itemize -> describe

Author: rpruim

R/BirthsDoc.R

@@ -13,14 +13,16 @@
 #' @usage data(BirthsCDC)
 #' @format  A data.frame with the following 8 variables.
 #'
-#'  * `date`   Date
-#'  * `births` Number of births on `date` (integer)
-#'  * `wday`   Day of week (ordered factor)
-#'  * `year`   Year (integer)
-#'  * `month`  Month (integer)
-#'  * `day_of_year`  Day of year (integer)
-#'  * `day_of_month` Day of month (integer)
-#'  * `day_of_week`  Day of week (integer)
+#' \describe{
+#'  \item{\code{date}}{Date}
+#'  \item{\code{births}{Number of births on `date` (integer)}
+#'  \item{\code{wday}{Day of week (ordered factor)}
+#'  \item{\code{year}{Year (integer)}
+#'  \item{\code{month}{Month (integer)}
+#'  \item{\code{day_of_year}{Day of year (integer)}
+#'  \item{\code{day_of_month}{Day of month (integer)}
+#'  \item{\code{day_of_week}{Day of week (integer)}
+#'  }
 #'
 #' @source
 #' * Data source for `Births`: National Vital Statistics System natality data, as provided by
@@ -110,7 +112,7 @@ NA
 #' @usage data(Birthdays)
 #' @format
 #'   A data frame with 374221 observations on the following variables.
-#'   \itemize{
+#'   \describe{
 #'     \item{\code{state}}{state where child was born}
 #'     \item{\code{year}}{year (1969-1988)}
 #'     \item{\code{month}}{month (1-12)}

R/DimesDoc.R

@@ -1,19 +1,19 @@
 #' Weight of dimes
-#' 
+#'
 #' Weights of a sample of dimes.
-#' 
+#'
 #' @docType data
 #' @name Dimes
 #' @usage data(Dimes)
 #' @format  A data frame with 30 observations on the following 2 variables.
-#' \itemize{
+#' \describe{
 #' \item{\code{mass}}{ mass of dime in grams}
 #' \item{\code{year}}{ year the dime was minted}
 #' }
 #' @source Data were collected by Michael Stob.
-#' @details These data were collected on a sample taken from a large sack of dimes 
-#' for the purpose of estimating the total number of dimes in the sack based on the 
+#' @details These data were collected on a sample taken from a large sack of dimes
+#' for the purpose of estimating the total number of dimes in the sack based on the
 #' weights of the individual dimes.
-#' 
+#'
 NA
 

R/GoosePermitsDoc.R

@@ -1,36 +1,36 @@
 #' Goose Permit Study
-#' 
-#' 237 hunters were each offered one of 11 cash amounts (bids) ranging from 
-#' $1 to $200 in return for their goose permits.  Hunters returned either their 
+#'
+#' 237 hunters were each offered one of 11 cash amounts (bids) ranging from
+#' $1 to $200 in return for their goose permits.  Hunters returned either their
 #' permit or the cash.
-#' 
+#'
 #' @docType data
 #' @name GoosePermits
 #' @usage data(GoosePermits)
 #' @format  A data.frame with 11 observations on the following 3 variables.
-#' \itemize{
+#' \describe{
 #'    item{code{bid }}{amount offered for permit (US $) (numeric)}
 #'    item{code{keep }}{number of hunters who kept the permit and returned the cash (numeric)}
 #'    item{code{sell }}{number of hunters who kept the cash and returned the permit (numeric)}
 #' }
-#' @source  Bishop and Heberlein. 
-#' "Measuring values of extramarket goods: are indirect measures biased?". 
-#' Amer. J. Agr. Econ. 61, 1979. Available at 
+#' @source  Bishop and Heberlein.
+#' "Measuring values of extramarket goods: are indirect measures biased?".
+#' Amer. J. Agr. Econ. 61, 1979. Available at
 #' <https://onlinelibrary.wiley.com/doi/abs/10.2307/3180348>
 ### broken URL:  \url{http://www1.udel.edu/johnmack/frec343/bishop_and_heberlein.pdf}.
-#' 
-### This link seems to have broken: 
+#'
+### This link seems to have broken:
 ### See also \url{http://www.math.umt.edu/patterson/ProfileLikelihoodCI.pdf}.
-#' 
+#'
 #' @examples
 #' data(GoosePermits)
-#' 
-#' goose.model <- 
+#'
+#' goose.model <-
 #'   glm( cbind(keep, sell) ~ log(bid), data = GoosePermits, family = binomial())
 #' if (require(ggformula)) {
 #'   y.hat <- makeFun(goose.model)
 #'   gf_point( (keep/(keep+sell)) ~ bid, data = GoosePermits, ylim = c(0,1.05)) |>
-#'   gf_fun(y.hat(b) ~ b, add = TRUE, color = "red", alpha = 0.5) 
+#'   gf_fun(y.hat(b) ~ b, add = TRUE, color = "red", alpha = 0.5)
 #' }
-#' 
+#'
 NA

