vignettes/BiocPkgTools.Rmd
BiocPkgTools.RmdAbstract
Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R in a tidy data format. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages.
Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R in a tidy data format. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages.
Functionality includes access to :
The Bioconductor build reports are available online as HTML pages. However, they are not very computable. The biocBuildReport function does some heroic parsing of the HTML to produce a tidy data.frame for further processing in R.
## # A tibble: 6 × 11
## pkg author version git_last_commit git_last_commit_date Deprecated
## <chr> <chr> <chr> <chr> <dttm> <lgl>
## 1 a4 Laure Cougnaud 1.48.0 89ef9f4 2023-04-25 10:19:40 FALSE
## 2 a4 Laure Cougnaud 1.48.0 89ef9f4 2023-04-25 10:19:40 FALSE
## 3 a4 Laure Cougnaud 1.48.0 89ef9f4 2023-04-25 10:19:40 FALSE
## 4 a4 Laure Cougnaud 1.48.0 89ef9f4 2023-04-25 10:19:40 FALSE
## 5 a4 Laure Cougnaud 1.48.0 89ef9f4 2023-04-25 10:19:40 FALSE
## 6 a4 Laure Cougnaud 1.48.0 89ef9f4 2023-04-25 10:19:40 FALSE
## # ℹ 5 more variables: PackageStatus <chr>, node <chr>, stage <chr>,
## # result <chr>, bioc_version <chr>Because developers may be interested in a quick view of their own packages, there is a simple function, problemPage, to produce an HTML report of the build status of packages matching a given author regex supplied to the authorPattern argument. The default is to report only “problem” build statuses (ERROR, WARNING).
problemPage(authorPattern = "V.*Carey")In similar fashion, maintainers of packages that have many downstream packages that depend on them may wish to check that a change they introduced hasn’t suddenly broken a large number of these. You can use the dependsOn argument to produce the summary report of those packages that “depend on” the given package.
problemPage(dependsOn = "limma")When run in an interactive environment, the problemPage function will open a browser window for user interaction. Note that if you want to include all your package results, not just the broken ones, simply specify includeOK = TRUE.
Bioconductor supplies download stats for all packages. The biocDownloadStats function grabs all available download stats for all packages in all Experiment Data, Annotation Data, and Software packages. The results are returned as a tidy data.frame for further analysis.
## # A tibble: 6 × 7
## pkgType Package Year Month Nb_of_distinct_IPs Nb_of_downloads Date
## <chr> <chr> <int> <chr> <int> <int> <date>
## 1 software ABarray 2023 Jan 49 97 2023-01-01
## 2 software ABarray 2023 Feb 40 91 2023-02-01
## 3 software ABarray 2023 Mar 43 65 2023-03-01
## 4 software ABarray 2023 Apr 48 88 2023-04-01
## 5 software ABarray 2023 May 66 85 2023-05-01
## 6 software ABarray 2023 Jun 0 0 2023-06-01The download statistics reported are for all available versions of a package. There are no separate, publicly available statistics broken down by version.
The majority of Bioconductor Software packages are also available through other channels such as Anaconda, who also provided download statistics for packages installed from their repositories. Access to these counts is provided by the anacondaDownloadStats function:
## # A tibble: 6 × 7
## Package Year Month Nb_of_distinct_IPs Nb_of_downloads repo Date
## <chr> <chr> <chr> <int> <dbl> <chr> <date>
## 1 ABAData 2018 Apr NA 8 Anaconda 2018-04-01
## 2 ABAData 2018 Aug NA 5 Anaconda 2018-08-01
## 3 ABAData 2018 Dec NA 133 Anaconda 2018-12-01
## 4 ABAData 2018 Jul NA 6 Anaconda 2018-07-01
## 5 ABAData 2018 Jun NA 18 Anaconda 2018-06-01
## 6 ABAData 2018 Mar NA 13 Anaconda 2018-03-01Note that Anaconda do not provide counts for distinct IP addresses, but this column is included for compatibility with the Bioconductor count tables.
