renault_data <- data.frame(
model = c("Clio", "Megane", "Captur", "Zoe", "Twingo"),
year = c(2020, 2021, 2022, 2020, 2021),
price_euro = c(14500, 22500, 19500, 28500, 12500),
mpg = c(48, 52, 45, NA, 50), # NA for EV
range_km = c(NA, NA, NA, 395, NA),
sales_units = c(187000, 112000, 158000, 43000, 62000),
co2_g_km = c(98, 105, 110, 0, 102),
maintenance_cost_year = c(450, 520, 480, 380, 420)
)
Driving school cars get dented. Mirrors get clipped. Clutches burn out. Renault offers some of the lowest maintenance costs in the industry. A replacement wing mirror for a Clio costs a fraction of a German competitor’s part.
ggplot(renault_data, aes(x = price_euro, y = maintenance_cost_year,
label = model, color = sales_units)) +
geom_point(size = 4) +
geom_text(vjust = -0.5) +
scale_color_gradient(low = "blue", high = "gold") +
labs(title = "Renault: Price vs Annual Maintenance",
x = "Price (€)", y = "Maintenance cost (€/year)") +
theme_bw()
Raw data at Renault does not sit in CSV files; it sits in SQL databases (Oracle, PostgreSQL). The best R learners know how to connect to these. r learning renault best