Foundations and Applications of Statistics Using R (2nd Edition)

Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet function.


CRAN_Status_Badge

This package contains data sets and some utility functions to support Foundations and Applications of Statistics: An Introduction Using R by Randall Pruim.

The package can be installed from CRAN via

install.packages("fastR2")

or from github

devtools::install_github("rpruim/fastR2")

Snippets

In addtion to data sets, fastR2 contains a snippet() function that loads and executes code found in the text. Here is an example:

require(fastR2)
require(multcomp)
snippet("bugs")
#> 
#> ## snippet: bugs
#> 
#> > model <- aov(sqrt(trapped) ~ color, data = Bugs)
#> 
#> > TukeyHSD(model)
#>   Tukey multiple comparisons of means
#>     95% family-wise confidence level
#> 
#> Fit: aov(formula = sqrt(trapped) ~ color, data = Bugs)
#> 
#> $color
#>          diff        lwr        upr     p adj
#> G-B  1.750330  0.6458303  2.8548288 0.0013396
#> W-B  0.146892 -0.9576072  1.2513913 0.9818933
#> Y-B  3.060201  1.9557018  4.1647003 0.0000011
#> W-G -1.603438 -2.7079368 -0.4989383 0.0031308
#> Y-G  1.309872  0.2053723  2.4143708 0.0165743
#> Y-W  2.913309  1.8088098  4.0178083 0.0000022
#> 
#> 
#> > model <- lm(sqrt(trapped) ~ color, data = Bugs)
#> 
#> > glht(model, mcp(color = "Tukey")) %>%
#> +   summary()          
#> 
#>   Simultaneous Tests for General Linear Hypotheses
#> 
#> Multiple Comparisons of Means: Tukey Contrasts
#> 
#> 
#> Fit: lm(formula = sqrt(trapped) ~ color, data = Bugs)
#> 
#> Linear Hypotheses:
#>            Estimate Std. Error t value Pr(>|t|)    
#> G - B == 0   1.7503     0.3946   4.436  0.00136 ** 
#> W - B == 0   0.1469     0.3946   0.372  0.98189    
#> Y - B == 0   3.0602     0.3946   7.755  < 0.001 ***
#> W - G == 0  -1.6034     0.3946  -4.063  0.00312 ** 
#> Y - G == 0   1.3099     0.3946   3.319  0.01672 *  
#> Y - W == 0   2.9133     0.3946   7.383  < 0.001 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> (Adjusted p values reported -- single-step method)
#> 
#> 
#> ## snippet: fit-bugs-pois01
#> 
#> > o <- c(2, 10, 16, 11, 5, 3, 3)
#> 
#> > o.collapsed <- c(2 + 10, 16, 11, 5, 3 + 3)
#> 
#> > n <- sum(o)
#> 
#> > m <- sum(o * 0:6) / n     # mean count = MLE for lambda (full data)
#> 
#> > p <- dpois(0:6, m)  
#> 
#> > p.collapsed <- c(p[1] + p[2], p[3:5], 1 - sum(p[1:5]))   # collapsed probs
#> 
#> > e.collapsed <- p.collapsed * n
#> 
#> > cbind(o.collapsed, p.collapsed, e.collapsed)
#>      o.collapsed p.collapsed e.collapsed
#> [1,]          12   0.2752049   13.760244
#> [2,]          16   0.2533122   12.665609
#> [3,]          11   0.2161597   10.807986
#> [4,]           5   0.1383422    6.917111
#> [5,]           6   0.1169810    5.849050
#> 
#> > lrt  <- 2 * sum(o.collapsed * log(o.collapsed / e.collapsed)); lrt
#> [1] 1.640881
#> 
#> > pearson <- sum((o.collapsed - e.collapsed)^2 / e.collapsed); pearson
#> [1] 1.641642
#> 
#> > 1-pchisq(lrt, df = 3)
#> [1] 0.6501564
#> 
#> > 1-pchisq(pearson, df = 3)
#> [1] 0.6499852
#> 
#> ## snippet: fit-bugs-pois02
#> 
#> > 1-pchisq(pearson, df = 5-1)
#> [1] 0.8012892
#> 
#> > 1-pchisq(pearson, df = 5-1-1)
#> [1] 0.6499852

News

fastR2 0.2.0

  • First CRAN submission.
  • Data sets and code snippets have been modified to match second edition of book.
  • snippet() uses regex matching by default

Reference manual

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install.packages("fastR2")

0.2.0 by Randall Pruim, 6 months ago


Browse source code at https://github.com/cran/fastR2


Authors: Randall Pruim


Documentation:   PDF Manual  


GPL (>= 2) license


Imports lattice, ggplot2, grid, magrittr, dplyr, maxLik, numDeriv, miscTools

Depends on ggformula, mosaic

Suggests mosaicCalc, tidyr, readr, MASS, faraway, Hmisc, DAAG, multcomp, vcd, car, alr3, corrgram, BradleyTerry2, cubature, knitr, mosaicData, rmarkdown


See at CRAN