#> <chr> <chr> <ord> <chr> <chr>
#> 1 yII2VaCD9MnKmNKqEzOY… XhjsOuc2tAJ21Lh3ypsJ… 4 fast positive
#> 2 yII2VaCD9MnKmNKqEzOY… XhjsOuc2tAJ21Lh3ypsJ… 4 clean positive
#> 3 yII2VaCD9MnKmNKqEzOY… XhjsOuc2tAJ21Lh3ypsJ… 4 affordable positive
#> 4 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 recommend… positive
#> 5 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 impressed positive
#> 6 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 amazing positive
#> 7 yII2VaCD9MnKmNKqEzOY… eSPWyeP6Tv5Whj0hvdNv… 5 work positive
#> 8 yII2VaCD9MnKmNKqEzOY… I31QLakCUD999rTygBTF… 5 work positive
#> 9 yII2VaCD9MnKmNKqEzOY… I31QLakCUD999rTygBTF… 5 like positive
#> 10 rCxfVI-UQGzgUPkJDiC5… xIWyHJXOPRmDW29pYA68… 5 nice positive
#> # ... with 20 more rows
Yelp also provide information on events. Call event_search() to see them.
events_in_la <- event_search("los angeles")
glimpse(events_in_la)
#> Observations: 50
#> Variables: 28
#> $ event_id <chr> "los-angeles-3rd-annual-say-no-bullying-festi...
#> $ name <chr> "3rd Annual Say NO Bullying Festival", "A Tas...
#> $ category <chr> "festivals-fairs", "food-and-drink", "food-an...
#> $ description <chr> "Come one! Come ALL! 3rd Annual Say NO Bullyi...
#> $ is_free <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS...
#> $ cost <dbl> NA, 55, 75, NA, 3, 6, NA, 20, 26, 21, 26, 30,...
#> $ cost_max <dbl> NA, NA, 100, NA, 12, 14, NA, 50, 1895, 35, 40...
#> $ business_id <chr> "griffith-park-los-angeles-3", "old-town-monr...
#> $ event_site_url <chr> "https://www.yelp.com/events/los-angeles-3rd-...
#> $ image_url <chr> "https://s3-media2.fl.yelpcdn.com/ephoto/1WEI...
#> $ tickets_url <chr> "http://www.saynobullying.org", "https://www....
#> $ interested_count <int> 3, 13, 4, 2, 16, 5, 17, 11, 81, 3, 1, 7, 35, ...
#> $ attending_count <int> 3, 6, 3, 2, 17, 1, 20, 2, 63, 3, 2, 5, 24, 33...
#> $ time_start <chr> "2018-10-14 20:00", "2018-10-22 00:00", "2018...
#> $ time_end <chr> "2018-10-14 23:00", "2018-10-22 03:00", "", "...
#> $ is_canceled <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
#> $ is_official <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
#> $ latitude <dbl> 34.13662, 34.14807, 33.98179, 33.84865, 34.04...
#> $ longitude <dbl> -118.2944, -118.0010, -118.3924, -118.0079, -...
#> $ address1 <chr> "4730 Crystal Springs Dr", "400 S Myrtle Ave"...
#> $ address2 <chr> "", "", "", "", "", "", "", "", "", "", "", "...
#> $ address3 <chr> "", "", "", "", "", "", "", "", "", "", "", "...
#> $ city <chr> "Los Angeles", "Monrovia", "Culver City", "Bu...
#> $ zip_code <chr> "90027", "91016", "90230", "90620", "91768", ...
#> $ state <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA...
#> $ country <chr> "US", "US", "US", "US", "US", "US", "US", "US...
#> $ display_address <chr> "4730 Crystal Springs Dr, Los Angeles, CA 900...
#> $ cross_streets <chr> "", "", "", "", "", "", "", "", "", "", "", "...
There are some other functions with more niche usage.
autocomplete()takes a search term, and returns some related search terms that Yelp understands.business_match()provides a more targetted search for a business, when you know its name and rough address.event_lookup()returns details for a specific event given its Yelp ID.featured_event()returns the Yelp-curated featured event for a location.food_delivery_search()lets you find businesses that will deliver food to a given location.categories()provides more information on the search category aliases.phone_search()lets you search for a business by phone number.
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