7-Elevens by city (an exercise in learning R). Generate list of North American stores ("all-na.csv") with "getsevs.sh" before running R scripts.

7eleven-stores-city-ca.R

ca_cities <- read.csv(url("https://simplemaps.com/static/data/country-cities/ca/ca.csv"))
# add cities to table that will be in top 10 list
missing_1 <- c("Burnaby","","","Canada","CA","British Columbia","",232755,232755)
missing_2 <- c("Surrey","","","Canada","CA","British Columbia","",517887,517887)
missing_df <- data.frame(rbind(missing_1, missing_2))
names(missing_df) <- names(ca_cities)
ca_cities <- rbind.data.frame(ca_cities,missing_df)
prov_names <- c("British Columbia", "Alberta", "Saskatchewan", "Manitoba", "Ontario", "Québec", "New Brunswick", "Nova Scotia", "Prince Edward Island", "Newfoundland and Labrador", "Nunavut", "Northwest Territories", "Yukon")
prov_abb <- c("BC", "AB", "SK", "MB", "ON", "QC", "NB", "NS", "PE", "NL", "NU", "NT", "YT")
names(prov_abb) <- prov_names
names(ca_cities)[5] <- "country_abb"
ca_cities$state_prov_abb = prov_abb[as.character(ca_cities$admin)]

cols <- c("city", "state_prov_abb", "country_abb", "population", "population_proper")
ca_cities_pop <- subset(ca_cities, select=cols)

na_stores <- read.csv(file="all-na.csv", header=FALSE, sep=",")
names(na_stores) <- c("id", "address", "city", "state_prov_abb", "country_abb")
library(dplyr)
city_totals <- tally(group_by(filter(na_stores, country_abb=="CA"), city, state_prov_abb, country_abb), sort=TRUE)


get_pop <- function(city, state_prov, country) { 
    rows <- ca_cities_pop[which(ca_cities_pop$city==city & ca_cities_pop$state_prov_abb==state_prov & ca_cities_pop$country_abb==country),]
    if(nrow(rows) > 0) {
        return(as.numeric(rows$population))
    } else {
        return(NA)
    }
}

#city_totals$pop_per_store <- apply(city_totals, 1, function(x) get_pop(x["city"], x["state_prov_abb"], x["country_abb"])) / city_totals$n
city_totals$stores_per_100k <- city_totals$n / (apply(city_totals, 1, function(x) get_pop(x["city"], x["state_prov_abb"], x["country_abb"])) / 10^5)

library(ggplot2)
g <- ggplot(head(city_totals, 10), aes(x = reorder(city, -n), n)) + 
  geom_col(aes(fill=city)) + 
  scale_x_discrete(labels=paste(city_totals$city, city_totals$state_prov_abb, sep=", ")) + 
  geom_text(aes(label=n), vjust=-0.5, size=3) +
  guides(fill=FALSE) +
  geom_point(aes(x=city, y=n/2, size=stores_per_100k), shape=21, colour="#FF6A6A", fill="#FAFAD2", alpha=0.75, na.rm=TRUE) + 
  geom_text(aes(x=city, y=n/2, label=ifelse(stores_per_100k>0, sprintf("%0.2f", round(stores_per_100k, digits = 2)), "")), colour="#FF6A6A", size=2.5, na.rm = TRUE) +
  scale_size_continuous(range=c(8,16), name="Stores per 100,000 people", breaks=c(5), label=c("")) + 
  theme_minimal() + 
  theme(text=element_text(colour="grey40"), axis.text.x=element_text(angle=90, hjust=1, vjust=1, margin=margin(-15,0,0,0)), plot.title = element_text(hjust = 0.5), plot.caption = element_text(hjust = 0, margin=margin(10,250,0,50)), panel.grid = element_blank(), axis.title = element_blank(), axis.text.y = element_blank(), legend.title = element_text(size=10), legend.position = "bottom", legend.justification=c(1,0), legend.margin=margin(-10,5,0,0)) +
  labs(title = "7-Eleven stores by city in Canada (Top 10)",
       caption = "Sources: https://www.7-eleven.com/locator, https://simplemaps.com/data/ca-cities")

