nikkyxiong/coffee_rating_and_nyc_demographics

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In nikkyxiong/coffee_rating_and_nyc_demographics: 

knitr::opts_chunk$set(echo = TRUE)

devtools::install_github("OmaymaS/yelpr")
library(yelpr)
library(jsonlite)
library(dplyr)
library(tidyverse)
key <- Sys.getenv("YELP_KEY")

All NYC

nyc_restaurants <- business_search(api_key = key,
                location = 'New York City',
                term = "Starbucks",
                limit = 50)

varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")
restaurants_df <- data.frame(cbind(as.character(nyc_restaurants$businesses$id), 
                        as.character(nyc_restaurants$businesses$alias), 
                        as.character(nyc_restaurants$businesses$name),
                        as.character(nyc_restaurants$businesses$image_url), 
                        as.logical(nyc_restaurants$businesses$is_closed),
                        as.character(nyc_restaurants$businesses$url),
                        as.numeric(nyc_restaurants$businesses$review_count),
                        as.numeric(nyc_restaurants$businesses$rating),
                        as.numeric(nyc_restaurants$businesses$coordinates$latitude),
                        as.numeric(nyc_restaurants$businesses$coordinates$longitude),
                        as.character(nyc_restaurants$businesses$transactions), 
                        as.character(nyc_restaurants$businesses$price),
                        as.numeric(nyc_restaurants$businesses$distance),
                        as.character(nyc_restaurants$businesses$location$address1),
                        as.character(nyc_restaurants$businesses$location$city),
                        as.character(nyc_restaurants$businesses$location$state),
                        as.numeric(nyc_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(restaurants_df) <- varnames

# rest_cats <- data.frame(unlist(nyc_restaurants$businesses$categories[[1]]))

#restaurants_df$categories <- nyc_restaurants$businesses$categories[[1]]

for (i in 1:ncol(restaurants_df)) {
  restaurants_df$categories[i] <- paste0(flatten(nyc_restaurants$businesses$categories[[i]]), sep = ", ", collapse = "")
  restaurants_df$full_address[i] <- paste0(flatten(nyc_restaurants$businesses$location$display_address[i]), sep = ", ", collapse = "")
}

for (i in 1:19) {

    varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")

  offset_val <- i * 50
  new_restaurants <- business_search(api_key = key,
                location = 'New York City',
                term = "Starbucks",
                limit = 50,
                offset = offset_val)

new_restaurants_df <- data.frame(cbind(as.character(new_restaurants$businesses$id), 
                        as.character(new_restaurants$businesses$alias), 
                        as.character(new_restaurants$businesses$name),
                        as.character(new_restaurants$businesses$image_url), 
                        as.logical(new_restaurants$businesses$is_closed),
                        as.character(new_restaurants$businesses$url),
                        as.numeric(new_restaurants$businesses$review_count),
                        as.numeric(new_restaurants$businesses$rating),
                        as.numeric(new_restaurants$businesses$coordinates$latitude),
                        as.numeric(new_restaurants$businesses$coordinates$longitude),
                        as.character(new_restaurants$businesses$transactions), 
                        as.character(new_restaurants$businesses$price),
                        as.numeric(new_restaurants$businesses$distance),
                        as.character(new_restaurants$businesses$location$address1),
                        as.character(new_restaurants$businesses$location$city),
                        as.character(new_restaurants$businesses$location$state),
                        as.numeric(new_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(new_restaurants_df) <- varnames

for (j in 1:ncol(new_restaurants_df)) {

  new_restaurants_df$full_address[j] <- paste0(flatten(new_restaurants$businesses$location$display_address[j]), sep = ", ", collapse = "")

  new_restaurants_df$categories[j] <- paste0(flatten(new_restaurants$businesses$categories[[j]]), sep = ", ", collapse = "")

}

  restaurants_df <- rbind(restaurants_df, new_restaurants_df)
}

restaurants_df_backup <- restaurants_df
write_csv(restaurants_df, "./nyc_Starbucks.csv", na = "NA")

