It has almost been a standard to put sessionInfo() to the end of the R markdown document to keep track
of the environment where the analysis is done. sessionInfo() prints a list of packages that are loaded to the
R session directly or indirectly. But how about the dependency relations among those packages? In this blog post,
let’s check it out.
Let’s open a new R session and only load the ggplot2 package:
library(ggplot2)Next we obtain the session info by the sessionInfo() function:
x1 = sessionInfo()Let’s print x1:
x1## R version 4.3.1 (2023-06-16)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.2.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] C/UTF-8/C/C/C/C
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.4.4
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 R6_2.5.1 tidyselect_1.2.0 magrittr_2.0.3 gtable_0.3.4
## [6] glue_1.6.2 tibble_3.2.1 pkgconfig_2.0.3 generics_0.1.3 dplyr_1.1.3
## [11] lifecycle_1.0.3 cli_3.6.1 fansi_1.0.5 scales_1.2.1 grid_4.3.1
## [16] vctrs_0.6.4 withr_2.5.1 compiler_4.3.1 munsell_0.5.0 pillar_1.9.0
## [21] colorspace_2.1-0 rlang_1.1.1It seems only a few packages are loaded in the session.
x1 is an R object as a list with several elements that correspond to the packages that are loaded directly
or indirectly. Packages in session are put into three groups:
- base packages: Base packages shipped with R, e.g. grid, methods. Note packages like lattice or MASS, although also shipped with R, they are “recommended packages”.
- other packages: Other packages e.g. which you load by
library(). They will be visible on the search path (bysearch()). - loaded packages: Other packages which are also loaded into the R session by “other packages”, but not visible on the search path.
base_pkgs = x1$basePkgs
other_pkgs = sapply(x1$otherPkgs, function(x) x$Package)
loaded_pkgs = sapply(x1$loadedOnly, function(x) x$Package)For the packages in session, we need to know their dependency relations. Here we use the pkgndep package. All the packages installed locally are used as the “package database” for querying dependencies.
library(pkgndep)
db = reformat_db(installed.packages())## prepare dependency table...
## prepare reverse dependency table...Now we go through every “other packages” and obtain its direct and remote dependencies. Here we only use the “strong” dependencies, i.e. the packages with dependency relations of “Depends”, “Imports” and “LinkingTo”.
mat = matrix(nrow = 0, ncol = 3)
for(pkg in other_pkgs) {
mat = rbind(mat, db$package_dependencies(pkg, recursive = TRUE, which = "strong"))
}
mat = unique(mat)Base packages are bound to R, so we remove the dependency relation from base packages. And we only restrict the packages to “other packages” and “loaded packages”.
all_pkgs = c(other_pkgs, loaded_pkgs)
mat = mat[mat[, 1] %in% all_pkgs & mat[, 2] %in% all_pkgs, , drop = FALSE]
mat = mat[!mat[, 1] %in% pkgndep:::BASE_PKGS | mat[, 2] %in% pkgndep:::BASE_PKGS, , drop = FALSE]
head(mat)## package dependency dep_fields
## [1,] "ggplot2" "cli" "Imports"
## [2,] "ggplot2" "glue" "Imports"
## [3,] "ggplot2" "grid" "Imports"
## [4,] "ggplot2" "gtable" "Imports"
## [5,] "ggplot2" "lifecycle" "Imports"
## [6,] "ggplot2" "rlang" "Imports"Next we use the DiagrammeR package to visualize the dependency diagram. We first generate the DOT code:
all_nodes = unique(c(mat[, 1], mat[, 2], other_pkgs, loaded_pkgs))
node_col = rep("black", length(all_nodes))
