library(tidycensus)## Warning: package 'tidycensus' was built under R version 4.2.1library(sf)## Warning: package 'sf' was built under R version 4.2.1## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUElibrary(tmap)## Warning: package 'tmap' was built under R version 4.2.1library(jsonlite)## Warning: package 'jsonlite' was built under R version 4.2.1library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.1## ── Attaching packages
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## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
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library(reshape2)## Warning: package 'reshape2' was built under R version 4.2.1##
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##
## smithslibrary(here)## Warning: package 'here' was built under R version 4.2.1## here() starts at D:/Georgia Tech/Spec topic_library(yelpr)
library(knitr)## Warning: package 'knitr' was built under R version 4.2.1library(skimr)## Warning: package 'skimr' was built under R version 4.2.1tidycensus::census_api_key(Sys.getenv("google_api"))## To install your API key for use in future sessions, run this function with `install = TRUE`.install = TRUEtract <- 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",
trans.total = "B08006_001",
trans.car = "B08006_002",
trans.drovealone = "B08006_003",
trans.carpooled = "B08006_004",
trans.pubtrans = "B08006_008",
trans.bicycle = "B08006_014",
trans.walk = "B08006_015",
trans.WfH = "B08006_017"
),
year = 2020,
survey = "acs5", # American Community Survey 5-year estimate
geometry = TRUE, # returns sf objects
output = "wide") # wide vs. long
)##
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|======================================================================| 100%# visualize tract
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(tract) + tm_borders()# 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 = 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)
}
}get_r <- function(poly, epsg_id){
#---------------------
# Takes: a single POLYGON or LINE_STRTING
# Outputs: distance between the centroid of the bounding_box 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$radius <- r*1.2
return(bb_center)
}
## Using a loop -----------------------------------------------------------------
# Creating an empty vector of NA.
# Results will fill this vector
epsg_id <- 4326 # Edit (9/8/2022): 4326 measures distance in meter. Before the edit, I used 26967.
r4all_loop <- vector("list", nrow(tract))
# Starting a for-loop
for (i in 1:nrow(tract)){
r4all_loop[[i]] <- tract %>%
st_transform(crs = epsg_id) %>%
st_geometry() %>%
.[[i]] %>%
get_r(epsg_id = epsg_id)
}
r4all_loop <- bind_rows(r4all_loop)# Appending X Y coordinates as separate columns
ready_4_yelp <- r4all_loop %>%
mutate(x = st_coordinates(.)[,1],
y = st_coordinates(.)[,2])tmap_mode('view')## tmap mode set to interactive viewing#Select the first 10 rows
ready_4_yelp[1:10,] %>%
# Draw a buffer centered at the centroid of Tract polygons.
# Radius of the buffer is the radius we just calculated using loop
st_buffer(., dist = .$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')# Prepare a collector
yelp_bikerentals_list <- vector("list", nrow(ready_4_yelp))
# Looping through all Census Tracts
for (row in 1:nrow(ready_4_yelp)){
yelp_bikerentals_list[[row]] <- suppressMessages(get_yelp(ready_4_yelp[row,], "bikerentals"))
if (row %% 5 == 0){
Sys.sleep(10)
if (row %% 50 == 0) {
print(paste0("Current row: ", row))
}
}
}## Warning: Outer names are only allowed for unnamed scalar atomic inputs
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## 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: 500"## 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 inputsr4all_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# Collapsing the list into a data.frame
yelp_df_b <- yelp_bikerentals_list %>% bind_rows() %>% as_tibble()
# print
yelp_df_b %>% print(width=1000)## # A tibble: 231 × 16
## id alias name
## <chr> <chr> <chr>
## 1 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 2 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 3 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 4 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 5 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 6 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 7 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 8 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 9 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## 10 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain Aztec Cycles
## image_url
## <chr>
## 1 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 2 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 3 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 4 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 5 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 6 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 7 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 8 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 9 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 10 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/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/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 2 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 3 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 4 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 5 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 6 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 7 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 8 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 9 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## 10 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nv…
## review_count categories rating coordinates$latitude $longitude transactions
## <int> <list> <dbl> <dbl> <dbl> <list>
## 1 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 2 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 3 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 4 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 5 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 6 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 7 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 8 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 9 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 10 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## price location$address1 $address2 $address3 $city $zip_code $country
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 $$ 901 Main St "" "" Stone Mountain 30083 US
