MSDS 6306 Case Study 01
Group 3
6/26/2018
Brewery Market Study
You’re trying to start a craft brewery chain and it’s important to know what the market for such items would be. We analyze your data on a large number of beers and breweries. Here are some results of some analysis to help build the market analysis for the business plan.
1. How many breweries are present in each state?
breweries <- read.csv("https://raw.githubusercontent.com/tikisen/6306-case-study-1/master/Breweries.csv", quote = "", row.names = NULL, stringsAsFactors = FALSE, header = TRUE)
beers <- read.csv("https://raw.githubusercontent.com/tikisen/6306-case-study-1/master/Beers.csv", quote = "", row.names = NULL, stringsAsFactors = FALSE, header = TRUE)
str(breweries)## 'data.frame': 559 obs. of 4 variables:
## $ Brew_ID: chr "1" "2" "3" "4" ...
## $ Name : chr "NorthGate Brewing " "Against the Grain Brewery" "Jack's Abby Craft Lagers" "Mike Hess Brewing Company" ...
## $ City : chr "Minneapolis" "Louisville" "Framingham" "San Diego" ...
## $ State : chr " MN" " KY" " MA" " CA" ...names(breweries)[2]<-paste("Brewery_Name")
str(beers)## 'data.frame': 2412 obs. of 7 variables:
## $ Name : chr "Pub Beer" "Devil's Cup" "Rise of the Phoenix" "Sinister" ...
## $ Beer_ID : chr "1436" "2265" "2264" "2263" ...
## $ ABV : num 0.05 0.066 0.071 0.09 0.075 0.077 0.045 0.065 0.055 0.086 ...
## $ IBU : num NA NA NA NA NA NA NA NA NA NA ...
## $ Brewery_id: int 409 178 178 178 178 178 178 178 178 178 ...
## $ Style : chr "American Pale Lager" "American Pale Ale (APA)" "American IPA" "American Double / Imperial IPA" ...
## $ Ounces : chr "12" "12" "12" "12" ...names(beers)[1]<-paste("Beer_Name")
require(package)## Loading required package: package## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'package'summary(breweries)## Brew_ID Brewery_Name City
## Length:559 Length:559 Length:559
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## State
## Length:559
## Class :character
## Mode :characterinstall.packages('plyr',repos = "http://cran.us.r-project.org")##
## The downloaded binary packages are in
## /var/folders/3b/4yr_49g17gn2h9_h6n5zyl6w0000gr/T//Rtmph0nsps/downloaded_packageslibrary("plyr")
count(breweries, "State")## State freq
## 1 1
## 2 AK 7
## 3 AL 3
## 4 AR 2
## 5 AZ 11
## 6 CA 39
## 7 CO 47
## 8 CT 8
## 9 DC 1
## 10 DE 2
## 11 FL 15
## 12 GA 7
## 13 HI 4
## 14 IA 5
## 15 ID 5
## 16 IL 18
## 17 IN 22
## 18 KS 3
## 19 KY 4
## 20 LA 5
## 21 MA 23
## 22 MD 7
## 23 ME 9
## 24 MI 32
## 25 MN 12
## 26 MO 9
## 27 MS 2
## 28 MT 9
## 29 NC 19
## 30 ND 1
## 31 NE 5
## 32 NH 2
## 33 NJ 3
## 34 NM 4
## 35 NV 2
## 36 NY 16
## 37 OH 15
## 38 OK 6
## 39 OR 29
## 40 PA 25
## 41 RI 5
## 42 SC 4
## 43 SD 1
## 44 TN 3
## 45 TX 28
## 46 UT 4
## 47 VA 16
## 48 VT 10
## 49 WA 23
## 50 WI 20
## 51 WV 1
## 52 WY 4
## 53 North Woodstock 12. Merge beer data with the breweries data. Print the first 6
observations and the last six observations to check the merged file.
