Buenos Aires Real Estate Analysis

Introduction

This presentation explores real estate listings in Buenos Aires using interactive visualizations.

Dataset: Buenos Aires Real Estate (Kaggle)
Created on: 2026-04-25

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Load Libraries

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)

suppressPackageStartupMessages({
  library(plotly)
  library(dplyr)
  library(readr)
})

## Warning: package 'plotly' was built under R version 4.5.3

## Warning: package 'ggplot2' was built under R version 4.5.2

## Warning: package 'dplyr' was built under R version 4.5.3

## Warning: package 'readr' was built under R version 4.5.3

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Load Data

data <- read_csv("buenos-aires-real-estate.csv")

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Data Preparation

data_clean <- data %>%
  filter(!is.na(price), !is.na(property_type))

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Interactive Plot: Price Distribution

plot_ly(
  data = data_clean,
  x = ~price,
  type = "histogram",
  nbinsx = 50
) %>%
  layout(
    title = "Distribution of Property Prices",
    xaxis = list(title = "Price"),
    yaxis = list(title = "Count")
  )

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Interactive Plot: Price by Property Type

plot_ly(
  data = data_clean,
  x = ~property_type,
  y = ~price,
  type = "box",
  color = ~property_type
) %>%
  layout(
    title = "Price by Property Type",
    xaxis = list(title = "Property Type"),
    yaxis = list(title = "Price")
  )

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Insights

• Property prices vary significantly across types
• Some property types show high price variability

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Conclusion

This presentation demonstrated how to use Plotly in R Markdown to create interactive visualizations for real estate data.

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