Last updated on 2024-04-16 01:56:08 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2024.2.21 | 2.86 | 115.83 | 118.69 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2024.2.21 | 2.96 | 86.57 | 89.53 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2024.2.21 | 149.68 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2024.2.21 | 156.77 | OK | |||
| r-prerel-macos-arm64 | 2024.2.21 | 50.00 | ERROR | |||
| r-prerel-macos-x86_64 | 2024.2.21 | 139.00 | OK | |||
| r-prerel-windows-x86_64 | 2024.2.21 | 4.00 | 828.00 | 832.00 | OK | |
| r-patched-linux-x86_64 | 2024.2.21 | 4.67 | 110.56 | 115.23 | OK | |
| r-release-linux-x86_64 | 2024.2.21 | 3.21 | 106.86 | 110.07 | OK | |
| r-release-macos-arm64 | 2024.2.21 | 67.00 | OK | |||
| r-release-macos-x86_64 | 2024.2.21 | 139.00 | OK | |||
| r-release-windows-x86_64 | 2024.2.21 | 6.00 | 1859.00 | 1865.00 | OK | |
| r-oldrel-macos-arm64 | 2024.2.21 | 74.00 | OK | |||
| r-oldrel-windows-x86_64 | 2024.2.21 | 6.00 | 1567.00 | 1573.00 | OK |
Version: 2024.2.21
Check: examples
Result: ERROR
Running examples in ‘nc-Ex.R’ failed
The error most likely occurred in:
> ### Name: capture_first_glob
> ### Title: capture first glob
> ### Aliases: capture_first_glob
>
> ### ** Examples
>
>
> data.table::setDTthreads(1)
>
> ## Example 0: iris data, one file per species.
> library(data.table)
> dir.create(iris.dir <- tempfile())
> icsv <- function(sp)file.path(iris.dir, paste0(sp, ".csv"))
> data.table(iris)[, fwrite(.SD, icsv(Species)), by=Species]
Empty data.table (0 rows and 1 cols): Species
> dir(iris.dir)
[1] "setosa.csv" "versicolor.csv" "virginica.csv"
> data.table::fread(file.path(iris.dir,"setosa.csv"), nrows=2)
Sepal.Length Sepal.Width Petal.Length Petal.Width
<num> <num> <num> <num>
1: 5.1 3.5 1.4 0.2
2: 4.9 3.0 1.4 0.2
> (iglob <- file.path(iris.dir,"*.csv"))
[1] "/var/folders/k4/0jwzxmln0nb8y6rkzprptb640000gq/T//RtmpxNPzlE/file463c41145017/*.csv"
> nc::capture_first_glob(iglob, Species="[^/]+", "[.]csv")
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
<char> <num> <num> <num> <num>
1: setosa 5.1 3.5 1.4 0.2
2: setosa 4.9 3.0 1.4 0.2
3: setosa 4.7 3.2 1.3 0.2
4: setosa 4.6 3.1 1.5 0.2
5: setosa 5.0 3.6 1.4 0.2
---
146: virginica 6.7 3.0 5.2 2.3
147: virginica 6.3 2.5 5.0 1.9
148: virginica 6.5 3.0 5.2 2.0
149: virginica 6.2 3.4 5.4 2.3
150: virginica 5.9 3.0 5.1 1.8
>
> ## Example 1: four files, two capture groups, custom read function.
> db <- system.file("extdata/chip-seq-chunk-db", package="nc", mustWork=TRUE)
> suffix <- if(interactive())"gz" else "head"
> (glob <- paste0(db, "/*/*/counts/*", suffix))
[1] "/Volumes/Builds/packages/big-sur-arm64/results/4.4/nc.Rcheck/nc/extdata/chip-seq-chunk-db/*/*/counts/*head"
> Sys.glob(glob)
[1] "/Volumes/Builds/packages/big-sur-arm64/results/4.4/nc.Rcheck/nc/extdata/chip-seq-chunk-db/H3K36me3_AM_immune/9/counts/McGill0101.bedGraph.head"
[2] "/Volumes/Builds/packages/big-sur-arm64/results/4.4/nc.Rcheck/nc/extdata/chip-seq-chunk-db/H3K36me3_TDH_other/1/counts/McGill0019.bedGraph.head"
[3] "/Volumes/Builds/packages/big-sur-arm64/results/4.4/nc.Rcheck/nc/extdata/chip-seq-chunk-db/H3K4me3_TDH_immune/9/counts/McGill0024.bedGraph.head"
[4] "/Volumes/Builds/packages/big-sur-arm64/results/4.4/nc.Rcheck/nc/extdata/chip-seq-chunk-db/H3K4me3_XJ_immune/2/counts/McGill0024.bedGraph.head"
> read.bedGraph <- function(f)data.table::fread(
+ f, skip=1, col.names = c("chrom","start", "end", "count"))
> data.chunk.pattern <- list(
+ data="H.*?",
+ "/",
+ chunk="[0-9]+", as.integer)
> (data.chunk.dt <- nc::capture_first_glob(glob, data.chunk.pattern, READ=read.bedGraph))
data chunk chrom start end count
<char> <int> <char> <int> <int> <int>
1: H3K36me3_AM_immune 9 chr10 111456281 111456338 2
2: H3K36me3_AM_immune 9 chr10 111456338 111456381 1
3: H3K36me3_AM_immune 9 chr10 111456381 111459312 0
4: H3K36me3_AM_immune 9 chr10 111459312 111459316 5
5: H3K36me3_AM_immune 9 chr10 111459316 111459409 10
6: H3K36me3_AM_immune 9 chr10 111459409 111459411 8
