Census & Yelp API

Bonwoo Koo & Subhrajit Guhathakurta

2022-09-06

New functions introduced in this document

Function	Description
tidycensus::get_acs	Downloads ACS data
sf::st_bbox	Get the bounding box
sf::st_point	Create POINT simple feature geometry
sf::st_centroid	Get the centroid of a geometry
sf::st_distance	Calculate Euclidian distance between pairs of geometries
sf::st_geometry	Get geometry from an sf object
sf::st_transform	Transform coordinates of sf object
sf::st_coordinates	Get coordinates in matrix form
sf::st_set_geometry	Set geometry of an sf object
sf::st_as_sf	Convert foreign object to an sf object
sf::st_join	Spatial join
sapply	Apply a function over a list or vector
min	Return the minima of the input values
max	Return the minima of the input values
abs	Return absolute valueof the input value
seq	Generate a regular sequences
expand.grid	Create a data.frame from all combinations of supplied vectors or factors.
names	Get (or set) the names of an object
paste0	Concatenate vectors after converting to character
ceiling	Returns a numeric vector containing the smallest integers not less than x
bind_rows	Binding rows of data.frames into one.
Sys.getenv	Access the values of the environment variables
list.files	Returns a character vector of the names of files or folder in the named folder.
units::set_units	Creates object of class units
osmdata::getbb	Uses the free Nominatim API provided by OpenStreetMap to find the bounding box (bb) associated with place names.
yelpr::business_search	Search bussiness on Yelp using Yelp Fusion API’s business search endpoint
jsonlite::flatten	Flattens nested data frame (i.e., data frames with one or more columns consisting of another data frame)
purrr::map_lgl	Funtional that returns a logical vector

Getting data through API

In this document, we will use API (application programming interface) to acquire data from the Census Bureau & from Yelp Fusion API. According to Wiki, “An API is a way for two or more computer programs to communicate with each other.” To use API, we generally need to connect to the endpoint of API server, which often looks something like https://api.yelp.com/v3. We then append other parameters to complete it.

For example, the following is requesting business search service using a keyword delis for the location latitude of 37.787 and longitude of -122.399.

https://api.yelp.com/v3/businesses/search?term=delis&latitude=37.786882&longitude=-122.399972

In our everyday terms, think of this as a data request application form. Once this ‘form’ is created, we need to send this ‘form’ to the server (i.e., the data provider). We would use e.g., GET() function in httr package to send API request to the server, and the server would return something like the following to the authenticated requests.

This JSON format can be reformatted into a data.frame format using e.g., jsonlite and reshape2 packages.

Understanding Yelp Business Search

All APIs are different. Being familiar with Yelp Fusion API does not tell you anything about how Google Street View API is structured. So the first thing you need to do is to READ THE DOCUMENTATION OF YELP FUSION API.

Yelp Fusion API has various endpoints: Business Search, Phone Search, Transaction Search, Business Details etc. You will notice that for everyting except Business Search, you need to have some information about the business you are looking for (e.g., business id, phone number, exact location, etc). Business Search allows us to search business by keywords (e.g., Ray’s), catergories (e.g., cafe), location (e.g., coordinates), etc. This is what we need. Let’s look at the related parameters of Business Search.

The Business Search takes various input parameters, some optional and others required. Most importantly, we need to provide geographic information in which we want to do a search. We can provide it through one of the following ways:

location parameter: E.g., New York City, NYC, 350 5th Ave, etc.
longitude and latitude parameter: E.g., 33.7484, -84.3902.
radius parameter: Search radius. Note that the actual search radius may vary. Max 40,000 meters.

Other important parameters are limit and offset.

limit parameter: Number of business results to return. For results of more than 50 businesses, Yelp returns results in multiple pages. The limit parameter determines how many businesses are contained in one page.
offset parameter: This parameter defines the amount by which the returned results are offset. For example, with limit of 50, the first request will give us 50 businesses. To get 51th ~ 100th business, we should set limit = 50.

An important limitation of the Business Search in Yelp Fusion API is that it returns up to 1,000 businesses based on the provided search criteria. It means if you request Yelp to return all restaurants in Atlatna using one search criteria, you may not get all the restaurants in Atlanta if there are more than 1000 of them in Atlanta. You would need to break your search into smaller bits to ensure that you get less than 1000 hits for each search criteria.

There is Yelp Open Dataset. Why not use this instead?
It is a subset of the businesses, reviews, and user data for use in personal, educational, and academic purposes, containing 6,990,280 reviews and 150,346 businesses from 11 metropolitan areas.

However, I do not know whether this data is a sample or not. If it is a sample, we would need to know the sampling method, when the sampling was done, when the update will be made, etc. In other words, we don’t have information on velocity and exhaustivity of the data. Without these information, we cannot assess whether findings from this dataset is biased or not.

Downloading Census Track Polygons through Census API

In this document, we will use Census Tract boundary as a device to break down the city of Atlanta into smaller bits so that one search criteria would not generate more than 1000 hits. Census Tract is useful for this purpose because, by definition, the size of Census Tracts is proportional to the population size. Assuming that there are more POIs in more populous area, we can roughly expect that the size of Census Tracts would be proportional to the POI numbers.

The Census bureau provides API. There is a convenient R package that makes accessing Census API easy, called tidycensus. The package has get_acs() function that can download American Community Service (ACS) directly into your R session as an sf object.

Census API Key

First of all, we need to acquire Census API Key. Go to https://api.census.gov/data/key_signup.html and input Organization Name and Email Address. You will get a key emailed to your email account. You need to then use census_api_key("your-API-key-here") to do the authentication.

Remember that everything on Rpubs is public. If you include your API key in R Markdown, it will be public as well. This is DANGEROUS. Instead, you can store the key as your environment variable and call it using Sys.getenv() function. For example, my Census API Key is stored in Environment Variables as “census_api”. I can do the following code to authenticate without revealing my actual API key.

tidycensus::census_api_key(Sys.getenv("census_api"))

## To install your API key for use in future sessions, run this function with `install = TRUE`.

Getting Census Tract Boundary

To use get_acs(), we first need to understand what arguments are needed for this function. Try ?tidycensus::get_acs in your console to check the documentation for this function.

We will specify the following argument and leave the rest as default: geography, state, county, variables, year, survey, geometry, and output. Notice that we can also specify variables argument. You can provide variable ID to specify the variables you want to download. You can (1) use load_variables() function to see all variable ID, (2) go to Census Bureau website to see the variable ID, or (3) some other sources. I recommend NHGIS for its highly intuitive filtering system.

library(tidycensus)
library(sf)
library(tmap)
library(jsonlite)
library(tidyverse)
library(httr)
library(jsonlite)
library(reshape2)
library(here)
library(yelpr)
library(knitr)

#### Tract polygons for the Yelp query
tract <- suppressMessages(
  get_acs(geography = "tract", # or "block group", "county", "state" etc. 
          state = "GA",
          county = c("Fulton", "Dekalb"), 
          variables = c(hhincome = 'B19019_001',
                        race.tot = "B02001_001", 
                        race.white = "B02001_002", 
                        race.black = 'B02001_003'
                        ),
          year = 2019,
          survey = "acs5", # American Community Survey 5-year estimate
          geometry = TRUE, # returns sf objects
          output = "wide") # wide vs. long
)

Running the code above downloads a data.frame with geometry column-list (i.e., sf object). There is 349 rows and 11 columns. Also notice that we get two variables for each variable ID we requested, one with E and the other with M at the end of their names. E is the actual estimate and M is the associated margin of error coming from the fact that the ACS is based on sample.

