Walk-through pacs

Investigate by filtering

Packages are not installed (and should be) or have too low version:

lib <- pacs::lib_validate(checkred = list(scope = c("ERROR", "FAIL")))
# not installed (and should be) or too low version
lib[(lib$version_status == -1), ]
# not installed (and should be)
lib[is.na(lib$Version.have), ]
# too low version
lib[(!is.na(lib$Version.have)) & (lib$version_status == -1), ]

Packages which have at least one CRAN server which ERROR or FAIL:

red <- lib[(!is.na(lib$checkred)) & (lib$checkred == TRUE), ]
nrow(red)
head(red)

Packages that are not a dependency (default c("Depends", "Imports", "LinkingTo")) of any other package:

lib[is.na(lib$Version.expected.min), ]

Non-CRAN packages:

lib[lib$cran == FALSE, ]

Not newest packages:

lib[(!is.na(lib$newest)) & (lib$newest == FALSE), ]

Core idea behind lib_validate

The core idea behind the function comes from proper processing of the installed.packages function result.

# aggregate function is needed as we could have different versions
# installed under different `.libPaths()`.
installed_packages_unique <- stats::aggregate(
  installed.packages()[, c("Version", "Depends", "Imports", "LinkingTo")], 
  list(Package = installed.packages()[, "Package"]), 
  function(x) x[1]
)
# installed_descriptions function transforms direct dependencies DESCRIPTION file fields
# installed_packages_unique[, c("Depends", "Imports", "LinkingTo")] 
# to the two column data.frame with Package name 
# and minimum required Version i.e. max(version1, version2 , ...).
installed_descriptions <- pacs:::installed_descriptions(
  lib.loc = .libPaths(), 
  fields = c("Depends", "Imports", "LinkingTo")
)

merge(
  installed_descriptions,
  installed_packages_unique[, c("Package", "Version")],
  by = "Package",
  all = TRUE,
  suffix = c(".expected.min", ".have")
)

renv library

When a project is based on renv and all needed dependencies are installed in the renv directory, we mostly want to validate only the isolated renv library. In the new renv versions the .libPaths() contains the main library path too (renv library and the main library). Please remember to limit the library path when using pacs::lib_validate, to limit the validation to only renv library.

# renv::init()
pacs::lib_validate(lib.loc = .libPaths()[1])

Warning, at least rsconnect (and its packrat connected dependencies) related packages could still not be in the renv library.

renv lock file

There is a way to validate the renv lock file in the same way the local library or packages are validated.

# a path or url
url <- "https://raw.githubusercontent.com/Polkas/pacs/master/tests/testthat/files/renv_test.lock"
pacs::lock_validate(url)

pacs::lock_validate(
  url,
  checkred = list(scope = c("ERROR", "FAIL"), flavors = NULL)
)

pacs::lock_validate(
  url,
  lifeduration = TRUE,
  checkred = list(scope = c("ERROR", "FAIL"), flavors = NULL)
)

R CRAN packages check page statuses

checked_packages was built to extend the .packages family functions, like utils::installed.packages() and utils::available.packages(). pacs::checked_packages retrieves all current package checks from CRAN webpage.

pacs::checked_packages()

Use pacs::pac_checkpage("dplyr") to get the check page per package. However, pacs::checked_packages() will be more efficient for many packages. Remember that pacs::checked_packages() result is cached after the first invoke.

Package health and life duration

We could determine if a specific package version lived more than 14 days (or other x limit days). If not then we might assume something needed to be fixed with it, as had to be quickly updated.

e.g. dplyr under the “0.8.0” version seems to be a broken release, we could find out that it was published only for 1 day.

pacs::pac_lifeduration("dplyr", "0.8.0")

With a 14 day limit we get a proper health status. We are sure about this state as this is not the newest release. For the newest packages we are checking if there are any red messages on CRAN check pages too, specified with a scope argument.

pacs::pac_health("dplyr", version = "0.8.0", limit = 14)

For the newest package, we will check the CRAN check page too, the scope might be adjusted.

pacs::pac_health("dplyr", limit = 14, scope = c("ERROR", "FAIL", "WARN"))
pacs::pac_health("dplyr", limit = 14, scope = c("ERROR", "FAIL", "WARN"), 
                 flavors = pacs::cran_flavors()$Flavor[1])

Simulate a package download with the withr package

withr package is recommended for the isolated download process.
We could use a temporary library path (withr::with_temp_libpaths) to check if the process is as expected.

Checking what packages need to be installed/(optionally updated) parallel with a specific package, with remotes package. The full list even with packages which are already installed could be get with pacs::pac_deps_user.

remotes::package_deps("keras")
pacs::pac_deps_user("pacs")

Isolated download of a package and the validation.

