Mini Assignment 3
Ilsu
2022-10-02
Load packages and print the session info
## Load packages
library(tidycensus)
library(sf)## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUElibrary(tmap)
library(jsonlite)
library(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ purrr::flatten() masks jsonlite::flatten()
## ✖ dplyr::lag() masks stats::lag()library(httr)
library(jsonlite)
library(reshape2)##
## Attaching package: 'reshape2'
##
## The following object is masked from 'package:tidyr':
##
## smithslibrary(here)## here() starts at C:/Users/iskim/Dropbox (GaTech)/2022-2023/2022 Fall/CP 8883 Intro to Urban Analytics/UA_module1# devtools::install_github("OmaymaS/yelpr")
library(yelpr)
library(knitr)
## Session info
sessionInfo()## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22000)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.40 yelpr_0.1.0 here_1.0.1 reshape2_1.4.4
## [5] httr_1.4.4 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
## [9] purrr_0.3.4 readr_2.1.2 tidyr_1.2.1 tibble_3.1.8
## [13] ggplot2_3.3.6 tidyverse_1.3.2 jsonlite_1.8.0 tmap_3.3-3
## [17] sf_1.0-8 tidycensus_1.2.3
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.2 lubridate_1.8.0 RColorBrewer_1.1-3
## [4] rprojroot_2.0.3 tools_4.2.1 backports_1.4.1
## [7] bslib_0.4.0 utf8_1.2.2 rgdal_1.5-32
## [10] R6_2.5.1 KernSmooth_2.23-20 DBI_1.1.3
## [13] colorspace_2.0-3 raster_3.5-29 withr_2.5.0
## [16] sp_1.5-0 tidyselect_1.1.2 leaflet_2.1.1
## [19] compiler_4.2.1 leafem_0.2.0 cli_3.4.0
## [22] rvest_1.0.3 xml2_1.3.3 sass_0.4.2
## [25] scales_1.2.1 classInt_0.4-7 proxy_0.4-27
## [28] rappdirs_0.3.3 digest_0.6.29 foreign_0.8-82
## [31] rmarkdown_2.16 base64enc_0.1-3 dichromat_2.0-0.1
## [34] pkgconfig_2.0.3 htmltools_0.5.3 dbplyr_2.2.1
## [37] fastmap_1.1.0 readxl_1.4.1 htmlwidgets_1.5.4
## [40] rlang_1.0.5 rstudioapi_0.14 jquerylib_0.1.4
## [43] generics_0.1.3 crosstalk_1.2.0 googlesheets4_1.0.1
## [46] magrittr_2.0.3 Rcpp_1.0.9 munsell_0.5.0
## [49] fansi_1.0.3 abind_1.4-5 lifecycle_1.0.2
## [52] terra_1.6-17 stringi_1.7.8 leafsync_0.1.0
## [55] yaml_2.3.5 plyr_1.8.7 tmaptools_3.1-1
## [58] grid_4.2.1 maptools_1.1-4 parallel_4.2.1
## [61] crayon_1.5.1 lattice_0.20-45 haven_2.5.1
## [64] stars_0.5-6 hms_1.1.2 pillar_1.8.1
## [67] uuid_1.1-0 codetools_0.2-18 reprex_2.0.2
## [70] XML_3.99-0.10 glue_1.6.2 evaluate_0.16
## [73] modelr_0.1.9 png_0.1-7 vctrs_0.4.1
## [76] tzdb_0.3.0 cellranger_1.1.0 gtable_0.3.1
## [79] assertthat_0.2.1 cachem_1.0.6 xfun_0.32
## [82] lwgeom_0.2-8 broom_1.0.1 e1071_1.7-11
## [85] class_7.3-20 googledrive_2.0.0 viridisLite_0.4.1
## [88] gargle_1.2.1 tigris_1.6.1 units_0.8-0
## [91] ellipsis_0.3.2Download tract polygons in Fulton County and Dekalb County for Yelp queries
## Load my Census API key
load(here('data', 'census_api_key.RData')) # Load the API key save in my local machine
census_api_key(census.api.key) %>% suppressMessages() # Activate the key in this R session
