This is an R Markdown document for Mini 1!
#client_secret <- "heiQtuO2RnUpIIOYNIBNwQI_prhD0_we8HyhuISPJTY14XXQ-n3B0McQUxofxO6oWMBU1rH4Z6tbsJ3P1IQsdA8D_zKSX7oFCrM1Yv4fpFGqq8-9wNAFmKbL3gchY3Yx"
client_secret <- "d_8lzfKBfcZ1zp7QNd6DcLBDfQmnyio93Ab5DYjblRoh_75EL08VRMHFgW-0qm5zuChGQmPi7Ivm78TkIFXpe_2Cx20t4wNPNASZQlWPRUn-vOCaqeahzhScYi8qY3Yx"
Sys.getenv("CENSUS_API_KEY")## [1] "dd63c5ddc43a230bd9d8e634b9d9d1fc26703e3b"#state_pop <- get_decennial(geography = "state", variables = "P001001")
#head(state_pop)
tract <- suppressMessages(
get_acs(geography = ("tract"), # or "block group", "county", "state" etc.
state = ("NJ"),
county = c("Somerset"),
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
)##
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|======================================================================| 100%message(sprintf("nrow: %s, ncol: %s", nrow(tract), ncol(tract)))## nrow: 68, ncol: 11#print(tract)
tract <- tract %>%
select(GEOID,
county = NAME,
hhincome = hhincomeE, # New name = old name
race.tot = race.totE,
race.white = race.whiteE,
race.black = race.blackE)
#print(tract)
#print tract after
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(tract) + tm_borders()# 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$radius <- r*1.2
return(bb_center)
}
## Using a loop -----------------------------------------------------------------
# Creating an empty vector of NA.
# Results will fill this vector
epsg_id <- 4326 # Edit (9/8/2022): 4326 measures distance in meter. Before the edit, I used 26967.
r4all_loop <- vector("list", nrow(tract))
# Starting a for-loop
for (i in 1:nrow(tract)){
r4all_loop[[i]] <- tract %>%
st_transform(crs = epsg_id) %>%
st_geometry() %>%
.[[i]] %>%
get_r(epsg_id = epsg_id)
}
r4all_loop <- bind_rows(r4all_loop)
# Using a functional -----------------------------------------------------------
# We use a functional (sapply) to apply this custom function to each Census Tract.
r4all_apply <- tract %>%
st_geometry() %>%
st_transform(crs = epsg_id) %>%
lapply(., function(x) get_r(x, epsg_id = epsg_id))
r4all_apply <- bind_rows(r4all_apply)
# Are these two identical?
identical(r4all_apply, r4all_loop)## [1] TRUEready_4_yelp <- r4all_apply %>%
mutate(x = st_coordinates(.)[,1],
y = st_coordinates(.)[,2])
tmap_mode('view')## tmap mode set to interactive viewing# Select the first 10 rows
ready_4_yelp[1:10,] %>%
# Draw a buffer centered at the centroid of Tract polygons.
# Radius of the buffer is the radius we just calculated using loop
st_buffer(., dist = .$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')which_tract <- 1
test <- business_search(api_key = client_secret, # like we did for census, store your api key
categories = 'farms', # 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$radius[which_tract]), # radius requires integer value
limit = 50) # how many business per page## No encoding supplied: defaulting to UTF-8.test1 <- business_search(api_key = client_secret, # like we did for census, store your api key
categories = 'restaurants', # return only restaurant businesses
latitude = ready_4_yelp$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
## list()
##
## $total
## [1] 0
##
## $region
## $region$center
## $region$center$longitude
## [1] -74.43781
##
## $region$center$latitude
## [1] 40.61849names(test)## [1] "businesses" "total" "region"paste0("is it a data.frame?: ", is.data.frame(test$businesses), ", ",
" how many rows?: ", nrow(test$businesses), ", ",
" how many columns?: ", ncol(test$businesses))## [1] "is it a data.frame?: FALSE, how many rows?: , how many columns?: "# FUNCTION
