R Exploratory Analysis of Urban Housing Markets

Project setup

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library(tidyverse)
library(lubridate)
library(scales)

housing <- read_csv("data/housing_portugal.csv")
glimpse(housing)

Key checks before modeling:

• Inspect missing values with skimr::skim.
• Validate coordinate reference systems if spatial joins are required.
• Record assumptions in an analysis log (see /docs/analysis-playbook.md).

Feature engineering

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housing <- housing %>%
  mutate(
    price_per_m2 = price_eur / floor_area_m2,
    listing_month = floor_date(listing_date, "month"),
    energy_rating = fct_explicit_na(energy_rating, "Unknown"),
    is_new_build = if_else(construction_year > 2018, TRUE, FALSE)
  )

Visualizing distributions

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ggplot(housing, aes(price_per_m2, fill = property_type)) +
  geom_histogram(binwidth = 250) +
  scale_x_continuous(labels = label_dollar(prefix = "€")) +
  facet_wrap(~property_type) +
  labs(
    title = "Distribution of price per square meter",
    subtitle = "Segmented by property type",
    x = "Price per m²",
    y = "Count"
  ) +
  theme_minimal(base_size = 14)

Communicating findings

• Central Lisbon apartments average €5,100/m² with a right-skewed tail.
• New builds exhibit a 15% premium relative to comparable resale properties.
• Energy ratings remain missing for 32% of listings—prioritize data enrichment.

Reproducibility checklist

1. Render the R Markdown document with targets::tar_make() to guarantee order.
2. Publish companion notebooks via Netlify or GitHub Pages with quarto publish.
3. Pin package versions using renv::snapshot() and commit the lockfile.

Download the analysis repository or launch the interactive Observable notebook to explore alternative visual encodings.