Merge branch 'issue#11' into 'devel'

Issue#11: Datasets of the chapter 8

This branch brings
  - Datasets of the chapter 8. So, 5 data frames are included with the corresponding `data-raw/*.R` and `data/*.RData` files and documentation on the `R/legTools.R`;
  - @brunaw is included as a collaborator in the DESCRIPTION;

See merge request !13

DESCRIPTION

 Package: legTools
 Title: Convenience Functions, Small GUI to Teach Statistics and Some Datasets
 Version: 0.2.0
-Authors@R: person("LEG", "Core Team", email = "leg@ufpr.br", role =
-    c("aut", "cre"))
+Authors@R: as.person(c(
+    "LEG Core Team <leg@ufpr.br> [cre]",
+    "Walmes Marques Zeviani <walmes@ufpr.br> [aut]",
+    "Fernando de Pol Mayer <fernando.mayer@ufpr.br> [aut]",
+    "Bruna Davies Wundervald <brunadaviesw@gmail.com> [ctb]"
+    ))
 Description: legTools is a collection of R functions and datasets used for
     academic purposes. These functions mainly include small GUI to teach
     statistics, conveninece functions to visualize data and datasets, nost part

data-raw/RData2txt.R

@@ -3,6 +3,7 @@
 ## the package.
 ##======================================================================
 
+setwd("~/GitLab/legTools/data-raw")
 f <- list.files(path="../data", pattern="*.RData")
 
 sapply(f,

data-raw/castorbeansYield.R

+##----------------------------------------------------------------------
+## Data generation.
+
+castorbeansYield <- expand.grid(
+    variety=c("V 38", "L 41", "L 168", "L 176", "L 178", "L 881",
+              "L 882", "L 883", "L 1.000"),
+    loc=c("Ribeirão Preto", "Pindorama", "Mococa", "Tietê",
+          "Santa Rita"),
+    KEEP.OUT.ATTRS=FALSE)
+
+castorbeansYield$meanYield <-
+    c(1186, 1219, 1005, 1264, 1272, 1151, 1246, 1223, 1168, 1460, 1598,
+      1825, 1394, 1407, 1436, 1291, 1622, 1521, 1832, 1595, 1851, 1613,
+      1747, 2297, 2233, 2391, 1992, 1644, 1422, 1458, 1567, 1532, 1532,
+      1683, 1699, 1467, 2192, 2294, 1920, 1856, 2178, 2026, 2458, 2040,
+      1963)
+
+addmargins(with(castorbeansYield,
+                tapply(meanYield, list(variety, loc), FUN=sum)))
+
+castorbeansYield <- castorbeansYield[with(castorbeansYield,
+                                          order(loc, variety)),]
+
+## Put MSE as an attibute to the data.frame.
+mse <-  c(29930, 69170, 88210, 35720, 64520)
+names(mse) <- levels(castorbeansYield$loc)
+attr(castorbeansYield, which="MSE") <- mse
+str(castorbeansYield)
+
+save(castorbeansYield, file="../data/castorbeansYield.RData")
+
+##----------------------------------------------------------------------
+## Examples.
+
+require(lattice)
+
+data(castorbeansYield)
+str(castorbeansYield)
+
+xyplot(meanYield~variety, data=castorbeansYield,
+       groups=loc, type="o",
+       ylab=expression(Yield~(t~ha^{-1})),
+       xlab="Variety")
+
+rm(list=ls())
+load("../data/castorbeansYield.RData")
+ls()
+str(castorbeansYield)

