Keywords: Data Analysis, Methods, Sample Design, R Programming, Open Source

Governments, NGOs, and other research institutes spend billions of dollars each year collecting demographic, economic, and health information about their populations. These efforts form the basis of many official reports, academic journal articles, and public health surveillance systems, each of which motivate public policy or inform the public to varying degrees. Though dependent on the sensitivity of the topic, these sponsoring organizations often publish household-level, person-level, or company-level datasets alongside their final, summary report. This response-level data (commonly known as microdata) allows external researchers both to reproduce the original findings and also to more deeply focus on segments of the population perhaps not discussed in the data products released by the authors of the original investigation. For example, the Census Bureau publishes an annual report, "Income and Poverty in the United States" with a series of tables, and also a database with one record per individual within each sampled households. While the Bureau helpfully provides many different measures of income dispersion in their results, an external researcher might find utility in this dataset by investigating other measures of poverty or inequality (such as the laeken measures to make comparisons between the United States and the European Union), and so the public microdata files allow continued research where it otherwise might end. The website https://convey-r.org/ offers a wide range of poverty, inequality, and richness measures applicable to many publicly-available datasets using the R language. This textbook contains three core components, each with step-by-step instructions: (1) Data preparation of major economic wellbeing surveys from the United States and Brazil; (2) Poverty Indices; (3) Inequality Measurement.