## 2 $$ 901 Main St "" "" Stone Mountain 30083 US
## 3 $$ 901 Main St "" "" Stone Mountain 30083 US
## 4 $$ 901 Main St "" "" Stone Mountain 30083 US
## 5 $$ 901 Main St "" "" Stone Mountain 30083 US
## 6 $$ 901 Main St "" "" Stone Mountain 30083 US
## 7 $$ 901 Main St "" "" Stone Mountain 30083 US
## 8 $$ 901 Main St "" "" Stone Mountain 30083 US
## 9 $$ 901 Main St "" "" Stone Mountain 30083 US
## 10 $$ 901 Main St "" "" Stone Mountain 30083 US
## $state $display_address phone display_phone distance
## <chr> <list> <chr> <chr> <dbl>
## 1 GA <chr [2]> +16786369043 (678) 636-9043 2322.
## 2 GA <chr [2]> +16786369043 (678) 636-9043 31114.
## 3 GA <chr [2]> +16786369043 (678) 636-9043 23033.
## 4 GA <chr [2]> +16786369043 (678) 636-9043 5784.
## 5 GA <chr [2]> +16786369043 (678) 636-9043 7643.
## 6 GA <chr [2]> +16786369043 (678) 636-9043 5733.
## 7 GA <chr [2]> +16786369043 (678) 636-9043 12804.
## 8 GA <chr [2]> +16786369043 (678) 636-9043 25149.
## 9 GA <chr [2]> +16786369043 (678) 636-9043 22818.
## 10 GA <chr [2]> +16786369043 (678) 636-9043 7008.
## # … with 221 more rowswrite_rds(yelp_df_b, "yelp_bikerentals.rds")
getwd()## [1] "D:/Georgia Tech/Spec topic_"yelp_df_b <- read_rds(here("D:/Georgia Tech/Spec topic_/yelp_bikerentals.rds"))FD_tract <- tract %>%
select(GEOID,
hhincome = hhincomeE, # New name = old name
race.tot = race.totE,
race.white = race.whiteE,
race.black = race.blackE,
trans.total = trans.totalE,
trans.car = trans.carE,
trans.drovealone = trans.drovealoneE,
trans.carpooled = trans.carpooledE,
trans.pubtrans = trans.pubtransE,
trans.bicycle = trans.bicycleE,
trans.walk = trans.walkE,
trans.WfH = trans.WfHE)tmap_mode("view")## tmap mode set to interactive viewing## tmap mode set to interactive viewing
## tmap mode set to interactive viewing
bicycle <- tm_shape(FD_tract) + tm_polygons("trans.bicycle")
car <- tm_shape(FD_tract) + tm_polygons("trans.car")
tmap_arrange(bicycle, car)head(yelp_df_b)## # A tibble: 6 × 16
## id alias name image_url is_closed url review_count categories rating
## <chr> <chr> <chr> <chr> <lgl> <chr> <int> <list> <dbl>
## 1 FK7-M9BG… azte… Azte… https://… FALSE http… 54 <df> 5
## 2 FK7-M9BG… azte… Azte… https://… FALSE http… 54 <df> 5
## 3 FK7-M9BG… azte… Azte… https://… FALSE http… 54 <df> 5
## 4 FK7-M9BG… azte… Azte… https://… FALSE http… 54 <df> 5
## 5 FK7-M9BG… azte… Azte… https://… FALSE http… 54 <df> 5
## 6 FK7-M9BG… azte… Azte… https://… FALSE http… 54 <df> 5
## # … with 7 more variables: coordinates <df[,2]>, transactions <list>,
## # price <chr>, location <df[,8]>, phone <chr>, display_phone <chr>,
## # distance <dbl># Deleting duplicate businesses
yelp_unique <- yelp_df_b %>%
distinct(id, .keep_all=T)