node_col[all_nodes %in% other_pkgs] = "red"
node_col[all_nodes %in% loaded_pkgs] = "blue"
library(glue)
nodes = glue(" \"{all_nodes}\" [color=\"{node_col}\"];", collapse = FALSE)
dep_col = c(2, 4, 3, 5, 6)
dep_col = rgb(t(col2rgb(dep_col)), max = 255)
names(dep_col) = c("Depends", "Imports", "LinkingTo", "Suggests", "Enhances")
edges = glue(" \"{mat[, 1]}\" -> \"{mat[, 2]}\" [color=\"{dep_col[mat[, 3]]}\"];", collapse = FALSE)
dot = paste(
c("digraph {",
" nodesep=0.05",
" rankdir=LR;",
" graph [overlap = true];",
" node[shape = box];",
nodes,
edges,
"}"),
collapse = "\n"
)
cat(dot)## digraph {
## nodesep=0.05
## rankdir=LR;
## graph [overlap = true];
## node[shape = box];
## "ggplot2" [color="red"];
## "gtable" [color="blue"];
## "lifecycle" [color="blue"];
## "scales" [color="blue"];
## "tibble" [color="blue"];
## "vctrs" [color="blue"];
## "munsell" [color="blue"];
## "pillar" [color="blue"];
## "cli" [color="blue"];
## "glue" [color="blue"];
## "grid" [color="blue"];
## "rlang" [color="blue"];
## "withr" [color="blue"];
## "R6" [color="blue"];
## "fansi" [color="blue"];
## "magrittr" [color="blue"];
## "pkgconfig" [color="blue"];
## "colorspace" [color="blue"];
## "utf8" [color="blue"];
## "tidyselect" [color="blue"];
## "generics" [color="blue"];
## "dplyr" [color="blue"];
## "compiler" [color="blue"];
## "ggplot2" -> "cli" [color="#2297E6"];
## "ggplot2" -> "glue" [color="#2297E6"];
## "ggplot2" -> "grid" [color="#2297E6"];
## "ggplot2" -> "gtable" [color="#2297E6"];
## "ggplot2" -> "lifecycle" [color="#2297E6"];
## "ggplot2" -> "rlang" [color="#2297E6"];
## "ggplot2" -> "scales" [color="#2297E6"];
## "ggplot2" -> "tibble" [color="#2297E6"];
## "ggplot2" -> "vctrs" [color="#2297E6"];
## "ggplot2" -> "withr" [color="#2297E6"];
## "gtable" -> "cli" [color="#2297E6"];
## "gtable" -> "glue" [color="#2297E6"];
## "gtable" -> "grid" [color="#2297E6"];
## "gtable" -> "lifecycle" [color="#2297E6"];
## "gtable" -> "rlang" [color="#2297E6"];
## "lifecycle" -> "cli" [color="#2297E6"];
## "lifecycle" -> "glue" [color="#2297E6"];
## "lifecycle" -> "rlang" [color="#2297E6"];
## "scales" -> "lifecycle" [color="#2297E6"];
## "scales" -> "munsell" [color="#2297E6"];
## "scales" -> "R6" [color="#2297E6"];
## "scales" -> "rlang" [color="#2297E6"];
## "tibble" -> "fansi" [color="#2297E6"];
## "tibble" -> "lifecycle" [color="#2297E6"];
## "tibble" -> "magrittr" [color="#2297E6"];
## "tibble" -> "pillar" [color="#2297E6"];
## "tibble" -> "pkgconfig" [color="#2297E6"];
## "tibble" -> "rlang" [color="#2297E6"];
## "tibble" -> "vctrs" [color="#2297E6"];
## "vctrs" -> "cli" [color="#2297E6"];
## "vctrs" -> "glue" [color="#2297E6"];
## "vctrs" -> "lifecycle" [color="#2297E6"];
## "vctrs" -> "rlang" [color="#2297E6"];
## "munsell" -> "colorspace" [color="#2297E6"];
## "pillar" -> "cli" [color="#2297E6"];
## "pillar" -> "fansi" [color="#2297E6"];
## "pillar" -> "glue" [color="#2297E6"];
## "pillar" -> "lifecycle" [color="#2297E6"];
## "pillar" -> "rlang" [color="#2297E6"];
## "pillar" -> "utf8" [color="#2297E6"];
## "pillar" -> "vctrs" [color="#2297E6"];
## }Then we send the DOT code to grViz() function.
DiagrammeR::grViz(dot)It looks like the dependency relations are complicated than simply listing the packages. There are also isolated packages in the diagram that are not connected to ggplot2, e.g. dplyr. They are loaded to the R session as “weak dependencies” indirectly by ggplot2 or its upstream packages.
