Package 'xfun'

Description

Substitute certain (by default, non-alphanumeric) characters with dashes and remove extra dashes at both ends to generate ID strings. This function is intended for generating IDs for HTML elements, so HTML tags in the input text will be removed first.

Usage

alnum_id(x, exclude = "[^[:alnum:]]+")

Arguments

Value

A character vector of IDs.

Examples

x = c("Hello world 123!", "a  &b*^##c 456")
xfun::alnum_id(x)
xfun::alnum_id(x, "[^[:alpha:]]+")  # only keep alphabetical chars
# when text contains HTML tags
xfun::alnum_id("<h1>Hello <strong>world</strong>!")

Description

An abbreviation of base::attr(exact = TRUE).

Usage

attr(...)

Arguments

Examples

z = structure(list(a = 1), foo = 2)
base::attr(z, "f")  # 2
xfun::attr(z, "f")  # NULL
xfun::attr(z, "foo")  # 2

Description

Return base R package names.

Usage

base_pkgs()

Value

A character vector of base R package names.

Examples

xfun::base_pkgs()

Description

The function base64_encode() encodes a file or a raw vector into the base64 encoding. The function base64_decode() decodes data from the base64 encoding.

Usage

base64_encode(x)

base64_decode(x, from = NA)

Arguments

Value

base64_encode() returns a character string. base64_decode() returns a raw vector.

Examples

xfun::base64_encode(as.raw(1:10))
logo = xfun:::R_logo()
xfun::base64_encode(logo)
xfun::base64_decode("AQIDBAUGBwgJCg==")

Description

Encode the file in the base64 encoding, and add the media type. The data URI can be used to embed data in HTML documents, e.g., in the src attribute of the ⁠<img />⁠ tag.

Usage

base64_uri(x, type = mime_type(x))

Arguments

Value

A string of the form ⁠data:<media type>;base64,<data>⁠.

Examples

logo = xfun:::R_logo()
img = xfun::html_tag("img", src = xfun::base64_uri(logo), alt = "R logo")
if (interactive()) xfun::html_view(img)

Description

Start a background process using the PowerShell cmdlet Start-Process-PassThru on Windows or the ampersand & on Unix, and return the process ID.

Usage

bg_process(
  command,
  args = character(),
  verbose = getOption("xfun.bg_process.verbose", FALSE)
)

Arguments

Value

The process ID as a character string.

Note

On Windows, if PowerShell is not available, try to use system2(wait = FALSE) to start the background process instead. The process ID will be identified from the output of the command tasklist. This method of looking for the process ID may not be reliable. If the search is not successful in 30 seconds, it will throw an error (timeout). If a longer time is needed, you may set options(xfun.bg_process.timeout) to a larger value, but it should be very rare that a process cannot be started in 30 seconds. When you reach the timeout, it is more likely that the command actually failed.

See Also

proc_kill() to kill a process.

Description

If a package is broken (i.e., not loadable()), reinstall it.

Usage

broken_packages(reinstall = TRUE)

Arguments

Details

Installed R packages could be broken for several reasons. One common reason is that you have upgraded R to a newer x.y version, e.g., from ⁠4.0.5⁠ to ⁠4.1.0⁠, in which case you need to reinstall previously installed packages.

Value

A character vector of names of broken package.

Description

Increase the last digit of version numbers, e.g., from 0.1 to 0.2, or ⁠7.23.9⁠ to ⁠7.23.10⁠.

Usage

bump_version(x)

Arguments

Value

A vector of new version numbers.

Examples

xfun::bump_version(c("0.1", "91.2.14"))

Description

Caching is based on the assumption that if the input does not change, the output will not change. After an expression is executed for the first time, its result will be saved (either in memory or on disk). The next run will be skipped and the previously saved result will be loaded directly if all external inputs of the expression remain the same, otherwise the cache will be invalidated and the expression will be re-executed.

Usage

cache_exec(expr, path = "cache/", id = NULL, ...)

Arguments

Details

Arguments supported in ... include:

• vars: Names of local variables (which are created inside the expression). By default, local variables are automatically detected from the expression via codetools::findLocalsList(). Locally created variables are cached along with the value of the expression.

• hash and extra: R objects to be used to determine if cache should be loaded or invalidated. If (the MD5 hash of) the objects is not changed, the cache is loaded, otherwise the cache is invalidated and rebuilt. By default, hash is a list of values of global variables in the expression (i.e., variables created outside the expression). Global variables are automatically detected by codetools::findGlobals(). You can provide a vector of names to override the automatic detection if you want some specific global variables to affect caching, or the automatic detection is not reliable. You can also provide additional information via the extra argument. For example, if the expression reads an external file foo.csv, and you want the cache to be invalidated after the file is modified, you may use extra = file.mtime("foo.csv").

• keep: By default, only one copy of the cache corresponding to an id under path is kept, and all other copies for this id is automatically purged. If TRUE, all copies of the cache are kept. If FALSE, all copies are removed, which means the cache is always invalidated, and can be useful to force re-executing the expression.