glue::glue("Before dropping NA, there were {nrow(yelp_df_b)} rows. After dropping them, there are {nrow(yelp_unique)} rows") %>%
print()## Before dropping NA, there were 231 rows. After dropping them, there are 15 rows# Custom function that takes the data frame in "categories" column in Yelp data
# and returns a character vector
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)
}
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)) #find missing values
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 9 0
## display_phone distance coordinates.latitude
## 0 0 0
## coordinates.longitude location.address1 location.address2
## 0 1 4
## location.address3 location.city location.zip_code
## 6 0 0
## location.country location.state location.display_address
## 0 0 0yelp_sf <- yelp_flat %>%
st_as_sf(coords=c("coordinates.longitude", "coordinates.latitude"), crs = 4326)
tmap_mode("view")## tmap mode set to interactive viewing## tmap mode set to interactive viewing
tm_shape(yelp_sf) + tm_dots(col = "price")## Check to see that the CRS for both the sf files are the same
head(FD_tract)## Simple feature collection with 6 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.47101 ymin: 33.65339 xmax: -84.18201 ymax: 33.81229
## Geodetic CRS: NAD83
## GEOID hhincome race.tot race.white race.black trans.total trans.car
## 1 13089021908 43191 4829 323 3368 2421 2137
## 2 13089020400 123711 2743 2435 48 1691 1202
## 3 13089023410 41065 3903 131 3562 2030 1562
## 4 13121010601 55479 3673 1057 2514 1699 1290
## 5 13121008302 28214 1891 33 1836 481 401
## 6 13121004200 23906 2675 252 2423 1073 487
## trans.drovealone trans.carpooled trans.pubtrans trans.bicycle trans.walk
## 1 1759 378 86 0 101
## 2 1069 133 63 62 13
## 3 1339 223 307 0 87
## 4 1131 159 154 0 113
## 5 347 54 57 0 8
## 6 437 50 460 19 0
## trans.WfH geometry
## 1 97 MULTIPOLYGON (((-84.20619 3...
## 2 300 MULTIPOLYGON (((-84.34921 3...
## 3 55 MULTIPOLYGON (((-84.29784 3...
## 4 116 MULTIPOLYGON (((-84.46957 3...
## 5 0 MULTIPOLYGON (((-84.46315 3...
## 6 106 MULTIPOLYGON (((-84.42334 3...## Check to see that the CRS for both the sf files are the same
head(yelp_sf)## Simple feature collection with 6 features and 22 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -84.35797 ymin: 33.74172 xmax: -84.16982 ymax: 34.04687
## Geodetic CRS: WGS 84
## id alias
## 1 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain
## 2 OJVvH1CUZacuHjSLkhSKOg roswell-bicycles-roswell
## 3 kJJiJqGbiO_QXmdhol3hIQ british-and-american-bikes-atlanta
## 4 ot4UyUsRAlTFudTlCVRMrQ pedego-electric-bikes-alpharetta-alpharetta
## 5 rbf8bVY0cuqyGZtbn691lg pedego-electric-bikes-atlanta-atlanta-2
## 6 8PfRbXo6qhKliGDCp5l79g atlanta-pro-bikes-atlanta
## name
## 1 Aztec Cycles
## 2 Roswell Bicycles
## 3 British and