Manhattan Restaurants

nyc_restaurants <- business_search(api_key = key,
                location = 'Manhattan',
                term = "Starbucks",
                limit = 50)

varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")
restaurants_df <- data.frame(cbind(as.character(nyc_restaurants$businesses$id), 
                        as.character(nyc_restaurants$businesses$alias), 
                        as.character(nyc_restaurants$businesses$name),
                        as.character(nyc_restaurants$businesses$image_url), 
                        as.logical(nyc_restaurants$businesses$is_closed),
                        as.character(nyc_restaurants$businesses$url),
                        as.numeric(nyc_restaurants$businesses$review_count),
                        as.numeric(nyc_restaurants$businesses$rating),
                        as.numeric(nyc_restaurants$businesses$coordinates$latitude),
                        as.numeric(nyc_restaurants$businesses$coordinates$longitude),
                        as.character(nyc_restaurants$businesses$transactions), 
                        as.character(nyc_restaurants$businesses$price),
                        as.numeric(nyc_restaurants$businesses$distance),
                        as.character(nyc_restaurants$businesses$location$address1),
                        as.character(nyc_restaurants$businesses$location$city),
                        as.character(nyc_restaurants$businesses$location$state),
                        as.numeric(nyc_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(restaurants_df) <- varnames

# rest_cats <- data.frame(unlist(nyc_restaurants$businesses$categories[[1]]))

#restaurants_df$categories <- nyc_restaurants$businesses$categories[[1]]

for (i in 1:ncol(restaurants_df)) {
  restaurants_df$categories[i] <- paste0(flatten(nyc_restaurants$businesses$categories[[i]]), sep = ", ", collapse = "")
  restaurants_df$full_address[i] <- paste0(flatten(nyc_restaurants$businesses$location$display_address[i]), sep = ", ", collapse = "")
}

for (i in 1:19) {

    varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")

  offset_val <- i * 50
  new_restaurants <- business_search(api_key = key,
                location = 'Manhattan',
                term = "Starbucks",
                limit = 50,
                offset = offset_val)

new_restaurants_df <- data.frame(cbind(as.character(new_restaurants$businesses$id), 
                        as.character(new_restaurants$businesses$alias), 
                        as.character(new_restaurants$businesses$name),
                        as.character(new_restaurants$businesses$image_url), 
                        as.logical(new_restaurants$businesses$is_closed),
                        as.character(new_restaurants$businesses$url),
                        as.numeric(new_restaurants$businesses$review_count),
                        as.numeric(new_restaurants$businesses$rating),
                        as.numeric(new_restaurants$businesses$coordinates$latitude),
                        as.numeric(new_restaurants$businesses$coordinates$longitude),
                        as.character(new_restaurants$businesses$transactions), 
                        as.character(new_restaurants$businesses$price),
                        as.numeric(new_restaurants$businesses$distance),
                        as.character(new_restaurants$businesses$location$address1),
                        as.character(new_restaurants$businesses$location$city),
                        as.character(new_restaurants$businesses$location$state),
                        as.numeric(new_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(new_restaurants_df) <- varnames

for (j in 1:ncol(new_restaurants_df)) {

  new_restaurants_df$full_address[j] <- paste0(flatten(new_restaurants$businesses$location$display_address[j]), sep = ", ", collapse = "")

  new_restaurants_df$categories[j] <- paste0(flatten(new_restaurants$businesses$categories[[j]]), sep = ", ", collapse = "")

}

  restaurants_df <- rbind(restaurants_df, new_restaurants_df)
}

restaurants_df_backup <- restaurants_df
write_csv(restaurants_df, "./manhattan_Starbucks.csv", na = "NA")

Brooklyn

nyc_restaurants <- business_search(api_key = key,
                location = 'Brooklyn',
                term = "Starbucks",
                limit = 50)

varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")
restaurants_df <- data.frame(cbind(as.character(nyc_restaurants$businesses$id), 
                        as.character(nyc_restaurants$businesses$alias), 
                        as.character(nyc_restaurants$businesses$name),
                        as.character(nyc_restaurants$businesses$image_url), 
                        as.logical(nyc_restaurants$businesses$is_closed),
                        as.character(nyc_restaurants$businesses$url),
                        as.numeric(nyc_restaurants$businesses$review_count),
                        as.numeric(nyc_restaurants$businesses$rating),
                        as.numeric(nyc_restaurants$businesses$coordinates$latitude),
                        as.numeric(nyc_restaurants$businesses$coordinates$longitude),
                        as.character(nyc_restaurants$businesses$transactions), 
                        as.character(nyc_restaurants$businesses$price),
                        as.numeric(nyc_restaurants$businesses$distance),
                        as.character(nyc_restaurants$businesses$location$address1),
                        as.character(nyc_restaurants$businesses$location$city),
                        as.character(nyc_restaurants$businesses$location$state),
                        as.numeric(nyc_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(restaurants_df) <- varnames