# View the data
message(sprintf("nrow: %s, ncol: %s", nrow(tract), ncol(tract)))

## nrow: 349, ncol: 11

tract %>% head() %>% knitr::kable() # Ignore this kable() function. This function is for neatly displaying tables on HTML document.

GEOID	NAME	hhincomeE	hhincomeM	race.totE	race.totM	race.whiteE	race.whiteM	race.blackE	race.blackM	geometry
13121001100	Census Tract 11, Fulton County, Georgia	109426	7959	5193	453	4060	453	299	115	MULTIPOLYGON (((-84.38782 3…
13121009603	Census Tract 96.03, Fulton County, Georgia	83243	15103	4830	332	3561	363	749	307	MULTIPOLYGON (((-84.38738 3…
13121005800	Census Tract 58, Fulton County, Georgia	42679	16189	1479	250	300	87	1057	255	MULTIPOLYGON (((-84.41692 3…
13121010117	Census Tract 101.17, Fulton County, Georgia	60731	5573	4319	488	1701	361	2007	554	MULTIPOLYGON (((-84.36575 3…
13121009502	Census Tract 95.02, Fulton County, Georgia	86053	24545	3867	497	2627	530	796	635	MULTIPOLYGON (((-84.39472 3…
13089021213	Census Tract 212.13, DeKalb County, Georgia	154063	19674	3526	204	3196	249	67	89	MULTIPOLYGON (((-84.34783 3…

For simplicity, we will keep columns that end with E and discard those that end with M. Notice that the select() verb from dplyr can not only select columns but can change names as well.

# Retaining only those I want.
# Notice that select function can also change names when it selects columns.
tract <- tract %>% 
  select(GEOID, 
         hhincome = hhincomeE, # New name = old name
         race.tot = race.totE,
         race.white = race.whiteE,
         race.black = race.blackE)

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(tract) + tm_borders()

Using Census Tract Boundary for Yelp API

The plan is:

Pick one Census Tract.
Create a bounding box (bbox) and calculate its centroid. This will be latitude & longitude information for Yelp’s Business Search.
Calculate a radius X such that a circle with the radius X centered at the latitude & longitude would completely cover the Census Tract polygon.
Send an API request for this circle and check if there are less than 1000 business in it.

If there are more than 1000 businesses, further break the circle into smaller zones.

Run through the pagination process to collect all businesses within that circle.
Use a loop to repeat 1 ~ 5 for all other Census Tracts to acquire all businesses within Atlanta.

Because Step 1 will be done automatically in a for-loop, let’s perform step 2 ~ 3 first. st_centroid() in sf package gives you the centroid of any sf geometry. For radius, we define a function that calculates the radius value.

Step 2 + 3

To ensure that we draw a circle that encapsulates a given polygon, we need to figure out the distance between the centroid (which will be the center of the circle) and the furthest point of the polygon. By definition, bounding box of a polygon A is drawn such that all points that make A is inside the bounding box. If we can draw bounding box around each polygon and calculate the distance from the centroid to one of the corners of the bounding box, that will be the radius of the circle.

Here, we need to perform a certain task (i.e., creating a bounding box and calculate distance from its centroid) repeatedly to each polygon. We can define a function that does this task for one Census Tract and use a loop or functional to apply it to all census Tracts. I will show you both a loop and a functional – the results will be identical. Let’s first define a function for that certain task.

# Function: Get tract-wise radius
get_r <- function(poly, epsg_id){
  #---------------------
  # Takes: a single POLYGON or LINESTRTING
  # Outputs: distance between the centroid of the boundingbox and a corner of the bounding box
  #---------------------
  
  # Get bounding box of a given polygon
  bb <- st_bbox(poly)
  # Get lat & long coordinates of any one corner of the bounding box.
  bb_corner <- st_point(c(bb[1], bb[2])) %>% st_sfc(crs = epsg_id)
  # Get centroid of the bb
  bb_center_x <- (bb[3]+bb[1])/2
  bb_center_y <- (bb[4]+bb[2])/2
  bb_center <- st_point(c(bb_center_x, bb_center_y)) %>% st_sfc(crs = epsg_id) %>% st_sf()
    
  # Get the distance between bb_p and c
  r <- st_distance(bb_corner, bb_center)
  # Multiply 1.1 to make the circle a bit larger than the Census Tract.
  # See the Yelp explanation of their radius parameter to see why we do this.
  bb_center$radius <- r*1.2
  return(bb_center)
}

We can apply this function to each polygon using a loop and a functional. At the end, we compare the two vectors from loop and functional to see they are the same.

## Using a loop -----------------------------------------------------------------
# Creating an empty vector of NA. 
# Results will fill this vector
epsg_id <- 4326 # Edit (9/8/2022): 4326 measures distance in meter. Before the edit, I used 26967.

r4all_loop <- vector("list", nrow(tract))

# Starting a for-loop

for (i in 1:nrow(tract)){
  r4all_loop[[i]] <- tract %>% 
    st_transform(crs = epsg_id) %>% 
    st_geometry() %>% 
    .[[i]] %>% 
    get_r(epsg_id = epsg_id)
}

r4all_loop <- bind_rows(r4all_loop)


# Using a functional -----------------------------------------------------------
# We use a functional (sapply) to apply this custom function to each Census Tract.
r4all_apply <- tract %>%
  st_geometry() %>% 
  st_transform(crs = epsg_id) %>% 
  lapply(., function(x) get_r(x, epsg_id = epsg_id))

r4all_apply <- bind_rows(r4all_apply)

# Are these two identical?
identical(r4all_apply, r4all_loop)

## [1] TRUE

# Appending X Y coordinates as seprate columns
ready_4_yelp <- r4all_apply %>% 
  mutate(x = st_coordinates(.)[,1],
         y = st_coordinates(.)[,2])

To visualize what we’ve done, run the following code.

tmap_mode('view')

## tmap mode set to interactive viewing

# Select the first 10 rows
ready_4_yelp[1:10,] %>% 
  # Draw a buffer centered at the centroid of Tract polygons.
  # Radius of the buffer is the radius we just calculated using loop
  st_buffer(., dist = .$radius) %>% 
  # Display this buffer in red
  tm_shape(.) + tm_polygons(alpha = 0.5, col = 'red') +
  # Display the original polygon in blue
  tm_shape(tract[1:10,]) + tm_borders(col= 'blue')

For fun: Other ways to accomplish similar results (Optional)

Using Census Tract is not the only way to break down Atlanta into smaller bits. Can you read the code below and explain what is being done here?

