May 9, 2018

tidyTuesday: coffee chains

The tidyTuesday for this week is coffee chain locations

For this week: 1. The basic link to the #tidyTuesday shows an original article for Week 6.

First, let’s import the data; it is a single Excel spreadsheet. The page notes that starbucks, Tim Horton, and Dunkin Donuts have raw data available.

library(readxl)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.6     ✓ dplyr   1.0.4
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(geofacet)
library(ggbeeswarm)
library(ggrepel)
# A great function appears below that I grabbed from Stack Overflow a while back.....
# URL functionality with read_excel is tricky so I turned them in to rds
read_excel_allsheets <- function(filename, tibble = TRUE) {
    sheets <- readxl::excel_sheets(filename)
    x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
    if(!tibble) x <- lapply(x, as.data.frame)
    names(x) <- sheets
    x
}
download.file("https://github.com/rfordatascience/tidytuesday/raw/master/data/2018/2018-05-07/week6_coffee_chains.xlsx", "coffee.xlsx")
coffee.xl <- read_excel_allsheets("coffee.xlsx")
Starbucks <- readRDS(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/week6SB.rds"))
Dunkin.Donuts <- readRDS(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/week6DD.rds"))
Tim.Hortons <- readRDS(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/week6TH.rds"))

What do the data look like?

library(skimr)
skim(Starbucks)
Table 1: Data summary
Name Starbucks
Number of rows 25600
Number of columns 13
_______________________
Column type frequency:
character 11
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Brand 0 1.00 7 21 0 4 0
Store Number 0 1.00 5 12 0 25599 0
Store Name 0 1.00 2 60 0 25364 0
Ownership Type 0 1.00 8 13 0 4 0
Street Address 2 1.00 1 234 0 25353 0
City 14 1.00 2 29 0 5470 0
State/Province 0 1.00 1 3 0 338 0
Country 0 1.00 2 2 0 73 0
Postcode 1521 0.94 1 9 0 18888 0
Phone Number 6861 0.73 1 18 0 18559 0
Timezone 0 1.00 18 30 0 101 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Longitude 1 1 -27.87 96.84 -159.46 -104.66 -79.35 100.63 176.92 ▇▇▂▂▅
Latitude 1 1 34.79 13.34 -46.41 31.24 36.75 41.57 64.85 ▁▁▁▇▂ 
skim(Dunkin.Donuts)
Table 1: Data summary
Name Dunkin.Donuts
Number of rows 4898
Number of columns 22
_______________________
Column type frequency:
character 13
numeric 9
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
biz_name 0 1.00 8 38 0 33 0
e_address 0 1.00 6 61 0 4864 0
e_city 0 1.00 2 27 0 1770 0
e_state 0 1.00 2 2 0 41 0
e_zip_full 0 1.00 10 10 0 545 0
e_country 0 1.00 3 3 0 1 0
loc_county 0 1.00 3 21 0 395 0
loc_PMSA 0 1.00 2 4 0 53 0
loc_TZ 0 1.00 3 5 0 5 0
loc_DST 0 1.00 1 1 0 3 0
web_url 0 1.00 20 175 0 22 0
biz_info 4091 0.16 14 18 0 709 0
biz_phone 0 1.00 14 14 0 4562 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
id 0 1 2459.46 1420.28 1.00 1231.25 2458.50 3686.75 4920.00 ▇▇▇▇▇
e_postal 0 1 21528.34 20311.57 1001.00 6080.00 13334.50 32810.75 98112.00 ▇▃▁▁▁
loc_area_code 0 1 590.09 229.26 201.00 401.00 610.00 781.00 989.00 ▇▅▇▇▆
loc_FIPS 0 1 27911.17 12470.14 1069.00 17031.00 26125.00 36111.00 55111.00 ▂▅▆▇▂
loc_MSA 0 1 4284.65 2849.57 160.00 1520.00 3800.00 6880.00 9320.00 ▇▃▂▆▅
loc_LAT_centroid 0 1 39.62 4.33 21.42 39.39 41.22 42.11 47.63 ▁▁▁▇▂
loc_LAT_poly 0 1 39.62 4.32 21.39 39.38 41.20 42.09 47.64 ▁▁▁▇▂
loc_LONG_centroid 0 1 -77.55 7.31 -157.93 -81.44 -75.08 -72.66 -67.23 ▁▁▁▁▇
loc_LONG_poly 0 1 -77.55 7.31 -157.96 -81.44 -75.08 -72.66 -67.28 ▁▁▁▁▇ 
skim(Tim.Hortons)
Table 1: Data summary
Name Tim.Hortons
Number of rows 4955
Number of columns 6
_______________________
Column type frequency:
character 6
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
country 0 1 2 2 0 2 0
address 0 1 6 51 0 4803 0
city 0 1 3 38 0 1206 0
postal_code 0 1 4 7 0 4328 0
state 0 1 2 2 0 27 0
store_name 0 1 2 63 0 3167 0

A basic plot of the global Starbucks data.

library(ggmap)
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
mapWorld <- borders("world", colour="gray50", fill="gray50") # create a layer of borders
mp <- ggplot() +   mapWorld
mp <- mp + geom_point(aes(x=Starbucks$Longitude, y=Starbucks$Latitude) ,color="dark green", size=0.5) + xlab("") + ylab("")
mp <- mp + geom_point(aes(x=Dunkin.Donuts$loc_LONG_centroid, y=Dunkin.Donuts$loc_LAT_centroid) ,color="orange", size=0.5) + xlab("") + ylab("")
mp
## Warning: Removed 1 rows containing missing values (geom_point).

Starbucks and Dunkin

Google Maps interface changed and I have not updated this part. Shame.