Introduction

This report checks if the status of packages on CRAN are due to intermittent failures.

Failures defined as warnings, notes or errors without change on:

R version used (if not stable the same svn snapshot)
The package version (Note that CRAN might modify a package without changing the version)
Their dependencies

Reasons of these failures might be because the packages depend on:

Random generation numbers
Flacky external resources
Other ?

Why is this important?

Because package maintainers of dependencies of that package, R core and CRAN team need to check if the failures are false positives.

This report started because it was suggested as something that the R-repositories working group could help the CRAN team.

Retrieve data

It makes use of tools::CRAN_check_results to retrieve the data.

library("dplyr")
library("tools", include.only = c("package_dependencies", "CRAN_check_results"))
library("flextable", include.only = c("flextable", "autofit"))
# Use a LOCAL environment to check if files can be overwritten on my computer
local_build <- as.logical(Sys.getenv("LOCAL", "FALSE"))
yc <- readRDS("today.RDS")
tc <- CRAN_check_results()
# Added 2023/03/09: sometimes some flavors are reported without status: Omit those
tc <- tc[!is.na(tc$Status),]
if (!interactive() && !local_build) {
  message("Saving today's file.")
  saveRDS(tc, file = "today.RDS")
}

The checks are from multiple flavors release, devel, old release and patched on multiple machines and configurations.

old_flavors <- readRDS("flavors.RDS")
flavors <- unique(tc$Flavor)
# One flavor now present in all is the r-devel-windows-x86_64: skip
flavors <- setdiff(flavors, "r-devel-windows-x86_64")
proto <- data.frame(r_version = character(),
                    os = character(),
                    architecture = character(),
                    other = character())
flavors_df <- strcapture(
  pattern = "r-([[:alnum:]]+)-([[:alnum:]]+)-([[:alnum:]_\\+]+)-?(.*)", 
  x = flavors,
  proto = proto)

# Extract R version used and svn id
h <- "https://www.r-project.org/nosvn/R.check/%s/ggplot2-00check.html"
links <- sprintf(h, flavors)
extract_revision <- function(x) {
  r <- readLines(x, 12)[12]
  version <- strcapture(pattern = "([[:digit:]]\\.[[:digit:]]\\.[[:digit:]])",  
                        x = r, proto = data.frame(version = character()))
  revision <- strcapture(pattern = "(r[[:digit:]]+)",  x = r,
                         proto = data.frame(revision = character()))
  cbind(version, revision)
}
revision <- data.frame(version = character(),
                       revision = character())
for (i in links) {
  revision <- rbind(revision, extract_revision(i))
}

flavors_df <- cbind(flavors = flavors, flavors_df, revision)
if (!interactive() && !local_build) {
  saveRDS(flavors_df, "flavors.RDS")
}

m <- match(tc$Flavor, flavors_df$flavors)
tc_flavors <- cbind(tc, flavors_df[m, ])
flextable(flavors_df) |> 
  autofit()

flavors	r_version	os	architecture	other	version	revision
r-devel-linux-x86_64-debian-clang	devel	linux	x86_64	debian-clang		r89818
r-devel-linux-x86_64-debian-gcc	devel	linux	x86_64	debian-gcc		r89869
r-devel-linux-x86_64-fedora-clang	devel	linux	x86_64	fedora-clang		r89794
r-devel-linux-x86_64-fedora-gcc	devel	linux	x86_64	fedora-gcc		r89795
r-devel-macos-arm64	devel	macos	arm64		4.6.0	r89725
r-patched-linux-x86_64	patched	linux	x86_64		4.6.0	r89797
r-release-linux-x86_64	release	linux	x86_64		4.5.3
r-release-macos-arm64	release	macos	arm64		4.5.2
r-release-macos-x86_64	release	macos	x86_64		4.5.1
r-release-windows-x86_64	release	windows	x86_64		4.5.3
r-oldrel-macos-arm64	oldrel	macos	arm64		4.4.3
r-oldrel-macos-x86_64	oldrel	macos	x86_64		4.4.1
r-oldrel-windows-x86_64	oldrel	windows	x86_64		4.4.3	r89426

It assumes that the same configuration in one package is used for all. Or in other words that the reports of the configuration (svn revision and version) for the A3 package is the same as for all the other packages.

