CRAN Package Check Results for Package worcs

Last updated on 2025-01-08 20:49:17 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.1.15				ERROR
r-devel-linux-x86_64-debian-gcc	0.1.16	7.89	164.56	172.45	OK
r-devel-linux-x86_64-fedora-clang	0.1.16			333.08	OK
r-devel-linux-x86_64-fedora-gcc	0.1.16			411.41	OK
r-devel-windows-x86_64	0.1.16	14.00	254.00	268.00	OK
r-patched-linux-x86_64	0.1.15	12.41	197.25	209.66	OK
r-release-linux-x86_64	0.1.15	9.97	198.03	208.00	OK
r-release-macos-arm64	0.1.16			164.00	OK
r-release-macos-x86_64	0.1.16			217.00	ERROR
r-release-windows-x86_64	0.1.15	13.00	258.00	271.00	OK
r-oldrel-macos-arm64	0.1.16			150.00	OK
r-oldrel-macos-x86_64	0.1.16			231.00	ERROR
r-oldrel-windows-x86_64	0.1.16	12.00	254.00	266.00	OK

Check Details

Version: 0.1.15
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘citation.Rmd’ using rmarkdown --- finished re-building ‘citation.Rmd’ --- re-building ‘endpoints.Rmd’ using rmarkdown --- finished re-building ‘endpoints.Rmd’ --- re-building ‘git_cloud.Rmd’ using rmarkdown --- finished re-building ‘git_cloud.Rmd’ --- re-building ‘reproduce.Rmd’ using rmarkdown --- finished re-building ‘reproduce.Rmd’ --- re-building ‘setup-docker.Rmd’ using rmarkdown --- finished re-building ‘setup-docker.Rmd’ --- re-building ‘setup.Rmd’ using rmarkdown --- finished re-building ‘setup.Rmd’ --- re-building ‘synthetic_data.Rmd’ using rmarkdown Quitting from lines 54-60 [unnamed-chunk-3] (synthetic_data.Rmd) Error: processing vignette 'synthetic_data.Rmd' failed with diagnostics: there is no package called 'tidySEM' --- failed re-building ‘synthetic_data.Rmd’ --- re-building ‘workflow.Rmd’ using rmarkdown --- finished re-building ‘workflow.Rmd’ SUMMARY: processing the following file failed: ‘synthetic_data.Rmd’ Error: Vignette re-building failed. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.1.16
Check: tests
Result: ERROR Running ‘testthat.R’ [32s/35s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(worcs) <1b>[0;34mWelcome to WORCS: Workflow for Open Reproducible Code in Science. Please read the tutorial before using this package, and consider citing it: Van Lissa and colleagues (2020) <doi:10.3233/DS-210031><1b>[0m > > test_check("worcs") | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | 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|======================================================================| 100% | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% ▶ dispatched target data ● completed target data [0.012 seconds, 1.743 kilobytes] ▶ dispatched target model ● completed target model [0.003 seconds, 108 bytes] ▶ dispatched target manuscript ▶ recorded workspace manuscript ✖ errored target manuscript ✖ errored pipeline [0.304 seconds] # Created by use_targets(). # Follow the comments below to fill in this target script. # Then follow the manual to check and run the pipeline: # https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline # Load packages required to define the pipeline: library(targets) # library(tarchetypes) # Load other packages as needed. # Set target options: tar_option_set( packages = c("tibble") # Packages that your targets need for their tasks. # format = "qs", # Optionally set the default storage format. qs is fast. # # Pipelines that take a long time to run may benefit from # optional distributed computing. To use this capability # in tar_make(), supply a {crew} controller # as discussed at https://books.ropensci.org/targets/crew.html. # Choose a controller that suits your needs. For example, the following # sets a controller that scales up to a maximum of two workers # which run as local R processes. Each worker launches when there is work # to do and exits if 60 seconds pass with no tasks to run. # # controller = crew::crew_controller_local(workers = 2, seconds_idle = 60) # # Alternatively, if you want workers to run on a high-performance computing # cluster, select a controller from the {crew.cluster} package. # For the cloud, see plugin packages like {crew.aws.batch}. # The following example is a controller for Sun Grid Engine (SGE). # # controller = crew.cluster::crew_controller_sge( # # Number of workers that the pipeline can scale up to: # workers = 10, # # It is recommended to set an idle time so workers can shut themselves # # down if they are not running tasks. # seconds_idle = 120, # # Many clusters install R as an environment module, and you can load it # # with the script_lines argument. To select a specific verison of R, # # you