rm(census.api.key) # Remove from the environment
## Download tract polygons in Fulton and Dekalb (Year = 2019)
tract <- get_acs(geography = "tract",
state = "GA",
county = c("Fulton", "Dekalb"),
variables = c(workers = "B08006_001", # SEX OF WORKERS BY MEANS OF TRANSPORTATION TO WORK: Total (Male and Female)
workers.bicycle.commute = "B08006_014" # SEX OF WORKERS BY MEANS OF TRANSPORTATION TO WORK: Bicycle among Total (Male and Female)
),
year = 2019,
survey = "acs5",
geometry = T,
output = "wide"
) %>%
suppressMessages()##
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|======================================================================| 100%## View the data
message(sprintf("Number of rows: %s, Number of columns: %s", nrow(tract), ncol(tract)))## Number of rows: 349, Number of columns: 7## Retain only those with estimates and rename them
tract <- tract %>%
select(GEOID, NAME,
workers = workersE,
workers.bicycle.commute = workers.bicycle.commuteE)## Print the data
tract## Simple feature collection with 349 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.85071 ymin: 33.50251 xmax: -84.02371 ymax: 34.18629
## Geodetic CRS: NAD83
## First 10 features:
## GEOID NAME workers
## 1 13121001100 Census Tract 11, Fulton County, Georgia 3972
## 2 13121009603 Census Tract 96.03, Fulton County, Georgia 3293
## 3 13121005800 Census Tract 58, Fulton County, Georgia 736
## 4 13121010117 Census Tract 101.17, Fulton County, Georgia 2941
## 5 13121009502 Census Tract 95.02, Fulton County, Georgia 2087
## 6 13089021213 Census Tract 212.13, DeKalb County, Georgia 1365
## 7 13121011202 Census Tract 112.02, Fulton County, Georgia 1788
## 8 13089023506 Census Tract 235.06, DeKalb County, Georgia 2687
## 9 13121004900 Census Tract 49, Fulton County, Georgia 1665
## 10 13121004800 Census Tract 48, Fulton County, Georgia 402
## workers.bicycle.commute geometry
## 1 71 MULTIPOLYGON (((-84.38782 3...
## 2 0 MULTIPOLYGON (((-84.38738 3...
## 3 12 MULTIPOLYGON (((-84.41692 3...
## 4 24 MULTIPOLYGON (((-84.36575 3...
## 5 0 MULTIPOLYGON (((-84.39472 3...
## 6 0 MULTIPOLYGON (((-84.34783 3...
## 7 0 MULTIPOLYGON (((-84.46052 3...
## 8 0 MULTIPOLYGON (((-84.25237 3...
## 9 28 MULTIPOLYGON (((-84.38779 3...
## 10 4 MULTIPOLYGON (((-84.38771 3...Using Census tract boundaries for Yelp API
## Define a function that outputs the center of the bounding box of a polygon and the radius of a circle encompassing the bounding box tightly
# Function: Get tract-wise radius
get_r <- function(poly, epsg_id) {
#---------------------------------------------------------------------------------------------#
# Takes: a single POLYGON or LINESTRING #
# 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_corner and bb_center
r <- st_distance(bb_corner, bb_center)
# Multiply 1.2 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$radius <- r*1.2
return(bb_center)
}## Use a functional (lapply) to apply this custom function to each Census Tract.
