Hotel Bookings Data Cleaning

Hotel Bookings Data Cleaning

This project demonstrates how I cleaned a simulated hotel bookings dataset using R within RStudio/Posit Cloud, as part of the Google Data Analytics Capstone course.

Dataset Source

Provided by the Google Data Analytics Professoinal Certificate Program on Coursera.

Note: According to the course scenario, the dataset was compiled from two hotel systems and exported as a .csv file. The data contained inconsistencies such as null values, misnamed columns, and type mismatches. It was intended for data cleaning practice using R and RStudio. This entire course assignment was hosted in Posit cloud, a cloud-based version of RStudio. However, as of July 2025, Posit Cloud deprecated publishing for our projects, and is no longer accessible. I will republish this using the desktop version of RStudio and push this as a repo on GitHub in the future.

Tools & Packages Used

  • 📦 tidyverse – data wrangling and manipulation
  • 📦 skimr – quick data overviews
  • 📦 janitor – cleaning and renaming column names

Key Cleaning Tasks

  • Imported .csv file using read_csv()
  • Previewed structure with head(), str(), glimpse(), and skim_without_charts()
  • Selected and renamed relevant columns for clarity (hotel, lead_time, etc.)
  • Created derived fields (e.g., total guests per booking)
  • Combined year and month columns for date analysis
  • Summarized basic metrics (e.g., number of cancellations, average lead time)

While this project focused primarily on data cleaning and preparation, the cleaned dataset is ready for deeper analysis — such as:

  • Booking seasonality
  • Cancellation trends
  • Hotel occupancy patterns