“Other foods” which are items like condiments, baking ingredients, spices and herbs, and specialty foods is the category that has experienced the highest percentage increase in price over the past year. We can see this in the peak that all categories have around 2022, where the “Other Foods” represented by the purple line reaches the highest point compared to other categories at 12.7. Throughout the years leading up to 2020, the trend for Other foods aswell as all the other categories experience significant fluctuations, with an increase in growth that sharply rises in the last year. We recommend that FreshFare uses these analytical tools for continuous monitering of grocery categories. This will allow FreshFare to analyze how inflation impacts different grocery categories over time and identify the categories with the highest sensitivity for price changes.
Source: USDA
| Category | Correlation to Inflation |
|---|---|
| Meats | 0.38 |
| Dairy Products | 0.69 |
| Fruits and Vegetables | 0.56 |
These plots illustrate data FreshFare will use to track the prices of specific categories. As seen, they all behave differently. The most obvious being Egg’s correlation to inflation. With eggs having a very weak correltion to inflation, Freshfare can research and analyze why the price of eggs has very little relation to the inflation rate. Similiarly, meat has a weak correlation, 0.38, compared to inflation which signals that Meat should be researched into why the price fluctuates. Dairy products appear to follow the inflation rate more closely with a correlation of 0.69. FreshFare can use these plots and tables to pinpoint categories that need further research or categories that generally follow inflation. FreshFare can also use this table to track correlation change of a category to see if it is inflation related.
These visualizations highlight the significant regional differences in grocery costs across the United States, with a focus on the Northeast and Midwest regions. The map shows that the Northeast region, where FreshFare operates, has some of the highest average monthly grocery costs per person, as reflected in states like New York and Massachusetts. This underscores an opportunity for FreshFare to position itself as a premium brand by emphasizing quality and locally sourced products that align with customer expectations in high-cost areas. Meanwhile, the bar chart provides a detailed comparison of average grocery costs in key states, showing that the Northeast generally surpasses the Midwest in monthly costs. This data can guide FreshFare in tailoring its pricing, promotions, and inventory strategies to meet the economic realities of its primary market while also exploring potential expansion opportunities into lower-cost regions, where competitive pricing could be leveraged to attract cost-sensitive consumers. Source: World Population Review
In the following scatter plots, you can explore the relationship between the monthly inflation rate and the grocery store sales. Scatter plots are ideal for visualizing this data, as they help to show the correlation and strength of the relationship of the two variables, and they help to identify any specific patterns or outliers. Based on the graphs below, we are able to see that the CPI steadily increased from 2020 to 2024, showing that overall prices kept rising over time. On the other hand, the inflation rate went up and peaked around 2022, then started to drop, meaning prices were rising quickly at first but then slowed down. While the CPI shows the overall trend of rising prices, the inflation rate highlights how quickly or slowly those prices are changing in the short term. Together, they provide a clearer understanding of both long-term price trends and short-term price movements.
This memo and dashboard were created using Quarto version 1.5.57 in RStudio version 2024.09.1+394, Canva, and the R Language and Environment version 4.4.2.
The data used to create the dashboard were downloaded from:
USDA: https://www.ers.usda.gov
World Population Review: https://worldpopulationreview.com
Statista: https://www.statista.com
FRED: https://fred.stlouisfed.org/
Macrotrends: https://www.macrotrends.net
Allaire, J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2024). Quarto (Version 1.5.57). doi.org/10.5281/zenodo.5960048 https://doi.org/10.5281/zenodo.5960048
Posit team (2024). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.
R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software,4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
OpenAI. ChatGPT (December 5 version). 2024. OpenAI, https://openai.com/chatgpt
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
Bache S, Wickham H (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3, https://CRAN.R-project.org/package=magrittr.
Dancho M, Vaughan D (2024). tidyquant: Tidy Quantitative Financial Analysis. R package version 1.0.9, https://CRAN.R-project.org/package=tidyquant.
Arnold J (2024). ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 5.1.0, https://CRAN.R-project.org/package=ggthemes.
Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.
Kunst J (2022). highcharter: A Wrapper for the ‘Highcharts’ Library. R package version 0.9.4, https://CRAN.R-project.org/package=highcharter.
Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, https://CRAN.R-project.org/package=kableExtra.
Vanderkam D, Allaire J, Owen J, Gromer D, Thieurmel B (2018). dygraphs: Interface to ‘Dygraphs’ Interactive Time Series Charting Library. R package version 1.1.1.6, https://CRAN.R-project.org/package=dygraphs.
Auguie B (2017). gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.3, https://CRAN.R-project.org/package=gridExtra.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://CRAN.R-project.org/package=dplyr.
Becker OScbRA, Minka ARWRvbRBEbTP, Deckmyn. A (2024). maps: Draw Geographical Maps. R package version 3.4.2.1, https://CRAN.R-project.org/package=maps.
Wickham H, Bryan J (2023). readxl: Read Excel Files. R package version 1.4.3, https://CRAN.R-project.org/package=readxl.
C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
Garrett Grolemund, Hadley Wickham (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1-25. URL https://www.jstatsoft.org/v40/i03/.