Data analysis requires multiple iterations of data cleaning and wrangling, visualizations, and fitting models, but the statistical results reported are usually just one of the many possible analyses. A multiverse analysis (Seegan, Tuerlinckx, Gelman, Vanpaemel, 2016) aims to increase transparency by performing multiple analyses based on a set of reasonable data processing procedures for a given research question and report on whether the outcomes are sensible or robust across analysis flows. But defining and navigating a multiverse is complex. We created mverse to help students and analysts to more easily create, explore, and critically examine multiverse analyses in teaching and practice. We will describe how instructors can use mverse to demonstrate multiverse analyses and how students familiar with only basic R tidyverse syntax can conduct multiverse analyses and explore the implications of the decisions made in analyses through scaffolded use of mverse.