#change brewery ID name to match in both files
names(breweries)[1]<-paste("Brewery_id")
beer_merge <- merge(beers,breweries,by=c("Brewery_id"))
head(beer_merge)## Brewery_id Beer_Name Beer_ID ABV IBU
## 1 1 Get Together 2692 0.045 50
## 2 1 Maggie's Leap 2691 0.049 26
## 3 1 Wall's End 2690 0.048 19
## 4 1 Pumpion 2689 0.060 38
## 5 1 Stronghold 2688 0.060 25
## 6 1 Parapet ESB 2687 0.056 47
## Style Ounces Brewery_Name
## 1 American IPA 16 NorthGate Brewing
## 2 Milk / Sweet Stout 16 NorthGate Brewing
## 3 English Brown Ale 16 NorthGate Brewing
## 4 Pumpkin Ale 16 NorthGate Brewing
## 5 American Porter 16 NorthGate Brewing
## 6 Extra Special / Strong Bitter (ESB) 16 NorthGate Brewing
## City State
## 1 Minneapolis MN
## 2 Minneapolis MN
## 3 Minneapolis MN
## 4 Minneapolis MN
## 5 Minneapolis MN
## 6 Minneapolis MNtail(beer_merge)## Brewery_id Beer_Name Beer_ID ABV IBU
## 2403 556 Pilsner Ukiah 98 0.055 NA
## 2404 557 Heinnieweisse Weissebier 52 0.049 NA
## 2405 557 Snapperhead IPA 51 0.068 NA
## 2406 557 Moo Thunder Stout 50 0.049 NA
## 2407 557 Porkslap Pale Ale 49 0.043 NA
## 2408 558 Urban Wilderness Pale Ale 30 0.049 NA
## Style Ounces Brewery_Name
## 2403 German Pilsener 12 Ukiah Brewing Company
## 2404 Hefeweizen 12 Butternuts Beer and Ale
## 2405 American IPA 12 Butternuts Beer and Ale
## 2406 Milk / Sweet Stout 12 Butternuts Beer and Ale
## 2407 American Pale Ale (APA) 12 Butternuts Beer and Ale
## 2408 English Pale Ale 12 Sleeping Lady Brewing Company
## City State
## 2403 Ukiah CA
## 2404 Garrattsville NY
## 2405 Garrattsville NY
## 2406 Garrattsville NY
## 2407 Garrattsville NY
## 2408 Anchorage AK3. Report the number of NA’s in each column.
sapply(beer_merge, function(x) sum(is.na(x)))## Brewery_id Beer_Name Beer_ID ABV IBU
## 0 0 0 62 1003
## Style Ounces Brewery_Name City State
## 0 0 0 0 04. Compute the median alcohol content and international bitterness unit for each state. Plot a bar chart to compare.
median(beer_merge$ABV, na.rm = TRUE)## [1] 0.056median(beer_merge$IBU, na.rm = TRUE)## [1] 35median_ABV <- aggregate(ABV~State, beer_merge, FUN = median)
barplot(median_ABV$ABV,
space = .25,
xlab = "States",
ylab = "Alcohol by Volume",
main = "Median ABV by State",
names.arg = median_ABV$State,
las=2)
median_IBU <- aggregate(IBU~State, beer_merge, FUN = median)
barplot(median_IBU$IBU,
space = .25,
xlab = "States",
ylab = "Bitterness",
main = "Median IBU by State",
names.arg = median_IBU$State,
las=2)
###5. This code will look in the merged file to determine which state has the maximum alcoholic (ABV) beer and which state has the most bitter (IBU) beer.
beer_merge$State[which.max(beer_merge$ABV)]## [1] " CO"beer_merge$State[which.max(beer_merge$IBU)]## [1] " OR"The state with the beer with the highest alcohol by volume is Colorado. The state with the beer with the most bitter beer is Oregon.
6. Here is some code to produce some summary statistics for the ABV of the beers.
summary(beer_merge$ABV)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00100 0.05000 0.05600 0.05978 0.06700 0.12800 62The lowest ABV is 0.1%, the highest is 12.8%. The average is 5.98% and the middle value is 5.6%. The alcohol content is unreported for 62 beers.
7. Is there an apparent relationship between the bitterness of the
beer and its alcoholic content? Draw a scatter plot.
plot(x=beer_merge$IBU,
y= beer_merge$ABV,
xlab = "Bitterness",
ylab = "Alcohol by Volume",
main = "Bitterness vs ABV")
There doesn’t seem to be a strong relationship between bitterness and alcoholic content. You can see a range of values for alcohol content all along the range of values for bitterness.