• rw: A list of functions to read/write the cache files. The list is of the form list(name = 'xxx', load = function(file) {}, save = function(x, file) {}). By default, readRDS() and saveRDS() are used. This argument can also take a character string to use some built-in read/write methods. Currently available methods include rds (the default), raw (using serialize() and unserialize()), and qs (using qs::qread() and qs::qsave()). The rds and raw methods only use base R functions (the rds method generates smaller files because it uses compression, but is often slower than the raw method, which does not use compression). The qs method requires the qs package, which can be much faster than base R methods and also supports compression.

Value

If the cache is found, the cached value of the expression will be loaded and returned (other local variables will also be lazy-loaded into the current environment as a side-effect). If cache does not exist, the expression is executed and its value is returned.

Examples

# the first run takes about 1 second
y1 = xfun::cache_exec({
    x = rnorm(1e+05)
    Sys.sleep(1)
    x
}, path = ":memory:", id = "sim-norm")

# the second run takes almost no time
y2 = xfun::cache_exec({
    # comments won't affect caching
    x = rnorm(1e+05)
    Sys.sleep(1)
    x
}, path = ":memory:", id = "sim-norm")

# y1, y2, and x should be identical
stopifnot(identical(y1, y2), identical(y1, x))

Description

Save the value of an expression to a cache file (of the RDS format). Next time the value is loaded from the file if it exists.

Usage

cache_rds(
  expr = {
 },
  rerun = FALSE,
  file = "cache.rds",
  dir = "cache/",
  hash = NULL,
  clean = getOption("xfun.cache_rds.clean", TRUE),
  ...
)

Arguments

Details

Note that the file argument does not provide the full cache filename. The actual name of the cache file is of the form ‘BASENAME_HASH.rds’, where ‘BASENAME’ is the base name provided via the ‘file’ argument (e.g., if file = 'foo.rds', BASENAME would be ‘foo’), and ‘HASH’ is the MD5 hash (also called the ‘checksum’) calculated from the R code provided to the expr argument and the value of the hash argument, which means when the code or the hash argument changes, the ‘HASH’ string may also change, and the old cache will be invalidated (if it exists). If you want to find the cache file, look for ‘.rds’ files that contain 32 hexadecimal digits (consisting of 0-9 and a-z) at the end of the filename.

The possible ways to invalidate the cache are: 1) change the code in expr argument; 2) delete the cache file manually or automatically through the argument rerun = TRUE; and 3) change the value of the hash argument. The first two ways should be obvious. For the third way, it makes it possible to automatically invalidate the cache based on changes in certain R objects. For example, when you run cache_rds({ x + y }), you may want to invalidate the cache to rerun { x + y } when the value of x or y has been changed, and you can tell cache_rds() to do so by cache_rds({ x + y }, hash = list(x, y)). The value of the argument hash is expected to be a list, but it can also take a special value, "auto", which means cache_rds(expr) will try to automatically figure out the global variables in expr, return a list of their values, and use this list as the actual value of hash. This behavior is most likely to be what you really want: if the code in expr uses an external global variable, you may want to invalidate the cache if the value of the global variable has changed. Here a “global variable” means a variable not created locally in expr, e.g., for cache_rds({ x <- 1; x + y }), x is a local variable, and y is (most likely to be) a global variable, so changes in y should invalidate the cache. However, you know your own code the best. If you want to be completely sure when to invalidate the cache, you can always provide a list of objects explicitly rather than relying on hash = "auto".

By default (the argument clean = TRUE), old cache files will be automatically cleaned up. Sometimes you may want to use clean = FALSE (set the R global option options(xfun.cache_rds.clean = FALSE) if you want FALSE to be the default). For example, you may not have decided which version of code to use, and you can keep the cache of both versions with clean = FALSE, so when you switch between the two versions of code, it will still be fast to run the code.

Value

If the cache file does not exist, run the expression and save the result to the file, otherwise read the cache file and return the value.

Note

Changes in the code in the expr argument do not necessarily always invalidate the cache, if the changed code is parsed to the same expression as the previous version of the code. For example, if you have run cache_rds({Sys.sleep(5);1+1}) before, running cache_rds({ Sys.sleep( 5 ) ; 1 + 1 }) will use the cache, because the two expressions are essentially the same (they only differ in white spaces). Usually you can add/delete white spaces or comments to your code in expr without invalidating the cache. See the package vignette vignette('xfun', package = 'xfun') for more examples.

When this function is called in a code chunk of a knitr document, you may not want to provide the filename or directory of the cache file, because they have reasonable defaults.

Side-effects (such as plots or printed output) will not be cached. The cache only stores the last value of the expression in expr.

See Also

cache_exec(), which is more flexible (e.g., it supports in-memory caching and different read/write methods for cache files).

Examples

f = tempfile()  # the cache file
compute = function(...) {
    res = xfun::cache_rds({
        Sys.sleep(1)
        1:10
    }, file = f, dir = "", ...)
    res
}
compute()  # takes one second
compute()  # returns 1:10 immediately
compute()  # fast again
compute(rerun = TRUE)  # one second to rerun
compute()
unlink(paste0(f, "_*.rds"))

Description

Check the reverse dependencies of a package using the crandalf service: https://github.com/yihui/crandalf. If the number of reverse dependencies is large, they will be split into batches and pushed to crandalf one by one.

