L9
Harman Rai
2026-03-25
While doing this Lab I was curious to see if AI is able to create a full R mardown file completely by itslef. I honestly thought that it would take a few attempts for AI to make a functioning file. After uploading the file and answering some question on what I exactly wanted from the r markdown file it, claude was able to generate a RMD file. It wanted me to use the RMD file to create the R markdown file but I did not want to use that method. I asked claude to write a singular script that i can upload into R. It generated the text for me to simply copy and paste into R for the final R markdown file.
After doing this Lab I found it nice that AI is able to assist me in creating R markdown files. Now knowing this I will probably use AI in the future whenver I want to create my own website using R beccause of how simple it is now with the help of claude. I noticed that my groupmate was expericing some troubles with other AI platforms as they were generating scripts that were missing crucial bits of code that would not allow the file to knit properly. After sometime he was able to fix this by using gemini AI instead of chatgpt, this showed me how much better cladue was at creating R markdown files so I will continue to use them for any R related questions.
Overview
This report analyzes survey responses from grocery store customers. Each row represents one customer who rated various aspects of their shopping experience on a 1–5 Likert scale, alongside demographic variables (Gender, Age, Education).
The analysis covers four areas:
- Spending behavior & demographics — who the customers are
- Product category & service performance — what they rate highly or poorly
- Sales / engagement trends — patterns across demographic groups
- Customer segmentation (clustering) — natural groupings in the data
Data Preparation
## Rows: 22 | Columns: 18## Missing values: 0| Staff_Helpful | Recommend | Come_Again | All_Products | Professionalism | Limitation | Online_Grocery | Delivery | Pickup | Find_Items | Other_Shops | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.0 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | |
| 1st Qu.:1.000 | 1st Qu.:1.000 | 1st Qu.:1.000 | 1st Qu.:1.250 | 1st Qu.:1.000 | 1st Qu.:1.0 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:1.000 | 1st Qu.:1.250 | |
| Median :1.000 | Median :1.000 | Median :1.000 | Median :2.000 | Median :1.000 | Median :1.0 | Median :2.000 | Median :3.000 | Median :2.000 | Median :1.000 | Median :2.000 | |
| Mean :1.591 | Mean :1.318 | Mean :1.455 | Mean :2.091 | Mean :1.409 | Mean :1.5 | Mean :2.273 | Mean :2.409 | Mean :2.455 | Mean :1.455 | Mean :2.591 | |
| 3rd Qu.:2.000 | 3rd Qu.:1.000 | 3rd Qu.:2.000 | 3rd Qu.:2.000 | 3rd Qu.:2.000 | 3rd Qu.:2.0 | 3rd Qu.:3.000 | 3rd Qu.:3.000 | 3rd Qu.:3.000 | 3rd Qu.:2.000 | 3rd Qu.:3.750 | |
| Max. :3.000 | Max. :3.000 | Max. :3.000 | Max. :5.000 | Max. :3.000 | Max. :4.0 | Max. :3.000 | Max. :3.000 | Max. :5.000 | Max. :3.000 | Max. :5.000 |
Spending Behavior & Demographics
Gender Distribution
Age Distribution
Education Level
Mean Ratings by Gender
Product Category & Service Performance
Overall Mean Ratings
Rating Distributions
Correlation Heatmap
Sales & Engagement Trends
Mean Ratings by Age Group
Delivery vs. Pickup by Age
Online Grocery Adoption by Education
Customer Segmentation (Clustering)
Elbow Method — Optimal Cluster Count
K-Means Clustering (k = 3)
Cluster Profiles
Cluster Heatmap
Demographics by Cluster
Key Takeaways
| Segment | Customers | Avg Rating | Top Category | Weakest Category |
|---|---|---|---|---|
| Segment 1 | 10 | 1.60 | Other_Shops | Recommend |
| Segment 2 | 4 | 2.32 | All_Products | Pickup |
| Segment 3 | 8 | 1.98 | Pickup | Come_Again |
Interpretation guidance: - Segments with higher average ratings indicate loyal, satisfied shoppers. - Low scores in Delivery or Online_Grocery highlight digital service gaps. - Demographic breakdowns per segment can guide targeted marketing campaigns.
Report generated with R Markdown · 2026-03-26 ’
writeLines(rmd_content, “grocery_analysis.Rmd”) message(“Rmd file written. Knitting now…”)
── Knit to HTML ─────────────────────────────────────────────
rmarkdown::render( input = “grocery_analysis.Rmd”, output_file = “grocery_analysis.html” )
message(“Done! Report saved as grocery_analysis.html”) ```