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Abstract As genome-wide data has become increasingly available, software libraries for their analysis have proliferated. While new tools for downstream analyses are constantly emerging, existing workflows are hindered by inefficiencies. Switching between coding languages and object types in the early stages of pipelines wastes researchers’ time, impedes reproducibility, and creates opportunity for error. To confront these obstacles, we introduce tidypopgen , a comprehensive R package for population genetic analysis of biallelic SNP data. Genotype data can be read, filtered, and analysed within a single environment, without the need for prior data cleaning or setup with other software. tidypopgen ’s gen_tibble object structure makes analysis efficient and intuitive, while standardised tidy grammar makes data manipulation clear. Functionality within tidypopgen supports cleaning and merging datasets, basic descriptive statistics, multivariate analysis, clustering algorithms, and F-statistics, as well as integrating with existing tools for population genetic analyses in R. We use the Human Genome Diversity Project SNP dataset (Li et al., 2008) to show that a basic population genetic workflow can be executed in under 25 lines of code in a single environment using one file set, without the need to write superfluous outputs or change directories. By supporting data assembly through to data analysis, tidypopgen significantly streamlines workflows without compromising speed or functionality.