epsg_id <- 4326 # WGS 84 (World Geodetic System 1984) used in GPS
r4all_apply <- tract %>%
st_geometry() %>%
st_transform(crs = epsg_id) %>%
lapply(., function(x) get_r(x, epsg_id = epsg_id))
r4all_apply <- bind_rows(r4all_apply)## Appending XY coordinates as separate columns
ready_4_yelp <- r4all_apply %>%
mutate(x = st_coordinates(.)[, 1],
y = st_coordinates(.)[, 2])## Visualize the results
# Activate the interactive mode
tmap_mode('view') %>% suppressMessages()
# Display data (the first 10 rows)
ready_4_yelp[1:10, ] %>%
st_buffer(., dist = .$radius) %>%
tm_shape(.) + tm_polygons(alpha = 0.5, col = 'red') +
tm_shape(tract[1:10, ]) + tm_borders(col = 'blue')Getting a Yelp API Key
## Load my Yelp API key
load(here('data', 'yelp_api_key.RData')) # Load the API key save in my local machineDefining function for accessing Yelp API for one Census Tract
## Define a function that downloads all businesses of a selected category in a Census tract
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 = yelp.api.key,
categories = category,
latitude = tract$y,
longitude = tract$x,
offset = (n - 1) * 50,
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 n-th 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 more than 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 = yelp.api.key,
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
}
# Merge all elements in the list into a single data frame
out <- out %>% bind_rows()
return(out)
}
}Repeating for all other Census Tracts
## 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,], "bikerentals"))
if (row %% 1 == 0){
Sys.sleep(1)
print(paste0("Current row: ", row))
}
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## [1] "Current row: 174"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 175"
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## [1] "Current row: 199"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 200"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 201"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 202"
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## [1] "Current row: 220"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 221"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 222"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 223"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 224"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 225"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 226"
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## [1] "Current row: 232"
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## [1] "Current row: 235"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 236"
## [1] "Current row: 237"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 238"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 239"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 240"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 241"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 242"
## [1] "Current row: 243"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 244"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 245"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 246"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 247"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 248"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 249"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 250"
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## [1] "Current row: 262"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 263"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 264"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 265"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 266"
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## [1] "Current row: 268"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 269"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 270"
## [1] "Current row: 271"
## [1] "Current row: 272"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 273"
## [1] "Current row: 274"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 275"
## [1] "Current row: 276"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 277"
## [1] "Current row: 278"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 279"
## [1] "Current row: 280"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 281"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 282"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 283"
## [1] "Current row: 284"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 285"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 286"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 287"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 288"
## [1] "Current row: 289"
## [1] "Current row: 290"
## [1] "Current row: 291"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 292"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 