# Get bb for two counties
fulton_bb <- osmdata::getbb("Fulton County, GA")
dekalb_bb <- osmdata::getbb("DeKalb County, GA")

# Find coordinates for the four sides of the bb
left_x <- min(c(fulton_bb[1,],dekalb_bb[1,])) # <<-- smaller , larger(i.e., closer to zero) -->>
right_x <- max(c(fulton_bb[1,],dekalb_bb[1,]))
bottom_y <- min(c(fulton_bb[2,],dekalb_bb[2,]))
top_y <- max(c(fulton_bb[2,],dekalb_bb[2,]))

# Break the bb into a grid
fishnet_n <- 40
steps <- abs(left_x - right_x)/fishnet_n


# Fishnet points
fish_x <- seq(from = left_x, to = right_x, by = steps)
fish_y <- seq(from = bottom_y, to = top_y, by = steps)

fishnet <- expand.grid(fish_x, fish_y) %>% 
  rename(x = Var1, y = Var2) %>% 
  st_as_sf(coords = c('x', 'y'), crs = 4326)

# Visualize it
tm_shape(fishnet %>% 
           st_buffer(dist = units::set_units(steps, "degree"))) + tm_polygons(alpha = 0.2, col = 'red')

Accessing Yelp API

Getting Yelp API key

Just like we did for the Census data, we need to get an API key for Yelp data. Go to https://www.yelp.com/developers/documentation/v3/business_search and sign up and log in. Then, click Introduction on the left panel to open up more menus. You will see Authentication (see the image below if you can’t find it). It will direct you to Create App. Go ahead and fill out required information. Once you’re done with the form and email verification process, we will have your API key displayed on the webpage. KEEP THE KEY TO YOURSELF and DO NOT POST IT ANYWHERE PUBLIC. I recommend storing it in as the environment variable.

Request through URL (Optional)

The beginning of this document, we saw a sample form for API request (e.g., https://api.yelp.com/v3/businesses/search?term=delis&latitude=37.786882&longitude=-122.399972). Let’s try creating a URL for API request, retrieve a response using GET (a HTTP method), and parse the response.

# Function for formatting the url for API request
url_format <- function(latitude = NULL, longitude = NULL, 
                         radius = NULL, categories = NULL, offset = NULL, limit = NULL){
  # Parameters
  base_url <- "https://api.yelp.com/v3/businesses/search?"
  latitude <- paste0("latitude=", latitude)
  longitude <- paste0("longitude=", longitude)
  radius <- paste0("radius=", radius)
  categories <- paste0("categories=", categories)
  offset <- paste0("offset=", offset)
  limit <- paste0("limit=", limit)
  
  # Out
  full_url <- paste0(c(base_url, latitude, longitude, radius, categories, offset, limit), collapse = "&")
  return(full_url)
}

# Run url_format function
full_url <- url_format(latitude = 33.792479078196294, # This is Atlantic station
                       longitude = -84.39676077444938, # This is Atlantic station 
                       radius=1000, 
                       categories="restaurants", 
                       offset = 0, 
                       limit = 50)

# GET response
resp <- httr::GET(full_url, add_headers(Authorization = paste("Bearer", Sys.getenv("yelp_api"))))

Formatted response

# Parse the body of the response (json format)
resp_parsed <- content(resp, as="text", encoding = "UTF-8")

# Parse json into a list
resp_parsed <- jsonlite::fromJSON(resp_parsed)
resp_parsed$businesses %>% head %>% kable() # Ignore this kable() function. This function is for neatly displaying tables on HTML document.

## Warning in `[<-.data.frame`(`*tmp*`, , j, value = structure(list(latitude =
## structure(c("33.78464", : provided 2 variables to replace 1 variables

## Warning in `[<-.data.frame`(`*tmp*`, , j, value = structure(list(address1
## = structure(c("1093 Hemphill Ave NW", : provided 8 variables to replace 1
## variables

id	alias	name	image_url	is_closed	url	review_count	categories	rating	coordinates	transactions	price	location	phone	display_phone	distance
CxQ1m2iY4wQpXC64tSfWgQ	antico-pizza-atlanta	Antico Pizza	https://s3-media2.fl.yelpcdn.com/bphoto/gUgTSYImx_45CqJaYWSiWg/o.jpg	FALSE	https://www.yelp.com/biz/antico-pizza-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA	3441	pizza, Pizza	4.5	33.78464	delivery, pickup	\[ \|1093 Hemphill Ave NW \|+14047242333 \|(404) 724-2333 \| 1197.7814\| \|kNwadCO8DEJLo07Dpd3W0w \|endive-publik-house-atlanta-3 \|Endive Publik House \|https://s3-media2.fl.yelpcdn.com/bphoto/-3K6uME7CBa3VPokyzGoZw/o.jpg \|FALSE \|https://www.yelp.com/biz/endive-publik-house-atlanta-3?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA \| 212\|newamerican , American (New) \| 4.5\|33.79497 \|delivery \|\]	1468 Mecaslin St NW	+14045049040	(404) 504-9040	407.2230
24–2r1ZEgnmJs5iVVAOgQ	wagaya-westside-atlanta	Wagaya - Westside	https://s3-media1.fl.yelpcdn.com/bphoto/0nATXrPNKu4W1dwAZc7XNQ/o.jpg	FALSE	https://www.yelp.com/biz/wagaya-westside-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA	641	sushi , ramen , seafood , Sushi Bars, Ramen , Seafood	4.0	33.78635	delivery, pickup	\[ \|339 14th St NW \|+14705755799 \|(470) 575-5799 \| 695.6814\| \|3BzwagIBPQEf_Ic44oZtYQ \|nan-thai-fine-dining-atlanta \|Nan Thai Fine Dining \|https://s3-media3.fl.yelpcdn.com/bphoto/nJkL5SZXqDtoNU94M-ok6Q/o.jpg \|FALSE \|https://www.yelp.com/biz/nan-thai-fine-dining-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA \| 602\|thai , wine_bars, Thai , Wine Bars \| 4.0\|33.79164 \|delivery, pickup \|\]	1350 Spring St NW	+14048709933	(404) 870-9933	651.0051
6VTj9WahLNw8i6bKAA7kvw	yard-house-atlanta-4	Yard House	https://s3-media2.fl.yelpcdn.com/bphoto/E30GU78wy1fzEP3JhOlDnQ/o.jpg	FALSE	https://www.yelp.com/biz/yard-house-atlanta-4?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA	1314	newamerican , vegetarian , bars , American (New), Vegetarian , Bars	3.0	33.79356	delivery, pickup	\[ \|261 19th St NW \|+14048158990 \|(404) 815-8990 \| 131.3623\| \|nQkcvNHx8FH00CefJV_0MA \|sri-thai-kitchen-and-sushi-bar-atlanta-atlanta \|Sri Thai Kitchen & Sushi Bar - Atlanta \|https://s3-media2.fl.yelpcdn.com/bphoto/rV4AJQdmggTaqClXHvjsLg/o.jpg \|FALSE \|https://www.yelp.com/biz/sri-thai-kitchen-and-sushi-bar-atlanta-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA \| 222\|thai , sushi , japanese , Thai , Sushi Bars, Japanese \| 4.0\|33.79341 \|delivery, pickup \|\]	1380 Atlantic Dr NW	+16787051679	(678) 705-1679	139.0467