Usage

crandalf_check(pkg, size = 400, jobs = Inf, which = "all")

crandalf_results(pkg, repo = NA, limit = 200, wait = 5 * 60)

Arguments

Details

Due to the time limit of a single job on GitHub Actions (6 hours), you will have to split the large number of reverse dependencies into batches and check them sequentially on GitHub (at most 5 jobs in parallel). The function crandalf_check() does this automatically when necessary. It requires the git command to be available.

The function crandalf_results() fetches check results from GitHub after all checks are completed, merge the results, and show a full summary of check results. It requires gh (GitHub CLI: https://cli.github.com/manual/) to be installed and you also need to authenticate with your GitHub account beforehand.

Description

For knitr and R Markdown documents, code chunk options can be written using the comma-separated syntax (e.g., ⁠opt1=value1, opt2=value2⁠). This function parses these options and returns a list. If an option is not named, it will be treated as the chunk label.

Usage

csv_options(x)

Arguments

Value

A list of chunk options.

Examples

xfun::csv_options("foo, eval=TRUE, fig.width=5, echo=if (TRUE) FALSE")

Description

Sometimes it is necessary to use the dot character as the decimal separator. In R, this could be affected by two settings: the global option options(OutDec) and the LC_NUMERIC locale. This function sets the former to . and the latter to C before evaluating an expression, such as coercing a number to character.

Usage

decimal_dot(x)

Arguments

Value

The value of x.

Examples

opts = options(OutDec = ",")
as.character(1.234)  # using ',' as the decimal separator
print(1.234)  # same
xfun::decimal_dot(as.character(1.234))  # using dot
xfun::decimal_dot(print(1.234))  # using dot
options(opts)

Description

Use list.file() to check if there are any files or subdirectories under a directory. If not, delete this empty directory.

Usage

del_empty_dir(dir)

Arguments

Description

First check if a directory exists. If it does, return TRUE, otherwise create it with dir.create(recursive = TRUE) by default.

Usage

dir_create(x, recursive = TRUE, ...)

Arguments

Value

A logical value indicating if the directory either exists or is successfully created.

Description

These are wrapper functions of [⁠utils::file_test()]⁠ to test the existence of directories and files. Note that file_exists() only tests files but not directories, which is the main difference between file.exists() in base R. If you use are using the R version 3.2.0 or above, dir_exists() is the same as dir.exists() in base R.

Usage

dir_exists(x)

file_exists(x)

Arguments

Value

A logical vector.

Description

Chunk options can be written in special comments (e.g., after ⁠#|⁠ for R code chunks) inside a code chunk. This function partitions these options from the chunk body.

Usage

divide_chunk(engine, code)

Arguments

Value

A list with the following items:

• options: The parsed options (if there are any) as a list.

• src: The part of the input that contains the options.

• code: The part of the input that contains the code.

Note

Chunk options must be written on continuous lines (i.e., all lines must start with the special comment prefix such as ⁠#|⁠) at the beginning of the chunk body.

Examples

# parse yaml-like items
yaml_like = c("#| label: mine", "#| echo: true", "#| fig.width: 8", "#| foo: bar",
    "1 + 1")
writeLines(yaml_like)
xfun::divide_chunk("r", yaml_like)

# parse CSV syntax
csv_like = c("#| mine, echo = TRUE, fig.width = 8, foo = 'bar'", "1 + 1")
writeLines(csv_like)
xfun::divide_chunk("r", csv_like)

Description

Perform a task once in an R session, e.g., emit a message or warning. Then give users an optional hint on how not to perform this task at all.

Usage

do_once(
  task,
  option,
  hint = c("You will not see this message again in this R session.",
    "If you never want to see this message,",
    sprintf("you may set options(%s = FALSE) in your .Rprofile.", option))
)

Arguments

Value

The value returned by the task, invisibly.

Examples

do_once(message("Today's date is ", Sys.Date()), "xfun.date.reminder")
# if you run it again, it will not emit the message again
do_once(message("Today's date is ", Sys.Date()), "xfun.date.reminder")

do_once({
    Sys.sleep(2)
    1 + 1
}, "xfun.task.1plus1")
do_once({
    Sys.sleep(2)
    1 + 1
}, "xfun.task.1plus1")

Description

This object provides methods to download files and cache them on disk.

Usage

download_cache

Format

A list of methods:

• ⁠$get(url, type, handler)⁠ downloads a URL, caches it, and returns the file content according to the value of type (possible values: "text" means the text content; "base64" means the base64 encoded data; "raw" means the raw binary content; "auto" is the default and means the type is determined by the content type in the URL headers). Optionally a handler function can be applied to the content.

• ⁠$list()⁠ gives the list of cache files.

• ⁠$summary()⁠ gives a summary of existing cache files.

• ⁠$remove(url, type)⁠ removes a single cache file.

• ⁠$purge()⁠ deletes all cache files.