get_yelp <- function(tract, category){
# ----------------------------------
# Gets one row of tract information (1,) and category name (str),
# Outputs a list of business data.frame
n <- 1
# First request --------------------------------------------------------------
resp <- business_search(api_key = client_secret,
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 = client_secret,
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)
}
}
yelp_first_tract <- get_yelp(ready_4_yelp[1,], "farms") %>%
as_tibble()## No encoding supplied: defaulting to UTF-8.## Warning: Outer names are only allowed for unnamed scalar atomic inputsyelp_first_tract %>% print## # A tibble: 0 × 0# 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,], "farms"))
if (row %% 50 == 0){
print(paste0("Current row: ", row))
}
Sys.sleep(.5)}## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 50"## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs#Start Test For Second Category
yelp_first_tract1 <- get_yelp(ready_4_yelp[1,], "indpak") %>%
as_tibble()## No encoding supplied: defaulting to UTF-8.## Warning: Outer names are only allowed for unnamed scalar atomic inputsyelp_first_tract1 %>% print## # A tibble: 0 × 0# Prepare a collector
yelp_all_list1 <- vector("list", nrow(ready_4_yelp))
# Looping through all Census Tracts
for (row in 1:nrow(ready_4_yelp)){
yelp_all_list1[[row]] <- suppressMessages(get_yelp(ready_4_yelp[row,], "indpak"))
if (row %% 50 == 0){
print(paste0("Current row: ", row))
}
Sys.sleep(.5)
}## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 50"## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputs
## Warning: Outer names are only allowed for unnamed scalar atomic inputsyelp_all_list <- c(yelp_all_list1,yelp_all_list)
#End Test For Second Category
# Collapsing the list into a data.frame
yelp_all <- yelp_all_list %>% bind_rows() %>% as_tibble()
# print
yelp_all %>% print(width=1000)## # A tibble: 283 × 16
## id alias
## <chr> <chr>
## 1 bFTPiSm6MNuAIJe9mirp4w tandoori-mela-basking-ridge
## 2 WpUy2Y9Bva8WHfPwHMqnpg arusuvai-somerville
## 3 co3tnIE4STWkI4Mv51uOoA curry-tub-express-bridgewater-township
## 4 PtmdgOoEeyb5KiIR-t_jTQ the-curry-and-grill-factory-martinsville
## 5 d9GmqiY1Q2oZm1UXa4EdWw tacowala-somerville
## 6 NYKMBrepIhgEZPDP4vXmRw honest-somerville
## 7 EHLxcSpBEupBVTh2EGlPWw punjabi-rasoi-somerset-3
## 8 4b8rxMf3fYPS0aWfV07_eg bayleaf-grill-somerset
## 9 6AnY7MrBubrFd5yTSppOZg sankranthi-somerset
## 10 SgCOSBup7gtXvQi0HUKz2Q masala-art-branchburg
## name
## <chr>
## 1 Tandoori Mela
## 2 Arusuvai
## 3 Curry Tub Express
## 4 The Curry and Grill Factory
## 5 Tacowala
## 6 Honest
## 7 Punjabi Rasoi
## 8 Bayleaf Grill
## 9 Sankranthi
## 10 Masala Art
## image_url
## <chr>
## 1 https://s3-media3.fl.yelpcdn.com/bphoto/ScRK-Wy01DLw_u6UEBzOWg/o.jpg
## 2 https://s3-media2.fl.yelpcdn.com/bphoto/kAn0kz2k1afX-D5cu0M33Q/o.jpg
## 3 https://s3-media2.fl.yelpcdn.com/bphoto/rZTVLbb47tHCJRq6yNUegg/o.jpg
## 4 https://s3-media1.fl.yelpcdn.com/bphoto/CR3vCDrpStEC5cEQ5ecRhg/o.jpg
## 5 https://s3-media1.fl.yelpcdn.com/bphoto/zjS79zXgIxAd9horOLjAFg/o.jpg
## 6 https://s3-media4.fl.yelpcdn.com/bphoto/VA3bD3sA2LGEA5bUHQbR9Q/o.jpg
## 7 https://s3-media3.fl.yelpcdn.com/bphoto/r5TVSqhGZL9LhFyFVZGl3w/o.jpg
## 8 https://s3-media4.fl.yelpcdn.com/bphoto/ym8PMIfZ2-0JdNZ1G6E5vA/o.jpg
## 9 https://s3-media2.fl.yelpcdn.com/bphoto/TgVsPewCAML9oSwPyJ_RTg/o.jpg
## 10 https://s3-media2.fl.yelpcdn.com/bphoto/WExT67guyQM3KDW0wK4pBw/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/tandoori-mela-basking-ridge?adjust_creative=YsnbCt8…