Warning: This assumption is not always true, but this would require to check each log file on each flavor to verify the R and svn id of each package (which could take too much time and resources).

Overview

Briefly an introduction of how much effort goes into checking

library("ggplot2")
theme_set(theme_minimal())
tc |> 
  filter(!is.na(T_install)) |> 
  ggplot() +
  geom_violin(aes(T_install, Flavor)) +
  scale_x_log10() +
  labs(x = "seconds", title = "Time to install", y = element_blank())

Machines (y axis) vs install time (seconds, x axis), violing plot usually around 10 seconds.

Distribution of install time on each machine.

This means that just to install all the packages on the multiple flavors with a single CPU would take 68 days.

tc |> 
  filter(!is.na(T_check)) |> 
  ggplot() +
  geom_violin(aes(T_check, Flavor), trim = FALSE) +
  scale_x_log10() +
  labs(x = "seconds", title = "Time to check", y = element_blank())

Machines (y axis) vs check time (seconds, x axis), violing plot usually around 100 seconds.

Distribution of checking time on each machine.

This means that to check all the packages on the multiple flavors with a single CPU would take 305 days.

tc |> 
  filter(!is.na(T_total)) |> 
  ggplot() +
  geom_violin(aes(T_total, Flavor)) +
  scale_x_log10() +
  labs(x = "seconds", title = "Time to check and install", y = element_blank())

Machines (y axis) vs total time (seconds, x axis), violing plot usually around 100 seconds.

Distribution of total time on each machine.

This means that to install and check all the packages with a single CPU would take 387 days.

I don’t know the computational cost of 266 days of CPU (every day), but a rough calculation of 2.5 cents per hour means 232.39 dollars daily dedicated to this.

tc |> 
  group_by(Package) |> 
  summarize(Versions = n_distinct(Version)) |> 
  ungroup() |> 
  count(Versions, name = "Packages", sort = TRUE) |> 
  flextable() |> 
  autofit()

Versions	Packages
1	22,614
2	947
3	21

This was surprising, but sometimes checks have multiple versions. Probably when a new version is added and the system don’t catch it for a certain machine.

tc |> 
  group_by(Package) |> 
  summarize(Flavors = n_distinct(Flavor)) |> 
  ungroup() |> 
  count(Flavors, name = "Packages", sort = TRUE) |> 
  flextable() |> 
  autofit()

Flavors	Packages
14	23,234
13	213
11	50
9	24
12	23
3	13
7	9
8	8
10	8

Similarly, often packages are only tested on few configurations.

Combining both we can have packages with few configurations that have multiple versions being tested.

tc |> 
  group_by(Package) |> 
  summarize(Versions = as.character(n_distinct(Version)),
            Flavors = n_distinct(Flavor)) |> 
  ungroup() |> 
  count(Flavors, Versions, name = "Packages") |> 
  ggplot() +
  geom_tile(aes(Flavors, Versions, fill = log10(Packages))) +
  scale_x_continuous(expand = expansion())

Flavors of machines and versions of packages

Most packages are just tested one version.

But focusing on those that have just one version of the package being tested, most of the machines have packages either OK or with some notes.

man_colors <- c("OK" = "green", "NOTE" = "darkgreen", 
                "WARNING" = "yellow", "ERROR" = "red", "FAILURE" = "black")
tc |> 
  group_by(Package) |> 
  filter(n_distinct(Version) == 1) |> 
  ungroup() |> 
  group_by(Flavor) |> 
  count(Status, name = "packages") |> 
  mutate(perc = packages/sum(packages),
         Status = forcats::fct_relevel(Status, names(man_colors))) |> 
  ggplot() + 
  geom_col(aes(perc, Flavor, fill = Status)) +
  scale_x_continuous(expand = expansion(), labels = scales::percent_format()) +
  scale_fill_manual(values = man_colors) +
  labs(title = "Packages check status", x = element_blank())

On the vertical axis the machine, on the horitzonal axis the packages colored by the status.