American Bikes
## 4 Pedego Electric Bikes Alpharetta
## 5 Pedego Electric Bikes Atlanta
## 6 Atlanta Pro Bikes
## image_url
## 1 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 2 https://s3-media2.fl.yelpcdn.com/bphoto/1YUjKn4QEkVQG_eIH7wiXQ/o.jpg
## 3 https://s3-media2.fl.yelpcdn.com/bphoto/9I0G3Ge2yKFg6184t1XYEQ/o.jpg
## 4 https://s3-media2.fl.yelpcdn.com/bphoto/gPNidtknlSWKu3Bh4iOJ3Q/o.jpg
## 5 https://s3-media2.fl.yelpcdn.com/bphoto/Z7KlxC0vcyoxOCSxbH3Rrg/o.jpg
## 6 https://s3-media1.fl.yelpcdn.com/bphoto/1s0fhhJvN3z-SUPizRy5sw/o.jpg
## is_closed
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## url
## 1 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=uBLr9nvKTe93WzGVCOhb0Q&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=uBLr9nvKTe93WzGVCOhb0Q
## 2 https://www.yelp.com/biz/roswell-bicycles-roswell?adjust_creative=uBLr9nvKTe93WzGVCOhb0Q&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=uBLr9nvKTe93WzGVCOhb0Q
## 3 https://www.yelp.com/biz/british-and-american-bikes-atlanta?adjust_creative=uBLr9nvKTe93WzGVCOhb0Q&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=uBLr9nvKTe93WzGVCOhb0Q
## 4 https://www.yelp.com/biz/pedego-electric-bikes-alpharetta-alpharetta?adjust_creative=uBLr9nvKTe93WzGVCOhb0Q&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=uBLr9nvKTe93WzGVCOhb0Q
## 5 https://www.yelp.com/biz/pedego-electric-bikes-atlanta-atlanta-2?adjust_creative=uBLr9nvKTe93WzGVCOhb0Q&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=uBLr9nvKTe93WzGVCOhb0Q
## 6 https://www.yelp.com/biz/atlanta-pro-bikes-atlanta?adjust_creative=uBLr9nvKTe93WzGVCOhb0Q&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=uBLr9nvKTe93WzGVCOhb0Q
## review_count categories rating transactions
## 1 54 Bikes, Bike Repair/Maintenance, Bike Rentals 5.0
## 2 70 Bikes, Bike Rentals 4.0
## 3 2 Motorcycle Repair, Bike Rentals 5.0
## 4 7 Bike Rentals, Bikes, Bike Repair/Maintenance 5.0
## 5 9 Bikes, Bike Rentals, Bike Repair/Maintenance 4.5
## 6 40 Bike Repair/Maintenance, Bikes, Bike Rentals 4.5
## price phone display_phone distance location.address1
## 1 $$ +16786369043 (678) 636-9043 2322.2660 901 Main St
## 2 $$$ +17706424057 (770) 642-4057 2841.9264 670 Houze Way
## 3 <NA> +17704518868 (770) 451-8868 597.5853 4264 F Winters Chapel Rd
## 4 <NA> +14042810264 (404) 281-0264 1931.4652 6480 N Point Pkwy
## 5 <NA> +14049753915 (404) 975-3915 8603.6159 414 Bill Kennedy Way
## 6 <NA> +14042541230 (404) 254-1230 1096.5805 1039 N Highland Ave NE
## location.address2 location.address3 location.city location.zip_code
## 1 Stone Mountain 30083
## 2 Roswell 30076
## 3 Atlanta 30360
## 4 Ste 1100b <NA> Alpharetta 30022
## 5 Ste 101 <NA> Atlanta 30316
## 6 <NA> Atlanta 30306
## location.country location.state
## 