We wrap the code as a function which we will use repeatedly. Internally, we call library(pkg) in a
fresh R session by using the callr package.
loaded_pkgs = function(pkg) {
for(i in seq_along(pkg)) {
library(pkg[i], character.only=TRUE)
}
session_info = sessionInfo()
base_pkgs = session_info$basePkgs
other_pkgs = sapply(session_info$otherPkgs, function(x)x$Package)
loaded_pkgs = sapply(session_info$loadedOnly, function(x)x$Package)
lt = list(base_pkgs = base_pkgs,
other_pkgs = other_pkgs,
loaded_pkgs = loaded_pkgs)
jsonlite::toJSON(lt)
}
dep_in_session = function(pkg, db, dep_group = "strong", rankdir = "LR") {
session_info = jsonlite::fromJSON(callr::r(loaded_pkgs, args = list(pkg = pkg)))
base_pkgs = session_info$base_pkgs
other_pkgs = session_info$other_pkgs
loaded_pkgs = session_info$loaded_pkgs
mat = matrix(nrow = 0, ncol = 3)
for(pkg in other_pkgs) {
mat = rbind(mat, db$package_dependencies(pkg, recursive = TRUE, which = dep_group))
}
mat = unique(mat)
mat = mat[!mat[, 1] %in% pkgndep:::BASE_PKGS | mat[, 2] %in% pkgndep:::BASE_PKGS, , drop = FALSE]
all_pkgs = c(other_pkgs, loaded_pkgs)
mat = mat[mat[, 1] %in% all_pkgs & mat[, 2] %in% all_pkgs, , drop = FALSE]
all_nodes = unique(c(mat[, 1], mat[, 2], other_pkgs, loaded_pkgs))
node_col = rep("black", length(all_nodes))
node_col[all_nodes %in% other_pkgs] = "red"
node_col[all_nodes %in% loaded_pkgs] = "blue"
nodes = glue::glue(" \"{all_nodes}\" [color=\"{node_col}\"];", collapse = FALSE)
dep_col = c(2, 4, 3, 5, 6)
dep_col = rgb(t(col2rgb(dep_col)), max = 255)
names(dep_col) = c("Depends", "Imports", "LinkingTo", "Suggests", "Enhances")
edges = glue::glue(" \"{mat[, 1]}\" -> \"{mat[, 2]}\" [color=\"{dep_col[mat[, 3]]}\"];", collapse = FALSE)
dot = paste(
c("digraph {",
" nodesep=0.05",
glue::glue(" rankdir={rankdir};"),
" graph [overlap = true];",
" node[shape = box];",
nodes,
edges,
"}"),
collapse = "\n"
)
DiagrammeR::grViz(dot)
}In the previous example, we only consider “strong dependency relations” upstream of ggplot2. What if we include all dependency relations?
dep_in_session("ggplot2", db = db, dep_group = "all")It becomes much more complicated! Especially we can see there are many bi-directional dependencies,
e.g. A <-> B where A is a strong dependency of B, and B is a weak dependency of A.
Next let’s check a Bioconductor package DESeq2.
We first only consider the strong dependencies upstream of DESeq2.
dep_in_session("DESeq2", db = db, dep_group = "strong")It is already very complicated, but still interesting that DESeq2’s dependencies can be nicely separated into two groups. The first group is related to Bioconductor packages and a lot of them are directly attached to the search path (red box: visible in search path; red link: the “Depends” relation); while the second group is mainly related to ggplot2 and its upstream packages, which all are loaded indirectly into the R session (blue link: the “Imports” relation).
And what if we include all dependency types? Now we need to change the layout to “top-bottom” style.
As we can see, a simple command library(DESeq2) brings a dependency monster to your R session.
dep_in_session("DESeq2", db = db, dep_group = "all", rankdir = "TB")Last, let’s check when freshly loading a very heavy package Seurat.
dep_in_session("Seurat", db = db, dep_group = "strong")And you can try the moster below. I will not execute it in this document.
dep_in_session("Seurat", db = db, dep_group = "all")