ggEDA is implemented as a standard R package and published on CRAN and the R-universe. The interactiveEDA web app was written using the shiny framework ⁶ and takes ggEDA as a dependency to separate the user-interface codebase from the underlying business logic, which is easier to test. interactiveEDA is compiled into a purely client-side web-assembly app using shinylive ⁷ and hosted as a static web-page on GitHub Pages. Code to produce visualisations is run in the client’s browser instead of a third party server outside the direct control of end-users. The distributed nature of compute also provides scaling benefits compared to traditional server-side shiny apps that quickly slow as concurrent users grow. These security and scalability benefits do come at the cost of slower application startup time.

The ggEDA R package can be installed from CRAN ( install.packages( “ggEDA”)). It is compatible with Mac OS X, Windows, and all Unix-like operating systems where R (≥3.5.0) can be installed. Package dependencies are described on the ggEDA CRAN listing .

Data can be explored using the interactiveEDA web app in any modern browser that supports WebAssembly. We performed the most extensive testing in Chrome (version 137.0.7151.120), however the app is also compatible with Firefox, Safari and Microsoft Edge.

PCPs are a well-established EDA visualisation that reveal trends in predominantly quantitative datasets and detect outliers in one or more dimensions. Quantitative features are represented as a series of parallel axes with samples visualised as lines passing through each axis at the point of its value. Correlative relationships are revealed when feature axes are ordered based on line crossing minimisation algorithms or mutual information with a categorical feature. ggEDA can produce interactive PCPs from any dataset with quantitative features using the ggparallel(data) command ( Figure 2).

PCPs scale well with large datasets but have several limitations. Visualising the relationships between multiple categorical variables is challenging. Missing data is also difficult to meaningfully represent. For this reason, ggEDA introduces a complementary visualisation composed of vertically aligned tile and bar plots.

For small datasets (n < 1000), ggEDA can represent features as distinct, vertically aligned bar or tile plots, with plot types auto-selected based on whether variables are categorical or numeric ( Figure 3).

To demonstrate the advantages of ggEDA, we created the artificial Lazy Birdwatcher dataset. It describes magpie observations by two birdwatchers, one of whom routinely skips birdwatching on weekends. This introduces a missing data pattern dependent on both the birdwatcher and day of the week. The multidimensional pattern becomes immediately apparent from ggEDA stacked tile plots despite being difficult to detect using one-dimensional EDA tools like skimr, or two-dimensional tools like ggpairs from the GGally package ( Figure 4). Despite being n-dimensional, all PCP plot implementations in R also fail to uncover this trend due to either exclusion of missing data or inability to represent clearly the relationships between categorical features.

Despite the advancements provided by ggEDA and other tools in the R ecosystem, a key limitation remains: accessibility for non-programmers, particularly when visualising n-dimensional data. All existing R implementations lack graphical user interfaces ( Figure 1). While shiny web apps offer a potential solution, they often require uploading datasets to external servers, raising privacy concerns. To address these limitations, we developed interactiveEDA, a web-assembly compiled client-side web app for secure, interactive data exploration ( Figure 5). Operating entirely in the browser, interactiveEDA ensures data remains on the user’s machine, increasing ease of use without compromising data privacy. interactiveEDA is available at https://github.com/CCICB/interactiveEDA.