# ggsave("chart-ca.png")

7eleven-stores-city.R

ca_cities <- read.csv(url("https://simplemaps.com/static/data/country-cities/ca/ca.csv"))
prov_names <- c("British Columbia", "Alberta", "Saskatchewan", "Manitoba", "Ontario", "Québec", "New Brunswick", "Nova Scotia", "Prince Edward Island", "Newfoundland and Labrador", "Nunavut", "Northwest Territories", "Yukon")
prov_abb <- c("BC", "AB", "SK", "MB", "ON", "QC", "NB", "NS", "PE", "NL", "NU", "NT", "YT")
names(prov_abb) <- prov_names
names(ca_cities)[5] <- "country_abb"
ca_cities$state_prov_abb = prov_abb[as.character(ca_cities$admin)]

us_cities <- read.csv(url("https://simplemaps.com/static/data/us-cities/uscitiesv1.4.csv"))
names(us_cities)[3] <- "state_prov_abb"
us_cities$country_abb <- "US"
cols <- c("city", "state_prov_abb", "country_abb", "population", "population_proper")
na_cities_pop <- rbind(subset(ca_cities, select=cols), subset(us_cities, select=cols))

na_stores <- read.csv(file="all-na.csv", header=FALSE, sep=",")
names(na_stores) <- c("id", "address", "city", "state_prov_abb", "country_abb")
library(dplyr)
city_totals <- tally(group_by(na_stores, city, state_prov_abb, country_abb), sort=TRUE)

get_pop <- function(city, state_prov, country) { 
    rows <- na_cities_pop[which(na_cities_pop$city==city & na_cities_pop$state_prov_abb==state_prov & na_cities_pop$country_abb==country),]
    if(nrow(rows) > 0) {
        return(rows$population)
    } else {
        return(NA)
    }
}

#city_totals$pop_per_store <- apply(city_totals, 1, function(x) get_pop(x["city"], x["state_prov_abb"], x["country_abb"])) / city_totals$n
city_totals$stores_per_100k <- city_totals$n / (apply(city_totals, 1, function(x) get_pop(x["city"], x["state_prov_abb"], x["country_abb"])) / 10^5)

library(ggplot2)
g <- ggplot(head(city_totals, 25), aes(x = reorder(city, -n), n)) + 
  geom_col(aes(fill=city)) + 
  scale_x_discrete(labels=paste(city_totals$city, city_totals$state_prov_abb, sep=", ")) + 
  geom_text(aes(label=n), vjust=-0.5, size=3) +
  guides(fill=FALSE) +
  geom_point(aes(x=city, y=n/2, size=stores_per_100k), shape=21, colour="#FF6A6A", fill="#FAFAD2", alpha=0.75, na.rm=TRUE) +
  geom_text(aes(x=city, y=n/2, label=ifelse(stores_per_100k>0, sprintf("%0.2f", round(stores_per_100k, digits = 2)), "")), colour="#FF6A6A", size=2.5, na.rm = TRUE) +
  scale_size_continuous(range=c(6,12), name="Stores per 100,000 people", breaks=c(5), label=c("")) + 
  theme_minimal() + 
  theme(text=element_text(colour="grey40"), axis.text.x=element_text(angle=90, hjust=1, vjust=1, margin=margin(-15,0,0,0)), plot.title = element_text(hjust = 0.5), plot.caption = element_text(hjust = 0, margin=margin(10,250,0,50)), panel.grid = element_blank(), axis.title = element_blank(), axis.text.y = element_blank(), legend.title = element_text(size=10), legend.position = "bottom", legend.justification=c(1,0), legend.margin=margin(-10,5,0,0)) +
  labs(title = "7-Eleven stores by city in North America (Top 25)",
       caption = "Sources: https://www.7-eleven.com/locator, https://simplemaps.com/data/ca-cities, https://simplemaps.com/data/us-cities")

# ggsave("chart-na.png")

getsevs.sh

#!/bin/bash

offset=0
url='https://api.7-eleven.com/auth/token/'
# id and secret appear to be hard coded in https://www.7-eleven.com/locator
declare -a args=(--data "client_id=sl3rgdU5c5ZvsYj95FGIuexau5Nt7J5OTf7VRPfV&\
client_secret=11BBlWqIeLenwAmPOKqz8WN5NIZRCCSBSEcBtp9DikLh90WL217OlaCvghuDJu\
cGP5wG12VW2vQ7FRAzUMcYtOOrLtcd4eMqShsOJJKZnJOL5snAnih0uyUN8ZEURXPh&grant_type\
=client_credentials")
token="$(curl -s $url "${args[@]}" | jq -r '.access_token')"

url="https://api.7-eleven.com/v4/stores/?features=&limit=500&offset="
declare -a args=(-H "Authorization: Bearer $token" \
-H "Accept-Encoding: gzip, deflate, br" --compressed)

while : ; do
    loc="$(curl -s $url$offset "${args[@]}" | jq '.results')"
    if [[ $loc != "[]" ]]; then
        echo $loc| jq -r '.[] | [.id, .address, .city, .state, .country] | @csv'
    else
        break
    fi
    offset=$(($offset + 500))
done