Last updated on 2025-12-19 23:49:41 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.3.0 | 12.46 | 253.01 | 265.47 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.3.0 | 7.88 | 145.60 | 153.48 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.3.0 | 21.00 | 293.16 | 314.16 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.3.0 | 19.00 | 445.08 | 464.08 | ERROR | |
| r-devel-windows-x86_64 | 0.3.0 | 13.00 | 246.00 | 259.00 | OK | |
| r-patched-linux-x86_64 | 0.3.0 | 10.62 | 212.80 | 223.42 | OK | |
| r-release-linux-x86_64 | 0.3.0 | 12.27 | 209.99 | 222.26 | OK | |
| r-release-macos-arm64 | 0.3.0 | 3.00 | 35.00 | 38.00 | OK | |
| r-release-macos-x86_64 | 0.3.0 | 10.00 | 287.00 | 297.00 | OK | |
| r-release-windows-x86_64 | 0.3.0 | 13.00 | 194.00 | 207.00 | OK | |
| r-oldrel-macos-arm64 | 0.3.0 | 3.00 | 70.00 | 73.00 | OK | |
| r-oldrel-macos-x86_64 | 0.3.0 | 10.00 | 279.00 | 289.00 | OK | |
| r-oldrel-windows-x86_64 | 0.3.0 | 21.00 | 257.00 | 278.00 | OK |
Version: 0.3.0
Check: examples
Result: ERROR
Running examples in ‘BioTIMEr-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: getAlphaMetrics
> ### Title: Alpha diversity metrics
> ### Aliases: getAlphaMetrics
>
> ### ** Examples
>
> # Mean and sd values of the metrics for several resamplings
> gridding(BTsubset_meta, BTsubset_data) |>
+ resampling(measure = "BIOMASS", resamps = 2) |>
+ getAlphaMetrics(measure = "BIOMASS") |>
+ dplyr::summarise(
+ dplyr::across(
+ .cols = !resamp,
+ .fns = c(mean = mean, sd = sd)),
+ .by = c(assemblageID, YEAR)) |>
+ tidyr::pivot_longer(
+ col = dplyr::contains("_"),
+ names_to = c("metric", "stat"),
+ names_sep = "_",
+ names_transform = as.factor) |>
+ tidyr::pivot_wider(names_from = stat) |>
+ head(10)
OK: all SL studies have 1 grid cell
Warning: NA values found and removed.
Only a subset of `x` is used.
Error in `[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), :
attempt access index 20/20 in VECTOR_ELT
Calls: head ... resampling.default -> resampling_internal -> [ -> [.data.table
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [48s/27s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> test_check("BioTIMEr")
Loading required package: BioTIMEr
Starting 2 test processes.
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
Saving _problems/test-resampling-10.R
Saving _problems/test-resampling-11.R
Saving _problems/test-resampling-37.R
Saving _problems/test-resampling-46.R
Saving _problems/test-resampling-60.R
Saving _problems/test-resampling-85.R
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance_biomass.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance_biomass_conservative.R: OK: all SL studies have 1 grid cell
> test-resampling_biomass.R: OK: all SL studies have 1 grid cell
[ FAIL 6 | WARN 5 | SKIP 20 | PASS 65 ]
══ Skipped tests (20) ══════════════════════════════════════════════════════════
• On CRAN (20): 'test-metrics.R:59:3', 'test-metrics.R:82:3',
'test-metrics.R:95:3', 'test-metrics.R:107:3', 'test-metrics.R:120:3',
'test-workflow_alpha.R:3:3', 'test-workflow_beta.R:3:3', 'test-slopes.R:3:3',
'test-plots.R:4:3', 'test-scales.R:35:3', 'test-resampling.R:101:3',
'test-resampling_abundance.R:11:3', 'test-gridding.R:75:3',
'test-gridding.R:82:3', 'test-gridding.R:89:3',
'test-resampling_abundance_biomass.R:11:3',
'test-resampling_abundance_biomass_conservative.R:11:3',
'test-resampling_core.R:91:3', 'test-resampling_core.R:164:3',
'test-resampling_biomass.R:11:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-resampling.R:10:3'): resampling returns an object of same class as meta ──
Expected `resdf <- resampling(test_df, measure = "BIOMASS")` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Error ('test-resampling.R:11:3'): resampling returns an object of same class as meta ──
Error in `eval(code, test_env)`: object 'resdf' not found
Backtrace:
▆
1. └─testthat::expect_s3_class(resdf, "data.frame") at test-resampling.R:11:3
2. └─testthat::quasi_label(enquo(object))
3. └─rlang::eval_bare(expr, quo_get_env(quo))
── Failure ('test-resampling.R:34:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Failure ('test-resampling.R:40:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Failure ('test-resampling.R:53:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Error ('test-resampling.R:82:3'): resampling correctly excludes 1 year long studies ──
Error in ``[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), keyby = c("assemblageID", "YEAR"))`: attempt access index 20/20 in VECTOR_ELT
Backtrace:
▆
1. ├─testthat::expect_warning(resampling(test_df_1y, "BIOMASS"), regexp = "Some 1-year-long studies were removed.") at test-resampling.R:82:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. ├─BioTIMEr::resampling(test_df_1y, "BIOMASS")
8. └─BioTIMEr:::resampling.default(test_df_1y, "BIOMASS")
9. └─BioTIMEr:::resampling_internal(...)
10. ├─...[]
11. └─data.table:::`[.data.table`(...)
