vignettes/mosaic-resources.Rmd
mosaic-resources.Rmd
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, from cran.r-project.org/package=mosaic,
or from www.mosaic-web.org/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
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.
Some features of the mosaic package are provided through auxiliary packages. These include:
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/fjaraavilaa/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/fjaraavilaa/MOSAIC-LittleBooks-Spanish.)
Statistical Modeling: A Fresh Approach (DT Kaplan,
second edition)] is an introduction to statistics embracing a modeling
approach and employing resampling methods. The mosaic
package is used throughout.
Foundations and Applications of Statistics: An Introduction
Using R (R Pruim, second edition) is an R-infused probability and
mathematical statistics text that emphasizes connections between
probability and statistics. The first edition of the book predates the
mosaic
package, but much of the code originally in the
fastR
package has been moved into the mosaic
package. The second edition is supported by the fastR2
package and uses ggformula
for plotting.
Modern Data Science with R (BS Baumer, DT Kaplan, and NJ Horton) is an R-infused data science text that emphasizes conceptual understanding and computation.
The Statistical Sleuth in R (NJ Horton) describes how to undertake analyses in R for the examples in the Third Edition of the Statistical Sleuth: A Course in Methods of Data Analysis (2013), by Fred Ramsey and Dan Schafer.
Introduction to the Practice of Statistics in R (NJ Horton and BS Baumer) describes how to undertake analyses in R that are introduced as examples in Introduction to the Practice of Statistics, by David Moore, George McCabe, and Bruce Craig.
Statistics: Unlocking the Power of Data (Lock, Lock, Lock, Lock, and Lock) is an introductory statistics textbook that embraces a resampling approach.
An annotated companion to the examples in the book implemented using R can be found at
and the Lock5withR
R package provides all the data sets
used in the text.
Stats: Data and Models (NJ Horton) describes how to undertake analyses in R for the examples in the Fourth Edition of Stats: Data and Models (2015), by Dick de Veaux, Paul Velleman, and Dave Bock.
Intro Stats (P Frenett and NJ Horton) describes how to undertake analyses in R for the examples in the Fourth and Fifth Editions of Intro Stats (2013), by Dick de Veaux, Paul Velleman, and Dave Bock.
Introduction to Statistical Investigations (Tintle et al) is another introductory statistics textbook that embraces a resampling approach.
An annotated companion to the examples in the book implemented using R can be found at
The ISIwithR
R package provides all the data sets used
in the text. Additional information about the book and the approach used
there can be found at
OpenIntro Stats now has versions of their labs designed for use with
the mosaic
package.
The mosaic
labs were adapted by Ben Baumer and Galen Long
of Smith College and updated to ggformula
by Bonnie Lin and
Nicholas Horton of Amherst College.
GW Cobb, “The introductory statistics course: a Ptolemaic curriculum?”, Technology Innovations in Statistics Education, 2007, 1(1), escholarship.org/uc/item/6hb3k0nz.
Fieberg JR, Vitense K, Johnson DH. 2020. Resampling-based methods for biologists. PeerJ 8:e9089 https://doi.org/10.7717/peerj.9089
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/precursors
NJ 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, https://amstat.tandfonline.com/doi/full/10.1080/00031305.2015.1094283
D Nolan and D Temple Lang, “Computing in the statistics curricula”, The American Statistician, 2010, 64(2), www.stat.berkeley.edu/~statcur/Preprints/ComputingCurric3.pdf.