I find it quite interesting to identify formal strategies for removing
dependencies, but also a little outside my domain of expertise. This code
library(tools)
library(dplyr)
## non-base packages the user requires for GenomicScores
deps <- package_dependencies("GenomicScores", db, recursive=TRUE)[[1]]
deps <- intersect(deps, rownames(db))
## only need the 'universe' of GenomicScores dependencies
db1 <- db[c("GenomicScores", deps),]
## sub-graph of packages between each dependency and GenomicScores
revdeps <- package_dependencies(deps, db1, recursive = TRUE, reverse = TRUE)
tibble(
package = names(olap),
n_remove = lengths(revdeps),
) %>%
arrange(n_remove)
produces a tibble
# A tibble: 106 x 2
package n_remove
<chr> <int>
1 BSgenome 1
2 AnnotationHub 1
3 shinyjs 1
4 DT 1
5 shinycustomloader 1
6 data.table 1
7 shinythemes 1
8 rtracklayer 2
9 BiocFileCache 2
10 BiocManager 2
# … with 96 more rows
shows me, via n_remove, that I can remove the dependency on AnnotationHub by
removing the dependency on just one package (AnnotationHub!), but to remove
BiocFileCache I'd also have to remove another package (AnnotationHub, I'd
guess). So this provides some measure of the ease with which a package can be
removed.
I'd like a 'benefit' column, too -- if I were to remove AnnotationHub, how many
additional packages would I also be able to remove, because they are present
only to satisfy the dependency on AnnotationHub? More generally, perhaps there
is a dependency of AnnotationHub that is only used by AnnotationHub and
BSgenome. So removing AnnotationHub as a dependency would make it easier to
remove BSgenome, etc. I guess this is a graph optimization problem.
Probably also worth mentioning the itdepends package
(https://github.com/r-lib/itdepends), which I think tries primarily to
determine the relationship between package dependencies and lines of code,
which seems like complementary information.
Martin
On 2/6/20, 12:29 PM, "Robert Castelo" <robert.cast...@upf.edu> wrote:
true, i was just searching for the shortest path, we can search for all
simple (i.e., without repeating "vertices") paths and there are up to
five routes from "GenomicScores" to "Matrix"
igraph::all_simple_paths(igraph::igraph.from.graphNEL(g),
from="GenomicScores", to="Matrix", mode="out")
[[1]]
+ 7/117 vertices, named, from 04133ec:
[1] GenomicScores BSgenome rtracklayer
[4] GenomicAlignments SummarizedExperiment DelayedArray
[7] Matrix
[[2]]
+ 6/117 vertices, named, from 04133ec:
[1] GenomicScores BSgenome rtracklayer
[4] GenomicAlignments SummarizedExperiment Matrix
[[3]]
+ 6/117 vertices, named, from 04133ec:
[1] GenomicScores DT crosstalk ggplot2 mgcv
[6] Matrix
[[4]]
+ 6/117 vertices, named, from 04133ec:
[1] GenomicScores rtracklayer GenomicAlignments
[4] SummarizedExperiment DelayedArray Matrix
[[5]]
+ 5/117 vertices, named, from 04133ec:
[1] GenomicScores rtracklayer GenomicAlignments
[4] SummarizedExperiment Matrix
this is interesting, because it means that if i wanted to get rid of the
"Matrix" dependence i'd need to get rid not only of the "rtracklayer"
dependence but also of "BSgenome" and "DT".
robert.
On 2/6/20 5:41 PM, Martin Morgan wrote:
> Excellent! I think there are other, independent, paths between your
immediate dependents...
>
> RBGL::sp.between(g, start="DT", finish="Matrix",
detail=TRUE)[[1]]$path_detail
> [1] "DT" "crosstalk" "ggplot2" "mgcv" "Matrix"
>
> ??