[ FAIL 6 | WARN 5 | SKIP 20 | PASS 65 ]
Deleting unused snapshots: 'plots/themebiotime-plot.svg',
'scales/color-continuous-cool-false.svg',
'scales/color-continuous-cool-true.svg',
'scales/color-continuous-gradient-false.svg',
'scales/color-continuous-gradient-true.svg',
'scales/color-continuous-realms-false.svg',
'scales/color-continuous-realms-true.svg',
'scales/color-continuous-warm-false.svg',
'scales/color-continuous-warm-true.svg', 'scales/color-cool-false-false.svg',
'scales/color-cool-false-true.svg', 'scales/color-cool-true-false.svg',
'scales/color-cool-true-true.svg', 'scales/color-gradient-false-false.svg',
'scales/color-gradient-false-true.svg', 'scales/color-gradient-true-false.svg',
'scales/color-gradient-true-true.svg', 'scales/color-realms-false-false.svg',
…, 'scales/fill-warm-true-false.svg', and 'scales/fill-warm-true-true.svg'
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘introduction.qmd’ using html
<1b>[31m
processing file: introduction.qmd
<1b>[39mBTsubset_meta package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t _<08>m_<08>e_<08>t_<08>a_<08>d_<08>a_<08>t_<08>a
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of the metadata from BioTIME
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_meta
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_meta` A data frame with 12 rows and 25 columns:
STUDY_ID
BioTIME study unique identifier
REALM
Realm of study location, i.e. Marine
CLIMATE
Climate of study location, i.e. Temperate
HABITAT
Habitat of study location, i.e. Rivers
PROTECTED_AREA
Binary variable indicating if the study is within a protected
area
BIOME_MAP
Biome of study location (taken from the WWF biomes, i.e.
Temperate broadleaf and mixed forests
TAXA
High level taxonomic identity of study species, i.e. Fish
ORGANISMS
More detailed information on taxonomy, i.e. woody plants
TITLE
Title of study as identified in original source
AB_BIO
A, B or AB to designate abundance only, biomass only or both
DATA_POINTS
Number of unique data points in study, e.g. 10 data points
spanning 15 years = 10
START_YEAR
First year of study
END_YEAR
Last year of study
CENT_LAT
Central latitude taken from the convex hull around all study
coordinates
CENT_LONG
Central longitude taken from the convex hull around all study
coordinates
NUMBER_OF_SPECIES
Number of distinct species in study
NUMBER_OF_SAMPLES
Number of distinct samples in study
NUMBER_LAT_LONG
Number of distinct geographic coordinates in study
TOTAL
Total number of records in study
GRAIN_SIZE_TEXT
Grain size described in text, i.e. size of forest plots
AREA_SQ_KM
Total area of study in km2
DATE_STUDY_ADDED
Date that the study was added to the database
ABUNDANCE_TYPE
Type of abundance, i.e. count
BIOMASS_TYPE
Type of biomass, i.e. weight
SAMPLE_DESC
Concatenation of descriptors comprising the unique sampling
event
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
BTsubset_data package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of data from BioTIME temporal surveys.
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_data
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_data` A data frame with 81,084 rows and 17 columns:
ID_ALL_RAW_DATA
Unique BioTIME identifier for record
ABUNDANCE
Double representing the abundance for the record (see
metadata for details of ABUNDANCE_TYPE
BIOMASS
Double representing the biomass for the record (see metadata
for details of BIOMASS_TYPE
ID_SPECIES
Unique identifier linking to the species table
SAMPLE_DESC
Concatenation of variables comprising unique sampling event
LATITUDE
Latitude of record
LONGITUDE
Longitude of record
DEPTH
Depth or elevation of record if available
DAY
Numerical day of record
MONTH
Numerical value of month for record, i.e. January=1
YEAR
Year of record
STUDY_ID
BioTIME study unique identifier
newID
Validated species identifier key
valid_name
Highest taxonomic resolution of individual, preferred is
genus and species
resolution
Level of resolution, i.e. 'species' represented by genus and
species
taxon
Higher level taxonomic grouping, i.e. Fish
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
Vignettes with name or keyword or title matching 'BioTIMEr' using fuzzy
matching:
BioTIMEr::introduction
Introduction to BioTIMEr
Type 'vignette(PKG::FOO)' to inspect entries 'PKG::FOO'.
Help files with alias or concept or title matching 'BioTIMEr' using
fuzzy matching:
BioTIMEr::BTsubset_data
BioTIME subset
BioTIMEr::BTsubset_meta
BioTIME subset metadata
BioTIMEr::BioTIMEr-package
BioTIMEr: Tools to Use and Explore the
'BioTIME' Database
Aliases: BioTIMEr, BioTIMEr-package
BioTIMEr::getLinearRegressions
Get Linear Regressions BioTIME
BioTIMEr::gridding gridding BioTIME data
BioTIMEr::gridding_internal
gridding BioTIME data
BioTIMEr::plotSlopes Plot slopes BioTIME
BioTIMEr::resampling Rarefy BioTIME data to an equal number of
samples per year
BioTIMEr::resampling_core
Rarefy BioTIME data Applies sample-based
rarefaction to standardise the number of
samples per year within a cell-level time
series.
BioTIMEr::scale_color_biotime
Scale construction for ggplot use
Aliases: scale_color_biotime, scale_colour_biotime,
scale_fill_biotime
BioTIMEr::themeBioTIME
ggplot2 theme for BioTIME plots
Aliases: themeBioTIME
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
<1b>[31mError in `[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), :
attempt access index 20/20 in VECTOR_ELT
Calls: .main ... resampling.default -> resampling_internal -> [ -> [.data.table
Quitting from introduction.qmd:265-274 [resampling_ex1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `[.data.table`:
! attempt access index 20/20 in VECTOR_ELT
---
Backtrace:
▆
1. ├─BioTIMEr::resampling(...)
2. └─BioTIMEr:::resampling.default(...)
3. └─BioTIMEr:::resampling_internal(...)
4. ├─...[]
5. └─data.table:::`[.data.table`(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Execution halted
<1b>[39m<1b>[33mWARN: Error encountered when rendering files<1b>[39m
Error: processing vignette 'introduction.qmd' failed with diagnostics:
! Error running quarto CLI from R.
Caused by error in `quarto_render()`:
✖ Error returned by quarto CLI.
-----------------------------
processing file: introduction.qmd
BTsubset_meta package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t _<08>m_<08>e_<08>t_<08>a_<08>d_<08>a_<08>t_<08>a
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of the metadata from BioTIME
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_meta
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_meta` A data frame with 12 rows and 25 columns:
STUDY_ID
BioTIME study unique identifier
REALM
Realm of study location, i.e. Marine
CLIMATE
Climate of study location, i.e. Temperate
HABITAT
Habitat of study location, i.e. Rivers
PROTECTED_AREA
Binary variable indicating if the study is within a protected
area
BIOME_MAP
Biome of study location (taken from the WWF biomes, i.e.