## 2 https://www.yelp.com/biz/arusuvai-somerville?adjust_creative=YsnbCt8xw2zmSFX…
## 3 https://www.yelp.com/biz/curry-tub-express-bridgewater-township?adjust_creat…
## 4 https://www.yelp.com/biz/the-curry-and-grill-factory-martinsville?adjust_cre…
## 5 https://www.yelp.com/biz/tacowala-somerville?adjust_creative=YsnbCt8xw2zmSFX…
## 6 https://www.yelp.com/biz/honest-somerville?adjust_creative=YsnbCt8xw2zmSFXqf…
## 7 https://www.yelp.com/biz/punjabi-rasoi-somerset-3?adjust_creative=YsnbCt8xw2…
## 8 https://www.yelp.com/biz/bayleaf-grill-somerset?adjust_creative=YsnbCt8xw2zm…
## 9 https://www.yelp.com/biz/sankranthi-somerset?adjust_creative=YsnbCt8xw2zmSFX…
## 10 https://www.yelp.com/biz/masala-art-branchburg?adjust_creative=YsnbCt8xw2zmS…
## review_count categories rating coordinates$latitude $longitude transactions
## <int> <list> <dbl> <dbl> <dbl> <list>
## 1 117 <df [1 × 2]> 4 40.7 -74.6 <chr [2]>
## 2 199 <df [1 × 2]> 4 40.6 -74.6 <chr [2]>
## 3 14 <df [1 × 2]> 4.5 40.6 -74.6 <chr [2]>
## 4 5 <df [3 × 2]> 4 40.6 -74.6 <chr [2]>
## 5 20 <df [2 × 2]> 4 40.6 -74.6 <chr [0]>
## 6 11 <df [1 × 2]> 4.5 40.6 -74.6 <chr [2]>
## 7 102 <df [1 × 2]> 4.5 40.5 -74.5 <chr [2]>
## 8 91 <df [3 × 2]> 4.5 40.5 -74.5 <chr [2]>
## 9 13 <df [1 × 2]> 3.5 40.5 -74.5 <chr [2]>
## 10 192 <df [1 × 2]> 4 40.6 -74.7 <chr [2]>
## price location$address1 $address2 $address3
## <chr> <chr> <chr> <chr>
## 1 $$ 562 Allen Rd "" ""
## 2 $$ 30 E Main St <NA> ""
## 3 <NA> 400 Commons Way "" <NA>
## 4 <NA> 1801 Washington Valley Rd <NA> ""
## 5 <NA> 133 E Main St "" <NA>
## 6 <NA> 97 US Hwy 206 N "" <NA>
## 7 $$ 1483 Rte 27 S "" "Franklin Mall"
## 8 $$ 2 Jfk Blvd <NA> ""
## 9 <NA> 2 John F Kennedy Blvd <NA> ""
## 10 $$ 1049 US Hwy 202 N "" ""
## $city $zip_code $country $state $display_address
## <chr> <chr> <chr> <chr> <list>
## 1 Basking Ridge 07920 US NJ <chr [2]>
## 2 Somerville 08876 US NJ <chr [2]>
## 3 Bridgewater Township 08807 US NJ <chr [2]>
## 4 Martinsville 08836 US NJ <chr [2]>
## 5 Somerville 08876 US NJ <chr [2]>
## 6 Somerville 08876 US NJ <chr [2]>
## 7 Somerset 08873 US NJ <chr [3]>
## 8 Somerset 08873 US NJ <chr [2]>
## 9 Somerset 08873 US NJ <chr [2]>
## 10 Branchburg 08876 US NJ <chr [2]>
## phone display_phone distance
## <chr> <chr> <dbl>
## 1 "+19085061032" "(908) 506-1032" 1247.
## 2 "+17322002787" "(732) 200-2787" 5527.
## 3 "" "" 4751.
## 4 "+19084327077" "(908) 432-7077" 180.
## 5 "+19083008690" "(908) 300-8690" 5417.
## 6 "+19089893366" "(908) 989-3366" 6266.
## 7 "+17322464569" "(732) 246-4569" 2420.
## 8 "+17327070777" "(732) 707-0777" 2842.
## 9 "+17327070777" "(732) 707-0777" 2841.
## 10 "+19085752200" "(908) 575-2200" 2908.
## # … with 273 more rows
## # ℹ Use `print(n = ...)` to see more rows# Extract coordinates
yelp_sf <- yelp_all %>%
mutate(x = .$coordinates$longitude,
y = .$coordinates$latitude) %>%
filter(!is.na(x) & !is.na(y)) %>%
st_as_sf(coords = c("x", "y"), crs = 4326)
# Map
tm_shape(yelp_sf) +
tm_dots(col = "review_count", style="quantile")yelp_unique <- yelp_all %>%
distinct(id, .keep_all=T)
glue::glue("Before dropping NA, there were {nrow(yelp_all)} rows. After dropping them, there are {nrow(yelp_unique)} rows") %>%