# rest_cats <- data.frame(unlist(nyc_restaurants$businesses$categories[[1]]))

#restaurants_df$categories <- nyc_restaurants$businesses$categories[[1]]

for (i in 1:ncol(restaurants_df)) {
  restaurants_df$categories[i] <- paste0(flatten(nyc_restaurants$businesses$categories[[i]]), sep = ", ", collapse = "")
  restaurants_df$full_address[i] <- paste0(flatten(nyc_restaurants$businesses$location$display_address[i]), sep = ", ", collapse = "")
}

for (i in 1:19) {

    varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")

  offset_val <- i * 50
  new_restaurants <- business_search(api_key = key,
                location = 'Brooklyn',
                term = "Starbucks",
                limit = 50,
                offset = offset_val)

new_restaurants_df <- data.frame(cbind(as.character(new_restaurants$businesses$id), 
                        as.character(new_restaurants$businesses$alias), 
                        as.character(new_restaurants$businesses$name),
                        as.character(new_restaurants$businesses$image_url), 
                        as.logical(new_restaurants$businesses$is_closed),
                        as.character(new_restaurants$businesses$url),
                        as.numeric(new_restaurants$businesses$review_count),
                        as.numeric(new_restaurants$businesses$rating),
                        as.numeric(new_restaurants$businesses$coordinates$latitude),
                        as.numeric(new_restaurants$businesses$coordinates$longitude),
                        as.character(new_restaurants$businesses$transactions), 
                        as.character(new_restaurants$businesses$price),
                        as.numeric(new_restaurants$businesses$distance),
                        as.character(new_restaurants$businesses$location$address1),
                        as.character(new_restaurants$businesses$location$city),
                        as.character(new_restaurants$businesses$location$state),
                        as.numeric(new_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(new_restaurants_df) <- varnames

for (j in 1:ncol(new_restaurants_df)) {

  new_restaurants_df$full_address[j] <- paste0(flatten(new_restaurants$businesses$location$display_address[j]), sep = ", ", collapse = "")

  new_restaurants_df$categories[j] <- paste0(flatten(new_restaurants$businesses$categories[[j]]), sep = ", ", collapse = "")

}

  restaurants_df <- rbind(restaurants_df, new_restaurants_df)
}

restaurants_df_backup <- restaurants_df
write_csv(restaurants_df, "./Brooklyn_Starbucks.csv", na = "NA")

Queens

nyc_restaurants <- business_search(api_key = key,
                location = 'Queens',
                term = "Starbucks",
                limit = 50)

varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")
restaurants_df <- data.frame(cbind(as.character(nyc_restaurants$businesses$id), 
                        as.character(nyc_restaurants$businesses$alias), 
                        as.character(nyc_restaurants$businesses$name),
                        as.character(nyc_restaurants$businesses$image_url), 
                        as.logical(nyc_restaurants$businesses$is_closed),
                        as.character(nyc_restaurants$businesses$url),
                        as.numeric(nyc_restaurants$businesses$review_count),
                        as.numeric(nyc_restaurants$businesses$rating),
                        as.numeric(nyc_restaurants$businesses$coordinates$latitude),
                        as.numeric(nyc_restaurants$businesses$coordinates$longitude),
                        as.character(nyc_restaurants$businesses$transactions), 
                        as.character(nyc_restaurants$businesses$price),
                        as.numeric(nyc_restaurants$businesses$distance),
                        as.character(nyc_restaurants$businesses$location$address1),
                        as.character(nyc_restaurants$businesses$location$city),
                        as.character(nyc_restaurants$businesses$location$state),
                        as.numeric(nyc_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(restaurants_df) <- varnames

# rest_cats <- data.frame(unlist(nyc_restaurants$businesses$categories[[1]]))

#restaurants_df$categories <- nyc_restaurants$businesses$categories[[1]]

for (i in 1:ncol(restaurants_df)) {
  restaurants_df$categories[i] <- paste0(flatten(nyc_restaurants$businesses$categories[[i]]), sep = ", ", collapse = "")
  restaurants_df$full_address[i] <- paste0(flatten(nyc_restaurants$businesses$location$display_address[i]), sep = ", ", collapse = "")
}

for (i in 1:19) {

    varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")

  offset_val <- i * 50
  new_restaurants <- business_search(api_key = key,
                location = 'Queens',
                term = "Starbucks",
                limit = 50,
                offset = offset_val)