Temperate broadleaf and mixed forests
TAXA
High level taxonomic identity of study species, i.e. Fish
ORGANISMS
More detailed information on taxonomy, i.e. woody plants
TITLE
Title of study as identified in original source
AB_BIO
A, B or AB to designate abundance only, biomass only or both
DATA_POINTS
Number of unique data points in study, e.g. 10 data points
spanning 15 years = 10
START_YEAR
First year of study
END_YEAR
Last year of study
CENT_LAT
Central latitude taken from the convex hull around all study
coordinates
CENT_LONG
Central longitude taken from the convex hull around all study
coordinates
NUMBER_OF_SPECIES
Number of distinct species in study
NUMBER_OF_SAMPLES
Number of distinct samples in study
NUMBER_LAT_LONG
Number of distinct geographic coordinates in study
TOTAL
Total number of records in study
GRAIN_SIZE_TEXT
Grain size described in text, i.e. size of forest plots
AREA_SQ_KM
Total area of study in km2
DATE_STUDY_ADDED
Date that the study was added to the database
ABUNDANCE_TYPE
Type of abundance, i.e. count
BIOMASS_TYPE
Type of biomass, i.e. weight
SAMPLE_DESC
Concatenation of descriptors comprising the unique sampling
event
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
BTsubset_data package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of data from BioTIME temporal surveys.
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_data
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_data` A data frame with 81,084 rows and 17 columns:
ID_ALL_RAW_DATA
Unique BioTIME identifier for record
ABUNDANCE
Double representing the abundance for the record (see
metadata for details of ABUNDANCE_TYPE
BIOMASS
Double representing the biomass for the record (see metadata
for details of BIOMASS_TYPE
ID_SPECIES
Unique identifier linking to the species table
SAMPLE_DESC
Concatenation of variables comprising unique sampling event
LATITUDE
Latitude of record
LONGITUDE
Longitude of record
DEPTH
Depth or elevation of record if available
DAY
Numerical day of record
MONTH
Numerical value of month for record, i.e. January=1
YEAR
Year of record
STUDY_ID
BioTIME study unique identifier
newID
Validated species identifier key
valid_name
Highest taxonomic resolution of individual, preferred is
genus and species
resolution
Level of resolution, i.e. 'species' represented by genus and
species
taxon
Higher level taxonomic grouping, i.e. Fish
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
Vignettes with name or keyword or title matching 'BioTIMEr' using fuzzy
matching:
BioTIMEr::introduction
Introduction to BioTIMEr
Type 'vignette(PKG::FOO)' to inspect entries 'PKG::FOO'.
Help files with alias or concept or title matching 'BioTIMEr' using
fuzzy matching:
BioTIMEr::BTsubset_data
BioTIME subset
BioTIMEr::BTsubset_meta
BioTIME subset metadata
BioTIMEr::BioTIMEr-package
BioTIMEr: Tools to Use and Explore the
'BioTIME' Database
Aliases: BioTIMEr, BioTIMEr-package
BioTIMEr::getLinearRegressions
Get Linear Regressions BioTIME
BioTIMEr::gridding gridding BioTIME data
BioTIMEr::gridding_internal
gridding BioTIME data
BioTIMEr::plotSlopes Plot slopes BioTIME
BioTIMEr::resampling Rarefy BioTIME data to an equal number of
samples per year
BioTIMEr::resampling_core
Rarefy BioTIME data Applies sample-based
rarefaction to standardise the number of
samples per year within a cell-level time
series.
BioTIMEr::scale_color_biotime
Scale construction for ggplot use
Aliases: scale_color_biotime, scale_colour_biotime,
scale_fill_biotime
BioTIMEr::themeBioTIME
ggplot2 theme for BioTIME plots
Aliases: themeBioTIME
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
<1b>[31mError in `[.data.table`(x, j = `:=`(minsamp,<1b>[39m
<1b>[31mdata.table::uniqueN(SAMPLE_DESC)), :<1b>[39m
attempt access index 20/20 in VECTOR_ELT
Calls: .main ... resampling.default -> resampling_internal -> [ ->
[.data.table
Quitting from introduction.qmd:265-274 [resampling_ex1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `[.data.table`:
! attempt access index 20/20 in VECTOR_ELT
---
Backtrace:
▆
1. ├─BioTIMEr::resampling(...)
2. └─BioTIMEr:::resampling.default(...)
3. └─BioTIMEr:::resampling_internal(...)
4. ├─...[]
5. └─data.table:::`[.data.table`(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Execution halted
<1b>[33mWARN: Error encountered when rendering files<1b>[39m
Caused by error:
! System command 'quarto' failed
--- failed re-building ‘introduction.qmd’
SUMMARY: processing the following file failed:
‘introduction.qmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.0
Check: examples
Result: ERROR
Running examples in ‘BioTIMEr-Ex.R’ failed
The error most likely occurred in:
> ### Name: getAlphaMetrics
> ### Title: Alpha diversity metrics
> ### Aliases: getAlphaMetrics
>
> ### ** Examples
>
> # Mean and sd values of the metrics for several resamplings
> gridding(BTsubset_meta, BTsubset_data) |>
+ resampling(measure = "BIOMASS", resamps = 2) |>
+ getAlphaMetrics(measure = "BIOMASS") |>
+ dplyr::summarise(
+ dplyr::across(
+ .cols = !resamp,
+ .fns = c(mean = mean, sd = sd)),
+ .by = c(assemblageID, YEAR)) |>
+ tidyr::pivot_longer(
+ col = dplyr::contains("_"),
+ names_to = c("metric", "stat"),
+ names_sep = "_",
+ names_transform = as.factor) |>
+ tidyr::pivot_wider(names_from = stat) |>
+ head(10)
OK: all SL studies have 1 grid cell
Warning: NA values found and removed.