293"
## [1] "Current row: 294"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 295"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 296"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 297"
## [1] "Current row: 298"
## [1] "Current row: 299"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 300"
## [1] "Current row: 301"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 302"
## [1] "Current row: 303"
## [1] "Current row: 304"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 305"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 306"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 307"
## [1] "Current row: 308"
## [1] "Current row: 309"
## [1] "Current row: 310"
## [1] "Current row: 311"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 312"
## [1] "Current row: 313"
## [1] "Current row: 314"
## [1] "Current row: 315"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 316"
## [1] "Current row: 317"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 318"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 319"
## [1] "Current row: 320"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 321"
## [1] "Current row: 322"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 323"
## [1] "Current row: 324"
## [1] "Current row: 325"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 326"
## [1] "Current row: 327"
## [1] "Current row: 328"
## [1] "Current row: 329"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 330"
## [1] "Current row: 331"
## [1] "Current row: 332"
## [1] "Current row: 333"
## [1] "Current row: 334"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 335"
## [1] "Current row: 336"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 337"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 338"
## [1] "Current row: 339"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 340"
## [1] "Current row: 341"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 342"
## [1] "Current row: 343"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 344"
## [1] "Current row: 345"
## [1] "Current row: 346"
## [1] "Current row: 347"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 348"## Warning: Outer names are only allowed for unnamed scalar atomic inputs## [1] "Current row: 349"## Collapsing the list into a data.frame
yelp_all <- yelp_all_list %>% bind_rows() %>% as_tibble()
## Print
yelp_all %>% print(width=1000)## # A tibble: 208 × 16
## id alias
## <chr> <chr>
## 1 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain
## 2 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain
## 3 JkkHRgYj0mvdgbMXFm436w civil-bikes-atlanta
## 4 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain
## 5 UmftRC3h0h_owHEm5ZLp7Q jump-atlanta-2
## 6 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain
## 7 JkkHRgYj0mvdgbMXFm436w civil-bikes-atlanta
## 8 kJJiJqGbiO_QXmdhol3hIQ british-and-american-bikes-atlanta
## 9 FK7-M9BGyCgpEmVifcPfoA aztec-cycles-stone-mountain
## 10 ot4UyUsRAlTFudTlCVRMrQ pedego-electric-bikes-alpharetta-alpharetta
## name
## <chr>
## 1 Aztec Cycles
## 2 Aztec Cycles
## 3 Civil Bikes
## 4 Aztec Cycles
## 5 JUMP
## 6 Aztec Cycles
## 7 Civil Bikes
## 8 British and American Bikes
## 9 Aztec Cycles
## 10 Pedego Electric Bikes Alpharetta
## 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-media4.fl.yelpcdn.com/bphoto/JqTLT-chrqtbyuoB-52gdw/o.jpg
## 4 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 5 https://s3-media2.fl.yelpcdn.com/bphoto/D87H00XdLWZJS-LvQkTalA/o.jpg
## 6 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 7 https://s3-media4.fl.yelpcdn.com/bphoto/JqTLT-chrqtbyuoB-52gdw/o.jpg
## 8 https://s3-media2.fl.yelpcdn.com/bphoto/9I0G3Ge2yKFg6184t1XYEQ/o.jpg
## 9 https://s3-media3.fl.yelpcdn.com/bphoto/re-aoEuun-QS1SE7dQCySQ/o.jpg
## 10 https://s3-media2.fl.yelpcdn.com/bphoto/gPNidtknlSWKu3Bh4iOJ3Q/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=wA5TIrp…
## 2 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=wA5TIrp…
## 3 https://www.yelp.com/biz/civil-bikes-atlanta?adjust_creative=wA5TIrpKGJmySk7…
## 4 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=wA5TIrp…
## 5 https://www.yelp.com/biz/jump-atlanta-2?adjust_creative=wA5TIrpKGJmySk7ny-tL…
## 6 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=wA5TIrp…
## 7 https://www.yelp.com/biz/civil-bikes-atlanta?adjust_creative=wA5TIrpKGJmySk7…
## 8 https://www.yelp.com/biz/british-and-american-bikes-atlanta?adjust_creative=…
## 9 https://www.yelp.com/biz/aztec-cycles-stone-mountain?adjust_creative=wA5TIrp…
## 10 https://www.yelp.com/biz/pedego-electric-bikes-alpharetta-alpharetta?adjust_…
## review_count categories rating coordinates$latitude $longitude transactions
## <int> <list> <dbl> <dbl> <dbl> <list>
## 1 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 2 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 3 9 <df [2 × 2]> 4.5 33.7 -84.4 <list [0]>