data-raw/cottonFert.R

+##----------------------------------------------------------------------
+## Data generation.
+
+cottonFert <- expand.grid(
+    ## K=c(-1, 1),
+    ## N=c(-1, 1),
+    trt=c("N1K1", "N1K2", "N2K1", "N2K2", "CTRL"),
+    rept=1:4,
+    loc=gl(5, 1),
+    KEEP.OUT.ATTRS=FALSE)
+
+## x <- scan()
+## dput(x/10)
+
+cottonFert$y <- c(4.2, 3.6, 3.2, 3.6, 2.4, 2.4, 2.2, 2.6, 2.8, 1.2, 2.8,
+                  1.8, 3, 3, 3, 3.2, 3.2, 2, 2.4, 2.8, 11, 10, 12, 10.5,
+                  8.5, 10.5, 9.5, 9, 11.5, 8, 9, 9, 9.5, 10.5, 10, 9, 8,
+                  9.5, 10, 7, 7, 8.5, 9, 9, 7, 7.5, 7, 6, 9.5, 7, 6.5,
+                  8, 6, 8, 4.5, 5.5, 5.5, 5.5, 7, 6, 8, 8, 7, 9, 3.5,
+                  6.9, 5.5, 4.7, 6.5, 3, 6, 7.5, 5.5, 5.5, 3.9, 6.5, 8,
+                  9, 7, 7, 1.72, 2.38, 2.52, 2.78, 1.48, 1.81, 2.56,
+                  2.88, 3.01, 1.62, 1.73, 2.48, 2.76, 2.83, 1.58, 1.62,
+                  2.43, 2.54, 2.79, 1.56)
+
+## Check using the totals.
+aggregate(y~loc, data=cottonFert, FUN=sum)
+
+str(cottonFert)
+
+save(cottonFert, file="../data/cottonFert.RData")
+
+##----------------------------------------------------------------------
+## Examples.
+
+library(lattice)
+
+data(cottonFert)
+str(cottonFert)
+
+xyplot(y~trt|loc,
+       data=cottonFert, type=c("p", "a"),
+       ylab="y", xlab="Treatment")
+
+xyplot(log(y)~trt|loc,
+       data=cottonFert, type=c("p", "a"),
+       ylab="y", xlab="Treatment")
+
+m0 <- by(data=cottonFert, INDICES=cottonFert$loc,
+         FUN=lm, formula=y~trt)
+lapply(m0, anova)
+
+m1 <- lm(y~loc*trt, data=cottonFert)
+
+par(mfrow=c(2,2)); plot(m1); layout(1)
+MASS::boxcox(m1)
+
+m2 <- lm(log(y)~loc*trt, data=cottonFert)
+par(mfrow=c(2,2)); plot(m2); layout(1)
+anova(m2)

data-raw/peanutYield.R

+##----------------------------------------------------------------------
+## Data generation. Pimentel page 149.
+
+peanutYield <- expand.grid(
+    variety=c("40-Roxo", "54-Roxo", "49-Cateto", "53-Tatu"),
+    loc=c("Campinas", "Ribeirão Preto", "Pindorama"),
+    year=c("1941-42", "1942-43", "1949-50"),
+    KEEP.OUT.ATTRS=FALSE)
+
+peanutYield$meanYield <-
+    c(1780, 1450, 1430, 790, 690, 470, 520, 280, 4400, 4330, 3440, 3710,
+      2610, 2590, 2710, 1590, 1570, 1330, 1500, 1170, 1850, 2010, 2240,
+      1790, 2570, 2320, 2130, 2220, 2650, 2740, 1890, 1570, 2100, 2160,
+      1570, 870)
+
+addmargins(with(peanutYield,
+                tapply(meanYield, list(variety, loc), FUN=sum)))
+
+peanutYield <- peanutYield[with(peanutYield,
+                                order(year, loc, variety)),]
+
+str(peanutYield)
+
+## save(peanutYield, file="../data/peanutYield.RData")
+
+##----------------------------------------------------------------------
+## Examples.
+
+require(lattice)
+
+data(peanutYield)
+str(peanutYield)
+
+xyplot(meanYield~variety|year, data=peanutYield,
+       groups=loc, type="o",
+       ylab=expression(Yield~(t~ha^{-1})),
+       xlab="Variety")
+
+rm(list=ls())
+load("../data/peanutYield.RData")
+ls()
+str(peanutYield)

data-raw/peanutYield2.R

+##----------------------------------------------------------------------
+## Data generation. Pimentel page 156.
+
+peanutYield2 <- expand.grid(
+    variety=c("40-Roxo", "54-Roxo", "49-Cateto", "53-Tatu"),
+    loc=c("Pindorama 49/50", "Ribeirão Preto 49/50",
+          "Campinas 48/49", "Campinas 42/43"),
+    KEEP.OUT.ATTRS=FALSE)
+
+peanutYield2$meanYield <-
+    c(2100, 2160, 1570, 870, 2650, 2740, 1890, 1570, 2100, 1830, 1890,
+      1370, 2710, 2610, 2590, 1590)
+
+addmargins(with(peanutYield2,
+                tapply(meanYield, list(variety, loc), FUN=sum)))
+
+peanutYield2 <- peanutYield2[with(peanutYield2,
+                                  order(loc, variety)),]
+
+str(peanutYield2)
+
+## Put MSE as an attibute to the data.frame.
+mse <-  c(52900, 84700, 3970, 106900)
+names(mse) <- levels(peanutYield2$loc)
+attr(peanutYield2, which="MSE") <- mse
+str(peanutYield2)
+
+save(peanutYield2, file="../data/peanutYield2.RData")
+
+##----------------------------------------------------------------------
+## Examples.
+
+require(lattice)
+
+data(peanutYield2)
+str(peanutYield2)
+
+xyplot(meanYield~variety, data=peanutYield2,
+       groups=loc, type="o",
+       ylab=expression(Yield~(t~ha^{-1})),
+       xlab="Variety")
+
+rm(list=ls())
+load("../data/peanutYield2.RData")
+ls()
+str(peanutYield2)