1 US GA
## 2 US GA
## 3 US GA
## 4 US GA
## 5 US GA
## 6 US GA
## location.display_address geometry
## 1 901 Main St, Stone Mountain, GA 30083 POINT (-84.16982 33.80586)
## 2 670 Houze Way, Roswell, GA 30076 POINT (-84.34202 34.04687)
## 3 4264 F Winters Chapel Rd, Atlanta, GA 30360 POINT (-84.27059 33.91903)
## 4 6480 N Point Pkwy, Ste 1100b, Alpharetta, GA 30022 POINT (-84.29336 34.0444)
## 5 414 Bill Kennedy Way, Ste 101, Atlanta, GA 30316 POINT (-84.35797 33.74172)
## 6 1039 N Highland Ave NE, Atlanta, GA 30306 POINT (-84.35415 33.78293)## It turns out they are NOT! So we need to transform their CRS into the same
FD_tract_Geom <- st_transform(FD_tract, crs=4326)
Yelp_Geom_b <- st_transform(yelp_sf, crs=4326)
# sf subsets
yelp_Geom_b2 <- Yelp_Geom_b[FD_tract_Geom %>% st_union(), ,op = st_intersects]
# join
bikerentals_in_tract <- st_join(FD_tract_Geom, yelp_Geom_b2, join = st_intersects)
skim(bikerentals_in_tract)## Warning: Couldn't find skimmers for class: sfc_MULTIPOLYGON, sfc; No user-
## defined `sfl` provided. Falling back to `character`.| Name | bikerentals_in_tract |
| Number of rows | 530 |
| Number of columns | 36 |
| _______________________ | |
| Column type frequency: | |
| character | 20 |
| logical | 1 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| GEOID | 0 | 1.00 | 11 | 11 | 0 | 530 | 0 |
| id | 516 | 0.03 | 22 | 22 | 0 | 14 | 0 |
| alias | 516 | 0.03 | 14 | 43 | 0 | 14 | 0 |
| name | 516 | 0.03 | 4 | 32 | 0 | 14 | 0 |
| image_url | 516 | 0.03 | 68 | 68 | 0 | 14 | 0 |
| url | 516 | 0.03 | 171 | 200 | 0 | 14 | 0 |
| categories | 516 | 0.03 | 19 | 52 | 0 | 12 | 0 |
| transactions | 516 | 0.03 | 0 | 0 | 14 | 1 | 0 |
| price | 524 | 0.01 | 1 | 4 | 0 | 4 | 0 |
| phone | 516 | 0.03 | 12 | 12 | 0 | 14 | 0 |
| display_phone | 516 | 0.03 | 14 | 14 | 0 | 14 | 0 |
| location.address1 | 517 | 0.02 | 0 | 24 | 3 | 11 | 0 |
| location.address2 | 520 | 0.02 | 0 | 9 | 7 | 4 | 0 |
| location.address3 | 522 | 0.02 | 0 | 0 | 8 | 1 | 0 |
| location.city | 516 | 0.03 | 7 | 14 | 0 | 5 | 0 |
| location.zip_code | 516 | 0.03 | 5 | 5 | 0 | 11 | 0 |
| location.country | 516 | 0.03 | 2 | 2 | 0 | 1 | 0 |
| location.state | 516 | 0.03 | 2 | 2 | 0 | 1 | 0 |
| location.display_address | 516 | 0.03 | 17 | 50 | 0 | 14 | 0 |
| geometry | 0 | 1.00 | 193 | 3750 | 0 | 530 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| is_closed | 516 | 0.03 | 0 | FAL: 14 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| hhincome | 8 | 0.98 | 81559.87 | 49076.11 | 13577.00 | 46780.50 | 69623.0 | 100893.50 | 250001.00 | ▇▇▃▁▁ |
| race.tot | 0 | 1.00 | 3409.13 | 1276.00 | 0.00 | 2498.25 | 3267.0 | 4243.00 | 7401.00 | ▁▇▇▃▁ |
| race.white | 0 | 1.00 | 1337.87 | 1198.31 | 0.00 | 237.75 | 1062.5 | 2146.00 | 5039.00 | ▇▅▃▁▁ |