The R DESCRIPTION file contains a plethora of information regarding package authors, dependencies, versions, etc. In a repository such as Bioconductor, these details are available in bulk for all included packages. The biocPkgList returns a data.frame with a row for each package. Tons of information are available, as evidenced by the column names of the results.
bpi = biocPkgList()
colnames(bpi)## [1] "Package" "Version"
## [3] "Depends" "Suggests"
## [5] "License" "MD5sum"
## [7] "NeedsCompilation" "Title"
## [9] "Description" "biocViews"
## [11] "Author" "Maintainer"
## [13] "git_url" "git_branch"
## [15] "git_last_commit" "git_last_commit_date"
## [17] "Date/Publication" "source.ver"
## [19] "win.binary.ver" "mac.binary.big-sur-x86_64.ver"
## [21] "mac.binary.big-sur-arm64.ver" "vignettes"
## [23] "vignetteTitles" "hasREADME"
## [25] "hasNEWS" "hasINSTALL"
## [27] "hasLICENSE" "Rfiles"
## [29] "dependencyCount" "Imports"
## [31] "Enhances" "dependsOnMe"
## [33] "suggestsMe" "VignetteBuilder"
## [35] "URL" "SystemRequirements"
## [37] "BugReports" "Archs"
## [39] "importsMe" "LinkingTo"
## [41] "PackageStatus" "Video"
## [43] "linksToMe" "License_restricts_use"
## [45] "OS_type" "organism"
## [47] "License_is_FOSS"Some of the variables are parsed to produce list columns.
head(bpi)## # A tibble: 6 × 47
## Package Version Depends Suggests License MD5sum NeedsCompilation Title
## <chr> <chr> <list> <list> <chr> <chr> <chr> <chr>
## 1 a4 1.48.0 <chr [5]> <chr [6]> GPL-3 5d9452… no Auto…
## 2 a4Base 1.48.0 <chr [2]> <chr [4]> GPL-3 b47278… no Auto…
## 3 a4Classif 1.48.0 <chr [2]> <chr [4]> GPL-3 44dc4f… no Auto…
## 4 a4Core 1.48.0 <chr [1]> <chr [2]> GPL-3 5f0cb8… no Auto…
## 5 a4Preproc 1.48.0 <chr [1]> <chr [4]> GPL-3 268368… no Auto…
## 6 a4Reporting 1.48.0 <chr [1]> <chr [2]> GPL-3 aace06… no Auto…
## # ℹ 39 more variables: Description <chr>, biocViews <list>, Author <list>,
## # Maintainer <list>, git_url <chr>, git_branch <chr>, git_last_commit <chr>,
## # git_last_commit_date <chr>, `Date/Publication` <chr>, source.ver <chr>,
## # win.binary.ver <chr>, `mac.binary.big-sur-x86_64.ver` <chr>,
## # `mac.binary.big-sur-arm64.ver` <chr>, vignettes <list>,
## # vignetteTitles <list>, hasREADME <chr>, hasNEWS <chr>, hasINSTALL <chr>,
## # hasLICENSE <chr>, Rfiles <list>, dependencyCount <chr>, Imports <list>, …As a simple example of how these columns can be used, extracting the importsMe column to find the packages that import the GEOquery package.
require(dplyr)
bpi = biocPkgList()
bpi %>%
filter(Package=="GEOquery") %>%
pull(importsMe) %>%
unlist()## [1] "bigmelon" "ChIPXpress"
## [3] "crossmeta" "DExMA"
## [5] "EGAD" "EpiMix"
## [7] "GEOexplorer" "minfi"
## [9] "MoonlightR" "phantasus"
## [11] "recount" "SRAdb"
## [13] "BeadArrayUseCases" "BioPlex"
## [15] "GSE13015" "healthyControlsPresenceChecker"
## [17] "easyDifferentialGeneCoexpression" "geneExpressionFromGEO"
## [19] "MetaIntegrator" "seeker"For the end user of Bioconductor, an analysis often starts with finding a package or set of packages that perform required tasks or are tailored to a specific operation or data type. The biocExplore() function implements an interactive bubble visualization with filtering based on biocViews terms. Bubbles are sized based on download statistics. Tooltip and detail-on-click capabilities are included. To start a local session:
The Bioconductor ecosystem is built around the concept of interoperability and dependencies. These interdependencies are available as part of the biocPkgList() output. The BiocPkgTools provides some convenience functions to convert package dependencies to R graphs. A modular approach leads to the following workflow.
data.frame of dependencies using buildPkgDependencyDataFrame.igraph object from the dependency data frame using buildPkgDependencyIgraph
igraph functionality to perform arbitrary network operations. Convenience functions, inducedSubgraphByPkgs and subgraphByDegree are available.A dependency graph for all of Bioconductor is a starting place.
