CRAN release work.

Remove all cached data sets as well as vignettes and tests that depend on them.
ehrlinger · Sep 7, 2016 · c270275 · c270275
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,20 +2,19 @@ Package: ggRandomForests
 Type: Package
 Title: Visually Exploring Random Forests
 Version: 2.0.1
-Date: 2016-08-10
+Date: 2016-09-07
 Author: John Ehrlinger <john.ehrlinger@gmail.com>
 Maintainer: John Ehrlinger <john.ehrlinger@gmail.com>
 License: GPL (>=3)
-VignetteBuilder: knitr
 URL: https://github.com/ehrlinger/ggRandomForests
 BugReports: https://github.com/ehrlinger/ggRandomForests/issues
 Description: Graphic elements for exploring Random Forests using the 'randomForest' or
     'randomForestSRC' package for survival, regression and classification forests and
     'ggplot2' package plotting.
 Depends:
-    R (>= 3.1.0)
+    R (>= 3.1.0),
+    randomForestSRC (>= 1.5.5)
 Imports:
-    randomForestSRC (>= 1.5.5),
     randomForest,
     ggplot2,
     survival,

diff --git a/R/cache_rfsrc_datasets.R b/R/cache_rfsrc_datasets.R
@@ -17,21 +17,21 @@
 #' deal with thoses changes. We make the function available to end users to
 #' create objects for further experimentation.
 #'
-#' There are five cached data set types:
-#' '\itemize{
-#' \item \code{\link{rfsrc_data}} - \code{\link[randomForestSRC]{rfsrc}} objects. 
-#' \item \code{\link{varsel_data}} - \code{\link[randomForestSRC]{var.select}} 
-#' minimal depth variable selection objects.
-#' \item \code{\link{interaction_data}} - 
-#' \code{\link[randomForestSRC]{find.interaction}} minimal depth, 
-#' pairwise variable interaction matrices.
-#' \item \code{\link{partial_data}} - \code{\link[randomForestSRC]{plot.variable}} 
-#' objects 
-#' (\code{partial=TRUE}) for partial variable dependence. 
-#' \item \code{\link{partial_coplot_data}} - 
-#' \code{\link[randomForestSRC]{plot.variable}} objects 
-#' (\code{partial=TRUE}) for partial variable dependence. 
-#' }
+# There are five cached data set types:
+# '\itemize{
+# \item \code{\link{rfsrc_data}} - \code{\link[randomForestSRC]{rfsrc}} objects. 
+# \item \code{\link{varsel_data}} - \code{\link[randomForestSRC]{var.select}} 
+# minimal depth variable selection objects.
+# \item \code{\link{interaction_data}} - 
+# \code{\link[randomForestSRC]{find.interaction}} minimal depth, 
+# pairwise variable interaction matrices.
+# \item \code{\link{partial_data}} - \code{\link[randomForestSRC]{plot.variable}} 
+# objects 
+# (\code{partial=TRUE}) for partial variable dependence. 
+# \item \code{\link{partial_coplot_data}} - 
+# \code{\link[randomForestSRC]{plot.variable}} objects 
+# (\code{partial=TRUE}) for partial variable dependence. 
+# }
 #' 
 #' For the following data sets:
 #' #'\itemize{
@@ -46,11 +46,11 @@
 #' @seealso \code{iris} \code{airq} \code{mtcars} \code{\link[MASS]{Boston}} 
 #' \code{\link[randomForestSRC]{pbc}}
 #' \code{\link[randomForestSRC]{veteran}} 
-#' \code{\link{rfsrc_data}} 
-#' \code{\link{varsel_data}}
-#' \code{\link{interaction_data}}
-#' \code{\link{partial_data}} 
-#' \code{\link{partial_coplot_data}}
+# \code{\link{rfsrc_data}} 
+# \code{\link{varsel_data}}
+# \code{\link{interaction_data}}
+# \code{\link{partial_data}} 
+# \code{\link{partial_coplot_data}}
 #' 
 #' @importFrom randomForestSRC rfsrc var.select plot.variable find.interaction
 #' @importFrom utils data