Raw response

resp_raw <- content(resp, as = "raw")
resp_raw

##     [1] 7b 22 62 75 73 69 6e 65 73 73 65 73 22 3a 20 5b 7b 22 69 64 22 3a 20 22
##    [25] 43 78 51 31 6d 32 69 59 34 77 51 70 58 43 36 34 74 53 66 57 67 51 22 2c
##    [49] 20 22 61 6c 69 61 73 22 3a 20 22 61 6e 74 69 63 6f 2d 70 69 7a 7a 61 2d
##    [73] 61 74 6c 61 6e 74 61 22 2c 20 22 6e 61 6d 65 22 3a 20 22 41 6e 74 69 63
##    [97] 6f 20 50 69 7a 7a 61 22 2c 20 22 69 6d 61 67 65 5f 75 72 6c 22 3a 20 22
##   [121] 68 74 74 70 73 3a 2f 2f 73 33 2d 6d 65 64 69 61 32 2e 66 6c 2e 79 65 6c
##   [145] 70 63 64 6e 2e 63 6f 6d 2f 62 70 68 6f 74 6f 2f 67 55 67 54 53 59 49 6d
##   [169] 78 5f 34 35 43 71 4a 61 59 57 53 69 57 67 2f 6f 2e 6a 70 67 22 2c 20 22
##   [193] 69 73 5f 63 6c 6f 73 65 64 22 3a 20 66 61 6c 73 65 2c 20 22 75 72 6c 22
##   [217] 3a 20 22 68 74 74 70 73 3a 2f 2f 77 77 77 2e 79 65 6c 70 2e 63 6f 6d 2f
##   [241] 62 69 7a 2f 61 6e 74 69 63 6f 2d 70 69 7a 7a 61 2d 61 74 6c 61 6e 74 61
##   [265] 3f 61 64 6a 75 73 74 5f 63 72 65 61 74 69 76 65 3d 62 34 46 51 43 56 69
##   [289] 66 78 42 6d 37 49 43 6f 6f 46 73 37 48 69 41 26 75 74 6d 5f 63 61 6d 70
##   [313] 61 69 67 6e 3d 79 65 6c 70 5f 61 70 69 5f 76 33 26 75 74 6d 5f 6d 65 64
##   [337] 69 75 6d 3d 61 70 69 5f 76 33 5f 62 75 73 69 6e 65 73 73 5f 73 65 61 72
##   [361] 63 68 26 75 74 6d 5f 73 6f 75 72 63 65 3d 62 34 46 51 43 56 69 66 78 42
##   [385] 6d 37 49 43 6f 6f 46 73 37 48 69 41 22 2c 20 22 72 65 76 69 65 77 5f 63
##   [409] 6f 75 6e 74 22 3a 20 33 34 34 31 2c 20 22 63 61 74 65 67 6f 72 69 65 73
##   [433] 22 3a 20 5b 7b 22 61 6c 69 61 73 22 3a 20 22 70 69 7a 7a 61 22 2c 20 22
##   [457] 74 69 74 6c 65 22 3a 20 22 50 69 7a 7a 61 22 7d 5d 2c 20 22 72 61 74 69
##   [481] 6e 67 22 3a 20 34 2e 35 2c 20 22 63 6f 6f 72 64 69 6e 61 74 65 73 22 3a
##   [505] 20 7b 22 6c 61 74 69 74 75 64 65 22 3a 20 33 33 2e 37 38 34 36 34 31 36
##   [529] 32 34 33 35 37 36 2c 20 22 6c 6f 6e 67 69 74 75 64 65 22 3a 20 2d 38 34
##   [553] 2e 34 30 35 36 35 32 33 36 34 34 31 38 7d 2c 20 22 74 72 61 6e 73 61 63
##   [577] 74 69 6f 6e 73 22 3a 20 5b 22 64 65 6c 69 76 65 72 79 22 2c 20 22 70 69
##   [601] 63 6b 75 70 22 5d 2c 20 22 70 72 69 63 65 22 3a 20 22 24 24 22 2c 20 22
##   [625] 6c 6f 63 61 74 69 6f 6e 22 3a 20 7b 22 61 64 64 72 65 73 73 31 22 3a 20
##   [649] 22 31 30 39 33 20 48 65 6d 70 68 69 6c 6c 20 41 76 65 20 4e 57 22 2c 20
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## [47761] 69 6e 65 73 73 5f 73 65 61 72 63 68 26 75 74 6d 5f 73 6f 75 72 63 65 3d
## [47785] 62 34 46 51 43 56 69 66 78 42 6d 37 49 43 6f 6f 46 73 37 48 69 41 22 2c
## [47809] 20 22 72 65 76 69 65 77 5f 63 6f 75 6e 74 22 3a 20 38 38 2c 20 22 63 61
## [47833] 74 65 67 6f 72 69 65 73 22 3a 20 5b 7b 22 61 6c 69 61 73 22 3a 20 22 63
## [47857] 61 66 65 73 22 2c 20 22 74 69 74 6c 65 22 3a 20 22 43 61 66 65 73 22 7d
## [47881] 2c 20 7b 22 61 6c 69 61 73 22 3a 20 22 69 74 61 6c 69 61 6e 22 2c 20 22
## [47905] 74 69 74 6c 65 22 3a 20 22 49 74 61 6c 69 61 6e 22 7d 5d 2c 20 22 72 61
## [47929] 74 69 6e 67 22 3a 20 32 2e 35 2c 20 22 63 6f 6f 72 64 69 6e 61 74 65 73
## [47953] 22 3a 20 7b 22 6c 61 74 69 74 75 64 65 22 3a 20 33 33 2e 37 38 34 38 39
## [47977] 2c 20 22 6c 6f 6e 67 69 74 75 64 65 22 3a 20 2d 38 34 2e 34 30 35 36 36
## [48001] 35 7d 2c 20 22 74 72 61 6e 73 61 63 74 69 6f 6e 73 22 3a 20 5b 22 64 65
## [48025] 6c 69 76 65 72 79 22 5d 2c 20 22 70 72 69 63 65 22 3a 20 22 24 22 2c 20
## [48049] 22 6c 6f 63 61 74 69 6f 6e 22 3a 20 7b 22 61 64 64 72 65 73 73 31 22 3a
## [48073] 20 22 31 30 39 39 20 48 65 6d 70 68 69 6c 6c 20 41 76 65 22 2c 20 22 61
## [48097] 64 64 72 65 73 73 32 22 3a 20 22 22 2c 20 22 61 64 64 72 65 73 73 33 22
## [48121] 3a 20 22 22 2c 20 22 63 69 74 79 22 3a 20 22 41 74 6c 61 6e 74 61 22 2c
## [48145] 20 22 7a 69 70 5f 63 6f 64 65 22 3a 20 22 33 30 33 31 38 22 2c 20 22 63
## [48169] 6f 75 6e 74 72 79 22 3a 20 22 55 53 22 2c 20 22 73 74 61 74 65 22 3a 20
## [48193] 22 47 41 22 2c 20 22 64 69 73 70 6c 61 79 5f 61 64 64 72 65 73 73 22 3a
## [48217] 20 5b 22 31 30 39 39 20 48 65 6d 70 68 69 6c 6c 20 41 76 65 22 2c 20 22
## [48241] 