set.seed(123)
employees <- data.frame(
  employee_id = sprintf("%06d", 1:500),
  tenure_years = runif(500, 0, 10),
  satisfaction = pmax(pmin(rnorm(500, 3.5, 1), 5), 1),
  performance = sample(1:5, 500, replace = TRUE),
  department = sample(c("Engineering", "Sales", "HR", "Finance"), 500, replace = TRUE)
)

ggplot(employees, aes(department, satisfaction, fill = department)) +
  geom_boxplot(alpha = 0.8) +
  theme_brand("block") +
  labs(title = "Employee Satisfaction by Department", x = NULL, y = "Satisfaction (1-5)")
#> Loaded brand configuration from: /private/var/folders/_4/5zbk3mcx73qfmmscm52hpd2w0000gn/T/RtmpDR7Wic/Rinst7db41cb0075e/gooseR/brands/block/block_brand.yaml

set.seed(456)
sales <- data.frame(
  date = seq(as.Date("2024-01-01"), as.Date("2024-12-31"), by = "day"),
  revenue = pmax(rnorm(366, 10000, 2000), 0),
  transactions = rpois(366, 50),
  region = sample(c("North", "South", "East", "West"), 366, replace = TRUE)
)

# Simple time series plot
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
sales_daily <- sales %>% group_by(date) %>% summarise(revenue = sum(revenue))

ggplot(sales_daily, aes(date, revenue)) +
  geom_line(color = "#0055FF", linewidth = 1) +
  theme_brand("block") +
  labs(title = "Daily Revenue", x = NULL, y = "Revenue")
#> Loaded brand configuration from: /private/var/folders/_4/5zbk3mcx73qfmmscm52hpd2w0000gn/T/RtmpDR7Wic/Rinst7db41cb0075e/gooseR/brands/block/block_brand.yaml

slow_mean <- function(x) {
  s <- 0
  for (i in seq_along(x)) s <- s + x[i]
  s / length(x)
}
fast_mean <- function(x) mean(x)

# Example: generate test code (requires configured Goose CLI)
# tests <- goose_generate_tests(slow_mean)
# cat(tests)