R/SaratogaHousesDoc.R

@@ -1,12 +1,12 @@
 #' Houses in Saratoga County (2006)
-#' 
+#'
 #' Data on houses in Saratoga County, New York, USA in 2006
-#' 
+#'
 #' @docType data
 #' @name SaratogaHouses
 #' @usage data(SaratogaHouses)
 #' @format  A data frame with 1728 observations on the following 16 variables.
-#' \itemize{
+#' \describe{
 #' \item{\code{price}}{ price (US dollars)}
 #' \item{\code{lotSize}}{ size of lot (acres)}
 #' \item{\code{age}}{ age of house (years)}
@@ -24,11 +24,11 @@
 #' \item{\code{newConstruction}}{ whether the property is a new construction}
 #' \item{\code{centralAir}}{ whether the house has central air}
 #' }
-#' @source 
-#' Data collected by Candice Corvetti and used in the "Stat 101" case study 
-#' "How much is a Fireplace Worth".  
-#' See also 
+#' @source
+#' Data collected by Candice Corvetti and used in the "Stat 101" case study
+#' "How much is a Fireplace Worth".
+#' See also
 #' <https://www.saratogacountyny.gov/departments/real-property-tax-service-agency/>
 NA
 
-  
+

Rbuildignore/Scratch/SAT.R

@@ -1,17 +1,17 @@
-#' State by State SAT data in 1994 and 2010 
-#' 
+#' State by State SAT data in 1994 and 2010
+#'
 #' SAT data for two years.  The 1994 data was
-#' assembled for a statistics education journal article on the 
+#' assembled for a statistics education journal article on the
 #' link between SAT scores and measures of educational expenditures.
-#' The 2010 data has been put in the same format and concatenated to the 1994 format. 
-#' 
-#' 
+#' The 2010 data has been put in the same format and concatenated to the 1994 format.
+#'
+#'
 #' @name SAT
 #' @usage data(SAT)
 #' @docType data
-#' @format 
+#' @format
 #'   A data frame with 100 observations on the following variables.
-#'   \itemize{
+#'   \describe{
 #'     \item{\code{state}}{a factor with names of each state}
 #'     \item{\code{expend}}{expenditure per pupil in average daily attendance in
 #'  public elementary and secondary schools, 1994-95 (in thousands of US dollars)}
@@ -25,21 +25,21 @@
 #'     \item{\code{sat}}{average total SAT score}
 #'     \item{\code{year}}{1994 or 2010 (as a character string)}
 #'   }
-#' 
-#' @source 
+#'
+#' @source
 #' http://www.amstat.org/publications/jse/secure/v7n2/datasets.guber.cfm
-#' 
-#' @references 
+#'
+#' @references
 #' Deborah Lynn Guber, "Getting what you pay for: the debate over equity in
 #' public school expenditures" (1999), \emph{Journal of Statistics Education} 7(2).
 #' 2010 data collated by Jeff Witmer, Oberlin College
-#' 
-#' @examples 
+#'
+#' @examples
 #' data(SAT)
 #' SAT1994 <- subset(SAT, year=="1994") # the original SAT data
 #' if (require(lattice)) {
 #'   xyplot(sat ~ expend, group=year, SAT)
-#'   xyplot(sat ~ expend, group=year, SAT, 
+#'   xyplot(sat ~ expend, group=year, SAT,
 #' 	   panel=function(x,y){grid.text(abbreviate(SAT$state, 3), x, y, default.units='native')})
-#' } 
+#' }
 #' @keywords datasets