data-raw/potatoYield2.R

+##----------------------------------------------------------------------
+## Data generation. Pimentel page 147.
+
+potatoYield2 <- expand.grid(
+    variety=c("Kennebec", "B 25-50 E", "B 1-52", "Huinkul",
+              "B 116-51", "B 72-53 A", "S. Rafaela", "Buena Vista"),
+    loc=gl(7, 1),
+    KEEP.OUT.ATTRS=FALSE)
+
+potatoYield2$sumYield <- c(470, 483, 646, 822, 611, 694, 685, 477,
+                           318, 650, 1201, 1205, 1223, 1112, 1176, 426,
+                           428, 660, 891, 1002, 900, 912, 1018, 497,
+                           584, 780, 928, 970, 954, 865, 703, 682,
+                           364, 356, 386, 558, 546, 450, 558, 356,
+                           482, 358, 439, 624, 523, 519, 488, 496,
+                           492, 583, 940, 929, 928, 797, 929, 532)/10
+
+addmargins(with(potatoYield2,
+                tapply(sumYield, list(variety, loc), FUN=sum)))
+
+potatoYield2 <- potatoYield2[with(potatoYield2, order(loc, variety)),]
+
+## Put MSE as an attibute to the data.frame.
+mse <-  c(315, 263, 855, 209, 325, 199, 535)/100
+names(mse) <- paste0("loc:", 1:length(mse))
+attr(potatoYield2, which="MSE") <- mse
+str(potatoYield2)
+
+save(potatoYield2, file="../data/potatoYield2.RData")
+
+##----------------------------------------------------------------------
+## Examples.
+
+require(lattice)
+
+data(potatoYield2)
+str(potatoYield2)
+
+xyplot(sumYield/4~variety, data=potatoYield2,
+       groups=loc, type="o",
+       ylab=expression(Yield~(t~ha^{-1})),
+       xlab="Variety")
+
+rm(list=ls())
+load("../data/potatoYield2.RData")
+ls()
+str(potatoYield2)

man/cassavaYield.Rd

@@ -21,10 +21,13 @@ These data are from an experiment done by The Brazilian
     (t/ha) was recorded in each experimental unit.
 
 \itemize{
+
 \item \code{block} a categorical unordered factor with 4 levels.
-    \item \code{variety} a categorical unordered factor with 6
-    levels.
+
+\item \code{variety} a categorical unordered factor with 6 levels.
+
 \item \code{yield} cassava yield (t/ha).
+
 }
 }
 \examples{

man/castorbeansYield.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/legTools.R
+\docType{data}
+\name{castorbeansYield}
+\alias{castorbeansYield}
+\title{Castor beans variety competition experiments in some locations}
+\format{a \code{data.frame} with 45 records and 3 variables.}
+\source{
+Pimentel Gomes, F. (2009). Curso de Estatística Experimental
+    (15th ed.). Piracicaba, São Paulo: FEALQ. (page 149)
+
+Souza, O. Ferreira de.; Canecchio, F. V. (1952). Melhoramento de
+    mamoeira, VII. Bragantia 12:301-307.
+}
+\usage{
+data(castorbeansYield)
+}
+\description{
+These data are from a set of experiments evaluating
+    varieties of castor beans in terms of yield (kg/ha) for some
+    locations (counties).
+
+\itemize{
+
+\item \code{variety} a categorical unordered factor with 8 levels,
+    varieties and lines of castor beans.
+
+\item \code{loc} a categorical unordered factor with 5 levels, the
+    locations (counties) experimental stations.
+
+\item \code{meanYield} is the mean of yield for a variety in each
+    location. So, this the mean across all plots of the same variety
+    in each experiment.
+
+}
+}
+\details{
+The data in the book was not complete because doesn't report
+    individual plot values but the mean for a variety in each single
+    experiment. Neither mention which experimental design was used in
+    each station. The book report the Mean Square Error estimates for
+    each experiment. These values as provided as an attribute of the
+    object, \code{attr(peanut, "MSE")} and they comes from the ANOVA
+    table corresponding to an appropriate model for each
+    location. With these MSE is possible use them in a such a way
+    that a partial ANOVA table can be obtained to test the effect of
+    location, variety and its interaction.
+}
+\examples{
+require(lattice)
+
+data(castorbeansYield)
+str(castorbeansYield)
+
+xyplot(meanYield~variety, data=castorbeansYield,
+       groups=loc, type="o",
+       ylab=expression(Yield~(t~ha^{-1})),
+       xlab="Variety")
+}
+\keyword{datasets}
+

man/coffeeFert.Rd

@@ -22,13 +22,19 @@ These data are from a \eqn{2^3} factorial experiment
     branches in coffee trees.
 
