This vignette describes related resources and materials useful for teaching statistics with a focus on modeling and computation.

Package Vignettes

The mosaic package includes a number of vignettes. These are available from within R, from, or from

  • Minimal R describes a minimal set of R commands for use in Introductory Statistics and discusses why it is important to keep the set of commands small;

  • Resampling methods in R demonstrates how to use the mosaic package to compute p-values for randomization tests and bootstrap confidence intervals in a number of common situations. The examples are based on the ``resampling bake off’’ at USCOTS 2011.

  • ggformula/lattice conversion examples compares the lattice and ggformula formula interfaces for creating graphics.

  • Less Volume, More Creativity, based on slides from an ICOTS 2014 workshop, introduces the mosaic package and related tools and describes some of the philosophy behind the design choices made in the mosaic package.

  • Graphics with the mosaic package is gallery of plots made using tools from the mosaic package.

Auxiliary packages

Some features of the mosaic package are provided through auxiliary packages. These include:

  • mosaicModel – implements high-level systems for working with statistical models: effect-size calculation, bootstrapped confidence intervals, prediction error, graphics for models with multiple inputs. The package contains an introductory vignette.
  • mosaicCalc – provides the calculus components of mosaic, including integration, differentiation, and differential equation solving.

Install these packages using install.packages(c("mosaicCalc", "mosaicModel")).

Mosaic paper

Pruim R, Kaplan DT and Horton NJ (2017). The mosaic Package: Helping Students to ‘Think with Data’ Using R. The R Journal, 9(1), pp. 77-102.

Abstract: The mosaic package provides a simplified and systematic introduction to the core functionality related to descriptive statistics, visualization, modeling, and simulation-based inference required in first and second courses in statistics. This introduction to the package describes some of the guiding principles behind the design of the package and provides illustrative examples of several of the most important functions it implements. These can be combined to help students ‘think with data’ using R in their early course work, starting with simple, yet powerful, declarative commands.

Project MOSAIC Little Books

The following longer documents are available at


Other Resources