# restart of R session could be needed
withr::with_temp_libpaths({install.packages("keras"); pacs::lib_validate()})

# restart of R session could be needed
withr::with_temp_libpaths({install.packages("keras"); pacs::pac_validate("keras")})

Time Machine

Using R CRAN website to get packages version/versions used at a specific Date or a Date interval.

pacs::pac_timemachine("dplyr")
pacs::pac_timemachine("dplyr", version = "0.8.0")
pacs::pac_timemachine("dplyr", at = as.Date("2017-02-02"))
pacs::pac_timemachine("dplyr", from = as.Date("2017-02-02"), to = as.Date("2018-04-02"))
pacs::pac_timemachine("dplyr", at = Sys.Date())
pacs::pac_timemachine("tidyr", from = as.Date("2020-06-01"), to = Sys.Date())

Package dependencies

One of the main functionality is to get versions for all package dependencies. Versions might come from installed packages or DESCRIPTION files.

pac_deps for an extremely fast retrieving of package dependencies, packages versions might come from installed ones or from DESCRIPTION files (required minimum). The default setup is to show dependencies recursively, recursive = TRUE.

# Providing more than tools::package_dependencies and packrat:::recursivePackageVersion
# pacs::pac_deps is providing the min required version for each package
# Use it to answer what we should have
res <- pacs::pac_deps("shiny", description_v = TRUE)
res
attributes(res)

Packages dependencies with versions from DESCRIPTION files.

pacs::pac_deps("shiny", description_v = TRUE)

Remote (newest CRAN) package dependencies with versions.

pacs::pac_deps("shiny", local = FALSE)

Raw dependencies from DESCRIPTION file. The same which is needed by the the install.packages function. Depends/Imports/LinkingTo DESCRIPTION fields dependencies, recursively. pacs::pac_deps_user could be used to check them.

pacs::pac_deps("memoise", description_v = TRUE, recursive = FALSE, local = FALSE)
# or
pacs::pac_deps_user("memoise")

The field argument is used to change the scope of exploration. The field argument is equal to c("Depends", "Imports", "LinkingTo") by default as these are the dependencies installed when the install.packages function is used. When the field argument is extended the number of dependencies will grow. Remember that we are looking for dependencies recursively by default. At the moment of writing it the first invoke returns 3 dependencies whereas the second over an one thousand. It should be clear that when extending the scope (and recursively) with the "Suggests" field then the number of dependencies is exploding.

nrow(pacs::pac_deps("memoise", fields = c("Depends", "Imports", "LinkingTo")))
nrow(pacs::pac_deps("memoise", fields = c("Depends", "Imports", "LinkingTo", "Suggests")))

The developer dependencies are the ones needed when e.g. R CMD check is run. These are Depends/Imports/LinkingTo/Suggests DESCRIPTION fields dependencies, and for them Depends/Imports/LinkingTo recursively. pacs::pac_deps_dev could be used to check them. Obviously the list is much longer as the one for pacs::pac_deps_user.

pac_deps_dev("memoise")

For a certain version (archived), might take some time.

pacs::pac_deps_timemachine("dplyr", version = "0.8.1")

Package DESCRIPTION file

Reading raw dcf DESCRIPTION files scrapped from the github CRAN mirror or if not worked from the CRAN website.

pacs::pac_description("dplyr")
pacs::pac_description("dplyr", version = "0.8.0")
pacs::pac_description("dplyr", at = as.Date("2019-01-01"))

Package NAMESPACE file

Reading raw NAMESPACE files scrapped from the github CRAN mirror or if it did not work from the CRAN website.

pacs::pac_namespace("dplyr")
pacs::pac_namespace("dplyr", version = "0.8.0")
pacs::pac_namespace("dplyr", at = as.Date("2019-01-01"))

Comparing DESCRIPTION files between different package versions

Comparing DESCRIPTION file dependencies between local and the newest package. We will get duplicated columns if the local version is the newest one.

pacs::pac_compare_versions("shiny")

Comparing DESCRIPTION file dependencies between package versions.

pacs::pac_compare_versions("shiny", "1.4.0", "1.5.0")

pacs::pac_compare_versions("shiny", "1.4.0", "1.6.0")
# to newest release
pacs::pac_compare_versions("shiny", "1.4.0")

Comparing NAMESPACE files between different package versions

Comparing NAMESPACE between local and the newest package.

pacs::pac_compare_namespace("shiny")

Comparing NAMESPACE between package versions.

pacs::pac_compare_namespace("shiny", "1.0.0", "1.5.0")
# e.g. only exports 
pacs::pac_compare_namespace("shiny", "1.0.0", "1.5.0")$exports

# to newest release
pacs::pac_compare_namespace("shiny", "1.0.0")