Only a subset of `x` is used.
Error in `[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), :
attempt access index 20/20 in VECTOR_ELT
Calls: head ... resampling.default -> resampling_internal -> [ -> [.data.table
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 0.3.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [89s/65s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> test_check("BioTIMEr")
Loading required package: BioTIMEr
Starting 2 test processes.
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
Saving _problems/test-resampling-10.R
Saving _problems/test-resampling-11.R
Saving _problems/test-resampling-37.R
Saving _problems/test-resampling-46.R
Saving _problems/test-resampling-60.R
Saving _problems/test-resampling-85.R
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance_biomass.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance_biomass_conservative.R: OK: all SL studies have 1 grid cell
> test-resampling_biomass.R: OK: all SL studies have 1 grid cell
[ FAIL 6 | WARN 5 | SKIP 20 | PASS 65 ]
══ Skipped tests (20) ══════════════════════════════════════════════════════════
• On CRAN (20): 'test-metrics.R:59:3', 'test-metrics.R:82:3',
'test-metrics.R:95:3', 'test-metrics.R:107:3', 'test-metrics.R:120:3',
'test-workflow_alpha.R:3:3', 'test-workflow_beta.R:3:3', 'test-slopes.R:3:3',
'test-plots.R:4:3', 'test-scales.R:35:3', 'test-resampling.R:101:3',
'test-gridding.R:75:3', 'test-gridding.R:82:3', 'test-gridding.R:89:3',
'test-resampling_abundance.R:11:3',
'test-resampling_abundance_biomass.R:11:3',
'test-resampling_abundance_biomass_conservative.R:11:3',
'test-resampling_core.R:91:3', 'test-resampling_core.R:164:3',
'test-resampling_biomass.R:11:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-resampling.R:10:3'): resampling returns an object of same class as meta ──
Expected `resdf <- resampling(test_df, measure = "BIOMASS")` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Error ('test-resampling.R:11:3'): resampling returns an object of same class as meta ──
Error in `eval(code, test_env)`: object 'resdf' not found
Backtrace:
▆
1. └─testthat::expect_s3_class(resdf, "data.frame") at test-resampling.R:11:3
2. └─testthat::quasi_label(enquo(object))
3. └─rlang::eval_bare(expr, quo_get_env(quo))
── Failure ('test-resampling.R:34:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Failure ('test-resampling.R:40:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Failure ('test-resampling.R:53:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Error ('test-resampling.R:82:3'): resampling correctly excludes 1 year long studies ──
Error in ``[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), keyby = c("assemblageID", "YEAR"))`: attempt access index 20/20 in VECTOR_ELT
Backtrace:
▆
1. ├─testthat::expect_warning(resampling(test_df_1y, "BIOMASS"), regexp = "Some 1-year-long studies were removed.") at test-resampling.R:82:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. ├─BioTIMEr::resampling(test_df_1y, "BIOMASS")
8. └─BioTIMEr:::resampling.default(test_df_1y, "BIOMASS")
9. └─BioTIMEr:::resampling_internal(...)
10. ├─...[]
11. └─data.table:::`[.data.table`(...)
[ FAIL 6 | WARN 5 | SKIP 20 | PASS 65 ]
Deleting unused snapshots: 'plots/themebiotime-plot.svg',
'scales/color-continuous-cool-false.svg',
'scales/color-continuous-cool-true.svg',
'scales/color-continuous-gradient-false.svg',
'scales/color-continuous-gradient-true.svg',
'scales/color-continuous-realms-false.svg',
'scales/color-continuous-realms-true.svg',
'scales/color-continuous-warm-false.svg',
'scales/color-continuous-warm-true.svg', 'scales/color-cool-false-false.svg',
'scales/color-cool-false-true.svg', 'scales/color-cool-true-false.svg',
'scales/color-cool-true-true.svg', 'scales/color-gradient-false-false.svg',
'scales/color-gradient-false-true.svg', 'scales/color-gradient-true-false.svg',
'scales/color-gradient-true-true.svg', 'scales/color-realms-false-false.svg',
…, 'scales/fill-warm-true-false.svg', and 'scales/fill-warm-true-true.svg'
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.3.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘introduction.qmd’ using html
<1b>[31m
processing file: introduction.qmd
<1b>[39mBTsubset_meta package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t _<08>m_<08>e_<08>t_<08>a_<08>d_<08>a_<08>t_<08>a
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of the metadata from BioTIME
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_meta
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_meta` A data frame with 12 rows and 25 columns:
STUDY_ID
BioTIME study unique identifier
REALM
Realm of study location, i.e. Marine
CLIMATE
Climate of study location, i.e. Temperate
HABITAT
Habitat of study location, i.e. Rivers
PROTECTED_AREA
Binary variable indicating if the study is within a protected
area
BIOME_MAP
Biome of study location (taken from the WWF biomes, i.e.
Temperate broadleaf and mixed forests
TAXA
High level taxonomic identity of study species, i.e. Fish
ORGANISMS
More detailed information on taxonomy, i.e. woody plants
TITLE
Title of study as identified in original source
AB_BIO
A, B or AB to designate abundance only, biomass only or both
DATA_POINTS
Number of unique data points in study, e.g. 10 data points
spanning 15 years = 10
START_YEAR
First year of study
END_YEAR
Last year of study
CENT_LAT
Central latitude taken from the convex hull around all study
coordinates
CENT_LONG
Central longitude taken from the convex hull around all study
coordinates
NUMBER_OF_SPECIES
Number of distinct species in study
NUMBER_OF_SAMPLES
Number of distinct samples in study
NUMBER_LAT_LONG
Number of distinct geographic coordinates in study
TOTAL
Total number of records in study
GRAIN_SIZE_TEXT
Grain size described in text, i.e. size of forest plots
AREA_SQ_KM
Total area of study in km2
DATE_STUDY_ADDED
Date that the study was added to the database
ABUNDANCE_TYPE
Type of abundance, i.e. count
BIOMASS_TYPE
Type of biomass, i.e. weight
SAMPLE_DESC
Concatenation of descriptors comprising the unique sampling
event
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
BTsubset_data package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of data from BioTIME temporal surveys.
