resolve CRAN notes on debian with data.table usage

to skip computationally intensive tests and limit DT threads in vignette and testthat

R/vpcstats.R

@@ -129,6 +129,7 @@ simulated.tidyvpcobj <- function(o, data, ysim, ..., xsim) {
 #'  values for lower/upper limit of quantification. Logicals for \code{blq} and \code{alq} are returned that indicate whether the DV value lies below/above limit
 #'  of quantification.
 #' @examples
+#' \donttest{
 #' require(magrittr)
 #'
 #' vpc <- observed(obs_data, x=TIME, y=DV) %>%
@@ -147,7 +148,7 @@ simulated.tidyvpcobj <- function(o, data, ysim, ..., xsim) {
 #'     stratify(~ STUDY) %>%
 #'     binning(bin = "kmeans", nbins = 4) %>%
 #'     vpcstats()
-#'
+#' }
 #'
 #' @seealso \code{\link{observed}} \code{\link{simulated}} \code{\link{stratify}} \code{\link{predcorrect}} \code{\link{binning}} \code{\link{binless}} \code{\link{vpcstats}}
 
@@ -245,6 +246,7 @@ censoring.tidyvpcobj <- function(o, blq, lloq, alq, uloq, data=o$data, ...) {
 #'   is \code{obs} split by unique levels of stratification variable(s). Resulting datasets are of class object \code{data.frame}
 #'   and \code{data.table}.
 #' @examples
+#' \donttest{
 #' require(magrittr)
 #'
 #' vpc <- observed(obs_data, x=TIME, y=DV) %>%
@@ -259,6 +261,7 @@ censoring.tidyvpcobj <- function(o, blq, lloq, alq, uloq, data=o$data, ...) {
 #'     stratify(~ GENDER + STUDY) %>%
 #'     binning(bin = "centers", centers = c(1,3,5,7,10)) %>%
 #'     vpcstats()
+#' }
 #'
 #' @seealso \code{\link{observed}} \code{\link{simulated}} \code{\link{censoring}} \code{\link{predcorrect}} \code{\link{binning}} \code{\link{binless}} \code{\link{vpcstats}}
 
@@ -557,6 +560,7 @@ binning.tidyvpcobj <- function(o, bin, data=o$data, xbin="xmedian", centers, bre
 #'   prediction corrected VPC is to be performed, the \code{predcor.log} logical indicating whether the DV is on a log-scale, and the \code{pred} prediction
 #'   column from the original data.
 #' @examples
+#' \donttest{
 #' require(magrittr)
 #'
 #' obs_data <- obs_data[MDV == 0]
@@ -573,7 +577,6 @@ binning.tidyvpcobj <- function(o, bin, data=o$data, xbin="xmedian", centers, bre
 #'        predcorrect(pred=PRED) %>%
 #'        vpcstats()
 #'
-#'  \donttest{
 #'  # For binless loess prediction corrected, use predcorrect() before
 #'  # binless() and set loess.ypc = TRUE
 #'

tests/testthat.R

@@ -1,4 +1,6 @@
 library(testthat)
 library(tidyvpc)
-
+registered_threads <- data.table::getDTthreads()
+data.table::setDTthreads(2)
+on.exit(data.table::setDTthreads(registered_threads))
 test_check("tidyvpc")

tests/testthat/test-binless.R

@@ -114,6 +114,7 @@ test_that("cont vpc predcorrect binless vpcstats are correct", {
 })
 
 test_that("cont vpc binless censoring vpcstats are correct", {
+  skip_on_cran()
   obs_data <- obs_data[MDV == 0]
   sim_data <- sim_data[MDV == 0]
 