| race.black | 0 | 1.00 | 1624.68 | 1545.94 | 0.00 | 367.00 | 996.5 | 2739.25 | 7021.00 | ▇▂▂▁▁ |
| trans.total | 0 | 1.00 | 1718.82 | 683.32 | 0.00 | 1268.00 | 1653.5 | 2140.25 | 4114.00 | ▂▇▇▂▁ |
| trans.car | 0 | 1.00 | 1315.87 | 585.48 | 0.00 | 926.75 | 1250.0 | 1690.50 | 3342.00 | ▂▇▆▂▁ |
| trans.drovealone | 0 | 1.00 | 1179.78 | 536.94 | 0.00 | 831.25 | 1121.0 | 1525.75 | 3164.00 | ▃▇▆▂▁ |
| trans.carpooled | 0 | 1.00 | 136.09 | 134.39 | 0.00 | 46.25 | 99.0 | 191.25 | 1109.00 | ▇▂▁▁▁ |
| trans.pubtrans | 0 | 1.00 | 117.38 | 128.89 | 0.00 | 26.00 | 87.5 | 166.00 | 1194.00 | ▇▁▁▁▁ |
| trans.bicycle | 0 | 1.00 | 7.84 | 21.88 | 0.00 | 0.00 | 0.0 | 0.00 | 220.00 | ▇▁▁▁▁ |
| trans.walk | 0 | 1.00 | 38.72 | 84.26 | 0.00 | 0.00 | 8.5 | 38.00 | 788.00 | ▇▁▁▁▁ |
| trans.WfH | 0 | 1.00 | 203.40 | 161.01 | 0.00 | 83.00 | 165.0 | 292.75 | 856.00 | ▇▅▂▁▁ |
| review_count | 516 | 0.03 | 29.50 | 36.61 | 1.00 | 3.25 | 10.5 | 50.50 | 124.00 | ▇▁▂▁▁ |
| rating | 516 | 0.03 | 3.64 | 1.61 | 1.00 | 2.50 | 4.5 | 4.50 | 5.00 | ▂▁▁▁▇ |
| distance | 516 | 0.03 | 2199.77 | 2144.27 | 230.64 | 758.52 | 1820.0 | 2304.95 | 8603.62 | ▇▆▁▁▁ |
# Now count the bike studios by tract
br_count_tract <- count(as_tibble(bikerentals_in_tract), GEOID) %>%
print()## # A tibble: 530 × 2
## GEOID n
## <chr> <int>
## 1 13089020100 1
## 2 13089020200 1
## 3 13089020300 1
## 4 13089020400 1
## 5 13089020500 1
## 6 13089020600 1
## 7 13089020700 1
## 8 13089020801 1
## 9 13089020802 1
## 10 13089020901 1
## # … with 520 more rows# Join tract geometry with the number of Yoga studios in tract
test <- st_join(FD_tract_Geom, yelp_Geom_b2 %>% mutate(count = 1))
out <- test %>%
group_by(GEOID) %>%
summarise(count = sum(count, na.rm = T))
# Lets' check to see if the polygons and the poin data on Yoga Studios match
tm_shape(out) + tm_polygons(col = "count") + tm_shape(yelp_Geom_b2) + tm_dots()FD_tract_Geom_bikerentals <- FD_tract_Geom %>%
left_join(out %>% st_set_geometry(NULL), by = "GEOID")
print(skim(FD_tract_Geom_bikerentals))## Warning: Couldn't find skimmers for class: sfc_MULTIPOLYGON, sfc; No user-
## defined `sfl` provided. Falling back to `character`.## ── Data Summary ────────────────────────
## Values
## Name FD_tract_Geom_bikerentals
## Number of rows 530
## Number of columns 15
## _______________________
## Column type frequency:
## character 2
## numeric 13
## ________________________
## Group variables None
##
## ── Variable type: character ────────────────────────────────────────────────────
## skim_variable n_missing complete_rate min max empty n_unique whitespace
## 1 GEOID 0 1 11 11 0 530 0
## 2 geometry 0 1 193 3750 0 530 0
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable n_missing complete_rate mean sd p0 p25