41 74 6c 61 6e 74 61 2c 20 47 41 20 33 30 33 31 38 22 5d 7d 2c 20 22 70
## [48265] 68 6f 6e 65 22 3a 20 22 2b 31 34 30 34 33 34 37 33 38 37 34 22 2c 20 22
## [48289] 64 69 73 70 6c 61 79 5f 70 68 6f 6e 65 22 3a 20 22 28 34 30 34 29 20 33
## [48313] 34 37 2d 33 38 37 34 22 2c 20 22 64 69 73 74 61 6e 63 65 22 3a 20 31 31
## [48337] 37 38 2e 36 35 33 35 31 33 37 31 39 33 32 38 7d 2c 20 7b 22 69 64 22 3a
## [48361] 20 22 59 49 78 61 62 44 33 4e 6f 52 33 55 5f 71 4f 62 78 56 4c 5f 58 41
## [48385] 22 2c 20 22 61 6c 69 61 73 22 3a 20 22 67 69 6f 73 2d 73 69 63 69 6c 69
## [48409] 61 6e 2d 70 69 7a 7a 61 2d 73 68 6f 70 2d 61 74 6c 61 6e 74 61 22 2c 20
## [48433] 22 6e 61 6d 65 22 3a 20 22 47 69 6f 27 73 20 53 69 63 69 6c 69 61 6e 20
## [48457] 50 69 7a 7a 61 20 53 68 6f 70 22 2c 20 22 69 6d 61 67 65 5f 75 72 6c 22
## [48481] 3a 20 22 68 74 74 70 73 3a 2f 2f 73 33 2d 6d 65 64 69 61 31 2e 66 6c 2e
## [48505] 79 65 6c 70 63 64 6e 2e 63 6f 6d 2f 62 70 68 6f 74 6f 2f 41 4b 30 72 68
## [48529] 6f 33 59 75 71 6d 70 47 54 41 36 7a 6e 4a 72 66 67 2f 6f 2e 6a 70 67 22
## [48553] 2c 20 22 69 73 5f 63 6c 6f 73 65 64 22 3a 20 66 61 6c 73 65 2c 20 22 75
## [48577] 72 6c 22 3a 20 22 68 74 74 70 73 3a 2f 2f 77 77 77 2e 79 65 6c 70 2e 63
## [48601] 6f 6d 2f 62 69 7a 2f 67 69 6f 73 2d 73 69 63 69 6c 69 61 6e 2d 70 69 7a
## [48625] 7a 61 2d 73 68 6f 70 2d 61 74 6c 61 6e 74 61 3f 61 64 6a 75 73 74 5f 63
## [48649] 72 65 61 74 69 76 65 3d 62 34 46 51 43 56 69 66 78 42 6d 37 49 43 6f 6f
## [48673] 46 73 37 48 69 41 26 75 74 6d 5f 63 61 6d 70 61 69 67 6e 3d 79 65 6c 70
## [48697] 5f 61 70 69 5f 76 33 26 75 74 6d 5f 6d 65 64 69 75 6d 3d 61 70 69 5f 76
## [48721] 33 5f 62 75 73 69 6e 65 73 73 5f 73 65 61 72 63 68 26 75 74 6d 5f 73 6f
## [48745] 75 72 63 65 3d 62 34 46 51 43 56 69 66 78 42 6d 37 49 43 6f 6f 46 73 37
## [48769] 48 69 41 22 2c 20 22 72 65 76 69 65 77 5f 63 6f 75 6e 74 22 3a 20 31 36
## [48793] 2c 20 22 63 61 74 65 67 6f 72 69 65 73 22 3a 20 5b 7b 22 61 6c 69 61 73
## [48817] 22 3a 20 22 70 69 7a 7a 61 22 2c 20 22 74 69 74 6c 65 22 3a 20 22 50 69
## [48841] 7a 7a 61 22 7d 2c 20 7b 22 61 6c 69 61 73 22 3a 20 22 73 69 63 69 6c 69
## [48865] 61 6e 22 2c 20 22 74 69 74 6c 65 22 3a 20 22 53 69 63 69 6c 69 61 6e 22
## [48889] 7d 5d 2c 20 22 72 61 74 69 6e 67 22 3a 20 34 2e 30 2c 20 22 63 6f 6f 72
## [48913] 64 69 6e 61 74 65 73 22 3a 20 7b 22 6c 61 74 69 74 75 64 65 22 3a 20 33
## [48937] 33 2e 37 38 35 30 31 33 38 37 34 36 30 36 33 2c 20 22 6c 6f 6e 67 69 74
## [48961] 75 64 65 22 3a 20 2d 38 34 2e 34 30 35 36 39 37 36 32 34 35 37 30 34 7d
## [48985] 2c 20 22 74 72 61 6e 73 61 63 74 69 6f 6e 73 22 3a 20 5b 22 64 65 6c 69
## [49009] 76 65 72 79 22 5d 2c 20 22 6c 6f 63 61 74 69 6f 6e 22 3a 20 7b 22 61 64
## [49033] 64 72 65 73 73 31 22 3a 20 22 31 30 39 39 20 48 65 6d 70 68 69 6c 6c 20
## [49057] 41 76 65 20 4e 57 22 2c 20 22 61 64 64 72 65 73 73 32 22 3a 20 22 22 2c
## [49081] 20 22 61 64 64 72 65 73 73 33 22 3a 20 6e 75 6c 6c 2c 20 22 63 69 74 79
## [49105] 22 3a 20 22 41 74 6c 61 6e 74 61 22 2c 20 22 7a 69 70 5f 63 6f 64 65 22
## [49129] 3a 20 22 33 30 33 31 38 22 2c 20 22 63 6f 75 6e 74 72 79 22 3a 20 22 55
## [49153] 53 22 2c 20 22 73 74 61 74 65 22 3a 20 22 47 41 22 2c 20 22 64 69 73 70
## [49177] 6c 61 79 5f 61 64 64 72 65 73 73 22 3a 20 5b 22 31 30 39 39 20 48 65 6d
## [49201] 70 68 69 6c 6c 20 41 76 65 20 4e 57 22 2c 20 22 41 74 6c 61 6e 74 61 2c
## [49225] 20 47 41 20 33 30 33 31 38 22 5d 7d 2c 20 22 70 68 6f 6e 65 22 3a 20 22
## [49249] 2b 31 34 30 34 33 34 37 33 38 37 34 22 2c 20 22 64 69 73 70 6c 61 79 5f
## [49273] 70 68 6f 6e 65 22 3a 20 22 28 34 30 34 29 20 33 34 37 2d 33 38 37 34 22
## [49297] 2c 20 22 64 69 73 74 61 6e 63 65 22 3a 20 31 31 37 30 2e 39 35 35 34 30
## [49321] 39 37 34 32 38 38 30 39 7d 5d 2c 20 22 74 6f 74 61 6c 22 3a 20 37 39 2c
## [49345] 20 22 72 65 67 69 6f 6e 22 3a 20 7b 22 63 65 6e 74 65 72 22 3a 20 7b 22
## [49369] 6c 6f 6e 67 69 74 75 64 65 22 3a 20 2d 38 34 2e 33 39 36 37 36 30 37 37
## [49393] 34 34 34 39 34 2c 20 22 6c 61 74 69 74 75 64 65 22 3a 20 33 33 2e 37 39
## [49417] 32 34 37 39 30 37 38 31 39 36 33 7d 7d 7d