## 4 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 5 1 <df [2 × 2]> 1 33.7 -84.4 <list [0]>
## 6 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 7 9 <df [2 × 2]> 4.5 33.7 -84.4 <list [0]>
## 8 2 <df [2 × 2]> 5 33.9 -84.3 <list [0]>
## 9 54 <df [3 × 2]> 5 33.8 -84.2 <list [0]>
## 10 7 <df [3 × 2]> 5 34.0 -84.3 <list [0]>
## price location$address1 $address2 $address3 $city
## <chr> <chr> <chr> <chr> <chr>
## 1 $$ "901 Main St" "" "" Stone Mountain
## 2 $$ "901 Main St" "" "" Stone Mountain
## 3 <NA> "" <NA> "" Atlanta
## 4 $$ "901 Main St" "" "" Stone Mountain
## 5 <NA> "" "" <NA> Atlanta
## 6 $$ "901 Main St" "" "" Stone Mountain
## 7 <NA> "" <NA> "" Atlanta
## 8 <NA> "4264 F Winters Chapel Rd" "" "" Atlanta
## 9 $$ "901 Main St" "" "" Stone Mountain
## 10 <NA> "6480 N Point Pkwy" "Ste 1100b" <NA> Alpharetta
## $zip_code $country $state $display_address phone display_phone
## <chr> <chr> <chr> <list> <chr> <chr>
## 1 30083 US GA <chr [2]> +16786369043 (678) 636-9043
## 2 30083 US GA <chr [2]> +16786369043 (678) 636-9043
## 3 30312 US GA <chr [1]> +14043238754 (404) 323-8754
## 4 30083 US GA <chr [2]> +16786369043 (678) 636-9043
## 5 30301 US GA <chr [1]> +18333006106 (833) 300-6106
## 6 30083 US GA <chr [2]> +16786369043 (678) 636-9043
## 7 30312 US GA <chr [1]> +14043238754 (404) 323-8754
## 8 30360 US GA <chr [2]> +17704518868 (770) 451-8868
## 9 30083 US GA <chr [2]> +16786369043 (678) 636-9043
## 10 30022 US GA <chr [3]> +14042810264 (404) 281-0264
## distance
## <dbl>
## 1 22818.
## 2 29547.
## 3 366.
## 4 20724.
## 5 1238.
## 6 20887.
## 7 527.
## 8 1715.
## 9 1921.
## 10 2501.
## # … with 198 more rows## Save the yelp data
write_rds(yelp_all, file = here('data', 'yelp_all_mini_assignment3.rds'))Tidy data
Delete duplicated rows (from 208 rows to 16 rows)
## Check duplicated values in the "id" column and delete duplicated rows
yelp_all <- yelp_all %>%
distinct(id, .keep_all = T) # from 208 rows to 16 rows## Print
yelp_all## # A tibble: 16 × 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 FK7-M9BGyCg… azte… Azte… https:… FALSE http… 54 <df> 5 33.8
## 2 JkkHRgYj0mv… civi… Civi… https:… FALSE http… 9 <df> 4.5 33.7
## 3 UmftRC3h0h_… jump… JUMP https:… FALSE http… 1 <df> 1 33.7
## 4 kJJiJqGbiO_… brit… Brit… https:… FALSE http… 2 <df> 5 33.9
## 5 ot4UyUsRAlT… pede… Pede… https:… FALSE http… 7 <df> 5 34.0
## 6 LsNS77QoD4w… dads… Dad'… https:… FALSE http… 64 <df> 4.5 33.8
## 7 b3nacMG8PR7… atla… Atla… https:… FALSE http… 124 <df> 4.5 33.8
## 8 8PfRbXo6qhK… atla… Atla… https:… FALSE http… 40 <df> 4.5 33.8
## 9 rbf8bVY0cuq… pede… Pede… https:… FALSE http… 9 <df> 4.5 33.7
## 10 BozJwfoXvoD… podi… Podi… https:… FALSE http… 19 <df> 4.5 33.8
## 11 OJVvH1CUZac… rosw… Rosw… https:… FALSE http… 70 <df> 4 34.0
## 12 vfp82FZBVz1… rela… Rela… https:… FALSE http… 12 <df> 2 33.6
## 13 0H4julqllnl… ston… Ston… https:… FALSE http… 7 <df> 3.5 33.8
## 14 cRTM5f8ATvV… the-… The … https:… FALSE http… 1 <df> 1 34.1
## 15 xF82sx0_jxT… atla… Atla… https:… FALSE http… 13 <df> 4 33.4
## 16 gcl6O-ZWTTr… clou… Clou… https:… FALSE http… 1 <df> 1 33.8
## # … with 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$latitudeFlatten nested columns
## Define a custom function that takes a data frame in "categories" column in Yelp data and returns a character vector
concate_list <- function(x) {
# "x" is a data frame with columns "alias" and "title" from Yelp$categories
# The function returns a character vector
titles <- x$title %>% str_c(collapse = ", ")
return(titles)
}
## Flatten columns
yelp_all <- yelp_all %>%
# Flatten data-frame-columns ("coordinates" and "location")
jsonlite::flatten() %>%
