How do you show multivariate information in a simple 2D plot? Scatterplots work for two variables, maybe three with color, but what if you want to show even more? The PieGlyph package provides an elegant solution. It lets you place mini pie charts directly on a plot, so each point can display the distribution of several variables at once. This makes it possible to show complex data in a compact and intuitive way. Some highlights of PieGlyph: 🔹 Overlay pie-glyphs on scatterplots, maps, or grids 🔹 Display proportions for multiple variables per observation 🔹 Control colors, sizes, and legends for clear interpretation 🔹 Combine with ggplot2 for polished and publication-ready visuals 🔹 Create both static and interactive graphics for deeper exploration The example visualization below (from the package website) is interactive and shows how PieGlyph enriches a scatterplot with pie-glyphs, turning each data point into a multivariate summary that can be explored dynamically. Package website: cran.r-project.org/web/packa… Want to improve your data visualization skills in R? Check out my course "Data Visualization in R Using ggplot2 & Friends." For more information, visit this link: statisticsglobe.com/online-c… #Statistical #Rpackage #rstudioglobal #VisualAnalytics #DataScientist #RStats #Data

Working with high-dimensional categorical data is often overwhelming. Tables and bar plots quickly become cluttered, making it hard to spot meaningful patterns. The DicePlot package (available in R and Python) offers a clever alternative: it visualizes categories as “dice faces” arranged in a compact grid, so complex relationships become easy to see. It’s especially useful for survey data, biomedical research, or any dataset with many categorical variables. With DicePlot you can: 🔹 Explore large sets of categorical variables in one view 🔹 Detect rare or unusual category combinations 🔹 Communicate complex patterns in a clear, visual way 🔹 Compare multiple datasets side by side 🔹 Produce visuals that are compact, intuitive, and publication-ready The figure below (from the package website) shows biological processes on the y-axis and cell types on the x-axis. Each square is a “dice face” representing categorical combinations, with colors highlighting different functional groups. This compact view makes it much easier to compare categories and uncover hidden structure in the data. GitHub page: github.com/maflot/Diceplot If you’d like more tips and resources on R, Python, statistics, and data science, you might enjoy my newsletter. Take a look here for more details: eepurl.com/gH6myT #datavis #RStats #datasciencetraining #Python #Rpackage