This report creates a web map to analyze different store ownership
types for Starbucks locations, as well as the local competition, in the
city of Bellingham in Washington state. Bellingham is a small college in
northwestern Washington state, just 21-miles from the Canadian border.
It contains Western Washington University, a college the author is
visiting for his brother’s graduation commencement from the School of
Business and Economics.
Check out the college here: WWU
This report is Part B of Lab #4 for GEOG588.
Logos here are used for academic purposes only.
Load packages
First, we load the packages for this analysis.
library(dplyr) # For data manipulation.
library(leaflet) # Leaflet is what we'll use for the main mapping.
library(sf) # To use features.
library(leafem) # Leafem will be used to add other functionalities to our map.
library(readr) # To read our CSV file.
Data Wrangling
Then, we set up our data. The dataset comes from a global directory
of Starbucks stores with information such as store number, address, and
lat/long. Additionally, data was obtained for a local coffee chain
competition called Woods Coffee from their company website. The
addresses were geocoded manually in google maps and inputted into a new
csv file called “directory_v2.”
Check out Woods Coffee here: Woods Coffee
(starbucks_belwa = read_csv('directory_v2.csv') %>% # choose the dataset.
filter(City %in% c("Bellingham")) %>% # Select stores in Bellingham
filter(State %in% c("WA")) %>% # and the state is WA
st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) %>% # set coordiantes
select(brand = `Brand`, store_name = `Store Name`, ownership_type = `Ownership Type`, address = `Street Address`, city = City, State = State)) # These are the columns/variables to be selected.
## Rows: 25607 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): Brand, Store Number, Store Name, Ownership Type, Street Address, C...
## dbl (2): Longitude, Latitude
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Simple feature collection with 21 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -122.51 ymin: 48.71 xmax: -122.44 ymax: 48.81
## Geodetic CRS: WGS 84
## # A tibble: 21 × 7
## brand store_name ownership_type address city State geometry
## <chr> <chr> <chr> <chr> <chr> <chr> <POINT [°]>
## 1 Star… S. Sammis… Company Owned 222 36… Bell… WA (-122.47 48.73)
## 2 Star… Barkley V… Company Owned 2915 N… Bell… WA (-122.44 48.77)
## 3 Star… Bellis Fa… Company Owned 1 Bell… Bell… WA (-122.49 48.79)
## 4 Star… Sunset Dr… Company Owned 1185 E… Bell… WA (-122.46 48.77)
## 5 Star… Old Fairh… Company Owned 3105 O… Bell… WA (-122.48 48.71)
## 6 Star… Western W… Licensed 156 E … Bell… WA (-122.49 48.73)
## 7 Star… Iowa & Ki… Company Owned 814 Io… Bell… WA (-122.46 48.76)
## 8 Star… Bakerview… Company Owned 1031 W… Bell… WA (-122.51 48.79)
## 9 Star… Safeway -… Licensed 1275 E… Bell… WA (-122.46 48.77)
## 10 Star… Fred Meye… Licensed 1225 W… Bell… WA (-122.51 48.79)
## # ℹ 11 more rows
A view of the data:
starbucks_belwa
## Simple feature collection with 21 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -122.51 ymin: 48.71 xmax: -122.44 ymax: 48.81
## Geodetic CRS: WGS 84
## # A tibble: 21 × 7
## brand store_name ownership_type address city State geometry
## <chr> <chr> <chr> <chr> <chr> <chr> <POINT [°]>
## 1 Star… S. Sammis… Company Owned 222 36… Bell… WA (-122.47 48.73)
## 2 Star… Barkley V… Company Owned 2915 N… Bell… WA (-122.44 48.77)
## 3 Star… Bellis Fa… Company Owned 1 Bell… Bell… WA (-122.49 48.79)
## 4 Star… Sunset Dr… Company Owned 1185 E… Bell… WA (-122.46 48.77)
## 5 Star… Old Fairh… Company Owned 3105 O… Bell… WA (-122.48 48.71)
## 6 Star… Western W… Licensed 156 E … Bell… WA (-122.49 48.73)
## 7 Star… Iowa & Ki… Company Owned 814 Io… Bell… WA (-122.46 48.76)
## 8 Star… Bakerview… Company Owned 1031 W… Bell… WA (-122.51 48.79)
## 9 Star… Safeway -… Licensed 1275 E… Bell… WA (-122.46 48.77)
## 10 Star… Fred Meye… Licensed 1225 W… Bell… WA (-122.51 48.79)
## # ℹ 11 more rows
Analysis
Here, we set the color palette for our map. The colors chosen come
from local sports team colors such as the Seattle Seahawks.
pal <- colorFactor(c("#52E74B", "#808080", "#0000FF"), starbucks_belwa$ownership_type) # select the color by hex code and the column to be colorcoded.
Now, we create the map.
leaflet(data=starbucks_belwa) %>% # select the data
addProviderTiles(providers$Stamen.Toner) %>% # we'll use Stamen.Toner to make the map colors 'pop.'
addCircleMarkers(radius = 8, # add the markers
color = ~pal(ownership_type),
fillOpacity = 1,
stroke=FALSE,
label = ~store_name,
popup = paste0("Brand: ", starbucks_belwa$brand, ". ",
"Store Address: ", starbucks_belwa$address)) %>%
# The popup will show the name of the store, either Starbucks or Woods Coffee, and the address.
addLegend(pal = pal, # Add a legend
values = ~ownership_type,
opacity = 0.8,
title = "Store Ownership Type") %>%
addMiniMap(position = "bottomleft") %>% # Add a minimap to show Bellingham in WA state.
addMeasure() %>% # Add a measuring tool
leafem::addMouseCoordinates() # Add coordinates to show up wherever the mouse is, from leafem.