library(pacs)
library(withr)
library(remotes)

A set of tools that make life easier for developers and maintainers of R packages.

• Validating the library, packages and renv lock files.
• Exploring complexity of a certain package like evaluating its size in bytes with dependencies.
• The shiny app complexity could be explored too.
• Assessing life duration of a specific package version.
• Checking a CRAN package check page status for any errors and warnings.
• Retrieving a DESCRIPTION or NAMESPACE file for any package version.
• Comparing DESCRIPTION or NAMESPACE files between different package versions.
• Getting a list of all releases for a specific package.
• The Bioconductor is partly supported.

Functions Reference

## Hints

Hint0: An Internet connection is required to take full advantage of most of the features.

Hint1: Almost all calls which requiring an Internet connection are cached (for 30 minutes) by the memoise package, so the second invocation of the same command (and arguments) is immediate. Restart the R session if you want to clear cached data.

Hint2: Version variable is mostly a minimal required i.e. max(version1, version2 , …).

Hint3: When working with many packages it is recommended to use global functions, which retrieves data for many packages at once. An example will be usage of pacs::checked_packages() over pacs::pac_checkpage (or pacs::pac_checkred). Another example will be usage of utils::available.packages over pacs::pac_last. Finally, most important one will be pacs::lib_validate over pacs::pac_validate and pacs::pac_checkred and others.

Hint4: Character string “all” is shorthand for the c("Depends", "Imports", "LinkingTo", "Suggests", "Enhances") vector, character string “most” for the same vector without “Enhances”, character string “strong” (default setup) for the first three elements of that vector.

Hint5: Use parallel::mclapply (Linux and Mac) or parallel::parLapply (Windows, Linux and Mac) to speed up loop calculations. Nevertheless, under parallel::mclapply computation results are NOT cached with memoise package. Warning: Parallel computations might be unstable.

Hint6: withr and remotes packages are a valuable addition.

## Validate the library

pacs::lib_validate()

This procedure will be crucial for R developers as clearly showing the possible broken packages inside the local library.
Thus we could assess which packages require versions to update.

Default validation of the library with the pacs::lib_validate function.
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.

The full library validation requires activation of two additional arguments lifeduration and checkred. Additional arguments are on default turned off as are time consuming, for lifeduration assessment might take even a few minutes for bigger libraries.

Assessment of status on CRAN check pages takes only a few additional seconds even for all R CRAN packages. pacs::checked_packages() is used to gather all package check statuses for all CRAN servers.

pacs::lib_validate(checkred = list(scope = c("ERROR", "FAIL")))

When lifeduration is triggered then assessment might take even few minutes.

pacs::lib_validate(lifeduration = TRUE,
checkred = list(scope = c("ERROR", "FAIL")))

Not only scope field inside checkred list could be updated, to remind any of c("ERROR", "FAIL", "WARN", "NOTE"). We could specify flavors field inside the checkred list argument and narrow the tested machines. The full list of CRAN servers (flavors) might be get with pacs::cran_flavors()$Flavor. flavs <- pacs::cran_flavors()$Flavor[1:2]
pacs::lib_validate(checkred = list(scope = c("ERROR", "FAIL"),
flavors = flavs))

### 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 which 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, ]

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

### Core idea behind lib_validate

The core idea behind the function is coming 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 then 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 packages 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 find out if a certain package version lived more than 14 days (or other x limit days). If not then we might assume something was wrong 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 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")}) ## Per package utils ### 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")) ## Compare different package versions ### 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")

## Package size

Take into account that packages sizes are appropriate for your local system (Sys.info()). Installation with install.packages and some devtools functions might result in different packages sizes.

If you do not want to install anything in your current library (.libPaths()) and still inspect a package size, then a usage of the withr package is recommended. withr::with_temp_libpaths is recommended to isolate the download process.

# restart of R session could be needed
withr::with_temp_libpaths({install.packages("devtools"); cat(pacs::pac_true_size("devtools") / 10**6, "MB", "\n")})

Installation in your main library.

# if not have
install.packages("devtools")

Size of the devtools package:

cat(pacs::pac_size("devtools") / 10**6, "MB", "\n")

True size of the package as taking into account its dependencies. At the time of writing it, it is 113MB for devtools without base packages (Mac OS arm64).

cat(pacs::pac_true_size("devtools") / 10**6, "MB", "\n")

A reasonable assumption might be to count only dependencies which are not used by any other package. Then we could use exclude_joint argument to limit them. However hard to assume if your local installation is a reasonable proxy for an average user.

# exclude packages if at least one other package use it too
cat(pacs::pac_true_size("devtools", exclude_joint = 1L) / 10**6, "MB", "\n")

We could check out which of the direct dependencies are heaviest ones:

pac_deps_heavy("devtools")

## The shiny app utils

The shiny app dependencies packages are checked. By default the c("Depends", "Imports", "LinkingTo") DESCRIPTION files fields are check recursively for each package recognized with the renv::dependencies function. The required dependencies have to be installed in the local repository.

pacs::app_deps(system.file("examples/04_mpg", package = "shiny"), description_v = TRUE)
pacs::app_deps(system.file("examples/04_mpg", package = "shiny"), description_v = TRUE, local = FALSE)

When we want to check only direct dependencies, recursive argument has to be set to FALSE. Then you could use the renv::dependencies function directly.

pacs::app_deps(system.file("examples/04_mpg", package = "shiny"), recursive = FALSE)

The size of shiny app is a sum of dependencies and the app directory. The app dependencies (packages) are checked recursively.

cat(pacs::app_size(system.file("examples/04_mpg", package = "shiny")) / 10**6, "MB")

## Base packages

Useful functions to get a list of base packages. You might want to exclude them from final results.

pacs::pacs_base()
# start up loaded, base packages
pacs::pacs_base(startup = TRUE)