library(BiocPkgTools)
dep_df = buildPkgDependencyDataFrame()
g = buildPkgDependencyIgraph(dep_df)
g## IGRAPH e357dc7 DN-- 3499 27215 --
## + attr: name (v/c), edgetype (e/c)
## + edges from e357dc7 (vertex names):
## [1] a4 ->a4Base a4 ->a4Preproc a4 ->a4Classif
## [4] a4 ->a4Core a4 ->a4Reporting a4Base ->a4Preproc
## [7] a4Base ->a4Core a4Classif->a4Core a4Classif->a4Preproc
## [10] ABSSeq ->methods acde ->boot aCGH ->cluster
## [13] aCGH ->survival aCGH ->multtest ACME ->Biobase
## [16] ACME ->methods ACME ->BiocGenerics ADaCGH2 ->parallel
## [19] ADaCGH2 ->ff ADaCGH2 ->GLAD ADAM ->stats
## [22] ADAM ->utils ADAM ->methods ADAMgui ->stats
## + ... omitted several edges## + 6/3499 vertices, named, from e357dc7:
## [1] a4 a4Base a4Classif ABSSeq acde aCGH## + 6/27215 edges from e357dc7 (vertex names):
## [1] a4 ->a4Base a4 ->a4Preproc a4 ->a4Classif
## [4] a4 ->a4Core a4 ->a4Reporting a4Base->a4PreprocSee inducedSubgraphByPkgs and subgraphByDegree to produce subgraphs based on a subset of packages.
See the igraph documentation for more detail on graph analytics, setting vertex and edge attributes, and advanced subsetting.
The visNetwork package is a nice interactive visualization tool that implements graph plotting in a browser. It can be integrated into shiny applications. Interactive graphs can also be included in Rmarkdown documents (see vignette)
igraph_network = buildPkgDependencyIgraph(buildPkgDependencyDataFrame())The full dependency graph is really not that informative to look at, though doing so is possible. A common use case is to visualize the graph of dependencies “centered” on a package of interest. In this case, I will focus on the GEOquery package.
igraph_geoquery_network = subgraphByDegree(igraph_network, "GEOquery")The subgraphByDegree() function returns all nodes and connections within degree of the named package; the default degree is 1.
The visNework package can plot igraph objects directly, but more flexibility is offered by first converting the graph to visNetwork form.
library(visNetwork)
data <- toVisNetworkData(igraph_geoquery_network)The next few code chunks highlight just a few examples of the visNetwork capabilities, starting with a basic plot.
visNetwork(nodes = data$nodes, edges = data$edges, height = "500px")For fun, we can watch the graph stabilize during drawing, best viewed interactively.
visNetwork(nodes = data$nodes, edges = data$edges, height = "500px") %>%
visPhysics(stabilization=FALSE)Add arrows and colors to better capture dependencies.
data$edges$color='lightblue'
data$edges[data$edges$edgetype=='Imports','color']= 'red'
data$edges[data$edges$edgetype=='Depends','color']= 'green'
visNetwork(nodes = data$nodes, edges = data$edges, height = "500px") %>%
visEdges(arrows='from') Add a legend.
ledges <- data.frame(color = c("green", "lightblue", "red"),
label = c("Depends", "Suggests", "Imports"), arrows =c("from", "from", "from"))
visNetwork(nodes = data$nodes, edges = data$edges, height = "500px") %>%
visEdges(arrows='from') %>%
visLegend(addEdges=ledges)[Work in progress]
The biocViews package is a small ontology of terms describing Bioconductor packages. This is a work-in-progress section, but here is a small example of plotting the biocViews graph.