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_data
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_data` A data frame with 81,084 rows and 17 columns:
ID_ALL_RAW_DATA
Unique BioTIME identifier for record
ABUNDANCE
Double representing the abundance for the record (see
metadata for details of ABUNDANCE_TYPE
BIOMASS
Double representing the biomass for the record (see metadata
for details of BIOMASS_TYPE
ID_SPECIES
Unique identifier linking to the species table
SAMPLE_DESC
Concatenation of variables comprising unique sampling event
LATITUDE
Latitude of record
LONGITUDE
Longitude of record
DEPTH
Depth or elevation of record if available
DAY
Numerical day of record
MONTH
Numerical value of month for record, i.e. January=1
YEAR
Year of record
STUDY_ID
BioTIME study unique identifier
newID
Validated species identifier key
valid_name
Highest taxonomic resolution of individual, preferred is
genus and species
resolution
Level of resolution, i.e. 'species' represented by genus and
species
taxon
Higher level taxonomic grouping, i.e. Fish
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
Vignettes with name or keyword or title matching 'BioTIMEr' using fuzzy
matching:
BioTIMEr::introduction
Introduction to BioTIMEr
Type 'vignette(PKG::FOO)' to inspect entries 'PKG::FOO'.
Help files with alias or concept or title matching 'BioTIMEr' using
fuzzy matching:
BioTIMEr::BTsubset_data
BioTIME subset
BioTIMEr::BTsubset_meta
BioTIME subset metadata
BioTIMEr::BioTIMEr-package
BioTIMEr: Tools to Use and Explore the
'BioTIME' Database
Aliases: BioTIMEr, BioTIMEr-package
BioTIMEr::getLinearRegressions
Get Linear Regressions BioTIME
BioTIMEr::gridding gridding BioTIME data
BioTIMEr::gridding_internal
gridding BioTIME data
BioTIMEr::plotSlopes Plot slopes BioTIME
BioTIMEr::resampling Rarefy BioTIME data to an equal number of
samples per year
BioTIMEr::resampling_core
Rarefy BioTIME data Applies sample-based
rarefaction to standardise the number of
samples per year within a cell-level time
series.
BioTIMEr::scale_color_biotime
Scale construction for ggplot use
Aliases: scale_color_biotime, scale_colour_biotime,
scale_fill_biotime
BioTIMEr::themeBioTIME
ggplot2 theme for BioTIME plots
Aliases: themeBioTIME
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
<1b>[31mError in `[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), :
attempt access index 20/20 in VECTOR_ELT
C<1b>[39m<1b>[31malls: .main ... resampling.default -> resampling_internal -> [ -> [.data.table
Quitting from introduction.qmd:265-274 [resampling_ex1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `[.data.table`:
! attempt access index 20/20 in VECTOR_ELT
---
Backtrace:
▆
1. ├─BioTIMEr::resampling(...)
2. └─BioTIMEr:::resampling.default(...)
3. └─BioTIMEr:::resampling_internal(...)
4. ├─...[]
5. └─data.table:::`[.data.table`(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Execution halted
<1b>[39mError: processing vignette 'introduction.qmd' failed with diagnostics:
! Error running quarto CLI from R.
Caused by error in `quarto_render()`:
✖ Error returned by quarto CLI.
-----------------------------
processing file: introduction.qmd
BTsubset_meta package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t _<08>m_<08>e_<08>t_<08>a_<08>d_<08>a_<08>t_<08>a
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of the metadata from BioTIME
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_meta
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_meta` A data frame with 12 rows and 25 columns:
STUDY_ID
BioTIME study unique identifier
REALM
Realm of study location, i.e. Marine
CLIMATE
Climate of study location, i.e. Temperate
HABITAT
Habitat of study location, i.e. Rivers
PROTECTED_AREA
Binary variable indicating if the study is within a protected
area
BIOME_MAP
Biome of study location (taken from the WWF biomes, i.e.
Temperate broadleaf and mixed forests
TAXA
High level taxonomic identity of study species, i.e. Fish
ORGANISMS
More detailed information on taxonomy, i.e. woody plants
TITLE
Title of study as identified in original source
AB_BIO
A, B or AB to designate abundance only, biomass only or both
DATA_POINTS
Number of unique data points in study, e.g. 10 data points
spanning 15 years = 10
START_YEAR
First year of study
END_YEAR
Last year of study
CENT_LAT
Central latitude taken from the convex hull around all study
coordinates
CENT_LONG
Central longitude taken from the convex hull around all study
coordinates
NUMBER_OF_SPECIES
Number of distinct species in study
NUMBER_OF_SAMPLES
Number of distinct samples in study
NUMBER_LAT_LONG
Number of distinct geographic coordinates in study
TOTAL
Total number of records in study
GRAIN_SIZE_TEXT
Grain size described in text, i.e. size of forest plots
AREA_SQ_KM
Total area of study in km2
DATE_STUDY_ADDED
Date that the study was added to the database
ABUNDANCE_TYPE
Type of abundance, i.e. count
BIOMASS_TYPE
Type of biomass, i.e. weight
SAMPLE_DESC
Concatenation of descriptors comprising the unique sampling
event
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
BTsubset_data package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of data from BioTIME temporal surveys.
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_data
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_data` A data frame with 81,084 rows and 17 columns:
ID_ALL_RAW_DATA
Unique BioTIME identifier for record
ABUNDANCE
Double representing the abundance for the record (see
metadata for details of ABUNDANCE_TYPE
BIOMASS
Double representing the biomass for the record (see metadata
for details of BIOMASS_TYPE
ID_SPECIES
Unique identifier linking to the species table
SAMPLE_DESC
Concatenation of variables comprising unique sampling event
LATITUDE
Latitude of record
LONGITUDE
Longitude of record
DEPTH
Depth or elevation of record if available
DAY
Numerical day of record
MONTH
Numerical value of month for record, i.e. January=1
YEAR
Year of record
STUDY_ID
BioTIME study unique identifier
newID
Validated species identifier key
valid_name
Highest taxonomic resolution of individual, preferred is
genus and species
resolution
Level of resolution, i.e. 'species' represented by genus and
species
taxon
Higher level taxonomic grouping, i.e. Fish
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
Vignettes with name or keyword or title matching 'BioTIMEr' using fuzzy
matching:
BioTIMEr::introduction
Introduction to BioTIMEr
Type 'vignette(PKG::FOO)' to inspect entries 'PKG::FOO'.