new_restaurants_df <- data.frame(cbind(as.character(new_restaurants$businesses$id), 
                        as.character(new_restaurants$businesses$alias), 
                        as.character(new_restaurants$businesses$name),
                        as.character(new_restaurants$businesses$image_url), 
                        as.logical(new_restaurants$businesses$is_closed),
                        as.character(new_restaurants$businesses$url),
                        as.numeric(new_restaurants$businesses$review_count),
                        as.numeric(new_restaurants$businesses$rating),
                        as.numeric(new_restaurants$businesses$coordinates$latitude),
                        as.numeric(new_restaurants$businesses$coordinates$longitude),
                        as.character(new_restaurants$businesses$transactions), 
                        as.character(new_restaurants$businesses$price),
                        as.numeric(new_restaurants$businesses$distance),
                        as.character(new_restaurants$businesses$location$address1),
                        as.character(new_restaurants$businesses$location$city),
                        as.character(new_restaurants$businesses$location$state),
                        as.numeric(new_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(new_restaurants_df) <- varnames

for (j in 1:ncol(new_restaurants_df)) {

  new_restaurants_df$full_address[j] <- paste0(flatten(new_restaurants$businesses$location$display_address[j]), sep = ", ", collapse = "")

  new_restaurants_df$categories[j] <- paste0(flatten(new_restaurants$businesses$categories[[j]]), sep = ", ", collapse = "")

}

  restaurants_df <- rbind(restaurants_df, new_restaurants_df)
}

restaurants_df_backup <- restaurants_df
write_csv(restaurants_df, "./Queens_Starbucks.csv", na = "NA")

Bronx

nyc_restaurants <- business_search(api_key = key,
                location = 'Bronx',
                term = "Starbucks",
                limit = 50)

varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")
restaurants_df <- data.frame(cbind(as.character(nyc_restaurants$businesses$id), 
                        as.character(nyc_restaurants$businesses$alias), 
                        as.character(nyc_restaurants$businesses$name),
                        as.character(nyc_restaurants$businesses$image_url), 
                        as.logical(nyc_restaurants$businesses$is_closed),
                        as.character(nyc_restaurants$businesses$url),
                        as.numeric(nyc_restaurants$businesses$review_count),
                        as.numeric(nyc_restaurants$businesses$rating),
                        as.numeric(nyc_restaurants$businesses$coordinates$latitude),
                        as.numeric(nyc_restaurants$businesses$coordinates$longitude),
                        as.character(nyc_restaurants$businesses$transactions), 
                        as.character(nyc_restaurants$businesses$price),
                        as.numeric(nyc_restaurants$businesses$distance),
                        as.character(nyc_restaurants$businesses$location$address1),
                        as.character(nyc_restaurants$businesses$location$city),
                        as.character(nyc_restaurants$businesses$location$state),
                        as.numeric(nyc_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(restaurants_df) <- varnames

# rest_cats <- data.frame(unlist(nyc_restaurants$businesses$categories[[1]]))

#restaurants_df$categories <- nyc_restaurants$businesses$categories[[1]]

for (i in 1:ncol(restaurants_df)) {
  restaurants_df$categories[i] <- paste0(flatten(nyc_restaurants$businesses$categories[[i]]), sep = ", ", collapse = "")
  restaurants_df$full_address[i] <- paste0(flatten(nyc_restaurants$businesses$location$display_address[i]), sep = ", ", collapse = "")
}

for (i in 1:19) {

    varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")

  offset_val <- i * 50
  new_restaurants <- business_search(api_key = key,
                location = 'Bronx',
                term = "Starbucks",
                limit = 50,
                offset = offset_val)

new_restaurants_df <- data.frame(cbind(as.character(new_restaurants$businesses$id), 
                        as.character(new_restaurants$businesses$alias), 
                        as.character(new_restaurants$businesses$name),
                        as.character(new_restaurants$businesses$image_url), 
                        as.logical(new_restaurants$businesses$is_closed),
                        as.character(new_restaurants$businesses$url),
                        as.numeric(new_restaurants$businesses$review_count),
                        as.numeric(new_restaurants$businesses$rating),
                        as.numeric(new_restaurants$businesses$coordinates$latitude),
                        as.numeric(new_restaurants$businesses$coordinates$longitude),
                        as.character(new_restaurants$businesses$transactions), 
                        as.character(new_restaurants$businesses$price),
                        as.numeric(new_restaurants$businesses$distance),
                        as.character(new_restaurants$businesses$location$address1),
                        as.character(new_restaurants$businesses$location$city),
                        as.character(new_restaurants$businesses$location$state),
                        as.numeric(new_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(new_restaurants_df) <- varnames

for (j in 1:ncol(new_restaurants_df)) {

  new_restaurants_df$full_address[j] <- paste0(flatten(new_restaurants$businesses$location$display_address[j]), sep = ", ", collapse = "")

  new_restaurants_df$categories[j] <- paste0(flatten(new_restaurants$businesses$categories[[j]]), sep = ", ", collapse = "")

