Project#2-Data2

Approach

Code Base

library(tidyr)
library(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, union

library(ggplot2)

takeout_spending <- data.frame(
  Generation = c("Gen Z", "Millennials", "Gen X", "Baby Boomers", "Silent Gen"),
  `Jan.2025` = c(185, 240, 195, 120, 75),
  `Feb.2025` = c(172, 228, 188, 115, 70),
  `Mar.2025` = c(198, 255, 205, 130, 82),
  `Apr.2025` = c(210, 270, 215, 140, 88),
  `May.2025` = c(205, 265, 210, 135, 85),
  `Jun.2025` = c(225, 285, 220, 145, 90),
  check.names = FALSE
)

# ── Save as CSV ──────────────────────────────────────────────────
write.csv(takeout_spending, "takeout_spending.csv", row.names = FALSE)

url <- "https://raw.githubusercontent.com/AslamF/DATA607-Project-2/refs/heads/main/takeout_spending.csv"

raw_data <- read.csv(url)

glimpse(raw_data)

Rows: 5
Columns: 7
$ Generation <chr> "Gen Z", "Millennials", "Gen X", "Baby Boomers", "Silent Ge…
$ Jan.2025   <int> 185, 240, 195, 120, 75
$ Feb.2025   <int> 172, 228, 188, 115, 70
$ Mar.2025   <int> 198, 255, 205, 130, 82
$ Apr.2025   <int> 210, 270, 215, 140, 88
$ May.2025   <int> 205, 265, 210, 135, 85
$ Jun.2025   <int> 225, 285, 220, 145, 90

Convert from wide data

tidy_spending <- raw_data |>
  pivot_longer(
    cols      = -Generation,        # pivot everything EXCEPT Generation
    names_to  = "month",            # column names become values in "month"
    values_to = "spending"          # numbers go into "spending"
  )

## Rename and standarize naming 

tidy_spending <- tidy_spending |>
  rename_with(tolower) |>           # lowercase all column names
  mutate(
    month = gsub("\\.", "-", month),          # fix "Jan.2025" → "Jan-2025"
    month = as.Date(paste0("01-", month),     # convert to proper Date
                    format = "%d-%b-%Y")
  )

tidy_spending |>
  print(n = 20)

# A tibble: 30 × 3
   generation   month      spending
   <chr>        <date>        <int>
 1 Gen Z        2025-01-01      185
 2 Gen Z        2025-02-01      172
 3 Gen Z        2025-03-01      198
 4 Gen Z        2025-04-01      210
 5 Gen Z        2025-05-01      205
 6 Gen Z        2025-06-01      225
 7 Millennials  2025-01-01      240
 8 Millennials  2025-02-01      228
 9 Millennials  2025-03-01      255
10 Millennials  2025-04-01      270
11 Millennials  2025-05-01      265
12 Millennials  2025-06-01      285
13 Gen X        2025-01-01      195
14 Gen X        2025-02-01      188
15 Gen X        2025-03-01      205
16 Gen X        2025-04-01      215
17 Gen X        2025-05-01      210
18 Gen X        2025-06-01      220
19 Baby Boomers 2025-01-01      120
20 Baby Boomers 2025-02-01      115
# ℹ 10 more rows

ggplot(tidy_spending, aes(x = month, y = spending, color = generation, group = generation)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1) +
  labs(
    title  = "Monthly Takeout Spending Trends by Generation (2025)",
    x      = "Month",
    y      = "Average Spending ($)",
    color  = "Generation"
  ) +
  theme_minimal()

Conclusion

Millennials consistently led takeout spending in early 2025 with a peak of $285.00. Every generation had an upward trend although some is less noticable such as silent g and baby boomers. Younger generations spend more on takeout than older generations however all groups have an increasing trend to spend on takeout.