Cytometry continues to be the preferred technique employed in clinical and research settings to classify, characterise, and quantify heterogeneous suspensions of cells. Recent technological advancements in cytometry have resulted in an unprecedented increase in the size and dimensionality of cytometry data sets. Unfortunately, this rapid increase data complexity has not been adequately met with innovations in commercially available software platforms to efficiently analyse these immense data sets. Consequently, many cytometry users have resorted to developing their own computational tools, in the form of R packages, to provide the additional tools required to analyse their data. Despite the quality of these packages, adoption of these tools within the broader cytometry community is limited, due to a lack of prerequisite coding knowledge, interactivity, and coherence between packages required for end-to-end analysis. Accordingly, there is exists an urgent need to develop a robust unified framework for cytometry data analysis, that is intuitive, interactive, efficient, extensible, and freely accessible. At ACS 2021, I am excited to announce the official release of CytoExploreR, the next generation of open-source software for cytometry data analysis. During this condensed presentation, we will endeavour to explore the plethora of computational tools implemented within the new CytoExploreR framework for end-to-end cytometry data analysis.