diff --git a/R/calc_roc.R b/R/calc_roc.R
@@ -43,8 +43,8 @@
 #' 
 #' @examples
 #' ## Taken from the gg_roc example
-#' # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
-#' data(rfsrc_iris)
+#'  rfsrc_iris <- rfsrc(Species ~ ., data = iris)
+#' #data(rfsrc_iris)
 #' gg_dta <- calc_roc.rfsrc(rfsrc_iris, rfsrc_iris$yvar, which.outcome=1, oob=TRUE)
 #' gg_dta <- calc_roc.rfsrc(rfsrc_iris, rfsrc_iris$yvar, which.outcome=1, oob=FALSE)
 #' @export
@@ -130,8 +130,8 @@ calc_roc <- calc_roc.rfsrc
 #' @examples
 #' ##
 #' ## Taken from the gg_roc example
-#' # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
-#' data(rfsrc_iris)
+#' rfsrc_iris <- rfsrc(Species ~ ., data = iris)
+#' #data(rfsrc_iris)
 #' 
 #' \dontrun{
 #' gg_dta <- gg_roc(rfsrc_iris, which.outcome=1)

diff --git a/R/combine.gg_partial.R b/R/combine.gg_partial.R
@@ -28,6 +28,7 @@
 #' @importFrom parallel mclapply
 #' 
 #' @examples 
+#' \dontrun{
 #' # Load a set of plot.variable partial plot data
 #' data(partial_pbc)
 #' 
@@ -61,7 +62,7 @@
 #'    ggpart[[ind]] <- NULL
 #' }
 #' plot(ggpart, panel=TRUE) 
-#' 
+#' }
 #' 
 #' @export
 combine.gg_partial <- function(x, y, lbls, ...){

diff --git a/R/gg_error.R b/R/gg_error.R
@@ -50,9 +50,9 @@
 #' ## ------------------------------------------------------------
 #' ## ------------- iris data
 #' ## You can build a randomForest
-#' # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
+#' rfsrc_iris <- rfsrc(Species ~ ., data = iris)
 #' # ... or load a cached randomForestSRC object
-#' data(rfsrc_iris, package="ggRandomForests")
+#' # data(rfsrc_iris, package="ggRandomForests")
 #'
 #' # Get a data.frame containing error rates
 #' gg_dta<- gg_error(rfsrc_iris)
@@ -73,7 +73,7 @@
 #' # Plot the gg_error object
 #' plot(gg_dta)
 #' }
-#'
+#'\dontrun{
 #' ## ------------- Boston data
 #' data(rfsrc_Boston, package="ggRandomForests")
 #'
@@ -82,7 +82,7 @@
 #'
 #' # Plot the gg_error object
 #' plot(gg_dta)
-#'
+#'}
 #' \dontrun{
 #' ## ------------- mtcars data
 #'
@@ -105,14 +105,14 @@
 #' gg_dta <- gg_error(rfsrc_veteran)
 #' plot(gg_dta)
 #' }
-#'
+#'\dontrun{
 #' ## ------------- pbc data
 #' # Load a cached randomForestSRC object
 #' data(rfsrc_pbc, package="ggRandomForests")
 #'
 #' gg_dta <- gg_error(rfsrc_pbc)
 #' plot(gg_dta)
-#'
+#'}
 #' @export gg_error gg_error.rfsrc gg_error.randomForest gg_error.randomForest.formula
 gg_error <- function (object, ...) {
   UseMethod("gg_error", object)