yelpr package (Step 4)

As shown above, it is possible to format an URL, send request, and get the response. However, there is a package specifically designed to make this process easier: yelpr package. It has a function called business_search(). For a test, let’s try this function to acquire data for the first Census Tract.

which_tract <- 1
test <- business_search(api_key = Sys.getenv('yelp_api'), # like we did for census, store your api key
                        categories = 'restaurants', # return only restaurant businesses
                        latitude = ready_4_yelp$y[which_tract],
                        longitude = ready_4_yelp$x[which_tract],
                        offset = 0, # 1st page, 1st obs
                        radius = round(ready_4_yelp$radius[which_tract]), # radius requires integer value
                        limit = 50) # how many business per page

## No encoding supplied: defaulting to UTF-8.

lapply(test, head)

## $businesses
##                       id                                alias
## 1 eG-UO83g_5zDk70FIJbm2w south-city-kitchen-midtown-atlanta-2
## 2 _iqFvc3zToL08WZrNeFP3Q             stk-steakhouse-atlanta-2
## 3 3ehyrexo3WcoTy74c2jDKA  flying-biscuit-café-midtown-atlanta
## 4 tDv2qG4N7PsYLN0QYuuaZQ            bulla-gastrobar-atlanta-2
## 5 hmrRb7qX3K705MuxHHfgNA    cafe-intermezzo-midtown-atlanta-3
## 6 Gwi9PMVb61nrrpUNa9_wfQ                         lure-atlanta
##                            name
## 1    South City Kitchen Midtown
## 2                STK Steakhouse
## 3 Flying Biscuit Café - Midtown
## 4               Bulla Gastrobar
## 5     Cafe Intermezzo - Midtown
## 6                          Lure
##                                                              image_url
## 1 https://s3-media3.fl.yelpcdn.com/bphoto/L1qX2ttHqvNMqgsw_JQNLQ/o.jpg
## 2 https://s3-media1.fl.yelpcdn.com/bphoto/cHKMsDThzTVpoRqS39lPXA/o.jpg
## 3 https://s3-media4.fl.yelpcdn.com/bphoto/9PiLUl2SWUyIQhOECrBcsA/o.jpg
## 4 https://s3-media1.fl.yelpcdn.com/bphoto/ZCovd0lMaKQpasok3yivWA/o.jpg
## 5 https://s3-media4.fl.yelpcdn.com/bphoto/9zuh2IfmOwYmkJPnwgH7rw/o.jpg
## 6 https://s3-media2.fl.yelpcdn.com/bphoto/TIzVTGEQQfP2RN950zWrJw/o.jpg
##   is_closed
## 1     FALSE
## 2     FALSE
## 3     FALSE
## 4     FALSE
## 5     FALSE
## 6     FALSE
##                                                                                                                                                                                                     url
## 1     https://www.yelp.com/biz/south-city-kitchen-midtown-atlanta-2?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA
## 2                 https://www.yelp.com/biz/stk-steakhouse-atlanta-2?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA
## 3 https://www.yelp.com/biz/flying-biscuit-caf%C3%A9-midtown-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA
## 4                https://www.yelp.com/biz/bulla-gastrobar-atlanta-2?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA
## 5        https://www.yelp.com/biz/cafe-intermezzo-midtown-atlanta-3?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA
## 6                             https://www.yelp.com/biz/lure-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=b4FQCVifxBm7ICooFs7HiA
##   review_count
## 1         2945
## 2         1779
## 3         1802
## 4          761
## 5         1376
## 6          637
##                                                                              categories
## 1                                                                    southern, Southern
## 2          steak, newamerican, cocktailbars, Steakhouses, American (New), Cocktail Bars
## 3 breakfast_brunch, southern, newamerican, Breakfast & Brunch, Southern, American (New)
## 4                           spanish, gastropubs, tapas, Spanish, Gastropubs, Tapas Bars
## 5                                          cafes, bars, desserts, Cafes, Bars, Desserts
## 6                             seafood, bars, newamerican, Seafood, Bars, American (New)
##   rating coordinates.latitude coordinates.longitude     transactions price
## 1    4.5             33.78600             -84.38456         delivery    $$
## 2    3.5             33.78418             -84.38283 delivery, pickup  $$$$
## 3    4.0             33.78200             -84.38015 delivery, pickup    $$
## 4    4.0             33.78354             -84.38483 delivery, pickup    $$
## 5    3.5             33.78331             -84.38356 delivery, pickup    $$
## 6    4.0             33.78500             -84.38452         delivery    $$
##      location.address1 location.address2 location.address3 location.city
## 1 1144 Crescent Ave NE                                           Atlanta
## 2 1075 Peachtree St NE                                           Atlanta
## 3    1001 Piedmont Ave                                           Atlanta
## 4        60 11th St NE                                <NA>       Atlanta
## 5 1065 Peachtree St NE                                           Atlanta
## 6 1106 Crescent Ave NE                                           Atlanta
##   location.zip_code location.country location.state
## 1             30309               US             GA
## 2             30309               US             GA
## 3             30309               US             GA
## 4             30309               US             GA
## 5             30309               US             GA
## 6             30309               US             GA
##                  location.display_address        phone  display_phone
## 1 1144 Crescent Ave NE, Atlanta, GA 30309 +14048737358 (404) 873-7358
## 2 1075 Peachtree St NE, Atlanta, GA 30309 +14047930144 (404) 793-0144
## 3    1001 Piedmont Ave, Atlanta, GA 30309 +14048748887 (404) 874-8887
## 4        60 11th St NE, Atlanta, GA 30309 +14049006926 (404) 900-6926
## 5 1065 Peachtree St NE, Atlanta, GA 30309 +14708783137 (470) 878-3137
## 6 1106 Crescent Ave NE, Atlanta, GA 30309 +14048173650 (404) 817-3650
##     distance
## 1 242.850935
## 2   4.314353
## 3 344.764641
## 4 196.780359
## 5 118.986629
## 6 179.474837
## 
## $total
## [1] 119
## 
## $region
## $region$center
## $region$center$longitude
## [1] -84.38286
## 
## $region$center$latitude
## [1] 33.78421

What we received from just this one request is a lot of information. First of all, test object is a list. By running names(test), we can see that it contains (1) businesses, (2) total, and (3) region. Businesses contains the main information about restaurants. Total tells you that there are 119 restaurants in Yelp’s database within the given radius from the centroid of Census Tract Bounding box. Region gives us back the center of the search – the centroid of the first Census Tract bounding box.

Looking at the businesses, it is a data frame (you can test is using is.data.frame(test$businesses)) with 50 rows and 16 columns (this data frame is actually a nested data frame with list-columns in which one data frame is containing another data frame(s), list(s), etc. We will deal with it later). This 50 is the artifact of setting limit = 50 above in the previous code chuck. This means that we need to request 3 times in total to collect data on all 119 restaurants, because 119/50 = 2.38.

# See what's inside
names(test)

## [1] "businesses" "total"      "region"

# Business
paste0("is it a data.frame?: ", is.data.frame(test$businesses), ", ",
       " how many rows?: ", nrow(test$businesses), ", ",
       " how many columns?: ", ncol(test$businesses))

## [1] "is it a data.frame?: TRUE,  how many rows?: 50,  how many columns?: 16"

What this tells us that:

We need to do at least one API request to see how many more we need to make.
You can get up to 50 at a time.
The total number of restaurants is less than 1000.

Defining function for accessing Yelp API for one Census Tract (Step 5)

Okay, we learned that we need to make multiple requests even for just one Census Tract. We can create another function to make this process automatic. We will design this function such that it takes a row from ready_4_yelp, make the initial API request, calculate how many times more it need to request, complete the remaining request, gather the responses from the multiple requests.