Most frequent status is OK or NOTE on all machines.

If we look at the most frequent status report for packages we can see this table:

ts <- tc |> 
  group_by(Package) |> 
  filter(n_distinct(Version) == 1) |> 
  count(Status, name = "flavors") |> 
  ungroup() |> 
  tidyr::pivot_wider(values_from = flavors, names_from = Status, 
                     values_fill = 0) |> 
  count(OK, NOTE, WARNING, ERROR, FAILURE, name = "packages", sort = TRUE)
download.file("https://cran.r-project.org/web/packages/packages.rds", 
              destfile = "packages.RDS") # From the help page
ap <- readRDS("packages.RDS") |> 
  as.data.frame() |> 
  distinct(Package, .keep_all = TRUE)
ap_bioc <- available.packages(repos = BiocManager::repositories()[1:5])
ap_bioc <- cbind(ap_bioc, Additional_repositories = NA)
ap_colm <- intersect(colnames(ap), colnames(ap_bioc))
ap <- rbind(ap[, ap_colm], ap_bioc[, ap_colm])
head(ts) |> 
  flextable() |> 
  autofit()

OK	NOTE	packages
14	0	13,116
12	2	4,202
11	3	1,548
0	14	912
9	5	781
10	4	335

We can see that the most common occurrences are some sort of OK and notes on checks. We can also check the official results on CRAN.

We can see that 0.99%, 0.95%, 0.58%, 0.08%, 0.01% of packages pass all checks without notes.

Now let’s see which of the notes or failures are due to intermittent issues.

Compare

First we need to make sure that we compare the right configurations. They must be the same machine, the same R version and the same svn revision between yesterday and today.

# Compare the previous flavor with today's
m_flavor <- which(flavors_df$flavors %in% old_flavors$flavors)
m_version <- which(flavors_df$version %in% old_flavors$version)
m_revision <- which(flavors_df$revision %in% old_flavors$revision)
tm <- table(c(m_flavor, m_version, m_revision))
compare <- flavors_df$flavors[tm == 3] # Only missing the packages version

All changes

Next, compare the status of the packages if the version of the package is the same.

# Find package on the flavors to compare that haven't changed versions
library("dplyr")
tcc <- filter(tc, Flavor %in% compare) |> 
  select(Flavor, Package, Version, Status) |> 
  arrange(Flavor, Package)
ycc <- filter(yc, Flavor %in% compare) |> 
  select(Flavor, Package, Version, Status) |> 
  arrange(Flavor, Package)

all_checks <- merge(tcc, ycc, by = c("Flavor", "Package"), 
                    suffixes = c(".t", ".y"), all = TRUE) 

possible_packages <- all_checks |> 
  filter(Version.t == Version.y & # Same version
           Status.t != Status.y & # Different status
           !is.na(Status.y) & # No new version or removed package
           !is.na(Status.t)) |> 
  rename(Today = Status.t, Yesterday = Status.y)
possible_packages |> 
  select(Package, Flavor, Today, Yesterday, -Version.t, -Version.y) |> 
  arrange(Package, Flavor) |> 
  flextable() |> 
  autofit()