Examples

# the first time it may take a few seconds
x1 = xfun::download_cache$get("https://www.r-project.org/")
head(x1)

# now you can get the cached content
x2 = xfun::download_cache$get("https://www.r-project.org/")
identical(x1, x2)  # TRUE

# a binary file
x3 = xfun::download_cache$get("https://yihui.org/images/logo.png", "raw")
length(x3)

# show a summary
xfun::download_cache$summary()
# remove a specific cache file
xfun::download_cache$remove("https://yihui.org/images/logo.png", "raw")
# remove all cache files
xfun::download_cache$purge()

Description

Try all possible methods in download.file() (e.g., libcurl, curl, wget, and wininet) and see if any method can succeed. The reason to enumerate all methods is that sometimes the default method does not work, e.g., https://stat.ethz.ch/pipermail/r-devel/2016-June/072852.html.

Usage

download_file(
  url,
  output = url_filename(url),
  ...,
  .error = "No download method works (auto/wininet/wget/curl/lynx)"
)

Arguments

Value

The integer code 0 for success, or an error if none of the methods work.

Note

To allow downloading large files, the timeout option in options() will be temporarily set to one hour (3600 seconds) inside this function when this option has the default value of 60 seconds. If you want a different timeout value, you may set it via options(timeout = N), where N is the number of seconds (not 60).

Description

For a file, first encode it into base64 data (a character string). Then generate a hyperlink of the form ‘⁠<a href="base64 data" download="filename">Download filename</a>⁠’. The file can be downloaded when the link is clicked in modern web browsers. For a directory, it will be compressed as a zip archive first, and the zip file is passed to embed_file(). For multiple files, they are also compressed to a zip file first.

Usage

embed_file(path, name = basename(path), text = paste("Download", name), ...)

embed_dir(path, name = paste0(normalize_path(path), ".zip"), ...)

embed_files(path, name = with_ext(basename(path[1]), ".zip"), ...)

Arguments

Details

These functions can be called in R code chunks in R Markdown documents with HTML output formats. You may embed an arbitrary file or directory in the HTML output file, so that readers of the HTML page can download it from the browser. A common use case is to embed data files for readers to download.

Value

An HTML tag ‘⁠<a>⁠’ with the appropriate attributes.

Note

Windows users may need to install Rtools to obtain the zip command to use embed_dir() and embed_files().

Internet Explorer does not support downloading embedded files. Chrome has a 2MB limit on the file size.

Examples

logo = xfun:::R_logo()
link = xfun::embed_file(logo, text = "Download R logo")
link
if (interactive()) xfun::html_view(link)

Description

If the option exists in options(), use its value. If not, query the environment variable with the name R_NAME where NAME is the capitalized option name with dots substituted by underscores. For example, for an option xfun.foo, first we try getOption('xfun.foo'); if it does not exist, we check the environment variable R_XFUN_FOO.

Usage

env_option(name, default = NULL)

Arguments

Details

This provides two possible ways, whichever is more convenient, for users to set an option. For example, global options can be set in the .Rprofile file, and environment variables can be set in the .Renviron file.

Value

The option value.

Examples

xfun::env_option("xfun.test.option")  # NULL

Sys.setenv(R_XFUN_TEST_OPTION = "1234")
xfun::env_option("xfun.test.option")  # 1234

options(xfun.test.option = TRUE)
xfun::env_option("xfun.test.option")  # TRUE (from options())
options(xfun.test.option = NULL)  # reset the option
xfun::env_option("xfun.test.option")  # 1234 (from env var)

Sys.unsetenv("R_XFUN_TEST_OPTION")
xfun::env_option("xfun.test.option")  # NULL again

xfun::env_option("xfun.test.option", FALSE)  # use default

Description

This is a shorthand of x[file.exists(x)], and optionally returns the first existing file path.

Usage

existing_files(x, first = FALSE, error = TRUE)

Arguments

Value

A vector of existing file paths.

Examples

xfun::existing_files(c("foo.txt", system.file("DESCRIPTION", package = "xfun")))

Description

The function on.exit() is often used to perform tasks when the current function exits. This exit_call() function allows calling a function when a parent function exits (thinking of it as inserting an on.exit() call into the parent function).

Usage

exit_call(fun, n = 2, ...)

Arguments

References

This function was inspired by Kevin Ushey: https://yihui.org/en/2017/12/on-exit-parent/

Examples

f = function(x) {
    print(x)
    xfun::exit_call(function() print("The parent function is exiting!"))
}
g = function(y) {
    f(y)
    print("f() has been called!")
}
g("An argument of g()!")

Description

Wrap content with fence delimiters such as backticks (code blocks) or colons (fenced Div). Optionally the fenced block can have attributes. The function fenced_div() is a shorthand of fenced_block(char = ':').

Usage

fenced_block(x, attrs = NULL, fence = make_fence(x, char), char = "`")

fenced_div(...)

make_fence(x, char = "`", start = 3)

Arguments

Value

fenced_block() returns a character vector that contains both the fences and content.

make_fence() returns a character string. If the block content contains N fence characters (e.g., backticks), use N + 1 characters as the fence.