## A graphNEL graph with directed edges
## Number of Nodes = 496
## Number of Edges = 495
library(igraph)
g = igraph.from.graphNEL(biocViewsVocab)
library(visNetwork)
gv = toVisNetworkData(g)
visNetwork(gv$nodes, gv$edges, width="100%") %>%
visIgraphLayout(layout = "layout_as_tree", circular=TRUE) %>%
visNodes(size=20) %>%
visPhysics(stabilization=FALSE)The dependency burden of a package, namely the amount of functionality that a given package is importing, is an important parameter to take into account during package development. A package may break because one or more of its dependencies have changed the part of the API our package is importing or this part has even broken. For this reason, it may be useful for package developers to quantify the dependency burden of a given package. To do that we should first gather all dependency information using the function buildPkgDependencyDataFrame() but setting the arguments to work with packages in Bioconductor and CRAN and dependencies categorised as Depends or Imports, which are the ones installed by default for a given package.
library(BiocPkgTools)
depdf <- buildPkgDependencyDataFrame(repo=c("BioCsoft", "CRAN"),
dependencies=c("Depends", "Imports"))
dim(depdf)## [1] 129500 3
head(depdf) # too big to show all## Package dependency edgetype
## 1 a4 a4Base Depends
## 2 a4 a4Preproc Depends
## 3 a4 a4Classif Depends
## 4 a4 a4Core Depends
## 5 a4 a4Reporting Depends
## 6 a4Base a4Preproc DependsFinally, we call the function pkgDepMetrics() to obtain different metrics on the dependency burden of a package we want to analyze, in the case below, the package BiocPkgTools itself:
pkgDepMetrics("BiocPkgTools", depdf)## ImportedAndUsed Exported Usage DepOverlap DepGainIfExcluded
## graph 1 116 0.86 0.06 0
## utils 2 228 0.88 0.01 0
## rlang 4 438 0.91 0.02 0
## igraph 9 800 1.12 0.11 4
## RBGL 1 77 1.30 0.07 0
## htmltools 1 76 1.32 0.08 0
## xml2 1 66 1.52 0.01 0
## tools 2 120 1.67 0.01 0
## stringr 1 59 1.69 0.11 0
## tibble 1 45 2.22 0.12 0
## DT 1 42 2.38 0.40 5
## magrittr 1 42 2.38 0.01 0
## rvest 1 40 2.50 0.29 2
## dplyr 8 292 2.74 0.19 0
## rorcid 1 32 3.12 0.31 8
## httr 5 91 5.49 0.09 0
## htmlwidgets 1 14 7.14 0.35 0
## jsonlite 2 23 8.70 0.01 0
## gh 1 11 9.09 0.19 4
## BiocFileCache 4 29 13.79 0.41 8
## BiocManager 3 6 50.00 0.02 0
## biocViews NA 31 NA 0.14 6
## readr NA 115 NA 0.25 6In this resulting table, rows correspond to dependencies and columns provide the following information:
ImportedAndUsed: number of functionality calls imported and used in the package.Exported: number of functionality calls exported by the dependency.Usage: (ImportedAndUsedx 100) / Exported. This value provides an estimate of what fraction of the functionality of the dependency is actually used in the given package.DepOverlap: Similarity between the dependency graph structure of the given package and the one of the dependency in the corresponding row, estimated as the Jaccard index between the two sets of vertices of the corresponding graphs. Its values goes between 0 and 1, where 0 indicates that no dependency is shared, while 1 indicates that the given package and the corresponding dependency depend on an identical subset of packages.DepGainIfExcluded: The ‘dependency gain’ (decrease in the total number of dependencies) that would be obtained if this package was excluded from the list of direct dependencies.The reported information is ordered by the Usage column to facilitate the identification of dependencies for which the analyzed package is using a small fraction of their functionality and therefore, it could be easier remove them. To aid in that decision, the column DepOverlap reports the overlap of the dependency graph of each dependency with the one of the analyzed package. Here a value above, e.g., 0.5, could, albeit not necessarily, imply that removing that dependency could substantially lighten the dependency burden of the analyzed package.
An NA value in the ImportedAndUsed column indicates that the function pkgDepMetrics() could not identify what functionality calls in the analyzed package are made to the dependency. This may happen because pkgDepMetrics() has failed to identify the corresponding calls, as it happens with imported built-in constants such as DNA_BASES from Biostrings, or that although the given package is importing that dependency, none of its functionality is actually being used. In such a case, this dependency could be safely removed without any further change in the analyzed package.
We can find out what actually functionality calls are we importing as follows:
imp <- pkgDepImports("BiocPkgTools")
imp %>% filter(pkg == "DT")## # A tibble: 1 × 2
## pkg fun
## <chr> <chr>
## 1 DT datatableIt is important to be able to identify the maintainer of a package in a reliable way. The DESCRIPTION file for a package can include an Authors@R field. This field can capture metadata about maintainers and contributors in a programmatically accessible way. Each element of the role field of a person (see ?person) in the Authors@R field comes from a subset of the relations vocabulary of the Library of Congress.