Help files with alias or concept or title matching 'BioTIMEr' using
fuzzy matching:
BioTIMEr::BTsubset_data
BioTIME subset
BioTIMEr::BTsubset_meta
BioTIME subset metadata
BioTIMEr::BioTIMEr-package
BioTIMEr: Tools to Use and Explore the
'BioTIME' Database
Aliases: BioTIMEr, BioTIMEr-package
BioTIMEr::getLinearRegressions
Get Linear Regressions BioTIME
BioTIMEr::gridding gridding BioTIME data
BioTIMEr::gridding_internal
gridding BioTIME data
BioTIMEr::plotSlopes Plot slopes BioTIME
BioTIMEr::resampling Rarefy BioTIME data to an equal number of
samples per year
BioTIMEr::resampling_core
Rarefy BioTIME data Applies sample-based
rarefaction to standardise the number of
samples per year within a cell-level time
series.
BioTIMEr::scale_color_biotime
Scale construction for ggplot use
Aliases: scale_color_biotime, scale_colour_biotime,
scale_fill_biotime
BioTIMEr::themeBioTIME
ggplot2 theme for BioTIME plots
Aliases: themeBioTIME
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
<1b>[31mError in `[.data.table`(x, j = `:=`(minsamp,<1b>[39m
<1b>[31mdata.table::uniqueN(SAMPLE_DESC)), :<1b>[39m
attempt access index 20/20 in VECTOR_ELT
C<1b>[31malls: .main ... resampling.default -> resampling_internal -> [ -><1b>[39m
<1b>[31m[.data.table<1b>[39m
Quitting from introduction.qmd:265-274 [resampling_ex1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `[.data.table`:
! attempt access index 20/20 in VECTOR_ELT
---
Backtrace:
▆
1. ├─BioTIMEr::resampling(...)
2. └─BioTIMEr:::resampling.default(...)
3. └─BioTIMEr:::resampling_internal(...)
4. ├─...[]
5. └─data.table:::`[.data.table`(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Execution halted
Caused by error:
! System command 'quarto' failed
--- failed re-building ‘introduction.qmd’
SUMMARY: processing the following file failed:
‘introduction.qmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.3.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [73s/52s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> test_check("BioTIMEr")
Loading required package: BioTIMEr
Starting 2 test processes.
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
> test-gridding.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance.R: OK: all SL studies have 1 grid cell
> test-resampling.R: OK: all SL studies have 1 grid cell
Saving _problems/test-resampling-10.R
Saving _problems/test-resampling-11.R
Saving _problems/test-resampling-37.R
Saving _problems/test-resampling-46.R
Saving _problems/test-resampling-60.R
Saving _problems/test-resampling-85.R
> test-resampling_abundance_biomass.R: OK: all SL studies have 1 grid cell
> test-resampling_biomass.R: OK: all SL studies have 1 grid cell
> test-resampling_abundance_biomass_conservative.R: OK: all SL studies have 1 grid cell
[ FAIL 6 | WARN 5 | SKIP 20 | PASS 65 ]
══ Skipped tests (20) ══════════════════════════════════════════════════════════
• On CRAN (20): 'test-metrics.R:59:3', 'test-metrics.R:82:3',
'test-metrics.R:95:3', 'test-metrics.R:107:3', 'test-metrics.R:120:3',
'test-workflow_alpha.R:3:3', 'test-workflow_beta.R:3:3', 'test-slopes.R:3:3',
'test-plots.R:4:3', 'test-scales.R:35:3', 'test-gridding.R:75:3',
'test-gridding.R:82:3', 'test-gridding.R:89:3',
'test-resampling_abundance.R:11:3', 'test-resampling.R:101:3',
'test-resampling_abundance_biomass.R:11:3', 'test-resampling_biomass.R:11:3',
'test-resampling_abundance_biomass_conservative.R:11:3',
'test-resampling_core.R:91:3', 'test-resampling_core.R:164:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-resampling.R:10:3'): resampling returns an object of same class as meta ──
Expected `resdf <- resampling(test_df, measure = "BIOMASS")` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Error ('test-resampling.R:11:3'): resampling returns an object of same class as meta ──
Error in `eval(code, test_env)`: object 'resdf' not found
Backtrace:
▆
1. └─testthat::expect_s3_class(resdf, "data.frame") at test-resampling.R:11:3
2. └─testthat::quasi_label(enquo(object))
3. └─rlang::eval_bare(expr, quo_get_env(quo))
── Failure ('test-resampling.R:34:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Failure ('test-resampling.R:40:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Failure ('test-resampling.R:53:3'): gridded object passed to resampling is not changed by reference ──
Expected `{ ... }` not to throw any errors.
Actually got a <simpleError> with message:
attempt access index 20/20 in VECTOR_ELT
── Error ('test-resampling.R:82:3'): resampling correctly excludes 1 year long studies ──
Error in ``[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), keyby = c("assemblageID", "YEAR"))`: attempt access index 20/20 in VECTOR_ELT
Backtrace:
▆
1. ├─testthat::expect_warning(resampling(test_df_1y, "BIOMASS"), regexp = "Some 1-year-long studies were removed.") at test-resampling.R:82:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. ├─BioTIMEr::resampling(test_df_1y, "BIOMASS")
8. └─BioTIMEr:::resampling.default(test_df_1y, "BIOMASS")
9. └─BioTIMEr:::resampling_internal(...)
10. ├─...[]
11. └─data.table:::`[.data.table`(...)