Examples

# code block with class 'r' and ID 'foo'
xfun::fenced_block("1+1", c(".r", "#foo"))
# fenced Div
xfun::fenced_block("This is a **Div**.", char = ":")
# three backticks by default
xfun::make_fence("1+1")
# needs five backticks for the fences because content has four
xfun::make_fence(c("````r", "1+1", "````"))

Description

Functions to obtain (file_ext()), remove (sans_ext()), and change (with_ext()) extensions in filenames.

Usage

file_ext(x, extra = "")

sans_ext(x, extra = "")

with_ext(x, ext, extra = "")

Arguments

Details

file_ext() is similar to tools::file_ext(), and sans_ext() is similar to tools::file_path_sans_ext(). The main differences are that they treat tar.(gz|bz2|xz) and nb.html as extensions (but functions in the tools package doesn't allow double extensions by default), and allow characters ~ and ⁠#⁠ to be present at the end of a filename.

Value

A character vector of the same length as x.

Examples

library(xfun)
p = c("abc.doc", "def123.tex", "path/to/foo.Rmd", "backup.ppt~", "pkg.tar.xz")
file_ext(p)
sans_ext(p)
with_ext(p, ".txt")
with_ext(p, c(".ppt", ".sty", ".Rnw", "doc", "zip"))
with_ext(p, "html")

# allow for more characters in extensions
p = c("a.c++", "b.c--", "c.e##")
file_ext(p)  # -/+/# not recognized by default
file_ext(p, extra = "-+#")

Description

First try file.rename(). If it fails (e.g., renaming a file from one volume to another on disk is likely to fail), try file.copy() instead, and clean up the original files if the copy succeeds.

Usage

file_rename(from, to)

Arguments

Value

A logical vector (TRUE for success and FALSE for failure).

Description

The source code of this function should be self-explanatory.

Usage

file_string(file)

Arguments

Value

A character string of text lines concatenated by '\n'.

Examples

xfun::file_string(system.file("DESCRIPTION", package = "xfun"))

Description

Call the S3 method format.object_size() to format numbers of bytes.

Usage

format_bytes(x, units = "auto", ...)

Arguments

Value

A character vector.

Examples

xfun::format_bytes(c(1, 1024, 2000, 1e+06, 2e+08))
xfun::format_bytes(c(1, 1024, 2000, 1e+06, 2e+08), units = "KB")

Description

First compose an absolute path using the project root directory and the relative path components, i.e., file.path⁠(root, ...)⁠. Then convert it to a relative path with relative_path(), which is relative to the current working directory.

Usage

from_root(..., root = proj_root(), error = TRUE)

Arguments

Details

This function was inspired by here::here(), and the major difference is that it returns a relative path by default, which is more portable.

Value

A relative path, or an error when the project root directory cannot be determined or the conversion failed and error = TRUE.

Examples

## Not run: 
xfun::from_root("data", "mtcars.csv")

## End(Not run)

Description

Use the GitHub API (github_api()) to obtain the tags of the releases.

Usage

github_releases(
  repo,
  tag = "",
  pattern = "v[0-9.]+",
  use_jsonlite = loadable("jsonlite")
)

Arguments

Value

A character vector of (GIT) tags.

Examples

xfun::github_releases("yihui/xfun")
xfun::github_releases("gohugoio/hugo")

Description

This function is a shorthand of gsub(pattern, replacement, grep(pattern, x, value = TRUE)).

Usage

grep_sub(pattern, replacement, x, ...)

Arguments

Value

A character vector.

Examples

# find elements that matches 'a[b]+c' and capitalize 'b' with perl regex
xfun::grep_sub("a([b]+)c", "a\\U\\1c", c("abc", "abbbc", "addc", "123"), perl = TRUE)

Description

These functions provide the "file" version of gsub(), i.e., they perform searching and replacement in files via gsub().

Usage

gsub_file(file, ..., rw_error = TRUE)

gsub_files(files, ...)

gsub_dir(..., dir = ".", recursive = TRUE, ext = NULL, mimetype = ".*")

gsub_ext(ext, ..., dir = ".", recursive = TRUE)

Arguments

Note

These functions perform in-place replacement, i.e., the files will be overwritten. Make sure you backup your files in advance, or use version control!

Examples

library(xfun)
f = tempfile()
writeLines(c("hello", "world"), f)
gsub_file(f, "world", "woRld", fixed = TRUE)
readLines(f)

Description

Given a tag name, generate an HTML tag with optional attributes and content. html_tag() can be viewed as a simplified version of htmltools::tags, html_value() adds classes on the value so that it will be treated as raw HTML (not escaped by html_tag()), html_escape() escapes special characters in HTML, and html_view() launches a browser or viewer to view the HTML content.

Usage

html_tag(.name, .content = NULL, .attrs = NULL, ...)

html_value(x)

html_escape(x, attr = FALSE)

html_view(x, ...)

Arguments

Value

A character string.