}

  restaurants_df <- rbind(restaurants_df, new_restaurants_df)
}

restaurants_df_backup <- restaurants_df
write_csv(restaurants_df, "./Bronx_Starbucks.csv", na = "NA")

Staten Island

nyc_restaurants <- business_search(api_key = key,
                location = 'Staten Island',
                term = "Starbucks",
                limit = 50)

varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")
restaurants_df <- data.frame(cbind(as.character(nyc_restaurants$businesses$id), 
                        as.character(nyc_restaurants$businesses$alias), 
                        as.character(nyc_restaurants$businesses$name),
                        as.character(nyc_restaurants$businesses$image_url), 
                        as.logical(nyc_restaurants$businesses$is_closed),
                        as.character(nyc_restaurants$businesses$url),
                        as.numeric(nyc_restaurants$businesses$review_count),
                        as.numeric(nyc_restaurants$businesses$rating),
                        as.numeric(nyc_restaurants$businesses$coordinates$latitude),
                        as.numeric(nyc_restaurants$businesses$coordinates$longitude),
                        as.character(nyc_restaurants$businesses$transactions), 
                        as.character(nyc_restaurants$businesses$price),
                        as.numeric(nyc_restaurants$businesses$distance),
                        as.character(nyc_restaurants$businesses$location$address1),
                        as.character(nyc_restaurants$businesses$location$city),
                        as.character(nyc_restaurants$businesses$location$state),
                        as.numeric(nyc_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(restaurants_df) <- varnames

# rest_cats <- data.frame(unlist(nyc_restaurants$businesses$categories[[1]]))

#restaurants_df$categories <- nyc_restaurants$businesses$categories[[1]]

for (i in 1:ncol(restaurants_df)) {
  restaurants_df$categories[i] <- paste0(flatten(nyc_restaurants$businesses$categories[[i]]), sep = ", ", collapse = "")
  restaurants_df$full_address[i] <- paste0(flatten(nyc_restaurants$businesses$location$display_address[i]), sep = ", ", collapse = "")
}

for (i in 1:19) {

    varnames <- c("id", "alias", "name", "image_url", "is_closed", "url", "review_count", "rating", "latitude", "longitude", "transactions", "price", "distance", "street_address", "city", "state", "zip_code")

  offset_val <- i * 50
  new_restaurants <- business_search(api_key = key,
                location = 'Staten Island',
                term = "Starbucks",
                limit = 50,
                offset = offset_val)

new_restaurants_df <- data.frame(cbind(as.character(new_restaurants$businesses$id), 
                        as.character(new_restaurants$businesses$alias), 
                        as.character(new_restaurants$businesses$name),
                        as.character(new_restaurants$businesses$image_url), 
                        as.logical(new_restaurants$businesses$is_closed),
                        as.character(new_restaurants$businesses$url),
                        as.numeric(new_restaurants$businesses$review_count),
                        as.numeric(new_restaurants$businesses$rating),
                        as.numeric(new_restaurants$businesses$coordinates$latitude),
                        as.numeric(new_restaurants$businesses$coordinates$longitude),
                        as.character(new_restaurants$businesses$transactions), 
                        as.character(new_restaurants$businesses$price),
                        as.numeric(new_restaurants$businesses$distance),
                        as.character(new_restaurants$businesses$location$address1),
                        as.character(new_restaurants$businesses$location$city),
                        as.character(new_restaurants$businesses$location$state),
                        as.numeric(new_restaurants$businesses$location$zip_code)), stringsAsFactors = FALSE)

colnames(new_restaurants_df) <- varnames

for (j in 1:ncol(new_restaurants_df)) {

  new_restaurants_df$full_address[j] <- paste0(flatten(new_restaurants$businesses$location$display_address[j]), sep = ", ", collapse = "")

  new_restaurants_df$categories[j] <- paste0(flatten(new_restaurants$businesses$categories[[j]]), sep = ", ", collapse = "")

}

  restaurants_df <- rbind(restaurants_df, new_restaurants_df)
}

restaurants_df_backup <- restaurants_df
write_csv(restaurants_df, "./StatenIsland_Starbucks.csv", na = "NA")


nikkyxiong/coffee_rating_and_nyc_demographics documentation built on May 16, 2020, 9:27 a.m.

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