diff --git a/R/gg_interaction.R b/R/gg_interaction.R
@@ -56,6 +56,7 @@
 #' ## ------------------------------------------------------------
 #' ## find interactions, classification setting
 #' ## ------------------------------------------------------------
+#' \dontrun{
 #' ## -------- iris data
 #' ## iris.obj <- rfsrc(Species ~., data = iris)
 #' ## TODO: VIMP interactions not handled yet....
@@ -66,7 +67,7 @@
 #' 
 #' plot(gg_dta, xvar="Petal.Width")
 #' plot(gg_dta, panel=TRUE)
-#' 
+#' }
 #' ## ------------------------------------------------------------
 #' ## find interactions, regression setting
 #' ## ------------------------------------------------------------
@@ -85,13 +86,13 @@
 #' 
 #' plot(gg_dta, panel=TRUE)
 #' }
-#' 
+#' \dontrun{
 #' ## -------- Boston data
 #' data(interaction_Boston, package="ggRandomForests")
 #' gg_dta <- gg_interaction(interaction_Boston)
 #' 
 #' plot(gg_dta, panel=TRUE)
-#' 
+#' }
 #' \dontrun{
 #' ## -------- mtcars data
 #' data(interaction_mtcars, package="ggRandomForests")
@@ -107,12 +108,13 @@
 #' ## data(pbc, package = "randomForestSRC") 
 #' ## pbc.obj <- rfsrc(Surv(days,status) ~ ., pbc, nsplit = 10)
 #' ## interaction_pbc <- randomForestSRC::find.interaction(pbc.obj, nvar = 8)
+#' \dontrun{
 #' data(interaction_pbc, package="ggRandomForests")
 #' gg_dta <- gg_interaction(interaction_pbc)
 #' 
 #' plot(gg_dta, xvar="bili")
 #' plot(gg_dta, panel=TRUE)
-#' 
+#' }
 #' \dontrun{
 #' ## -------- veteran data
 #' data(interaction_veteran, package="ggRandomForests")

diff --git a/R/gg_minimal_depth.R b/R/gg_minimal_depth.R
@@ -42,7 +42,8 @@
 #' ## ------------------------------------------------------------
 #' ## classification example
 #' ## ------------------------------------------------------------
-#' ## -------- iris data
+#'\dontrun{
+#'  ## -------- iris data
 #' ## You can build a randomForest
 #' # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
 #' # varsel_iris <- randomForestSRC::var.select(rfsrc_iris)
@@ -54,7 +55,7 @@
 #' 
 #' # Plot the gg_minimal_depth object
 #' plot(gg_dta)
-#' 
+#' }
 #' ## ------------------------------------------------------------
 #' ## Regression example
 #' ## ------------------------------------------------------------
@@ -71,13 +72,13 @@
 #' # Plot the gg_minimal_depth object
 #' plot(gg_dta)
 #' }
-#' 
+#' \dontrun{
 #' ## -------- Boston data
 #' data(varsel_Boston, package="ggRandomForests")
 #' 
 #' # Get a data.frame containing error rates
 #' plot(gg_minimal_depth(varsel_Boston))
-#' 
+#' }
 #' \dontrun{
 #' ## -------- mtcars data
 #' data(varsel_mtcars, package="ggRandomForests")
@@ -102,13 +103,13 @@
 #' gg_dta <- gg_minimal_depth(varsel_veteran)
 #' plot(gg_dta)
 #' }
-#' 
+#' \dontrun{
 #' ## -------- pbc data
 #' data(varsel_pbc, package="ggRandomForests")
 #' 
 #' gg_dta <- gg_minimal_depth(varsel_pbc)
 #' plot(gg_dta)
-#' 
+#' }
 #' @aliases gg_minimal_depth  gg_minimal_depth.randomForest gg_minimal_depth.rfsrc
 #' 
 #' @export

diff --git a/R/gg_minimal_vimp.R b/R/gg_minimal_vimp.R
@@ -35,6 +35,7 @@
 #' ## ------------------------------------------------------------
 #' ## classification example
 #' ## ------------------------------------------------------------
+#' \dontrun{
 #' ## -------- iris data
 #' ## You can build a randomForest
 #' # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
@@ -47,7 +48,7 @@
 #' 
 #' # Plot the gg_minimal_depth object
 #' plot(gg_dta)
-#' 
+#' }
 #' ## ------------------------------------------------------------
 #' ## Regression example
 #' ## ------------------------------------------------------------
@@ -64,7 +65,7 @@
 #' # Plot the gg_minimal_vimp object
 #' plot(gg_dta)
 #' }
-#' 
+#' \dontrun{
 #' ## -------- Boston data
 #' data(varsel_Boston, package="ggRandomForests")
 #' 
@@ -73,7 +74,7 @@
 #' 
 #' # Plot the gg_minimal_vimp object
 #' plot(gg_dta)
-#' 
+#' }
 #' \dontrun{
 #' ## -------- mtcars data
 #' data(varsel_mtcars, package="ggRandomForests")
@@ -99,12 +100,13 @@
 #' gg_dta <- gg_minimal_vimp(varsel_veteran)
 #' plot(gg_dta)
 #' }
+#' \dontrun{
 #' ## -------- pbc data
 #' data(varsel_pbc, package="ggRandomForests")
 #' 
 #' gg_dta <- gg_minimal_vimp(varsel_pbc)
 #' plot(gg_dta)
-#' 
+#' }
 #' @aliases gg_minimal_vimp gg_minimal_vimp.randomForest gg_minimal_vimp.rfsrc
 #' @export
 gg_minimal_vimp <- function (object, ...) {