Examples

xfun::html_tag("a", "<R Project>", href = "https://www.r-project.org", target = "_blank")
xfun::html_tag("br")
xfun::html_tag("a", xfun::html_tag("strong", "R Project"), href = "#")
xfun::html_tag("a", list("<text>", xfun::html_tag("b", "R Project")), href = "#")
xfun::html_escape("\" quotes \" & brackets < >")
xfun::html_escape("\" & < > \r \n", attr = TRUE)

Description

Change the working directory, evaluate the expression, and restore the working directory.

Usage

in_dir(dir, expr)

Arguments

Examples

library(xfun)
in_dir(tempdir(), {
    print(getwd())
    list.files()
})

Description

Run R CMD build to build a tarball from a source directory, and run R CMD INSTALL to install it.

Usage

install_dir(pkg, build = TRUE, build_opts = NULL, install_opts = NULL)

Arguments

Value

Invisible status from R CMD INSTALL.

Description

This alias is to make autocomplete faster via xfun::install_github, because most ⁠remotes::install_*⁠ functions are never what I want. I only use install_github and it is inconvenient to autocomplete it, e.g. install_git always comes before install_github, but I never use it. In RStudio, I only need to type xfun::ig to get xfun::install_github.

Usage

install_github(...)

Arguments

Description

On Unix, check if the paths start with ‘/’ or ‘~’ (if they do, they are absolute paths). On Windows, check if a path remains the same (via same_path()) if it is prepended with ‘./’ (if it does, it is a relative path).

Usage

is_abs_path(x)

is_rel_path(x)

Arguments

Value

A logical vector.

Examples

xfun::is_abs_path(c("C:/foo", "foo.txt", "/Users/john/", tempdir()))
xfun::is_rel_path(c("C:/foo", "foo.txt", "/Users/john/", tempdir()))

Description

Converts the encoding of a character vector to 'ascii', and check if the result is NA.

Usage

is_ascii(x)

Arguments

Value

A logical vector indicating whether each element of the character vector is ASCII.

Examples

library(xfun)
is_ascii(letters)  # yes
is_ascii(intToUtf8(8212))  # no

Description

Return a logical vector indicating if elements of a character vector are blank (white spaces or empty strings).

Usage

is_blank(x)

Arguments

Value

TRUE for blank elements, or FALSE otherwise.

Examples

xfun::is_blank("")
xfun::is_blank("abc")
xfun::is_blank(c("", "  ", "\n\t"))
xfun::is_blank(c("", " ", "abc"))

Description

Check if the path starts with the dir path.

Usage

is_sub_path(x, dir, n = nchar(dir))

Arguments

Value

A logical vector.

Note

You may want to normalize the values of the x and dir arguments first (with normalize_path()), to make sure the path separators are consistent.

Examples

xfun::is_sub_path("a/b/c.txt", "a/b")  # TRUE
xfun::is_sub_path("a/b/c.txt", "d/b")  # FALSE
xfun::is_sub_path("a/b/c.txt", "a\\b")  # FALSE (even on Windows)

Description

Check if a path starts with ‘http://’ or ‘https://’ or ‘ftp://’ or ‘ftps://’.

Usage

is_web_path(x)

Arguments

Value

A logical vector.

Examples

xfun::is_web_path("https://www.r-project.org")  # TRUE
xfun::is_web_path("www.r-project.org")  # FALSE

Description

Functions based on .Platform$OS.type and Sys.info() to test if the current operating system is Windows, macOS, Unix, or Linux.

Usage

is_windows()

is_unix()

is_macos()

is_linux()

is_arm64()

Examples

library(xfun)
# only one of the following statements should be true
is_windows()
is_unix() && is_macos()
is_linux()
# In newer Macs, CPU can be either Intel or Apple
is_arm64()  # TRUE on Apple silicone machines

Description

The function lazy_save() saves objects to files with incremental integer names (e.g., the first object is saved to ⁠1.rds⁠, and the second object is saved to ⁠2.rds⁠, etc.). The function lazy_load() lazy-load objects from files saved via lazy_save(), i.e., a file will not be read until the object is used.

Usage

lazy_save(list = NULL, path = "./", method = "auto", envir = parent.frame())

lazy_load(path = "./", method = "auto", envir = parent.frame())

Arguments

Value

lazy_save() returns invisible NULL; lazy_load() returns the object names invisibly.

See Also

delayedAssign()

Description

Given a path, try to find it recursively under a root directory. The input path can be an incomplete path, e.g., it can be a base filename, and magic_path() will try to find this file under subdirectories.

Usage

magic_path(
  ...,
  root = proj_root(),
  relative = TRUE,
  error = TRUE,
  message = getOption("xfun.magic_path.message", TRUE),
  n_dirs = getOption("xfun.magic_path.n_dirs", 10000)
)

Arguments

Value

The path found under the root directory, or an error when error = TRUE and the path is not found (or multiple paths are found).

Examples

## Not run: 
xfun::magic_path("mtcars.csv")  # find any file that has the base name mtcars.csv

## End(Not run)

Description

Add a trailing backlash to a file path if this is a directory. This is useful in messages to the console for example to quickly identify directories from files.