# FUNCTION
get_yelp <- function(tract, category){
  # ----------------------------------
  # Gets one row of tract information (1,) and category name (str),
  # Outputs a list of business data.frame
  Sys.sleep(5)
  n <- 1
  # First request --------------------------------------------------------------
  resp <- business_search(api_key = Sys.getenv("yelp_api"), 
                          categories = category, 
                          latitude = tract$y, 
                          longitude = tract$x, 
                          offset = (n - 1) * 50, # = 0 when n = 1
                          radius = round(tract$radius), 
                          limit = 50)
  # Calculate how many requests are needed in total
  required_n <- ceiling(resp$total/50)
  
  # out is where the results will be appended to.
  out <- vector("list", required_n)
  
  # Store the business information to nth slot in out
  out[[n]] <- resp$businesses
  
  # Change the name of the elements to the total required_n
  # This is to know if there are more than 1000 businesses,
  # we know how many.
  names(out)[n] <- required_n
  
  # Throw error if more than 1000
  if (resp$total >= 1000)
  {
    # glue formats string by inserting {n} with what's currently stored in object n.
    print(glue::glue("{n}th row has >= 1000 businesses."))
    # Stop before going into the loop because we need to
    # break down Census Tract to something smaller.
    return(out)
  } 
  else 
  {
    # add 1 to n
    n <- n + 1
    
    # Now we know required_n -----------------------------------------------------
    # Starting a loop
    while(n <= required_n){
      resp <- business_search(api_key = Sys.getenv("yelp_api"), 
                              categories = category, 
                              latitude = tract$y, 
                              longitude = tract$x, 
                              offset = (n - 1) * 50, 
                              radius = round(tract$radius), 
                              limit = 50)
      
      out[[n]] <- resp$businesses
      
      n <- n + 1
    } #<< end of while loop
    
    # Merge all elements in the list into a single data frame
    out <- out %>% bind_rows()
    
    return(out)
  }
}

# Apply the function for the first Census Tract
yelp_first_tract <- get_yelp(ready_4_yelp[1,], "restaurants") %>% 
  as_tibble()

## No encoding supplied: defaulting to UTF-8.
## No encoding supplied: defaulting to UTF-8.
## No encoding supplied: defaulting to UTF-8.

# Print
yelp_first_tract %>% print

## # A tibble: 119 × 16
##    id           alias name  image…¹ is_cl…² url   revie…³ categ…⁴ rating coord…⁵
##    <chr>        <chr> <chr> <chr>   <lgl>   <chr>   <int> <list>   <dbl>   <dbl>
##  1 eG-UO83g_5z… sout… Sout… https:… FALSE   http…    2945 <df>       4.5    33.8
##  2 _iqFvc3zToL… stk-… STK … https:… FALSE   http…    1779 <df>       3.5    33.8
##  3 3ehyrexo3Wc… flyi… Flyi… https:… FALSE   http…    1802 <df>       4      33.8
##  4 tDv2qG4N7Ps… bull… Bull… https:… FALSE   http…     761 <df>       4      33.8
##  5 hmrRb7qX3K7… cafe… Cafe… https:… FALSE   http…    1376 <df>       3.5    33.8
##  6 Gwi9PMVb61n… lure… Lure  https:… FALSE   http…     637 <df>       4      33.8
##  7 Pl2c7HLD9Ug… tabl… Tabla https:… FALSE   http…     613 <df>       4      33.8
##  8 qS2Gg7_FH8i… past… Past… https:… FALSE   http…     815 <df>       4      33.8
##  9 Z2qMwUhnGt_… the-… The … https:… FALSE   http…    1903 <df>       4      33.8
## 10 DTALvZKBJH8… 5chu… 5Chu… https:… FALSE   http…    1184 <df>       4      33.8
## # … with 109 more rows, 7 more variables: coordinates$longitude <dbl>,
## #   transactions <list>, price <chr>, location <df[,8]>, phone <chr>,
## #   display_phone <chr>, distance <dbl>, and abbreviated variable names
## #   ¹image_url, ²is_closed, ³review_count, ⁴categories, ⁵coordinates$latitude
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

The code below applies this function to download Yelp data for the first Census Tract. The yelp_first_tract shows that it is a tibble of size 119 x 16, which is what we expected!

Repeating for all other Census Tracts (Step 6)

Now that we have a function that successfully downloaded Yelp data for the first Census Tract, we can repeat this process for the rest of the Census Tracts in tract_center. We can use a for-loop again (or a functional if you prefer) that loops through each Census Tract and perform what we did above.

# Prepare a collector
yelp_all_list <- vector("list", nrow(ready_4_yelp))

# Looping through all Census Tracts
for (row in 1:nrow(ready_4_yelp)){
  yelp_all_list[[row]] <- suppressMessages(get_yelp(ready_4_yelp[row,], "restaurants"))
  if (row %% 10 == 0){
    Sys.sleep(10)
    print(paste0("Current row: ", row))
  }
}

## [1] "Current row: 10"
## [1] "Current row: 20"
## [1] "Current row: 30"
## [1] "Current row: 40"
## [1] "Current row: 50"
## [1] "Current row: 60"
## [1] "Current row: 70"
## [1] "Current row: 80"
## [1] "Current row: 90"
## [1] "Current row: 100"
## [1] "Current row: 110"
## [1] "Current row: 120"

## Warning: Outer names are only allowed for unnamed scalar atomic inputs

## [1] "Current row: 130"
## [1] "Current row: 140"
## [1] "Current row: 150"

## Warning: Outer names are only allowed for unnamed scalar atomic inputs

## [1] "Current row: 160"
## [1] "Current row: 170"
## [1] "Current row: 180"
## [1] "Current row: 190"
## [1] "Current row: 200"
## [1] "Current row: 210"
## [1] "Current row: 220"
## [1] "Current row: 230"
## [1] "Current row: 240"
## [1] "Current row: 250"
## [1] "Current row: 260"
## [1] "Current row: 270"
## [1] "Current row: 280"
## [1] "Current row: 290"
## [1] "Current row: 300"
## [1] "Current row: 310"
## [1] "Current row: 320"
## [1] "Current row: 330"
## [1] "Current row: 340"