[ FAIL 6 | WARN 5 | SKIP 20 | PASS 65 ]
Deleting unused snapshots: 'plots/themebiotime-plot.svg',
'scales/color-continuous-cool-false.svg',
'scales/color-continuous-cool-true.svg',
'scales/color-continuous-gradient-false.svg',
'scales/color-continuous-gradient-true.svg',
'scales/color-continuous-realms-false.svg',
'scales/color-continuous-realms-true.svg',
'scales/color-continuous-warm-false.svg',
'scales/color-continuous-warm-true.svg', 'scales/color-cool-false-false.svg',
'scales/color-cool-false-true.svg', 'scales/color-cool-true-false.svg',
'scales/color-cool-true-true.svg', 'scales/color-gradient-false-false.svg',
'scales/color-gradient-false-true.svg', 'scales/color-gradient-true-false.svg',
'scales/color-gradient-true-true.svg', 'scales/color-realms-false-false.svg',
…, 'scales/fill-warm-true-false.svg', and 'scales/fill-warm-true-true.svg'
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.3.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘introduction.qmd’ using html
<1b>[31m
processing file: introduction.qmd
<1b>[39mBTsubset_meta package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t _<08>m_<08>e_<08>t_<08>a_<08>d_<08>a_<08>t_<08>a
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of the metadata from BioTIME
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_meta
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_meta` A data frame with 12 rows and 25 columns:
STUDY_ID
BioTIME study unique identifier
REALM
Realm of study location, i.e. Marine
CLIMATE
Climate of study location, i.e. Temperate
HABITAT
Habitat of study location, i.e. Rivers
PROTECTED_AREA
Binary variable indicating if the study is within a protected
area
BIOME_MAP
Biome of study location (taken from the WWF biomes, i.e.
Temperate broadleaf and mixed forests
TAXA
High level taxonomic identity of study species, i.e. Fish
ORGANISMS
More detailed information on taxonomy, i.e. woody plants
TITLE
Title of study as identified in original source
AB_BIO
A, B or AB to designate abundance only, biomass only or both
DATA_POINTS
Number of unique data points in study, e.g. 10 data points
spanning 15 years = 10
START_YEAR
First year of study
END_YEAR
Last year of study
CENT_LAT
Central latitude taken from the convex hull around all study
coordinates
CENT_LONG
Central longitude taken from the convex hull around all study
coordinates
NUMBER_OF_SPECIES
Number of distinct species in study
NUMBER_OF_SAMPLES
Number of distinct samples in study
NUMBER_LAT_LONG
Number of distinct geographic coordinates in study
TOTAL
Total number of records in study
GRAIN_SIZE_TEXT
Grain size described in text, i.e. size of forest plots
AREA_SQ_KM
Total area of study in km2
DATE_STUDY_ADDED
Date that the study was added to the database
ABUNDANCE_TYPE
Type of abundance, i.e. count
BIOMASS_TYPE
Type of biomass, i.e. weight
SAMPLE_DESC
Concatenation of descriptors comprising the unique sampling
event
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
BTsubset_data package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of data from BioTIME temporal surveys.
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_data
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_data` A data frame with 81,084 rows and 17 columns:
ID_ALL_RAW_DATA
Unique BioTIME identifier for record
ABUNDANCE
Double representing the abundance for the record (see
metadata for details of ABUNDANCE_TYPE
BIOMASS
Double representing the biomass for the record (see metadata
for details of BIOMASS_TYPE
ID_SPECIES
Unique identifier linking to the species table
SAMPLE_DESC
Concatenation of variables comprising unique sampling event
LATITUDE
Latitude of record
LONGITUDE
Longitude of record
DEPTH
Depth or elevation of record if available
DAY
Numerical day of record
MONTH
Numerical value of month for record, i.e. January=1
YEAR
Year of record
STUDY_ID
BioTIME study unique identifier
newID
Validated species identifier key
valid_name
Highest taxonomic resolution of individual, preferred is
genus and species
resolution
Level of resolution, i.e. 'species' represented by genus and
species
taxon
Higher level taxonomic grouping, i.e. Fish
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
Vignettes with name or keyword or title matching 'BioTIMEr' using fuzzy
matching:
BioTIMEr::introduction
Introduction to BioTIMEr
Type 'vignette(PKG::FOO)' to inspect entries 'PKG::FOO'.
Help files with alias or concept or title matching 'BioTIMEr' using
fuzzy matching:
BioTIMEr::BTsubset_data
BioTIME subset
BioTIMEr::BTsubset_meta
BioTIME subset metadata
BioTIMEr::BioTIMEr-package
BioTIMEr: Tools to Use and Explore the
'BioTIME' Database
Aliases: BioTIMEr, BioTIMEr-package
BioTIMEr::getLinearRegressions
Get Linear Regressions BioTIME
BioTIMEr::gridding gridding BioTIME data
BioTIMEr::gridding_internal
gridding BioTIME data
BioTIMEr::plotSlopes Plot slopes BioTIME
BioTIMEr::resampling Rarefy BioTIME data to an equal number of
samples per year
BioTIMEr::resampling_core
Rarefy BioTIME data Applies sample-based
rarefaction to standardise the number of
samples per year within a cell-level time
series.
BioTIMEr::scale_color_biotime
Scale construction for ggplot use
Aliases: scale_color_biotime, scale_colour_biotime,
scale_fill_biotime
BioTIMEr::themeBioTIME
ggplot2 theme for BioTIME plots
Aliases: themeBioTIME
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
<1b>[31mError in `[.data.table`(x, j = `:=`(minsamp, data.table::uniqueN(SAMPLE_DESC)), :
attempt access index 20/20 in VECTOR_ELT
Calls: .main ... resampling.default -> resampling_internal -> [ -> [.data.table
<1b>[39m<1b>[31m
Quitting from introduction.qmd:265-274 [resampling_ex1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `[.data.table`:
! attempt access index 20/20 in VECTOR_ELT
---
Backtrace:
▆
1. ├─BioTIMEr::resampling(...)
2. └─BioTIMEr:::resampling.default(...)
3. └─BioTIMEr:::resampling_internal(...)