may need to include a version string, e.g. "module load R/4.3.2". # # Check with your system administrator if you are unsure. # script_lines = "module load R" # ) # # Set other options as needed. ) # Run the R scripts in the R/ folder with your custom functions: tar_source() # tar_source("other_functions.R") # Source other scripts as needed. # Set default priority to 1 to ensure all results are completed before manuscript. tar_option_set(priority = 1) # Replace the target list below with your own: list( tar_target( name = data, command = tibble(x = rnorm(100), y = rnorm(100)) # format = "qs" # Efficient storage for general data objects. ), tar_target( name = model, command = coefficients(lm(y ~ x, data = data)) ), tarchetypes::tar_render(manuscript, "manuscript/manuscript.Rmd", priority = 0, cue = tar_cue(mode = 'always')) # Set priority to 0 to ensure the manuscript is rendered after other results are available )[1] "manuscript.Rmd" ✔ skipped target data ✔ skipped target model ▶ dispatched target manuscript ▶ recorded workspace manuscript ✖ errored target manuscript ✖ errored pipeline [0.328 seconds] Error : pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available). Variables to impute: Petal.Length, Sepal.Length, Petal.Width, Species Variables used to impute: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species iter 1 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% iter 2 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% iter 3 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% iter 4 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% Error in libgit2::git_remote_push : unsupported URL protocol [ FAIL 6 | WARN 0 | SKIP 6 | PASS 156 ] ══ Skipped tests (6) ═══════════════════════════════════════════════════════════ • empty test (6): 'test-citations.R:3:1', 'test-citations.R:33:1', 'test-citations.R:67:1', 'test-citations.R:111:1', 'test-codebook.R:1:1', 'test-codebook.R:24:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-checksum_markdown.R:21:3'): checksum works from within Rmarkdown ── Error: pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available). Backtrace: ▆ 1. └─rmarkdown::render("manuscript.Rmd") at test-checksum_markdown.R:21:3 2. └─rmarkdown::pandoc_available(required_pandoc, error = TRUE) 3. └─rmarkdown:::stop2(paste(msg, collapse = " ")) ── Failure ('test-dir_of_save_data.R:29:3'): data stored in correct dir ──────── all(sapply(f, file.exists)) is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:24:3'): targets works with apa6 ──────────────────── file.exists("manuscript/manuscript.html") is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:31:3'): targets works with apa6 ──────────────────── file.exists("manuscript/manuscript.html") is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:84:3'): targets works with target markdown ───────── file.exists("_targets.html") is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:90:3'): targets works with target markdown ───────── file.exists("_targets.html") is not TRUE `actual`: FALSE `expected`: TRUE [ FAIL 6 | WARN 0 | SKIP 6 | PASS 156 ] Error: Test failures Execution halted Flavor: r-release-macos-x86_64

Version: 0.1.16
Check: tests
Result: ERROR Running ‘testthat.R’ [32s/36s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(worcs) <1b>[0;34mWelcome to WORCS: Workflow for Open Reproducible Code in Science. Please read the tutorial before using this package, and consider citing it: Van Lissa and colleagues (2020) <doi:10.3233/DS-210031><1b>[0m > > test_check("worcs") | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | 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|======================================================================| 100% | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% ▶ dispatched target data ● completed target data [0.017 seconds, 1.745 kilobytes] ▶ dispatched target model ● completed target model [0.007 seconds, 108 bytes] ▶ dispatched target manuscript ▶ recorded workspace manuscript ✖ errored target manuscript ✖ errored pipeline [0.342 seconds] # Created by use_targets(). # Follow the comments below to fill in this target script. # Then follow the manual to check and run the pipeline: # https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline # Load packages required to define the pipeline: library(targets) # library(tarchetypes) # Load other packages as needed. # Set target options: tar_option_set( packages = c("tibble") # Packages that your targets need for their