print()## Before dropping NA, there were 283 rows. After dropping them, there are 79 rows#This makes sense as the Map only shows 8 points!!!
concate_list <- function(x){
# x is a data frame with columns "alias" and "title" from Yelp$categories
# returns a character vector containing category concatenated titles
titles <- x[["title"]] %>% str_c(collapse = ", ")
return(titles)
}
# Custom function that takes the data frame in "categories" column in Yelp data
# and returns a character vector
# Issue 3 ------------------------------
yelp_flat <- yelp_unique %>%
# 1. Flattening columns with data frame
jsonlite::flatten() %>%
# 2. Handling list-columns
mutate(transactions = transactions %>%
map_chr(., function(x) str_c(x, collapse=", ")),
location.display_address = location.display_address %>%
map_chr(., function(x) str_c(x, collapse=", ")),
categories = categories %>% map_chr(concate_list)) # concate_list is the custom function
yelp_flat %>%
map_dbl(., function(x) sum(is.na(x)))## id alias name
## 0 0 0
## image_url is_closed url
## 0 0 0
## review_count categories rating
## 0 0 0
## transactions price phone
## 0 27 0
## display_phone distance coordinates.latitude
## 0 0 0
## coordinates.longitude location.address1 location.address2
## 0 0 23
## location.address3 location.city location.zip_code
## 22 0 0
## location.country location.state location.display_address
## 0 0 0identical(is.na(yelp_flat$coordinates.latitude),
is.na(yelp_flat$coordinates.longitude))## [1] TRUEyelp_dropna1 <- yelp_flat %>%
drop_na(coordinates.longitude)
# Dropping NAs in price
yelp_dropna2 <- yelp_dropna1 %>%
drop_na(price)
census <- tract
yelp_sf <- yelp_dropna2 %>%
st_as_sf(coords=c("coordinates.longitude", "coordinates.latitude"), crs = 4326)
# sf subsets
print(st_crs(yelp_sf))## Coordinate Reference System:
## User input: EPSG:4326
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["World."],
## BBOX[-90,-180,90,180]],
## ID["EPSG",4326]]print(st_crs(tract))## Coordinate Reference System:
## User input: NAD83
## wkt:
## GEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["latitude",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["longitude",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]]crs_to_use <- st_crs(yelp_sf) # store crs information from the van_boundary dataset
crs_epsg <- crs_to_use$epsg # store the epsg code from the van_boundary dataset
tract <- st_transform(tract,crs=crs_epsg)
yelp_in <- yelp_sf[tract %>% st_union(), ,op = st_intersects]
glue::glue("nrow before: {nrow(yelp_all)} -> nrow after: {nrow(yelp_in)} \n
ncol before: {ncol(yelp_all)} -> ncol after: {ncol(yelp_in)} \n") %>%
print()## nrow before: 283 -> nrow after: 24
##
## ncol before: 16 -> ncol after: 23print(colnames(yelp_in))## [1] "id" "alias"
## [3] "name" "image_url"
## [5] "is_closed" "url"
## [7] "review_count" "categories"
## [9] "rating" "transactions"
## [11] "price" "phone"
## [13] "display_phone" "distance"
## [15] "location.address1" "location.address2"
## [17] "location.address3" "location.city"
## [19] "location.zip_code" "location.country"
## [21] "location.state" "location.display_address"
## [23] "geometry"tmap_mode("view")## tmap mode set to interactive viewingtm_shape(yelp_in) + tm_dots(col = "price")