Package	Flavor	Today	Yesterday
BALLI	r-oldrel-windows-x86_64	NOTE	ERROR
BioVizSeq	r-release-windows-x86_64	OK	ERROR
DIscBIO	r-oldrel-windows-x86_64	ERROR	OK
DIscBIO	r-release-windows-x86_64	OK	ERROR
DRviaSPCN	r-oldrel-windows-x86_64	ERROR	OK
DeclareDesign	r-oldrel-windows-x86_64	ERROR	OK
GLMMcosinor	r-release-windows-x86_64	OK	ERROR
R4GoodPersonalFinances	r-oldrel-windows-x86_64	OK	ERROR
RcensusPkg	r-release-windows-x86_64	OK	ERROR
SpatialPosition	r-release-windows-x86_64	OK	ERROR
TaxaNorm	r-oldrel-windows-x86_64	OK	ERROR
algaeClassify	r-oldrel-windows-x86_64	OK	ERROR
algaeClassify	r-release-windows-x86_64	OK	ERROR
annotaR	r-oldrel-windows-x86_64	ERROR	OK
blockr.dag	r-oldrel-windows-x86_64	ERROR	OK
blockr.dock	r-oldrel-windows-x86_64	ERROR	OK
cardx	r-oldrel-windows-x86_64	ERROR	OK
cartography	r-release-windows-x86_64	OK	ERROR
cdcatR	r-devel-linux-x86_64-fedora-gcc	OK	ERROR
cdmTools	r-devel-linux-x86_64-fedora-gcc	OK	ERROR
cgmanalysis	r-release-windows-x86_64	OK	ERROR
coursekata	r-oldrel-windows-x86_64	ERROR	OK
cvdprevent	r-devel-linux-x86_64-fedora-gcc	ERROR	OK
cvdprevent	r-oldrel-windows-x86_64	ERROR	OK
damAOI	r-release-windows-x86_64	OK	ERROR
divvy	r-release-windows-x86_64	ERROR	OK
ediblecity	r-release-windows-x86_64	ERROR	OK
eva3dm	r-release-windows-x86_64	ERROR	OK
flightplot	r-oldrel-windows-x86_64	ERROR	OK
fractalforest	r-release-windows-x86_64	ERROR	OK
gander	r-oldrel-windows-x86_64	OK	ERROR
ggmapcn	r-release-windows-x86_64	OK	ERROR
ggpattern	r-release-windows-x86_64	ERROR	OK
ggplot2	r-oldrel-windows-x86_64	ERROR	NOTE
ggpp	r-oldrel-windows-x86_64	OK	ERROR
glossa	r-release-windows-x86_64	OK	ERROR
gridpattern	r-release-windows-x86_64	ERROR	OK
gt	r-release-windows-x86_64	OK	ERROR
h5lite	r-oldrel-windows-x86_64	OK	NOTE
icosa	r-release-windows-x86_64	OK	ERROR
infectiousR	r-release-windows-x86_64	OK	ERROR
insight	r-oldrel-windows-x86_64	ERROR	NOTE
jlview	r-release-windows-x86_64	FAILURE	ERROR
leaflet.extras	r-oldrel-windows-x86_64	ERROR	OK
malariaAtlas	r-release-windows-x86_64	ERROR	OK
mixKernel	r-release-windows-x86_64	ERROR	OK
mlr3pipelines	r-release-windows-x86_64	ERROR	OK
mosaic	r-oldrel-windows-x86_64	ERROR	OK
muttest	r-oldrel-windows-x86_64	OK	ERROR
pkggraph	r-oldrel-windows-x86_64	WARNING	OK
polymatching	r-release-windows-x86_64	FAILURE	OK
rbranding	r-oldrel-windows-x86_64	ERROR	OK
readepi	r-oldrel-windows-x86_64	ERROR	NOTE
reshape2	r-release-windows-x86_64	ERROR	OK
restfulr	r-release-windows-x86_64	OK	ERROR
revert	r-oldrel-windows-x86_64	ERROR	OK
rsimsum	r-oldrel-windows-x86_64	WARNING	OK
scPOEM	r-release-windows-x86_64	OK	ERROR
searchAnalyzeR	r-oldrel-windows-x86_64	ERROR	OK
secrdesign	r-release-windows-x86_64	OK	ERROR
sfclust	r-release-windows-x86_64	ERROR	OK
sonicscrewdriver	r-oldrel-windows-x86_64	ERROR	OK
sonicscrewdriver	r-release-windows-x86_64	ERROR	NOTE
spMaps	r-release-windows-x86_64	ERROR	OK
spatsoc	r-release-windows-x86_64	OK	ERROR
tall	r-devel-linux-x86_64-fedora-gcc	NOTE	OK
testthat	r-oldrel-windows-x86_64	ERROR	OK
tibble	r-oldrel-windows-x86_64	OK	ERROR
tibble	r-release-windows-x86_64	ERROR	OK
tidychangepoint	r-oldrel-windows-x86_64	WARNING	OK
tidynorm	r-oldrel-windows-x86_64	WARNING	OK
tidytlg	r-release-windows-x86_64	ERROR	OK
tipitaka	r-oldrel-windows-x86_64	FAILURE	OK
treePlotArea	r-release-windows-x86_64	ERROR	OK
treesitter.c	r-oldrel-windows-x86_64	OK	ERROR
treesitter.c	r-release-windows-x86_64	OK	ERROR
uteals	r-oldrel-windows-x86_64	WARNING	OK
xpose.xtras	r-release-windows-x86_64	OK	ERROR