Usage

mark_dirs(x)

Arguments

Details

If x is a vector of relative paths, directory test is done with path relative to the current working dir. Use in_dir() or use absolute paths.

Examples

mark_dirs(list.files(find.package("xfun"), full.names = TRUE))

Description

A minimal Markdown table generator using the pipe | as column separators.

Usage

md_table(x, digits = NULL, na = NULL, newline = NULL, limit = NULL)

Arguments

Details

The default argument values can be set via global options with the prefix xfun.md_table., e.g., ⁠options(xfun.md_table.digits 2, xfun.md_table.na = 'n/a')⁠.

Value

A character vector.

See Also

knitr::kable() (which supports more features)

Examples

xfun::md_table(head(iris))
xfun::md_table(mtcars, limit = c(10, 6))

Description

Serialize an object and calculate the checksum via tools::md5sum(). If tools::md5sum() does not have the argument bytes, the object will be first serialized to a temporary file, which will be deleted after the checksum is calculated, otherwise the raw bytes of the object will be passed to the bytes argument directly (which will be faster than writing to a temporary file).

Usage

md5(...)

Arguments

Value

A character vector of the checksums of objects passed to md5(). If the arguments are named, the results will also be named.

Examples

x1 = 1
x2 = 1:10
x3 = seq(1, 10)
x4 = iris
x5 = paste
(m = xfun::md5(x1, x2, x3, x4, x5))
stopifnot(m[2] == m[3])  # x2 and x3 should be identical

xfun::md5(x1 = x1, x2 = x2)  # named arguments

Description

If the mime package is installed, call mime::guess_type(), otherwise use the system command file --mime-type to obtain the MIME type of a file. Typically, the file command exists on *nix. On Windows, the command should exist if Cygwin or Rtools is installed. If it is not found, .NET's MimeMapping class will be used instead (which requires the .NET framework).

Usage

mime_type(x, use_mime = loadable("mime"), empty = "text/plain")

Arguments

Value

A character vector of MIME types.

Note

When querying the MIME type via the system command, the result will be cached to xfun:::cache_dir(). This will make future queries much faster, since running the command in real time can be a little slow.

Examples

f = list.files(R.home("doc"), full.names = TRUE)
mime_type(f)
mime_type(f, FALSE)  # don't use mime
mime_type(f, FALSE, NA)  # run command for files without extension

Description

This function is similar to message(), and the difference is that msg_cat() uses cat() to write out the message, which is sent to stdout() instead of stderr(). The message can be suppressed by suppressMessages().

Usage

msg_cat(...)

Arguments

Value

Invisible NULL, with the side-effect of printing the message.

Note

By default, a newline will not be appended to the message. If you need a newline, you have to explicitly add it to the message (see ‘Examples’).

See Also

This function was inspired by rlang::inform().

Examples

{
    # a message without a newline at the end
    xfun::msg_cat("Hello world!")
    # add a newline at the end
    xfun::msg_cat(" This message appears right after the previous one.\n")
}
suppressMessages(xfun::msg_cat("Hello world!"))

Description

Apply enc2native() to the character vector, and check if enc2utf8() can convert it back without a loss. If it does, return enc2native(x), otherwise return the original vector with a warning.

Usage

native_encode(x)

Arguments

Note

On platforms that supports UTF-8 as the native encoding (l10n_info()⁠[['UTF-8']]⁠ returns TRUE), the conversion will be skipped.

Examples

library(xfun)
s = intToUtf8(c(20320, 22909))
Encoding(s)

s2 = native_encode(s)
Encoding(s2)

Description

An experimental function to create a local web application based on R's internal httpd server (which is primarily for running R's dynamic help system).

Usage

new_app(name, handler, open = interactive(), ports = 4321 + 1:10)

Arguments

Value

The app URL of the form ⁠http://127.0.0.1:port/custom/name/⁠.

Note

This function is not based on base R's public API, and is possible to break in the future, which is also why the documentation here is terse. Please avoid creating public-facing web apps with it. You may consider packages like httpuv and Rserve for production web apps.

Description

Read the package news with news(), convert the result to Markdown, and write to an output file (e.g., ‘NEWS.md’). Each package version appears in a first-level header, each category (e.g., ‘⁠NEW FEATURES⁠’ or ‘⁠BUG FIXES⁠’) is in a second-level header, and the news items are written into bullet lists.

Usage

news2md(package, ..., output = "NEWS.md", category = TRUE)

Arguments

Value

If output = NA, returns the Markdown content as a character vector, otherwise the content is written to the output file.

Examples

# news for the current version of R
xfun::news2md("R", Version == getRversion(), output = NA)

Description

A wrapper function of normalizePath() with different defaults.

Usage

normalize_path(x, winslash = "/", must_work = FALSE, resolve_symlink = TRUE)

Arguments

Examples

library(xfun)
normalize_path("~")

Description

This can be helpful when writing reports with knitr/rmarkdown if we want to print numbers as English words in the output. The function n2w() is an alias of numbers_to_words().

Usage

numbers_to_words(x, cap = FALSE, hyphen = TRUE, and = FALSE)

n2w(x, cap = FALSE, hyphen = TRUE, and = FALSE)

Arguments

Value

A character vector.