>
> Martin
>
> On 2/6/20, 10:47 AM, "Robert Castelo" <robert.cast...@upf.edu> wrote:
>
> hi Martin,
>
> thanks for hint!! i wasn't aware of 'tools::package_dependencies()',
> adding a bit of graph sorcery i get the result i was looking for:
>
> repos <- BiocManager::repositories()[c(1,5)]
> repos
> BioCsoft
> "https://bioconductor.org/packages/3.11/bioc";
> CRAN
> "https://cran.rstudio.com";
>
> db <- available.packages(repos=repos)
>
> deps <- tools::package_dependencies("GenomicScores", db,
> recursive=TRUE)[[1]]
>
> deps <- tools::package_dependencies(c("GenomicScores", deps), db)
>
> g <- graph::graphNEL(nodes=names(deps), edgeL=deps,
edgemode="directed")
>
> RBGL::sp.between(g, start="GenomicScores", finish="Matrix",
> detail=TRUE)[[1]]$path_detail
> [1] "GenomicScores" "rtracklayer" "GenomicAlignments"
> [4] "SummarizedExperiment" "Matrix"
>
> so, it was the rtracklayer dependency that leads to Matrix through
> GenomeAlignments and SummarizedExperiment.
>
> maybe the BioC package 'pkgDepTools' should be deprecated if its
> functionality is part of 'tools' and it does not even work as fast
and
> correct as 'tools'.
>
> cheers,
>
> robert.
>
>
> On 2/6/20 2:51 PM, Martin Morgan wrote:
> > The first thing is to get the correct repositories
> >
> > repos = BiocManager::repositories()
> >
> > (maybe trim the experiment and annotation repos from this). I also
tried pkgDepTools::makeDepGraph() but it took so long that I moved on... it has
an option 'keep.builtin' which might include Matrix.
> >
> > There is also BiocPkgTools::buildPkgDependencyDataFrame() &
friends, but this seems to build dependencies within a single repository...
> >
> > The building block for a solution is
`tools::package_dependencies()`, and I can confirm that "Matrix" _is_ a
dependency
> >
> > db = available.packages(repos = BiocManager::repositories())
> > revdeps <- tools::package_dependencies("GenomicScores", db,
recursive = TRUE)
> > "Matrix" %in% revdeps[[1]]
> > ## [1] TRUE
> >
> > so I'll leave the clever recursive or graph-based algorithm up to
you, to report back to the mailing list?
> >
> > For what it's worth I think the last time this came up Martin
Maechler pointed to a function in base R (probably the tools package) that
implements this, too...?
> >
> > Martin Morgan
> >
> > On 2/6/20, 6:40 AM, "Bioc-devel on behalf of Robert Castelo"
<bioc-devel-boun...@r-project.org on behalf of robert.cast...@upf.edu> wrote:
> >
> > hi,
> >
> > when i load the package 'GenomicScores' in a clean session i
see thorugh
> > the 'sessionInfo()' that the package 'Matrix' is listed under
"loaded
> > via a namespace (and not attached)".
> >
> > i'd like to know what is the dependency that 'GenomicsScores'
has that
> > ends up requiring the package 'Matrix'.
> >
> > i've tried using the package 'pkgDepTools' without success,
because the
> > dependency graph does not list any path from 'GenomicScores'
to 'Matrix'.
> >
> > i've been manually browsing the Bioc website and, unless i've
overlooked
> > something, the only association with 'Matrix' i could find is
that
> > 'S4Vectors' and 'GenomicRanges', which are required by
'GenomicScores',
> > list 'Matrix' in the 'Suggests' field, but my understanding
is that
> > those packages are not required and should not be loaded.
> >
> > so, is there any way in which i can figure out what of the
> > 'GenomicScores' dependencies leads to loading the package
'Matrix'?
