CraftBeer_DataAnalysis

Data Analysis of Craft Beers and Breweries in the US - A Case Study

Purpose

This is a data analysis of craft beers and breweries in the United States, all 50 states plus Washington, DC. The study has three main objectives, to evaluate the number of craft breweries in each state, to provide summary statistics of craft beer metrics, and to investigate statistical relationships between these metrics. We aim to provide useful insight into the craft beer industry of value to executives of Budweiser.

• Analysis team: Willis Jones and Kristin Henderson
• Stakeholders: Budweiser CEO and CFO

Repository structure

Root directory

• README.md: Readme file detailing purpose, contents and usage.
• Analysis_BeersAndBreweries.Rmd: Rmarkdown file with introduction, questions to be addressed, all code to interact with the data and build the plots, written explanations, and conclusion.
• Analysis_BeersAndBreweries.html: HTML file knitted with the above and with embedded data and visualizations.
• Presentation_Final_Analysis.ppt: Powerpoint slides for our final statistical analysis with link to seven-minute YouTube video team presentation.
• Presentation_Initial_EDA.ppt: Powerpoint slides for our initial EDA presentation.

Data directory containing the two data files.

• Beers.csv
• Breweries.csv

Codebook

Data dictionary

• Beers.csv: a dataset of US craft beers with 2410 observations of 7 variables.

  • Name: Name of the beer.
  • Beer_ID: Unique identifier of the beer.
  • ABV: Alcohol by volume of the beer.
  • IBU: International Bitterness Units of the beer.
  • Brewery_ID: Brewery id associated with the beer.
  • Style: Style of the beer.
  • Ounces: Ounces of beer.

• Breweries.csv: a dataset of US breweries with 558 observations of 4 variables.

  • Brew_ID: Unique identifier of the brewery.
  • Name: Name of the brewery.
  • City: City where the brewery is located.
  • State: U.S. State where the brewery is located.

Data transformation

Here are descriptions of the two primary dataframes used in the analysis.

In the code, the two datasets (Beers.csv and Breweries.csv) are imported and merged into a dataframe called bbDF by the unique identifier of the brewery, Brew_ID. bbDF includes all variables and observations from both datasets. Prior to the merge, a variable count was added to the breweries dataframe, to enable finding the sum of breweries per state.

A new dataframe cleanDF was created when imputing and cleaning missing data. A variable medianIBU was created, which held the median IBU value based on each unique style of beer. Missing values in the IBU variable were imputed with medianIBU values. The remaining missing IBU observations were deleted in this dataframe (those from styles with no data from which to compute medians). Missing ABV observations were also deleted in this dataframe. One other variable was added to this dataframe, IBU_Profile, to categorize styles into "IPA", "Ale", or "Other".

Usage

• Clone this repository git clone https://github.com/kdhenderson/CraftBeer_DataAnalysis.git
• The files were created with RStudio version 2023.12.0+369. (Machine: MacBook Pro, OS: macOS Monterey 12.7.1)
• All required libraries are listed and loaded in the Rmarkdown file in one of the first code chunks. If not already installed, run install.packages("package_name") substituting the needed package for package_name, prior to loading the libraries.