tasks. # format = "qs", # Optionally set the default storage format. qs is fast. # # Pipelines that take a long time to run may benefit from # optional distributed computing. To use this capability # in tar_make(), supply a {crew} controller # as discussed at https://books.ropensci.org/targets/crew.html. # Choose a controller that suits your needs. For example, the following # sets a controller that scales up to a maximum of two workers # which run as local R processes. Each worker launches when there is work # to do and exits if 60 seconds pass with no tasks to run. # # controller = crew::crew_controller_local(workers = 2, seconds_idle = 60) # # Alternatively, if you want workers to run on a high-performance computing # cluster, select a controller from the {crew.cluster} package. # For the cloud, see plugin packages like {crew.aws.batch}. # The following example is a controller for Sun Grid Engine (SGE). # # controller = crew.cluster::crew_controller_sge( # # Number of workers that the pipeline can scale up to: # workers = 10, # # It is recommended to set an idle time so workers can shut themselves # # down if they are not running tasks. # seconds_idle = 120, # # Many clusters install R as an environment module, and you can load it # # with the script_lines argument. To select a specific verison of R, # # you may need to include a version string, e.g. "module load R/4.3.2". # # Check with your system administrator if you are unsure. # script_lines = "module load R" # ) # # Set other options as needed. ) # Run the R scripts in the R/ folder with your custom functions: tar_source() # tar_source("other_functions.R") # Source other scripts as needed. # Set default priority to 1 to ensure all results are completed before manuscript. tar_option_set(priority = 1) # Replace the target list below with your own: list( tar_target( name = data, command = tibble(x = rnorm(100), y = rnorm(100)) # format = "qs" # Efficient storage for general data objects. ), tar_target( name = model, command = coefficients(lm(y ~ x, data = data)) ), tarchetypes::tar_render(manuscript, "manuscript/manuscript.Rmd", priority = 0, cue = tar_cue(mode = 'always')) # Set priority to 0 to ensure the manuscript is rendered after other results are available )[1] "manuscript.Rmd" ✔ skipped target data ✔ skipped target model ▶ dispatched target manuscript ▶ recorded workspace manuscript ✖ errored target manuscript ✖ errored pipeline [0.378 seconds] Error : pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available). Variables to impute: Petal.Length, Sepal.Length, Petal.Width, Species Variables used to impute: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species iter 1 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% iter 2 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% iter 3 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% iter 4 | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% Error in libgit2::git_remote_push : unsupported URL protocol [ FAIL 6 | WARN 0 | SKIP 6 | PASS 156 ] ══ Skipped tests (6) ═══════════════════════════════════════════════════════════ • empty test (6): 'test-citations.R:3:1', 'test-citations.R:33:1', 'test-citations.R:67:1', 'test-citations.R:111:1', 'test-codebook.R:1:1', 'test-codebook.R:24:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-checksum_markdown.R:21:3'): checksum works from within Rmarkdown ── Error: pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available). Backtrace: ▆ 1. └─rmarkdown::render("manuscript.Rmd") at test-checksum_markdown.R:21:3 2. └─rmarkdown::pandoc_available(required_pandoc, error = TRUE) 3. └─rmarkdown:::stop2(paste(msg, collapse = " ")) ── Failure ('test-dir_of_save_data.R:29:3'): data stored in correct dir ──────── all(sapply(f, file.exists)) is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:24:3'): targets works with apa6 ──────────────────── file.exists("manuscript/manuscript.html") is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:31:3'): targets works with apa6 ──────────────────── file.exists("manuscript/manuscript.html") is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:84:3'): targets works with target markdown ───────── file.exists("_targets.html") is not TRUE `actual`: FALSE `expected`: TRUE ── Failure ('test-targets.R:90:3'): targets works with target markdown ───────── file.exists("_targets.html") is not TRUE `actual`: FALSE `expected`: TRUE [ FAIL 6 | WARN 0 | SKIP 6 | PASS 156 ] Error: Test failures Execution halted Flavor: r-oldrel-macos-x86_64