7: H3K36me3_AM_immune 9 chr10 111459411 111459415 5
8: H3K36me3_AM_immune 9 chr10 111459415 111463412 0
9: H3K36me3_AM_immune 9 chr10 111463412 111463512 2
10: H3K36me3_AM_immune 9 chr10 111463512 111466726 0
11: H3K36me3_TDH_other 1 chr21 43119165 43119386 0
12: H3K36me3_TDH_other 1 chr21 43119386 43119407 1
13: H3K36me3_TDH_other 1 chr21 43119407 43119475 2
14: H3K36me3_TDH_other 1 chr21 43119475 43119502 1
15: H3K36me3_TDH_other 1 chr21 43119502 43119987 0
16: H3K36me3_TDH_other 1 chr21 43119987 43120007 1
17: H3K36me3_TDH_other 1 chr21 43120007 43120086 2
18: H3K36me3_TDH_other 1 chr21 43120086 43120107 1
19: H3K36me3_TDH_other 1 chr21 43120107 43120743 0
20: H3K36me3_TDH_other 1 chr21 43120743 43120789 1
21: H3K4me3_TDH_immune 9 chr1 36926536 36926549 10
22: H3K4me3_TDH_immune 9 chr1 36926549 36926554 9
23: H3K4me3_TDH_immune 9 chr1 36926554 36926565 11
24: H3K4me3_TDH_immune 9 chr1 36926565 36926569 9
25: H3K4me3_TDH_immune 9 chr1 36926569 36926571 8
26: H3K4me3_TDH_immune 9 chr1 36926571 36926580 7
27: H3K4me3_TDH_immune 9 chr1 36926580 36926593 8
28: H3K4me3_TDH_immune 9 chr1 36926593 36926606 7
29: H3K4me3_TDH_immune 9 chr1 36926606 36926622 8
30: H3K4me3_TDH_immune 9 chr1 36926622 36926634 9
31: H3K4me3_XJ_immune 2 chr22 20688396 20688502 0
32: H3K4me3_XJ_immune 2 chr22 20688502 20688602 1
33: H3K4me3_XJ_immune 2 chr22 20688602 20688869 0
34: H3K4me3_XJ_immune 2 chr22 20688869 20688932 2
35: H3K4me3_XJ_immune 2 chr22 20688932 20688934 3
36: H3K4me3_XJ_immune 2 chr22 20688934 20688936 4
37: H3K4me3_XJ_immune 2 chr22 20688936 20688963 5
38: H3K4me3_XJ_immune 2 chr22 20688963 20688968 7
39: H3K4me3_XJ_immune 2 chr22 20688968 20688969 6
40: H3K4me3_XJ_immune 2 chr22 20688969 20688979 5
data chunk chrom start end count
>
> ## Write same data set in Hive partition, then re-read.
> if(requireNamespace("arrow")){
+ path <- tempfile()
+ max_rows_per_file <- if(interactive())3 else 1000
+ arrow::write_dataset(
+ dataset=data.chunk.dt,
+ path=path,
+ format="csv",
+ partitioning=c("data","chunk"),
+ max_rows_per_file=max_rows_per_file)
+ hive.glob <- file.path(path, "*", "*", "*.csv")
+ hive.pattern <- list(
+ nc::field("data","=",".*?"),
+ "/",
+ nc::field("chunk","=",".*?", as.integer),
+ "/",
+ nc::field("part","-","[0-9]+", as.integer))
+ hive.dt <- nc::capture_first_glob(hive.glob, hive.pattern)
+ hive.dt[, .(rows=.N), by=.(data,chunk,part)]
+ }
Loading required namespace: arrow
Error in dataset___HivePartitioning(schm, null_fallback = null_fallback_or_default(null_fallback), :
Cannot call dataset___HivePartitioning(). See https://arrow.apache.org/docs/r/articles/install.html for help installing Arrow C++ libraries.
Calls: <Anonymous> -> <Anonymous> -> dataset___HivePartitioning
Execution halted
Flavor: r-prerel-macos-arm64
Version: 2024.2.21
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘v0-overview.Rmd’ using rmarkdown
--- finished re-building ‘v0-overview.Rmd’
--- re-building ‘v1-capture-first.Rmd’ using rmarkdown
--- finished re-building ‘v1-capture-first.Rmd’
--- re-building ‘v2-capture-all.Rmd’ using rmarkdown
--- finished re-building ‘v2-capture-all.Rmd’
--- re-building ‘v3-capture-melt.Rmd’ using rmarkdown
--- finished re-building ‘v3-capture-melt.Rmd’
--- re-building ‘v4-comparisons.Rmd’ using rmarkdown
--- finished re-building ‘v4-comparisons.Rmd’
--- re-building ‘v5-helpers.Rmd’ using rmarkdown
--- finished re-building 'v5-helpers.Rmd'
--- re-building ‘v6-engines.Rmd’ using rmarkdown
--- finished re-building ‘v6-engines.Rmd’
--- re-building ‘v7-capture-glob.Rmd’ using rmarkdown
Quitting from lines 152-163 [unnamed-chunk-11] (v7-capture-glob.Rmd)
Error: processing vignette 'v7-capture-glob.Rmd' failed with diagnostics:
Cannot call dataset___HivePartitioning(). See https://arrow.apache.org/docs/r/articles/install.html for help installing Arrow C++ libraries.
--- failed re-building ‘v7-capture-glob.Rmd’
SUMMARY: processing the following file failed:
‘v7-capture-glob.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-prerel-macos-arm64