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

The `mosaic`

package includes a number of vignettes. These are available from within R or from cran.r-project.org/package=mosaic.

*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 2013.*ggformula/lattice conversion examples*compares the lattice and ggformula formula interfaces for creating graphics.

The following vignette-like documents are available via github

*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.

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. See
*Modeling-based calculus with R/mosaic*for an instructor-oriented introduction and*Start R in Calculus*for a student-facing guide.

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

.

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. https://journal.r-project.org/archive/2017/RJ-2017-024/index.html.

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.

The following longer documents are available at github.com/ProjectMOSAIC/LittleBooks.

*Start Teaching Statistics Using R*includes some strategies for teaching beginners, and introduction to the`mosaic`

package, and some additional things that instructors should know about using R. (A spanish language translation can be found at https://github.com/jarochoeltrocho/MOSAIC-LittleBooks-Spanish.)*A Student’s Guide to R*provides a brief introduction to the R commands needed for all the basic statistical procedures in an Intro Stats course.

(A spanish language translation can be found at https://github.com/jarochoeltrocho/MOSAIC-LittleBooks-Spanish.)*Start R in Calculus*highlights features of R and the`mosaic`

package that can be used to teach calculus with R.

GW Cobb, “The introductory statistics course: a Ptolemaic curriculum?”,

*Technology Innovations in Statistics Education*, 2007, 1(1), escholarship.org/uc/item/6hb3k0nz.NJ Horton, BS Baumer, and H Wickham, “Teaching precursors to data science in introductory and second courses in statistics,”

*CHANCE*, 2015, 28(2):40-50, nhorton.people.amherst.edu/precursorsNJ Horton, and J Hardin, “Teaching the next generation of statistics students to”Think With Data“: special issue on statistics and the undergraduate curriculum,”

*TAS*, 2015, 69(4):259-265, http://amstat.tandfonline.com/doi/full/10.1080/00031305.2015.1094283D Nolan and D Temple Lang, “Computing in the statistics curricula”,

*The American Statistician*, 2010, 64(2), www.stat.berkeley.edu/~statcur/Preprints/ComputingCurric3.pdf.