diff --git a/R/gg_partial.R b/R/gg_partial.R
@@ -50,7 +50,7 @@
 #' ## classification
 #' ## ------------------------------------------------------------
 #' ## -------- iris data
-#' 
+#' \dontrun{
 #' ## iris "Petal.Width" partial dependence plot
 #' ##
 #' # rfsrc_iris <- rfsrc(Species ~., data = iris)
@@ -60,7 +60,7 @@
 #' 
 #' gg_dta <- gg_partial(partial_iris)
 #' plot(gg_dta)
-#' 
+#' }
 #' ## ------------------------------------------------------------
 #' ## regression
 #' ## ------------------------------------------------------------
@@ -82,13 +82,13 @@
 #' gg_dta[["Month"]] <- NULL
 #' plot(gg_dta, panel=TRUE)
 #' }
-#' 
+#' \dontrun{
 #' ## -------- Boston data
 #' data(partial_Boston, package="ggRandomForests")
 #'
 #' gg_dta <- gg_partial(partial_Boston)
 #' plot(gg_dta, panel=TRUE)
-#'
+#' }
 #' \dontrun{
 #' ## -------- mtcars data
 #' data(partial_mtcars, package="ggRandomForests")
@@ -145,6 +145,7 @@
 #' gg_dta.cat[["karno"]] <- gg_dta.cat[["diagtime"]] <- gg_dta.cat[["age"]] <- NULL 
 #' plot(gg_dta.cat, panel=TRUE, notch=TRUE)
 #' }
+#' \dontrun{
 #' ## -------- pbc data
 #' data("partial_pbc", package = "ggRandomForests")
 #' data("varsel_pbc", package = "ggRandomForests")
@@ -173,7 +174,7 @@
 #' 
 #' #plot(pbc_ggpart[["edema"]], panel=TRUE) #,
 #'      # notch = TRUE, alpha = .3, outlier.shape = NA) 
-#'   
+#'   }
 #' @aliases gg_partial gg_partial_list gg_partial.rfsrc gg_partial.randomForest
 #' @name gg_partial
 #' @name gg_partial_list

diff --git a/R/gg_partial_coplot.R b/R/gg_partial_coplot.R
@@ -17,6 +17,7 @@
 #' @importFrom parallel mclapply
 #' 
 #' @examples
+#' \dontrun{
 #' # Load the forest
 #' data(rfsrc_pbc, package="ggRandomForests")
 #' 
@@ -28,7 +29,7 @@
 #' 
 #' # Create the conditional groups and add to the gg_variable object
 #' copper_grp <- cut(ggvar$copper, breaks = copper_cts)
-#' 
+#' }
 #' \dontrun{
 #' ## We would run this, but it's expensive 
 #' partial_coplot_pbc <- gg_partial_coplot(rfsrc_pbc, xvar = "bili", 
@@ -37,12 +38,13 @@
 #'                                          time = 1, 
 #'                                          show.plots = FALSE)
 #' }
+#' \dontrun{
 #' ## so load the cached set
 #' data(partial_coplot_pbc, package="ggRandomForests")
 #' 
 #' # Partial coplot
 #' plot(partial_coplot_pbc) #, se = FALSE)
-#'  
+#'  }
 #' 
 #' @export
 gg_partial_coplot.rfsrc <- function(object,