Lab 9: Customer Segmentation Analysis (Grocery Dataset)
Nathaniel Maritnez
2026-03-25
Reflection
This lab helped me understand how AI can be used as a support tool when writing code and analyzing data. My first prompt gave me a general R Markdown template, but it was not very specific to the dataset I was working with. After improving my prompt, I was able to generate a much more useful and structured analysis that included reflection, visualizations, and even clustering. This showed me that better prompts lead to better AI outputs, and that I need to be clear and detailed when asking for help.
One challenge I faced was that the AI did not initially know the structure of my dataset, so I had to adjust the code to match the actual variables. I also noticed that AI-generated code is not always perfect and needs to be checked carefully. However, there were many opportunities as well. AI helped me organize my work, think of useful analyses, and save time writing code. Overall, this lab showed me that AI is a helpful assistant, but I still need to understand the data and make decisions myself.
library(tidyverse)
library(janitor)
library(corrplot)
customers <- read.csv("customer_segmentation.csv") %>%
clean_names()
head(customers)## id cs_helpful recommend come_again all_products profesionalism limitation
## 1 1 2 2 2 2 2 2
## 2 2 1 2 1 1 1 1
## 3 3 2 1 1 1 1 2
## 4 4 3 3 2 4 1 2
## 5 5 2 1 3 5 2 1
## 6 6 1 1 3 2 1 1
## online_grocery delivery pick_up find_items other_shops gender age education
## 1 2 3 4 1 2 1 2 2
## 2 2 3 3 1 2 1 2 2
## 3 3 3 2 1 3 1 2 2
## 4 3 3 2 2 2 1 3 5
## 5 2 3 1 2 3 2 4 2
## 6 1 2 1 1 4 1 2 5dim(customers)## [1] 22 15names(customers)## [1] "id" "cs_helpful" "recommend" "come_again"
## [5] "all_products" "profesionalism" "limitation" "online_grocery"
## [9] "delivery" "pick_up" "find_items" "other_shops"
## [13] "gender" "age" "education"str(customers)## 'data.frame': 22 obs. of 15 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ cs_helpful : int 2 1 2 3 2 1 2 1 1 1 ...
## $ recommend : int 2 2 1 3 1 1 1 1 1 1 ...
## $ come_again : int 2 1 1 2 3 3 1 1 1 1 ...
## $ all_products : int 2 1 1 4 5 2 2 2 2 1 ...
## $ profesionalism: int 2 1 1 1 2 1 2 1 2 1 ...
## $ limitation : int 2 1 2 2 1 1 1 2 1 1 ...
## $ online_grocery: int 2 2 3 3 2 1 2 1 2 3 ...
## $ delivery : int 3 3 3 3 3 2 2 1 1 2 ...
## $ pick_up : int 4 3 2 2 1 1 2 2 3 2 ...
## $ find_items : int 1 1 1 2 2 1 1 2 1 1 ...
## $ other_shops : int 2 2 3 2 3 4 1 4 1 1 ...
## $ gender : int 1 1 1 1 2 1 1 1 2 2 ...
## $ age : int 2 2 2 3 4 2 2 2 2 2 ...
## $ education : int 2 2 2 5 2 5 3 2 1 2 ...summary(customers)## id cs_helpful recommend come_again
## Min. : 1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 6.25 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :11.50 Median :1.000 Median :1.000 Median :1.000
## Mean :11.50 Mean :1.591 Mean :1.318 Mean :1.455
## 3rd Qu.:16.75 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :22.00 Max. :3.000 Max. :3.000 Max. :3.000
## all_products profesionalism limitation online_grocery delivery
## Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
## 1st Qu.:1.250 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :1.0 Median :2.000 Median :3.000
## Mean :2.091 Mean :1.409 Mean :1.5 Mean :2.273 Mean :2.409
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :3.000 Max. :4.0 Max. :3.000 Max. :3.000
## pick_up find_items other_shops gender
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.250 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2.000 Median :1.000
## Mean :2.455 Mean :1.455 Mean :2.591 Mean :1.273
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.750 3rd Qu.:1.750
## Max. :5.000 Max. :3.000 Max. :5.000 Max. :2.000
## age education
## Min. :2.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.500
## Mean :2.455 Mean :3.182
## 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :4.000 Max. :5.000satisfaction_vars <- customers %>%
select(cs_helpful, recommend, come_again)
summary(satisfaction_vars)## cs_helpful recommend come_again
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000
## Mean :1.591 Mean :1.318 Mean :1.455
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :3.000 Max. :3.000 Max. :3.000satisfaction_long <- satisfaction_vars %>%
pivot_longer(cols = everything(), names_to = "variable", values_to = "value")
ggplot(satisfaction_long, aes(x = value)) +
geom_bar() +
facet_wrap(~variable) +
labs(title = "Customer Satisfaction Ratings (1–3 Scale)")
service_vars <- customers %>%
select(online_grocery, delivery, pick_up, find_items)
service_long <- service_vars %>%
pivot_longer(cols = everything(), names_to = "variable", values_to = "value")
ggplot(service_long, aes(x = value)) +
geom_bar() +
facet_wrap(~variable) +
labs(title = "Service Usage Ratings")
ggplot(customers, aes(x = factor(gender))) +
geom_bar() +
labs(title = "Gender Distribution")
ggplot(customers, aes(x = factor(age))) +
geom_bar() +
labs(title = "Age Distribution")
ggplot(customers, aes(x = factor(education))) +
geom_bar() +
labs(title = "Education Levels")
numeric_data <- customers %>% select(where(is.numeric))
cor_matrix <- cor(numeric_data)
corrplot(cor_matrix, method = "color", tl.cex = 0.7)
set.seed(123)
scaled_data <- scale(numeric_data)
kmeans_result <- kmeans(scaled_data, centers = 3)
customers$cluster <- factor(kmeans_result$cluster)
table(customers$cluster)##
## 1 2 3
## 10 4 8ggplot(customers, aes(x = cs_helpful, y = recommend, color = cluster)) +
geom_point(size = 3) +
labs(title = "Customer Segments")