relocate(coordinates.latitude, coordinates.longitude, .after = rating) %>%
relocate(location.address1, location.address2, location.address3, location.city,
location.zip_code, location.country, location.state, location.display_address, .after = price) %>%
as_tibble() %>%
# Flatten list-columns ("transactions" (empty), "location.display_address", and "categories")
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)
)## Print
yelp_all## # A tibble: 16 × 24
## id alias name image…¹ is_cl…² url revie…³ categ…⁴ rating coord…⁵
## <chr> <chr> <chr> <chr> <lgl> <chr> <int> <chr> <dbl> <dbl>
## 1 FK7-M9BGyCg… azte… Azte… https:… FALSE http… 54 Bikes,… 5 33.8
## 2 JkkHRgYj0mv… civi… Civi… https:… FALSE http… 9 Bike R… 4.5 33.7
## 3 UmftRC3h0h_… jump… JUMP https:… FALSE http… 1 Scoote… 1 33.7
## 4 kJJiJqGbiO_… brit… Brit… https:… FALSE http… 2 Motorc… 5 33.9
## 5 ot4UyUsRAlT… pede… Pede… https:… FALSE http… 7 Bike R… 5 34.0
## 6 LsNS77QoD4w… dads… Dad'… https:… FALSE http… 64 Smog C… 4.5 33.8
## 7 b3nacMG8PR7… atla… Atla… https:… FALSE http… 124 Bike R… 4.5 33.8
## 8 8PfRbXo6qhK… atla… Atla… https:… FALSE http… 40 Bike R… 4.5 33.8
## 9 rbf8bVY0cuq… pede… Pede… https:… FALSE http… 9 Bikes,… 4.5 33.7
## 10 BozJwfoXvoD… podi… Podi… https:… FALSE http… 19 Bikes,… 4.5 33.8
## 11 OJVvH1CUZac… rosw… Rosw… https:… FALSE http… 70 Bikes,… 4 34.0
## 12 vfp82FZBVz1… rela… Rela… https:… FALSE http… 12 Party … 2 33.6
## 13 0H4julqllnl… ston… Ston… https:… FALSE http… 7 Bikes,… 3.5 33.8
## 14 cRTM5f8ATvV… the-… The … https:… FALSE http… 1 Bike R… 1 34.1
## 15 xF82sx0_jxT… atla… Atla… https:… FALSE http… 13 Bike R… 4 33.4
## 16 gcl6O-ZWTTr… clou… Clou… https:… FALSE http… 1 Scoote… 1 33.8
## # … with 14 more variables: coordinates.longitude <dbl>, transactions <chr>,
## # price <chr>, location.address1 <chr>, location.address2 <chr>,
## # location.address3 <chr>, location.city <chr>, location.zip_code <chr>,
## # location.country <chr>, location.state <chr>,
## # location.display_address <chr>, phone <chr>, display_phone <chr>,
## # distance <dbl>, and abbreviated variable names ¹image_url, ²is_closed,
## # ³review_count, ⁴categories, ⁵coordinates.latitudeDelete rows with missing data in “coordinates” variables (No missing values -> all rows are kept)
## Check missing values in "coordinates.latitude" and "coordinates.longitude" variables
yelp_all %>% select(coordinates.latitude, coordinates.longitude) %>% summary()## coordinates.latitude coordinates.longitude
## Min. :33.44 Min. :-84.59
## 1st Qu.:33.75 1st Qu.:-84.38
## Median :33.79 Median :-84.35
## Mean :33.81 Mean :-84.34
## 3rd Qu.:33.85 3rd Qu.:-84.29
## Max. :34.06 Max. :-84.10## No missing values in "coordinates.latitude" and "coordinates.longitude" -> Keep all rowsDelete rows that fall outside of Fulton and Dekalb County (from 16 rows to 14 rows)
## Create an sf object (crs = 4326)
yelp_sf <- yelp_all %>%
mutate(x = .$coordinates.longitude,
y = .$coordinates.latitude) %>%
filter(!is.na(x) & !is.na(y)) %>% # No row is removed
st_as_sf(coords = c("x", "y"), crs = 4326)
## Change the CRS of "tract" so that the CRS is the same as that of "yelp_sf"
tract <- st_transform(tract, crs = 4326)## Check current data
tmap_mode('view') %>% suppressMessages()
tm_shape(yelp_sf) + tm_dots() +
tm_shape(tract %>% st_union()) + tm_borders()## Keep only cases fall inside Fulton and Dekalb County
yelp_sf <- yelp_sf[tract %>% st_union(), , op = st_intersects] # from 16 rows to 14 rows
## Check the output
tmap_mode('view') %>% suppressMessages()
tm_shape(yelp_sf) + tm_dots() +
tm_shape(tract) + tm_borders()Join Census and Yelp