 \itemize{
+
 \item \code{N} content of nitrogen in the fertilizer (low/high).
+
 \item \code{P} content of phosphorus in the fertilizer (low/high).
+
 \item \code{K} content of potassium in the fertilizer (low/high).
+
 \item \code{block} an unordered factor representing the blocks
     used.
+
 \item \code{branches} an integer variable, the number of dry
     branches in a coffee the.
+
 }
 }
 \details{

man/cornYield.Rd

@@ -18,11 +18,17 @@ These data are from an \eqn{2^3} factorial experiment
     (K) on corn yield in a randomized block design.
 
 \itemize{
+
 \item \code{block} a factor with 4 levels.
+
 \item \code{N} low (-1) and high (+1) levels of nitrogen.
+
 \item \code{P} low (-1) and high (+1) levels of phosporus.
+
 \item \code{K} low (-1) and high (+1) levels of potassium.
+
 \item \code{yield} corn yield (ton/ha).
+
 }
 }
 \examples{

man/cornYield2.Rd

@@ -22,11 +22,17 @@ These data are from an axial 3 factorial experiment
     plus presence of limestone.
 
 \itemize{
+
 \item \code{N} content of nitrogen in the fertilizer.
+
 \item \code{P} content of phosphorus in the fertilizer.
+
 \item \code{K} content of potassium in the fertilizer.
+
 \item \code{limestone} presence (1) or absence of limestone (0).
+
 \item \code{acid} mean of corn yield in 16 locations (ton/ha).
+
 }
 }
 \details{

man/cottonFert.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/legTools.R
+\docType{data}
+\name{cottonFert}
+\alias{cottonFert}
+\title{A set of experiments in different locations studing NK on
+    cotton}
+\format{a \code{data.frame} with 100 records and 4 variables.}
+\source{
+Pimentel Gomes, F. (2009). Curso de Estatística Experimental
+    (15th ed.). Piracicaba, São Paulo: FEALQ. (page 142)
+}
+\usage{
+data(cottonFert)
+}
+\description{
+These data is a set of experiments carried out in
+    different locations studing NK fertilization in cotton. All the 5
+    experiments are a complete randomized design with 4 replications
+    and 5 levels of fertilization based on N and K levels and a
+    control.
+
+\itemize{
+
+\item \code{trt} unordered factor, treatment that consist of 4 cells
+    from a 2^2 factorial design (\eqn{N\times K}) and a control.
+
+\item \code{rept} integer, indexes experimental units.
+
+\item \code{loc} an unordered factor representing the locations where
+    the experiment was carried out.
+
+\item \code{y} numeric, the response variable of the experiment. The
+    text book didn't give details.
+
+}
+}
+\examples{
+library(lattice)
+
+data(cottonFert)
+str(cottonFert)
+
+xyplot(y~trt|loc,
+       data=cottonFert, type=c("p", "a"),
+       ylab="y", xlab="Treatment")
+
+xyplot(log(y)~trt|loc,
+       data=cottonFert, type=c("p", "a"),
+       ylab="y", xlab="Treatment")
+}
+\keyword{datasets}
+

man/defoliation.Rd

@@ -3,14 +3,14 @@
 \docType{data}
 \name{defoliation}
 \alias{defoliation}
-\title{Bolls in cotton as function of artifitial defoliation}
+\title{Bolls in cotton as function of artificial defoliation}
 \format{a \code{data.frame} with 125 records and 4 variables.}
 \usage{
 data(defoliation)
 }
 \description{
 This dataset contais the result of a real experiment to
-    evaluate the effect of artifitial defoliation in combination with
+    evaluate the effect of artificial defoliation in combination with
     phenological stage of occurence on the production of cotton
     represented by the number of bolls produced at the end of the
     crop cycle. The experiment is a \eqn{5\times 5} factorial with 5
@@ -21,15 +21,21 @@ This dataset contais the result of a real experiment to
     variance less than the sample mean).
 
 \itemize{
-\item \code{phenol} a categorical ordered factor with 5 levels
-    that represent the phenological stages of the cotton plant in
-    which defoliation was applied.
+
+\item \code{phenol} a categorical ordered factor with 5 levels that
+    represent the phenological stages of the cotton plant in which
+    defoliation was applied.
+
 \item \code{defol} a numeric factor with 5 levels that represents the
     artifical level of defoliation (percent in leaf area removed with
     scissors) applied for all leaves in the plant.
-\item \code{rept} index for each experimenal unit in each treatment cell.
+
+\item \code{rept} index for each experimenal unit in each treatment
+    cell.
+
 \item \code{bolls} the number of bolls produced (count variable)
     evaluated at harvest.
+
 }
 }
 \details{