This final part of the project focuses on tidying a travel pricing dataset. By converting wide-format seasonal data and separating combined fields, it became easier to analyze and visualize price trends across agencies and service types. This final section concludes the full data tidying workflow by emphasizing organization, transformation, and clarity in analysis.

In this continuation of the project, I tidied and transformed a wide-format dataset containing population and GDP data for the USA, China, and India from 2000 to 2010. Converting the dataset into long format made it easier to analyze growth patterns and visualize economic trends across countries and years.

An example of taking untidy data from https://glaad.org/trump-accountability-tracker/ and processing it into a form that makes analysis more accessible

In the first part of this project, I worked with an untidy Travel Expense Report dataset to demonstrate how data can be cleaned, structured, and transformed using R. The analysis compared total and average spending across two cities, San Jose and Seattle, and the final visualization highlighted how data tidying enables clear and reliable insights.

Problem Set #2 for PSYCH 251