Marketing Analytics Project - Spring, 2017
Rafael Guimaraes, Brian Kangogo, and Nicholas Matthew
Date April 27 2017
About Starbucks Corporation
Founded in Seattle, WA in 1971, Starbucks Corporation is the world’s largest coffeehouse chain. With 23,768 locations worldwide, 13,107 of which located in the United States, Starbucks brands itself as a “second wave” coffee house that offers superior quality and service.
According to our research, 83% of Starbucks locations cater to white communiities. The two hypothesis we’d like to test are:
- BLAH
- NAH
Interesting Discoverys in the Dataset
Maps of Starbucks Locations Around the World
Starbucks on all seven continents.
- Comment about map above
Starbucks in the United States.
- Comment on map above.
Starbucks in Massachusetts.
- Comment on map above.
Interactive Maps and Graphs.
- Comment on map above.
Top 10 Countries with Starbucks
## # A tibble: 10 × 3
## Country Total Percentage
## <chr> <int> <dbl>
## 1 US 13608 53.16
## 2 CN 2734 10.68
## 3 CA 1468 5.73
## 4 JP 1237 4.83
## 5 KR 993 3.88
## 6 GB 901 3.52
## 7 MX 579 2.26
## 8 TW 394 1.54
## 9 TR 326 1.27
## 10 PH 298 1.16- Comment
Top 10 States with Starbucks
## # A tibble: 10 × 3
## state Total Percentage
## <chr> <int> <dbl>
## 1 CA 2821 11.02
## 2 TX 1042 4.07
## 3 ENG 787 3.07
## 4 WA 757 2.96
## 5 11 706 2.76
## 6 FL 694 2.71
## 7 NY 645 2.52
## 8 31 580 2.27
## 9 IL 575 2.25
## 10 AZ 536 2.09- Comment
Starbucks Ownership Type
## # A tibble: 4 × 3
## `Ownership Type` Total Percentage
## <chr> <int> <dbl>
## 1 Company Owned 11932 46.61
## 2 Licensed 9375 36.62
## 3 Joint Venture 3976 15.53
## 4 Franchise 317 1.24- Comment
Heat Maps … Not functional yet
```*** NEEDS FIXING**
income <- read_csv(“https://raw.githubusercontent.com/IBSBigData/BUS256TeamE/master/income.csv?token=AY4rQleHlRuBRgTlKwfbWh3n9P3mtvYjks5ZCMJ-wA%3D%3D”)
income <- rename(income, c(postalcode=“state”))
map <- US[,c(6:7,12:13)]
map <- aggregate(map\(City ~ map\)state + map\(Longitude + map\)Latitude, FUN = sum)
income\(region <- income\)state
new <- merge(x =map, y = income, by = “state”, all.x = TRUE)
new <- na.omit(new)
new\(household_income <- as.numeric(new\)household_income)
ag <- aggregate( map\(state ~ income\)household_income + map\(Longitude + map\)Latitude, FUN = mean)
ag <- rename(ag, c(new$state =“state”, new$household_income=“household_incomee”, new$Latitude=“Latitude”,new$Longitude=“Longitude”))
qplot(data=ag, Longitude, Latitude, group=state , fill= household_incomee, geom=“polygon”) ############ Map <- gvisGeoChart(ag, “state”, “household_incomee”, #select the state and number of stores options=list(region=“US”, # Map from the US displayMode=“region”, colorAxis=“{colors:[‘green’]}”, backgroundColor=“lightblue”, chartid=“EQ”, resolution=“provinces”,# Displays the regions width=1200, height=900)) # Controls the size plot(Map) #############
states <- map_data(“state”)
states <- rename(states, c(region=“state”))
map.df <- merge(states,ag, by=“state”, all.y=T)
map.df <- na.omit
map.df <- map.df[order(map.df$order),]
ggplot(map.df, aes(x=Longitude,y=Latitude,group=state))+ geom_polygon(aes(fill=state))+ geom_path()+ scale_fill_gradientn(colours=rev(heat.colors(10)),na.value=“grey90”)+ coord_map()
AndrewMap <- gvisMap(US, “LatLong” , “Tip”, options=list(showTip=TRUE, showLine=TRUE, enableScrollWheel=TRUE, mapType=‘terrain’, useMapTypeControl=TRUE)) plot(AndrewMap)
```
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