## Get the number of bike rentals by tract
bike_rentals_by_tract <- st_join(tract, yelp_sf %>% mutate(count = 1)) %>%
group_by(GEOID) %>%
summarize(count = sum(count, na.rm = T))
# Attach the result to the census data
tract_with_bike_rentals_count <- tract %>%
left_join(bike_rentals_by_tract %>% st_drop_geometry(), by = "GEOID")# Mapping
tm_shape(tract_with_bike_rentals_count %>% mutate(count = as.integer(count))) + tm_polygons(col="count") +
tm_shape(yelp_sf) + tm_dots()Examine the associations among the variables bike rentals and bike commuting
## Create a variable for the proportion of workers commuting via bicycle by tract
tract_with_bike_rentals_count <- tract_with_bike_rentals_count %>%
mutate(proportion.workers.bicycle.commute = workers.bicycle.commute/workers)
## Remove 4 census tracts with no workers (among 349 census tracts)
## They do not have bike rental shops
tract_with_bike_rentals_count <- tract_with_bike_rentals_count %>%
filter(workers != 0)## Existence of bike rental shops and the proportion of workers commuting via bike
# Create a binary variable
tract_with_bike_rentals_count$Bike.rental <- ifelse(tract_with_bike_rentals_count$count > 0, "Yes", "No")
# Visualize using a box plot
boxplot(proportion.workers.bicycle.commute ~ Bike.rental, data = tract_with_bike_rentals_count,
main = "Boxplot of Bike Rental Shop and Proportion of Bike Commuters",
xlab = "Whether Bike Rental Shops are Present",
ylab = "Proportion of Bike Commuters")
## Mapping bike rental shops and the "number" of bike commuters
tm_shape(tract_with_bike_rentals_count) +
tm_polygons(col = "workers.bicycle.commute") tm_shape(yelp_sf) + tm_dots()## Mapping bike rental shops and the "proportion" of bike commuters
tm_shape(tract_with_bike_rentals_count %>% rename()) + tm_polygons(col = "proportion.workers.bicycle.commute") +
tm_shape(yelp_sf) + tm_dots()Findings
- One thing to note is that I excluded 4 tracts among 349 in the study area before drawing the boxplot and creating maps. Those tracts have 0 workers.
- Of the remaining 345 tracts, 236 (68.4%) tracts are with 0% of
workers commuting via bike. Less than a third of tracts have at least
one bike commuters, the maximum proportion being only 7.0%. The number
of bike rental shops is 14 and the number of tracts with at least one
bike rental shops is 13 because one tract located at the northern part
of Fulton County (GEOID = 13121011611) has two bike rental shops and the
other 12 tracts have one.
- Even though the boxplot presents that the median value of proportion
of bike commuters is higher for tracts with one or more bike rental
shops than for those without a bike rental shop, it is hard to be sure
whether the difference is “substantial” or “meaningful” by just looking
at the plot because (1) the number of tracts with bike rental shops is
too small and (2) a huge proportion of tracts do not have any workers
who use bike as their commute mode. It is possible to test the
statistical test regarding the “significance” of the difference but the
statistical test would be less helpful in determining the location to
open a bike rental shop than just mapping the information.
- Looking at the maps showing the spatial distribution of bike rental shops and the number (or proportion) of workers commuting via bike, I was able to easily pick one tract with high demand but with no supply. The tract is the one near the center of Dekalb County with orange color in the two maps above (GEOID =13089023206). The proportion of workers commuting via bike is 5.0% abut there is no bike rental shop around. If I were to set up a bike rental shop, I would consider the tract as the main candidate for my bike rental shop location.