## 1 hhincome 8 0.985 81560. 49076. 13577 46780.
## 2 race.tot 0 1 3409. 1276. 0 2498.
## 3 race.white 0 1 1338. 1198. 0 238.
## 4 race.black 0 1 1625. 1546. 0 367
## 5 trans.total 0 1 1719. 683. 0 1268
## 6 trans.car 0 1 1316. 585. 0 927.
## 7 trans.drovealone 0 1 1180. 537. 0 831.
## 8 trans.carpooled 0 1 136. 134. 0 46.2
## 9 trans.pubtrans 0 1 117. 129. 0 26
## 10 trans.bicycle 0 1 7.84 21.9 0 0
## 11 trans.walk 0 1 38.7 84.3 0 0
## 12 trans.WfH 0 1 203. 161. 0 83
## 13 count 0 1 0.0264 0.161 0 0
## p50 p75 p100 hist
## 1 69623 100894. 250001 ▇▇▃▁▁
## 2 3267 4243 7401 ▁▇▇▃▁
## 3 1062. 2146 5039 ▇▅▃▁▁
## 4 996. 2739. 7021 ▇▂▂▁▁
## 5 1654. 2140. 4114 ▂▇▇▂▁
## 6 1250 1690. 3342 ▂▇▆▂▁
## 7 1121 1526. 3164 ▃▇▆▂▁
## 8 99 191. 1109 ▇▂▁▁▁
## 9 87.5 166 1194 ▇▁▁▁▁
## 10 0 0 220 ▇▁▁▁▁
## 11 8.5 38 788 ▇▁▁▁▁
## 12 165 293. 856 ▇▅▂▁▁
## 13 0 0 1 ▇▁▁▁▁## $character
##
## ── Variable type: character ────────────────────────────────────────────────────
## skim_variable n_missing complete_rate min max empty n_unique whitespace
## 1 GEOID 0 1 11 11 0 530 0
## 2 geometry 0 1 193 3750 0 530 0
##
## $numeric
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable n_missing complete_rate mean sd p0 p25 p50
## 1 hhincome 8 0.985 8.16e+4 4.91e+4 13577 4.68e4 6.96e4
## 2 race.tot 0 1 3.41e+3 1.28e+3 0 2.50e3 3.27e3
## 3 race.white 0 1 1.34e+3 1.20e+3 0 2.38e2 1.06e3
## 4 race.black 0 1 1.62e+3 1.55e+3 0 3.67e2 9.96e2
## 5 trans.total 0 1 1.72e+3 6.83e+2 0 1.27e3 1.65e3
## 6 trans.car 0 1 1.32e+3 5.85e+2 0 9.27e2 1.25e3
## 7 trans.drovealone 0 1 1.18e+3 5.37e+2 0 8.31e2 1.12e3
## 8 trans.carpooled 0 1 1.36e+2 1.34e+2 0 4.62e1 9.9 e1
## 9 trans.pubtrans 0 1 1.17e+2 1.29e+2 0 2.6 e1 8.75e1
## 10 trans.bicycle 0 1 7.84e+0 2.19e+1 0 0 0
## 11 trans.walk 0 1 3.87e+1 8.43e+1 0 0 8.5 e0
## 12 trans.WfH 0 1 2.03e+2 1.61e+2 0 8.3 e1 1.65e2
## 13 count 0 1 2.64e-2 1.61e-1 0 0 0
## # … with 3 more variables: p75 <dbl>, p100 <dbl>, hist <chr>br_census_dropnaHH2 <- FD_tract_Geom_bikerentals[!is.na(FD_tract_Geom_bikerentals$hhincome),]
skim(br_census_dropnaHH2)## Warning: Couldn't find skimmers for class: sfc_MULTIPOLYGON, sfc; No user-
## defined `sfl` provided. Falling back to `character`.| Name | br_census_dropnaHH2 |
| Number of rows | 522 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| GEOID | 0 | 1 | 11 | 11 | 0 | 522 | 0 |
| geometry | 0 | 1 | 194 | 3750 | 0 | 522 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| hhincome | 0 | 1 | 81559.87 | 49076.11 | 13577 | 46780.50 | 69623.0 | 100893.50 | 250001 | ▇▇▃▁▁ |
| race.tot | 0 | 1 | 3434.26 | 1262.91 | 176 | 2548.50 | 3303.5 | 4252.00 | 7401 | ▁▇▇▃▁ |
| race.white | 0 | 1 | 1348.84 | 1200.38 | 0 | 240.25 | 1094.5 | 2168.75 | 5039 | ▇▅▃▁▁ |
| race.black | 0 | 1 | 1634.44 | 1552.84 | 0 | 367.00 | 994.5 | 2764.75 | 7021 | ▇▂▂▁▁ |
| trans.total | 0 | 1 | 1737.83 | 668.07 | 54 | 1274.25 | 1663.0 | 2149.25 | 4114 | ▂▇▇▂▁ |
| trans.car | 0 | 1 | 1331.53 | 574.70 | 54 | 944.50 | 1256.0 | 1710.50 | 3342 | ▂▇▆▂▁ |
| trans.drovealone | 0 | 1 | 1193.61 | 527.88 | 54 | 834.50 | 1130.5 | 1528.50 | 3164 | ▃▇▆▂▁ |
| trans.carpooled | 0 | 1 | 137.92 | 134.54 | 0 | 47.25 | 101.5 | 192.75 | 1109 | ▇▂▁▁▁ |
| trans.pubtrans | 0 | 1 | 118.63 | 129.35 | 0 | 26.25 | 88.5 | 167.75 | 1194 | ▇▁▁▁▁ |
| trans.bicycle | 0 | 1 | 7.89 | 22.00 | 0 | 0.00 | 0.0 | 0.00 | 220 | ▇▁▁▁▁ |
| trans.walk | 0 | 1 | 38.43 | 83.92 | 0 | 0.00 | 9.0 | 38.00 | 788 | ▇▁▁▁▁ |
| trans.WfH | 0 | 1 | 205.55 | 160.85 | 0 | 86.00 | 169.0 | 293.00 | 856 | ▇▅▂▁▁ |
| count | 0 | 1 | 0.02 | 0.16 | 0 | 0.00 | 0.0 | 0.00 | 1 | ▇▁▁▁▁ |
tm_shape(FD_tract_Geom_bikerentals) + tm_polygons(col="count") +tm_shape(yelp_Geom_b2) +tm_dots()#changing name because it's too long..