# Collapsing the list into a data.frame
yelp_all <- yelp_all_list %>% bind_rows() %>% as_tibble()

# print
yelp_all %>% print(width=1000)

## # A tibble: 33,724 × 16
##    id                     alias                                   
##    <chr>                  <chr>                                   
##  1 eG-UO83g_5zDk70FIJbm2w south-city-kitchen-midtown-atlanta-2    
##  2 _iqFvc3zToL08WZrNeFP3Q stk-steakhouse-atlanta-2                
##  3 3ehyrexo3WcoTy74c2jDKA flying-biscuit-café-midtown-atlanta     
##  4 tDv2qG4N7PsYLN0QYuuaZQ bulla-gastrobar-atlanta-2               
##  5 hmrRb7qX3K705MuxHHfgNA cafe-intermezzo-midtown-atlanta-3       
##  6 Gwi9PMVb61nrrpUNa9_wfQ lure-atlanta                            
##  7 Pl2c7HLD9UgFL2zHP0cZVQ tabla-atlanta                           
##  8 qS2Gg7_FH8iHFiorOVd7hg pasta-da-pulcinella-atlanta             
##  9 Z2qMwUhnGt_2pA9uQbS7Uw the-vortex-bar-and-grill-midtown-atlanta
## 10 DTALvZKBJH8XVAKfcQseYQ 5church-midtown-atlanta                 
##    name                              
##    <chr>                             
##  1 South City Kitchen Midtown        
##  2 STK Steakhouse                    
##  3 Flying Biscuit Café - Midtown     
##  4 Bulla Gastrobar                   
##  5 Cafe Intermezzo - Midtown         
##  6 Lure                              
##  7 Tabla                             
##  8 Pasta Da Pulcinella               
##  9 The Vortex Bar And Grill - Midtown
## 10 5Church Midtown                   
##    image_url                                                           
##    <chr>                                                               
##  1 https://s3-media3.fl.yelpcdn.com/bphoto/L1qX2ttHqvNMqgsw_JQNLQ/o.jpg
##  2 https://s3-media1.fl.yelpcdn.com/bphoto/cHKMsDThzTVpoRqS39lPXA/o.jpg
##  3 https://s3-media4.fl.yelpcdn.com/bphoto/9PiLUl2SWUyIQhOECrBcsA/o.jpg
##  4 https://s3-media1.fl.yelpcdn.com/bphoto/ZCovd0lMaKQpasok3yivWA/o.jpg
##  5 https://s3-media4.fl.yelpcdn.com/bphoto/9zuh2IfmOwYmkJPnwgH7rw/o.jpg
##  6 https://s3-media2.fl.yelpcdn.com/bphoto/TIzVTGEQQfP2RN950zWrJw/o.jpg
##  7 https://s3-media4.fl.yelpcdn.com/bphoto/LV8ixrMFmRGT0RLKfS3iYg/o.jpg
##  8 https://s3-media1.fl.yelpcdn.com/bphoto/dGJg82WtO0Tx9xWFG_pamw/o.jpg
##  9 https://s3-media4.fl.yelpcdn.com/bphoto/G3yU2A_ejrasV5tzoxMzew/o.jpg
## 10 https://s3-media4.fl.yelpcdn.com/bphoto/ryCZZheFgt5iyHfp0nzw0A/o.jpg
##    is_closed
##    <lgl>    
##  1 FALSE    
##  2 FALSE    
##  3 FALSE    
##  4 FALSE    
##  5 FALSE    
##  6 FALSE    
##  7 FALSE    
##  8 FALSE    
##  9 FALSE    
## 10 FALSE    
##    url                                                                          
##    <chr>                                                                        
##  1 https://www.yelp.com/biz/south-city-kitchen-midtown-atlanta-2?adjust_creativ…
##  2 https://www.yelp.com/biz/stk-steakhouse-atlanta-2?adjust_creative=b4FQCVifxB…
##  3 https://www.yelp.com/biz/flying-biscuit-caf%C3%A9-midtown-atlanta?adjust_cre…
##  4 https://www.yelp.com/biz/bulla-gastrobar-atlanta-2?adjust_creative=b4FQCVifx…
##  5 https://www.yelp.com/biz/cafe-intermezzo-midtown-atlanta-3?adjust_creative=b…
##  6 https://www.yelp.com/biz/lure-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7HiA…
##  7 https://www.yelp.com/biz/tabla-atlanta?adjust_creative=b4FQCVifxBm7ICooFs7Hi…
##  8 https://www.yelp.com/biz/pasta-da-pulcinella-atlanta?adjust_creative=b4FQCVi…
##  9 https://www.yelp.com/biz/the-vortex-bar-and-grill-midtown-atlanta?adjust_cre…
## 10 https://www.yelp.com/biz/5church-midtown-atlanta?adjust_creative=b4FQCVifxBm…
##    review_count categories   rating coordinates$latitude $longitude transactions
##           <int> <list>        <dbl>                <dbl>      <dbl> <list>      
##  1         2945 <df [1 × 2]>    4.5                 33.8      -84.4 <chr [1]>   
##  2         1779 <df [3 × 2]>    3.5                 33.8      -84.4 <chr [2]>   
##  3         1802 <df [3 × 2]>    4                   33.8      -84.4 <chr [2]>   
##  4          761 <df [3 × 2]>    4                   33.8      -84.4 <chr [2]>   
##  5         1376 <df [3 × 2]>    3.5                 33.8      -84.4 <chr [2]>   
##  6          637 <df [3 × 2]>    4                   33.8      -84.4 <chr [1]>   
##  7          613 <df [1 × 2]>    4                   33.8      -84.4 <chr [2]>   
##  8          815 <df [3 × 2]>    4                   33.8      -84.4 <chr [1]>   
##  9         1903 <df [3 × 2]>    4                   33.8      -84.4 <chr [1]>   
## 10         1184 <df [2 × 2]>    4                   33.8      -84.4 <chr [1]>   
##    price location$address1      $address2 $address3 $city   $zip_code $country
##    <chr> <chr>                  <chr>     <chr>     <chr>   <chr>     <chr>   
##  1 $$    1144 Crescent Ave NE   ""        ""        Atlanta 30309     US      
##  2 $$$$  1075 Peachtree St NE   ""        ""        Atlanta 30309     US      
##  3 $$    1001 Piedmont Ave      ""        ""        Atlanta 30309     US      
##  4 $$    60 11th St NE          ""        <NA>      Atlanta 30309     US      
##  5 $$    1065 Peachtree St NE   ""        ""        Atlanta 30309     US      
##  6 $$    1106 Crescent Ave NE   ""        ""        Atlanta 30309     US      
##  7 $$    77TH 12th St NE        "Ste 2"   ""        Atlanta 30309     US      
##  8 $$    1123 Peachtree Walk NE ""        ""        Atlanta 30309     US      
##  9 $$    878 Peachtree St NE    ""        ""        Atlanta 30309     US      
## 10 $$    1197 Peachtree St NE   ""        ""        Atlanta 30361     US      
##    $state $display_address phone        display_phone  distance
##    <chr>  <list>           <chr>        <chr>             <dbl>
##  1 GA     <chr [2]>        +14048737358 (404) 873-7358   243.  
##  2 GA     <chr [2]>        +14047930144 (404) 793-0144     4.31
##  3 GA     <chr [2]>        +14048748887 (404) 874-8887   345.  
##  4 GA     <chr [2]>        +14049006926 (404) 900-6926   197.  
##  5 GA     <chr [2]>        +14708783137 (470) 878-3137   119.  
##  6 GA     <chr [2]>        +14048173650 (404) 817-3650   179.  
##  7 GA     <chr [3]>        +14044648571 (404) 464-8571   213.  
##  8 GA     <chr [2]>        +14048761114 (404) 876-1114   323.  
##  9 GA     <chr [2]>        +14048751667 (404) 875-1667   592.  
## 10 GA     <chr [2]>        +14044003669 (404) 400-3669   333.  
## # … with 33,714 more rows
## # ℹ Use `print(n = ...)` to see more rows

Glimpse

We have yelp information on restaurant in data frame format. We have 16 columns. We will take a closer look at them next week. For now, let’s just map them.

# Extract coordinates
yelp_sf <- yelp_all %>% 
  mutate(x = .$coordinates$longitude,
         y = .$coordinates$latitude) %>% 
  filter(!is.na(x) & !is.na(y)) %>% 
  st_as_sf(coords = c("x", "y"), crs = 4326)

# Map
tm_shape(yelp_sf) +
  tm_dots(col = "review_count", style="quantile")