This repository houses a project that seeks to examine whether household groceries can provide a metric for tracking inflation in Canada. It contains all of the files and code necessary for an analysis, and provides a paper summarizing the research and findings.
Note
The research paper associated with this study is available here.
This project requires both the R programming language and Quarto. If you do not have these tools in your development environment, please install them now. You will also need an integrated development environment (IDE) capable of running R scripts. I recommend RStudio (local) or Posit Cloud (cloud-based).
Once your environment is set up, you must install several packages that handle various tasks, like graphing data, creating tables, and general organization and processing. You will find a complete list of these packages in the file scripts/00-install_dependencies.r. You only need to run this file once to install the required dependencies.
The first step in working with this repository is downloading two data sets: one from Statistics Canada, and one from the Bank of Canada:
- The Bank of Canada's inflation data set is available here. Download the data in a CSV format.
- Statistics Canada's data set on the price of retail products is available here. Downloading the correct data from this data set is an involved process, and a step-by-step guide is available here.
Once you download both data sets, place the two CSV files in inputs/data. At the end of this process, you should have two files in the aforementioned folder:
1810024501-eng.csv: Statistics Canada's price of retail products data setINDINF_CPI.csv: The Bank of Canada Inflation Data Set
Before moving to data analysis, we must clean the data to help us filter, format, and make use of the relevant data points. The scripts/01-data_cleaning.r file handles all of the data cleaning, including summarizing the data by year, selecting relevant years, and providing meaningful column names, among other things.
Run the file to fetch the raw data sets, clean them, and then create new .parquet files with the clean data. At the end of this process, you should have two new files in inputs/data:
clean_grocery.parquetclean_inflation.parquet
The core data analysis of this project occurs in the outputs/paper/paper.qmd Quarto document. Once you render paper.qmd, Quarto will generate a paper.pdf file in the same folder. The raw references used in paper.qmd are available under the same folder in the references.bib file.
If you're experiencing problems with the data, I've compiled a document that tests the data against several parameters, like data types, number ranges, etc. This testing document is available under the scripts/03-data_testing.r file. The file contains a number of tests to run on the two .parquet files.
Before running these tests, you must first download and clean the data following the steps outlined above. All of these tests should return true. If they do not, feel free to create an issue.
If you'd like to debug the problem yourself, or if you'd like to use a service like Stack Overflow for help, it's important to have some simulated data to reproduce the problem. The scripts/04-data_simulation.r file generates random, fake data based on the cleaned information initially downloaded from Statistics Canada and the Bank of Canada.
Created by Sebastian Rodriguez, licensed under the BSD 3-Clause License. Contains data from Statistics Canada, licensed under the Statistics Canada Open Licence. Created using R, an open-source statistical programming language.
This project uses a number of R packages, including: arrow, dplyr, ggplot2, here, janitor, kableExtra, knitr, lubridate, readr, RColorBrewer, scales, and tidyverse.
Thank you to Chloe Thierstein, Linrong Li, and Sagith Kalaichelvam for peer reviewing this project and providing invaluable guidance and insight.
Much of this project's development was informed by Rohan Alexander's book Telling Stories with Data.