4. ├─...[]
5. └─data.table:::`[.data.table`(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Execution halted
<1b>[39mError: processing vignette 'introduction.qmd' failed with diagnostics:
! Error running quarto CLI from R.
Caused by error in `quarto_render()`:
✖ Error returned by quarto CLI.
-----------------------------
processing file: introduction.qmd
BTsubset_meta package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t _<08>m_<08>e_<08>t_<08>a_<08>d_<08>a_<08>t_<08>a
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of the metadata from BioTIME
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_meta
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_meta` A data frame with 12 rows and 25 columns:
STUDY_ID
BioTIME study unique identifier
REALM
Realm of study location, i.e. Marine
CLIMATE
Climate of study location, i.e. Temperate
HABITAT
Habitat of study location, i.e. Rivers
PROTECTED_AREA
Binary variable indicating if the study is within a protected
area
BIOME_MAP
Biome of study location (taken from the WWF biomes, i.e.
Temperate broadleaf and mixed forests
TAXA
High level taxonomic identity of study species, i.e. Fish
ORGANISMS
More detailed information on taxonomy, i.e. woody plants
TITLE
Title of study as identified in original source
AB_BIO
A, B or AB to designate abundance only, biomass only or both
DATA_POINTS
Number of unique data points in study, e.g. 10 data points
spanning 15 years = 10
START_YEAR
First year of study
END_YEAR
Last year of study
CENT_LAT
Central latitude taken from the convex hull around all study
coordinates
CENT_LONG
Central longitude taken from the convex hull around all study
coordinates
NUMBER_OF_SPECIES
Number of distinct species in study
NUMBER_OF_SAMPLES
Number of distinct samples in study
NUMBER_LAT_LONG
Number of distinct geographic coordinates in study
TOTAL
Total number of records in study
GRAIN_SIZE_TEXT
Grain size described in text, i.e. size of forest plots
AREA_SQ_KM
Total area of study in km2
DATE_STUDY_ADDED
Date that the study was added to the database
ABUNDANCE_TYPE
Type of abundance, i.e. count
BIOMASS_TYPE
Type of biomass, i.e. weight
SAMPLE_DESC
Concatenation of descriptors comprising the unique sampling
event
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
BTsubset_data package:BioTIMEr R Documentation
_<08>B_<08>i_<08>o_<08>T_<08>I_<08>M_<08>E _<08>s_<08>u_<08>b_<08>s_<08>e_<08>t
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
A subset of data from BioTIME temporal surveys.
_<08>U_<08>s_<08>a_<08>g_<08>e:
BTsubset_data
_<08>F_<08>o_<08>r_<08>m_<08>a_<08>t:
## `BTsubset_data` A data frame with 81,084 rows and 17 columns:
ID_ALL_RAW_DATA
Unique BioTIME identifier for record
ABUNDANCE
Double representing the abundance for the record (see
metadata for details of ABUNDANCE_TYPE
BIOMASS
Double representing the biomass for the record (see metadata
for details of BIOMASS_TYPE
ID_SPECIES
Unique identifier linking to the species table
SAMPLE_DESC
Concatenation of variables comprising unique sampling event
LATITUDE
Latitude of record
LONGITUDE
Longitude of record
DEPTH
Depth or elevation of record if available
DAY
Numerical day of record
MONTH
Numerical value of month for record, i.e. January=1
YEAR
Year of record
STUDY_ID
BioTIME study unique identifier
newID
Validated species identifier key
valid_name
Highest taxonomic resolution of individual, preferred is
genus and species
resolution
Level of resolution, i.e. 'species' represented by genus and
species
taxon
Higher level taxonomic grouping, i.e. Fish
_<08>S_<08>o_<08>u_<08>r_<08>c_<08>e:
<https://biotime.st-andrews.ac.uk/download.php>
Vignettes with name or keyword or title matching 'BioTIMEr' using fuzzy
matching:
BioTIMEr::introduction
Introduction to BioTIMEr
Type 'vignette(PKG::FOO)' to inspect entries 'PKG::FOO'.
Help files with alias or concept or title matching 'BioTIMEr' using
fuzzy matching:
BioTIMEr::BTsubset_data
BioTIME subset
BioTIMEr::BTsubset_meta
BioTIME subset metadata
BioTIMEr::BioTIMEr-package
BioTIMEr: Tools to Use and Explore the
'BioTIME' Database
Aliases: BioTIMEr, BioTIMEr-package
BioTIMEr::getLinearRegressions
Get Linear Regressions BioTIME
BioTIMEr::gridding gridding BioTIME data
BioTIMEr::gridding_internal
gridding BioTIME data
BioTIMEr::plotSlopes Plot slopes BioTIME
BioTIMEr::resampling Rarefy BioTIME data to an equal number of
samples per year
BioTIMEr::resampling_core
Rarefy BioTIME data Applies sample-based
rarefaction to standardise the number of
samples per year within a cell-level time
series.
BioTIMEr::scale_color_biotime
Scale construction for ggplot use
Aliases: scale_color_biotime, scale_colour_biotime,
scale_fill_biotime
BioTIMEr::themeBioTIME
ggplot2 theme for BioTIME plots
Aliases: themeBioTIME
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
<1b>[31mError in `[.data.table`(x, j = `:=`(minsamp,<1b>[39m
<1b>[31mdata.table::uniqueN(SAMPLE_DESC)), :<1b>[39m
attempt access index 20/20 in VECTOR_ELT
Calls: .main ... resampling.default -> resampling_internal -> [ ->
[.data.table
Quitting from introduction.qmd:265-274 [resampling_ex1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `[.data.table`:
! attempt access index 20/20 in VECTOR_ELT
---
Backtrace:
▆
1. ├─BioTIMEr::resampling(...)
2. └─BioTIMEr:::resampling.default(...)
3. └─BioTIMEr:::resampling_internal(...)
4. ├─...[]
5. └─data.table:::`[.data.table`(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Execution halted
Caused by error:
! System command 'quarto' failed
--- failed re-building ‘introduction.qmd’
SUMMARY: processing the following file failed:
‘introduction.qmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc