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Kernel smoothers are essential tools for data analysis due to their abilityto convey complex statistical information with concise graphicalvisualisations. Their inclusion in the base distribution and in the manyuser-contributed add-on packages of the R statistical analysis environmentcaters well to many practitioners. Though there remain some important gaps forspecialised data types, most notably for tibbles (tidy data) within thetidyverse, and for simple features (geospatial data) within geospatialanalysis. The proposed eks package fills in these gaps. In addition to kerneldensity estimation, this package also caters for more complex data analysissituations, such as density derivative estimation, density-based classification(supervised learning) and mean shift clustering (unsupervised learning). Weillustrate with experimental data how to obtain and to interpret thestatistical visualisations for these kernel smoothing methods.

Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package