> >
> > here are the depends, import and suggests fields from
'GenomicScores':
> >
> > Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges,
methods,
> > BiocGenerics (>= 0.13.8)
> > Imports: utils, XML, Biobase, IRanges (>= 2.3.23), Biostrings,
> > BSgenome, GenomeInfoDb, AnnotationHub, shiny,
shinyjs,
> > DT, shinycustomloader, rtracklayer, data.table,
shinythemes
> > Suggests: BiocStyle, knitr, rmarkdown,
BSgenome.Hsapiens.UCSC.hg19,
> > phastCons100way.UCSC.hg19,
MafDb.1Kgenomes.phase1.hs37d5,
> > SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation,
> > TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat,
RColorBrewer
> >
> > and here a session information in a fresh R-devel session
after loading
> > the package 'GenomicScores':
> >
> > R Under development (unstable) (2020-01-29 r77745)
> > Platform: x86_64-pc-linux-gnu (64-bit)
> > Running under: CentOS Linux 7 (Core)
> >
> > Matrix products: default
> > BLAS: /opt/R/R-devel/lib64/R/lib/libRblas.so
> > LAPACK: /opt/R/R-devel/lib64/R/lib/libRlapack.so
> >
> > locale:
> > [1] LC_CTYPE=en_US.UTF8 LC_NUMERIC=C
> > [3] LC_TIME=en_US.UTF8 LC_COLLATE=en_US.UTF8
> > [5] LC_MONETARY=en_US.UTF8 LC_MESSAGES=en_US.UTF8
> > [7] LC_PAPER=en_US.UTF8 LC_NAME=C
> > [9] LC_ADDRESS=C LC_TELEPHONE=C
> > [11] LC_MEASUREMENT=en_US.UTF8 LC_IDENTIFICATION=C
> >
> > attached base packages:
> > [1] parallel stats4 stats graphics grDevices utils
datasets
> > [8] methods base
> >
> > other attached packages:
> > [1] GenomicScores_1.11.4 GenomicRanges_1.39.2
GenomeInfoDb_1.23.10
> > [4] IRanges_2.21.3 S4Vectors_0.25.12
BiocGenerics_0.33.0
> > [7] colorout_1.2-2
> >
> > loaded via a namespace (and not attached):
> > [1] Rcpp_1.0.3 lattice_0.20-38
> > [3] shinycustomloader_0.9.0 Rsamtools_2.3.3
> > [5] Biostrings_2.55.4 assertthat_0.2.1
> > [7] digest_0.6.23 mime_0.9
> > [9] BiocFileCache_1.11.4 R6_2.4.1
> > [11] RSQLite_2.2.0 httr_1.4.1
> > [13] pillar_1.4.3 zlibbioc_1.33.1
> > [15] rlang_0.4.4 curl_4.3
> > [17] data.table_1.12.8 blob_1.2.1
> > [19] DT_0.12 Matrix_1.2-18
> > [21] shinythemes_1.1.2 shinyjs_1.1
> > [23] BiocParallel_1.21.2 AnnotationHub_2.19.7
> > [25] htmlwidgets_1.5.1 RCurl_1.98-1.1
> > [27] bit_1.1-15.1 shiny_1.4.0
> > [29] DelayedArray_0.13.3 compiler_4.0.0
> > [31] httpuv_1.5.2 rtracklayer_1.47.0
> > [33] pkgconfig_2.0.3 htmltools_0.4.0
> > [35] tidyselect_1.0.0 SummarizedExperiment_1.17.1
> > [37] tibble_2.1.3 GenomeInfoDbData_1.2.2
> > [39] interactiveDisplayBase_1.25.0 matrixStats_0.55.0
> > [41] XML_3.99-0.3 crayon_1.3.4
> > [43] dplyr_0.8.4 dbplyr_1.4.2
> > [45] later_1.0.0 GenomicAlignments_1.23.1
> > [47] bitops_1.0-6 rappdirs_0.3.1
> > [49] grid_4.0.0 xtable_1.8-4
> > [51] DBI_1.1.0 magrittr_1.5
> > [53] XVector_0.27.0 promises_1.1.0
> > [55] vctrs_0.2.2 tools_4.0.0
> > [57] bit64_0.9-7 BSgenome_1.55.3
> > [59] Biobase_2.47.2 glue_1.3.1
> > [61] purrr_0.3.3 BiocVersion_3.11.1
> > [63] fastmap_1.0.1 yaml_2.2.1
> > [65] AnnotationDbi_1.49.1 BiocManager_1.30.10
> > [67] memoise_1.1.0
> >
> >
> >
> > thanks!!
> >
> > robert.
> >
> > _______________________________________________
> > Bioc-devel@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/bioc-devel
> >
> >
>
> --
> Robert Castelo, PhD
> Associate Professor
> Dept. of Experimental and Health Sciences
> Universitat Pompeu Fabra (UPF)
> Barcelona Biomedical Research Park (PRBB)
> Dr Aiguader 88
> E-08003 Barcelona, Spain
> telf: +34.933.160.514
> fax: +34.933.160.550
>
>
--
Robert Castelo, PhD
Associate Professor
Dept. of Experimental and Health Sciences
Universitat Pompeu Fabra (UPF)
Barcelona Biomedical Research Park (PRBB)
Dr Aiguader 88
E-08003 Barcelona, Spain
telf: +34.933.160.514
fax: +34.933.160.550
_______________________________________________
Bioc-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/bioc-devel