If the machine and R versions is the same but the check of the package is different there might be some discrepancy between the dependencies.

# Extract dependencies
dependencies <- package_dependencies(unique(possible_packages$Package),
                                     # Should it check all the recursive dependencies or only direct?
                                     db = ap, # Only considering those dependencies on CRAN and Bioconductor but not any Additional_repositories. 
                                     recursive = TRUE, 
                                     which = c("Depends", "Imports", "LinkingTo", "Suggests"))

# Prepare to compare versions (as they are sorted by everything else we can compare directly)
intermittent_failures <- rep(FALSE, length(dependencies))
names(intermittent_failures) <- names(dependencies)
dep_0 <- lengths(dependencies) == 0
intermittent_failures[dep_0] <- TRUE

If they do not have any recursive dependency on Depends, Imports, LinkingTo and Suggests they might be have some intermittent problems on the packages. These is only on dependencies on CRAN and Bioconductor but not in other additional repositories (There are 195 packages with additional repositories).

If they have some dependencies and those dependencies didn’t change as far as we can tell then there might be some problems with random numbers or connectivity.

for (pkg in names(intermittent_failures[!intermittent_failures])) {
  dep <- dependencies[[pkg]]
  fl <- possible_packages$Flavor[possible_packages$Package == pkg]
  intermittent_failures[pkg] <- all_checks |> 
    filter(Package %in% dep,
           Flavor %in% fl,
           Version.t == Version.y,
           Status.t != Status.y) |> 
    nrow() == 0 # If packages outside || any(!dep %in% rownames(ap)) 
}
packages <- names(intermittent_failures)[intermittent_failures]

We finally show the differences on the status of those without any dependency change on version or status¹:

keep_files <- filter(possible_packages, Package %in% packages) |> 
  merge(y = flavors_df, by.x = "Flavor", by.y = "flavors", all.x = TRUE, all.y = FALSE) |> 
  select(Package, Flavor, Version = Version.t, R_version = r_version, OS = os, 
         architecture, other, version, revision) |> 
  mutate(Date = Sys.time())

if (nrow(keep_files >= 1)) {
  write.csv(keep_files, 
            paste0("cran-failing-", format(Sys.time(), "%Y%m%dT%H%M"), ".csv"),
            row.names = FALSE,
            quote = FALSE,
  )
}

filter(possible_packages, Package %in% packages) |> 
  select(Package, Flavor, Today, Yesterday, -Version.t, -Version.y) |> 
  flextable() |> 
  autofit()

Package	Flavor	Today	Yesterday
tall	r-devel-linux-x86_64-fedora-gcc	NOTE	OK

Conclusion

cat("There are no packages detected with differences between yesterday and today attributable to intermittent failures.\n")
knitr::knit_exit()

cat("This suggests that these packages might have some problems with random numbers or connectivity:\n\n")

This suggests that these packages might have some problems with random numbers or connectivity:

if (any(dep_0)) {
  cat("\n## Packages with dependencies\n\n")
  cat(paste0(" - ", sort(intersect(packages, 
                                   names(dependencies)[dep_0])), "\n"), sep = "")
  cat("\n## Packages without dependencies\n\n")
  cat(paste0(" - ", sort(intersect(packages,
                                   names(dependencies)[!dep_0])), "\n"), sep = "")
  
} else {
  cat(paste0(" - ", sort(packages), "\n"), sep = "")
}

tall

I think a new version might not propagate to check other packages until 24 hours later as checks might have already started for that day.↩︎

Packages intermittent failtures

Lluís Revilla Sancho

2026-04-12