Retail Store Location Analysis with R (draft)
your name, taught by Dr. Zhenning ‘Jimmy’ Xu, follow him on Twitter: https://twitter.com/MKTJimmyxu or find him on his website at utjimmyx.github.io
2024-12-03
Selecting the ideal location for a retail store is a critical factor in ensuring its success. Location analysis involves evaluating multiple aspects such as demographic trends, population density, competitor presence, and ease of accessibility. This helps businesses identify the most strategic sites for maximizing customer reach and profitability. In this tutorial, we will explore how to conduct retail store location analysis using the R. By combining real-world data with analytical techniques, this guide offers a practical approach to making data-driven decisions in retail planning. The example is based on resources from Geo-computation with R and a few other technical articles.
This tutorial aims to:
Highlight the significance of location analysis for retail businesses.
Showcase how to utilize R for analyzing and visualizing potential store locations.
Provide insights and recommendations for strategic retail decision-making.
If you never used R before, please check out this introduction here: https://www.geeksforgeeks.org/r-programming-language-introduction/ .
In this tutorial, we evaluate retail store locations using real-world city data. The analysis includes median household income, population, and competitor count.
Step 1: Set Up the Environment
First, load the required libraries.
## install.packages(c("ggplot2", "dplyr"))
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionStep 2: Create the Dataset
The dataset includes data from authoritative sources like the Census Bureau and World Population Review.
retail_data <- data.frame(
City = c("San Francisco", "San Jose", "Charlotte", "Austin", "Seattle"),
Median_Income = c(124000, 116000, 63500, 81000, 97500),
Population = c(873965, 1013616, 879709, 1028225, 737015),
Competitor_Count = c(15, 12, 18, 16, 14) # Hypothetical values
)
# View the dataset
print(retail_data)## City Median_Income Population Competitor_Count
## 1 San Francisco 124000 873965 15
## 2 San Jose 116000 1013616 12
## 3 Charlotte 63500 879709 18
## 4 Austin 81000 1028225 16
## 5 Seattle 97500 737015 14Step 3: Data Analysis
We now calculate a score for each city based on income, population, and competition.
retail_data <- retail_data %>%
mutate(
Score = Median_Income / Competitor_Count * log(Population)
) %>%
arrange(desc(Score)) # Sort by score
print(retail_data)## City Median_Income Population Competitor_Count Score
## 1 San Jose 116000 1013616 12 133680.67
## 2 San Francisco 124000 873965 15 113094.58
## 3 Seattle 97500 737015 14 94090.03
## 4 Austin 81000 1028225 16 70081.93
## 5 Charlotte 63500 879709 18 48285.92Step 4: Visualization
Median Income vs. Competitor Count
ggplot(retail_data, aes(x = reorder(City, -Median_Income), y = Median_Income, fill = Competitor_Count)) +
geom_bar(stat = "identity") +
labs(title = "Median Income vs Competitor Count by City",
x = "City",
y = "Median Income",
fill = "Competitor Count") +
theme_minimal()
Let’s explore the relationship between Location Score and Median Income
ggplot(retail_data, aes(x = Median_Income, y = Score, color = City)) +
geom_point(size = 4) +
labs(title = "Location Score vs Median Income",
x = "Median Income",
y = "Score") +
theme_minimal()
Step 5: Save the Results
Export the analyzed data to a CSV file for further use.
write.csv(retail_data, "real_retail_analysis_results.csv", row.names = FALSE)