##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:ClustAll.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings ClustAll_1.1.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/ClustAll.Rcheck’
* using R version 4.4.0 Patched (2024-04-24 r86482)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.4
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘ClustAll/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘ClustAll’ version ‘1.1.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘ClustAll’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                     user system elapsed
createClustAll     13.356  0.223  18.385
JACCARD_DISTANCE_F  3.091  0.184  47.214
summary_clusters    2.266  0.101  45.883
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘createClustAll_example.R’
  Running ‘runClustAll_example.R’
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: OK

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL ClustAll
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘ClustAll’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (ClustAll)

R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> # All possibilities to create the object
> ###########################################
> require("ClustAll")
Loading required package: ClustAll
> data("BreastCancerWisconsin", package = "ClustAll")
> data("BreastCancerWisconsinMISSING", package = "ClustAll")
> 
> test_creatingClustAll1 <- function() {
+   # Scenario 1: 1.No NA, 2.No imputation, 3.No imputation implemented
+   createClustAll(data = wdbc, colValidation = "Diagnosis",
+                  nImputation = NULL, dataImputed = NULL)
+ }
> 
> test_creatingClustAll2 <- function() {
+   # Scenario 2: 1.Yes NA, 2.Yes imputation, 3.Imputations automatically (default)
+   createClustAll(wdbcNA, nImputation = 2,  colValidation = "Diagnosis")
+   createClustAll(wdbcNA, dataImputed = wdbcMIDS,
+                  colValidation = "Diagnosis")
+ }
> 
> test_creatingClustAll3 <- function() {
+   # Scenario 3: 1.Yes NA, 2.Yes imputation, 3.Imputations manually
+   createClustAll(wdbcNA, dataImputed = wdbcMIDS,
+                  colValidation = "Diagnosis")
+ }
> 
> proc.time()
   user  system elapsed 
 14.003   0.946  20.009 

R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ##########################################################
> # Running the pipeline with parallelization
> ##########################################################
> require("ClustAll")
Loading required package: ClustAll
> data("BreastCancerWisconsin", package = "ClustAll") # load example data
> wdbc <- subset(wdbc,select=c(-ID, -Diagnosis))
> wdbc <- wdbc[1:15,1:8] # reduce the number of variables for a less computational time
> obj <- createClustAll(data = wdbc, colValidation = NULL,
+                       nImputation = NULL, dataImputed = NULL)
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
> 
> 
> test_pipeline1 <- function() {
+   # 1. No parallelization, 2. YES simplified (use all depth)
+   runClustAll(obj, threads = 1, simplify = TRUE)
+ }
> #
> test_pipeline2 <- function() {
+   # 1. YES parallelization, 2. YES simplified (one depth every four)
+   runClustAll(obj, threads = 2, simplify = TRUE)
+ }
> 
> 
> 
> proc.time()
   user  system elapsed 
 14.058   0.957  20.446 

R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage("ClustAll")
The following cluster(s) do(es) not exist:
NOTexist

You may want to check the clusters using resStratification.
The following cluster(s) do(es) not exist:
NOTexist

You may want to check the clusters using resStratification.
The following cluster(s) do(es) not exist:
NOTexist

You may want to check the clusters using resStratification.
The ClustALL Object does not include labels.
Please create a new object or modidy the object with the original labelling data. 
For that check addValidationData method.
The ClustALL Object does not include labels.
Please create a new object or modify the object with the original labelling data. 
For that check addValidationData method
Loading required package: mice

Attaching package: 'mice'

The following object is masked from 'package:stats':

    filter

The following objects are masked from 'package:base':

    cbind, rbind


 iter imp variable
  1   1
  1   2
  2   1
  2   2
  3   1
  3   2
  4   1
  4   2
  5   1
  5   2
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

The introduced columnname is not present in the dataset.
Please, make sure to introduce it correctly.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
The introduced data  and the original data labelling have different lenghts.
Make sure the introduced data is correct.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Categorical variables detected! Applying One Hot encoding...
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Categorical variables detected! Applying One Hot encoding...
Before continuing, check that the transformation has been processed correctly.


ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

The dataset does NOT contain NA values.
The imputation process will not be applied.

ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

Running default multiple imputation method.
For more information check mice package.

 iter imp variable
  1   1
  1   2
  2   1
  2   2
  3   1
  3   2
  4   1
  4   2
  5   1
  5   2

ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

Decimal number included, the decimal value will be rounded.
Running default multiple imputation method.
For more information check mice package.

 iter imp variable
  1   1
  1   2
  2   1
  2   2
  3   1
  3   2
  4   1
  4   2
  5   1
  5   2

ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

Decimal number included, the decimal value will be rounded.
Running default multiple imputation method.
For more information check mice package.

 iter imp variable
  1   1
  1   2
  1   3
  2   1
  2   2
  2   3
  3   1
  3   2
  3   3
  4   1
  4   2
  4   3
  5   1
  5   2
  5   3

ClustALL object created successfully. You can use runClustAll.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

The dataset contains NA values.
Specify the number of imputations (nImputation) to be computed.
The dataset contains character values.
They are converted to categorical (more than one class) or to binary (one class).
Before continuing, check that the transformation has been processed correctly.

Argument nImputation is not valid.
Please introduce a positive number of imputations.


RUNIT TEST PROTOCOL -- Mon Jun 10 01:33:18 2024 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
ClustAll RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: Number of logged events: 2 
2: Number of logged events: 1 
3: Number of logged events: 1 
4: Number of logged events: 1 
> 
> proc.time()
   user  system elapsed 
 15.115   1.000  21.513