Metadata about maintainers can be extracted from DESCRIPTION in various ways. As of October 2022, we focus on the use of the ORCID field which is an optional comment component in a person element. For example, in the DESCRIPTION for the AnVIL package we have
Authors@R:
c(person(
"Martin", "Morgan", role = c("aut", "cre"),
email = "mtmorgan.bioc@gmail.com",
comment = c(ORCID = "0000-0002-5874-8148")
),This convention is used for a fair number of Bioconductor and CRAN packages.
We’ll demonstrate the use of get_cre_orcids with some packages.
inst = rownames(installed.packages())
cands = c("devtools", "evaluate", "ggplot2", "GEOquery", "gert", "utils")
totry = intersect(cands, inst)
oids = get_cre_orcids(totry)
oids## evaluate ggplot2 utils
## "0000-0003-0645-5666" "0000-0002-5147-4711" NAWe use the ORCID API to tabulate metadata about the holders of these IDs. We’ll avoid evaluating this because a token must be refreshed for the query to succeed.
orcid_table(oids)In October 2022 the result is
> orcid_table(.Last.value)
name org
devtools Jennifer Bryan RStudio
evaluate Yihui Xie RStudio, Inc.
ggplot2 Thomas Lin Pedersen RStudio
GEOquery Sean Davis University of Colorado Anschutz Medical Campus
gert Jeroen Ooms Berkeley Institute for Data Science
utils <NA> <NA>
city region country orcid
devtools Boston MA US 0000-0002-6983-2759
evaluate Elkhorn NE US 0000-0003-0645-5666
ggplot2 Copenhagen <NA> DK 0000-0002-5147-4711
GEOquery Aurora Colorado US 0000-0002-8991-6458
gert Berkeley CA US 0000-0002-4035-0289
utils <NA> <NA> <NA> <NA>## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] biocViews_1.68.1 visNetwork_2.1.2 igraph_1.4.3
## [4] dplyr_1.1.2 BiocPkgTools_1.19.2 htmlwidgets_1.6.2
## [7] knitr_1.43 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] xfun_0.39 bslib_0.4.2 gh_1.4.0
## [4] Biobase_2.60.0 tzdb_0.4.0 vctrs_0.6.2
## [7] tools_4.3.0 bitops_1.0-7 generics_0.1.3
## [10] stats4_4.3.0 curl_5.0.0 RUnit_0.4.32
## [13] tibble_3.2.1 fansi_1.0.4 RSQLite_2.3.1
## [16] blob_1.2.4 pkgconfig_2.0.3 dbplyr_2.3.2
## [19] desc_1.4.2 graph_1.78.0 lifecycle_1.0.3
## [22] compiler_4.3.0 stringr_1.5.0 textshaping_0.3.6
## [25] htmltools_0.5.5 sass_0.4.6 RCurl_1.98-1.12
## [28] yaml_2.3.7 pkgdown_2.0.7 pillar_1.9.0
## [31] jquerylib_0.1.4 whisker_0.4.1 ellipsis_0.3.2
## [34] DT_0.28 cachem_1.0.8 rvest_1.0.3
## [37] tidyselect_1.2.0 digest_0.6.31 stringi_1.7.12
## [40] purrr_1.0.1 bookdown_0.34 rprojroot_2.0.3
## [43] fastmap_1.1.1 cli_3.6.1 magrittr_2.0.3
## [46] utf8_1.2.3 RBGL_1.76.0 XML_3.99-0.14
## [49] crul_1.4.0 withr_2.5.0 readr_2.1.4
## [52] filelock_1.0.2 bit64_4.0.5 rmarkdown_2.22
## [55] httr_1.4.6 bit_4.0.5 hms_1.1.3
## [58] ragg_1.2.5 memoise_2.0.1 evaluate_0.21
## [61] BiocFileCache_2.8.0 rlang_1.1.1 glue_1.6.2
## [64] DBI_1.1.3 httpcode_0.3.0 BiocManager_1.30.20
## [67] xml2_1.3.4 fauxpas_0.5.2 BiocGenerics_0.46.0
## [70] rorcid_0.7.0 jsonlite_1.8.4 R6_2.5.1
## [73] systemfonts_1.0.4 fs_1.6.2