#br_census_dropnaHH2 <- br_census_dropnaHH2
# (1) Household Income and Bike Rentals
#
# It’s hard to see any relationship between household income and bike rental shops through the plot below.
ggplot(br_census_dropnaHH2, aes(x=hhincome, y=count)) +
geom_point() +
ylab("No. of bike rentals in middle income")
#looking at a binary field that shows the presence or absence of bike Studios in tract
ggplot(br_census_dropnaHH2, aes(x=trans.bicycle, y=count)) +
geom_point() +
ylab("Number of bike rental shops and bicycle commuters in Tract")
br_census_dropnaHH2$bikerental <- ifelse(br_census_dropnaHH2$count>0, 1, 0)
boxplot(trans.bicycle~bikerental, data=br_census_dropnaHH2, main="Boxplot of bike rentals by bicycle commuters", xlab="Whether bike rental shops are present", ylab="number of commuters using bicycle")
#Creating new features (1) Population density
br_census_dropnaHH2$pop_density <- 1000*br_census_dropnaHH2$race.tot/st_area(br_census_dropnaHH2$geometry)#Creating new features (2) Proportion of bike commuters
br_census_dropnaHH2$bike_commute_p <- 100*br_census_dropnaHH2$trans.bicycle/br_census_dropnaHH2$trans.total#Finding relationship between features and the presence of bike rental shops again.
boxplot(pop_density~bikerental, data=br_census_dropnaHH2, main="Boxplot of bike rentals by population density of a tract", xlab="Whether bike rental shops are present", ylab="population density")
binary_br <- glm(bikerental~pop_density, family=binomial, data=br_census_dropnaHH2)
summary(binary_br)##
## Call:
## glm(formula = bikerental ~ pop_density, family = binomial, data = br_census_dropnaHH2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.2546 -0.2384 -0.2294 -0.2149 2.8097
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.4088 0.4536 -7.514 5.71e-14 ***
## pop_density -0.1670 0.2517 -0.664 0.507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 121.68 on 521 degrees of freedom
## Residual deviance: 121.09 on 520 degrees of freedom
## AIC: 125.09
##
## Number of Fisher Scoring iterations: 7boxplot(bike_commute_p~bikerental, data=br_census_dropnaHH2, main="Boxplot of bike rentals by percentage of bike commuters", xlab="Whether bike rental shops are present", ylab="percentage of people using bicycle to commute")
tmap_mode("view")## tmap mode set to interactive viewing## tmap mode set to interactive viewing
t_pop_den <- tm_shape(br_census_dropnaHH2) + tm_polygons(col ="pop_density") +tm_shape(yelp_Geom_b2) +tm_dots()
t_bike_commute <- tm_shape(br_census_dropnaHH2) + tm_polygons(col="bike_commute_p") +tm_shape(yelp_Geom_b2) +tm_dots()
tmap_arrange(t_pop_den, t_bike_commute)binary_br2 <- glm(bikerental~bike_commute_p, family=binomial, data=br_census_dropnaHH2)
summary(binary_br2)##
## Call:
## glm(formula = bikerental ~ bike_commute_p, family = binomial,
## data = br_census_dropnaHH2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.3389 -0.2211 -0.2211 -0.2211 2.7288
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.69885 0.30010 -12.32 <2e-16 ***
## bike_commute_p 0.05981 0.18102 0.33 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 121.68 on 521 degrees of freedom
## Residual deviance: 121.59 on 520 degrees of freedom
## AIC: 125.59
##
## Number of Fisher Scoring iterations: 6