@@ -200,6 +201,7 @@ test_that("binless.tidyvpcobj returns correct errors and warnings", {
 
 test_that("binless.tidyvpcobj uses supplied lambda and span if optimize = FALSE", {
   # continuous VPC
+  skip_on_cran()
   obs_data <- obs_data[MDV == 0]
   sim_data <- sim_data[MDV == 0]
   obs_data$PRED <- sim_data[REP == 1, PRED]

vignettes/tidyvpc_cat.Rmd

@@ -18,6 +18,10 @@ library(data.table)
 set.seed(1014)
 ```
 
+```{r, echo=FALSE}
+data.table::setDTthreads(2)
+```
+
 
 ## Introduction
 

vignettes/tidyvpc_cont.Rmd

@@ -16,6 +16,11 @@ library(magrittr)
 set.seed(1014)
 ```
 
+```{r, echo=FALSE}
+data.table::setDTthreads(2)
+```
+
+
 ## Introduction 
 
 **When deriving a Visual Predictive Check (VPC) you must:**
@@ -232,27 +237,7 @@ vpc <- observed(obs_data, x=TIME, y=DV) %>%
 plot(vpc, censoring.type = "blq")
 ```
 
-Censoring using `LLOQ` variable in the data.
-
-First, let's add an LLOQ variable to the observed data.
-```{r}
-obs_data$LLOQ <- 50
-```
-
-Then, we'll specify lower limit of quantification as the unquoted variable name `LLOQ` in our data. Let's also provide our own lambda values.
-
-```{r  fig.width = 9, fig.height = 6, out.width=640, warning = FALSE}
-vpc <- observed(obs_data, x=TIME, y=DV) %>%
-    simulated(sim_data, y=DV) %>%
-    censoring(blq=(DV < LLOQ), lloq=LLOQ) %>%
-    binless(optimize = FALSE, lambda = c(1.5, 2.5, 1.7)) %>%
-    vpcstats()
-
-plot(vpc, censoring.type = "blq")
-```
-
-
-The `tidyvpc` package also allows you to use different LLOQ for each level of stratification variable. We'll set an `LLOQ` value of `50` for `Study A` and `25` for `Study B` and calculate statistics at 10%, 50%, 90% quantiles.
+The `tidyvpc` package also allows you to use LLOQ values within your data and different LLOQ for each level of stratification variable. We'll set an `LLOQ` value of `50` for `Study A` and `25` for `Study B` and calculate statistics at 10%, 50%, 90% quantiles.
 
 ```{r  fig.width = 9, fig.height = 6, out.width=640, warning = FALSE}
 obs_data$LLOQ <- obs_data[, ifelse(STUDY == "Study A", 50, 25)]
@@ -269,7 +254,7 @@ plot(vpc, censoring.type = "blq", facet.scales = "fixed")
 
 The `tidyvpc` package also supports usage of `censoring()` with ALQ data, similar to above usage with BLQ data.
 
-```{r  fig.width = 9, fig.height = 6, out.width=640, warning = FALSE}
+```{r  fig.width = 9, fig.height = 6, out.width=640, warning = FALSE, eval = FALSE}
 obs_data$ULOQ <- obs_data[, ifelse(STUDY == "Study A", 125, 100)]
 
 vpc <- observed(obs_data, x = TIME, y = DV) |>
@@ -278,11 +263,10 @@ vpc <- observed(obs_data, x = TIME, y = DV) |>
   stratify(~ STUDY) |>
   binning(bin = NTIME) |>
   vpcstats(qpred = c(0.1, 0.5, 0.9))
-
-plot(vpc, censoring.type = "alq")
-
 ```
 
+To plot the above `vpc` and include the percentage of ALQ plot, you'd run `plot(vpc, censoring.type = "alq")`.
+
 If using `censoring()` with both ALQ and BLQ data, set `censoring.type = "both"` in the `plot()` function to display both percentage of BLQ and ALQ plots as a grid in the resulting VPC plot.
 
 ```{r  fig.width = 9, fig.height = 6, out.width=640, warning = FALSE}

vignettes/tidyvpc_nlmixr2.Rmd

@@ -14,6 +14,10 @@ knitr::opts_chunk$set(
 )
 ```
 
+```{r, echo=FALSE}
+data.table::setDTthreads(2)
+```
+
 # Introduction
 
 `tidyvpc` and `nlmixr2` can work together seamlessly.  The information below