Author(s)

Daijiang Li

Examples

library(xfun)
n2w(0, cap = TRUE)
n2w(0:121, and = TRUE)
n2w(1e+06)
n2w(1e+11 + 12345678)
n2w(-987654321)
n2w(1e+15 - 1)
n2w(123.456)
n2w(123.45678901)
n2w(123.456789098765)

Description

Call the command optipng via system2() to optimize all PNG files under a directory.

Usage

optipng(dir = ".", files = all_files("[.]png$", dir), ...)

Arguments

References

OptiPNG: https://optipng.sourceforge.net.

Description

An abbreviation of parse(keep.source = FALSE).

Usage

parse_only(code)

Arguments

Value

R expression()s.

Examples

library(xfun)
parse_only("1+1")
parse_only(c("y~x", "1:5 # a comment"))
parse_only(character(0))

Description

pkg_attach() is a vectorized version of library() over the package argument to attach multiple packages in a single function call. pkg_load() is a vectorized version of requireNamespace() to load packages (without attaching them). The functions pkg_attach2() and pkg_load2() are wrappers of pkg_attach(install = TRUE) and pkg_load(install = TRUE), respectively. loadable() is an abbreviation of requireNamespace(quietly = TRUE). pkg_available() tests if a package with a minimal version is available.

Usage

pkg_attach(
  ...,
  install = FALSE,
  message = getOption("xfun.pkg_attach.message", TRUE)
)

pkg_load(..., error = TRUE, install = FALSE)

loadable(pkg, strict = TRUE, new_session = FALSE)

pkg_available(pkg, version = NULL)

pkg_attach2(...)

pkg_load2(...)

Arguments

Details

These are convenience functions that aim to solve these common problems: (1) We often need to attach or load multiple packages, and it is tedious to type several library() calls; (2) We are likely to want to install the packages when attaching/loading them but they have not been installed.

Value

pkg_attach() returns NULL invisibly. pkg_load() returns a logical vector, indicating whether the packages can be loaded.

See Also

pkg_attach2() is similar to pacman::p_load(), but does not allow non-standard evaluation (NSE) of the ... argument, i.e., you must pass a real character vector of package names to it, and all names must be quoted. Allowing NSE adds too much complexity with too little gain (the only gain is that it saves your effort in typing two quotes).

Examples

library(xfun)
pkg_attach("stats", "graphics")
# pkg_attach2('servr') # automatically install servr if it is not installed

(pkg_load("stats", "graphics"))

Description

Run the command taskkill /f /pid on Windows and kill on Unix, respectively, to kill a process.

Usage

proc_kill(pid, recursive = TRUE, ...)

Arguments

Value

The status code returned from system2().

Description

Read a text file with the UTF-8 encoding, apply a function to the text, and write back to the original file if the processed text is different with the original input.

Usage

process_file(file, fun = identity, x = read_utf8(file))

sort_file(..., fun = sort)

Arguments

Details

sort_file() is an application of process_file(), with the processing function being sort(), i.e., it sorts the text lines in a file and write back the sorted text.

Value

If file is provided, invisible NULL (the file is updated as a side effect), otherwise the processed content (as a character vector).

Examples

f = tempfile()
xfun::write_utf8("Hello World", f)
xfun::process_file(f, function(x) gsub("World", "woRld", x))
xfun::read_utf8(f)  # see if it has been updated
file.remove(f)

Description

Given a path of a file (or dir) in a potential project (e.g., an R package or an RStudio project), return the path to the project root directory.

Usage

proj_root(path = "./", rules = root_rules)

root_rules

Arguments

Format

An object of class matrix (inherits from array) with 2 rows and 2 columns.

Details

The search for the root directory is performed by a series of tests, currently including looking for a ‘DESCRIPTION’ file that contains ⁠Package: *⁠ (which usually indicates an R package), and a ‘*.Rproj’ file that contains ⁠Version: *⁠ (which usually indicates an RStudio project). If files with the expected patterns are not found in the initial directory, the search will be performed recursively in upper-level directories.

Value

Path to the root directory if found, otherwise NULL.

Note

This function was inspired by the rprojroot package, but is much less sophisticated. It is a rather simple function designed to be used in some of packages that I maintain, and may not meet the need of general users until this note is removed in the future (which should be unlikely). If you are sure that you are working on the types of projects mentioned in the ‘Details’ section, this function may be helpful to you, otherwise please consider using rprojroot instead.

Description

Filter out the indices of lines between code block fences such as ⁠```⁠ (could be three or four or more backticks).

Usage

prose_index(x, warn = TRUE)

Arguments

Value

An integer vector of indices of lines that are prose in Markdown.

Note

If the code fences are not balanced (e.g., a starting fence without an ending fence), this function will treat all lines as prose.

Examples

library(xfun)
prose_index(c("a", "```", "b", "```", "c"))
prose_index(c("a", "````", "```r", "1+1", "```", "````", "c"))

Description

For Markdown renderers that do not support LaTeX math, we need to protect math expressions as verbatim code (in a pair of backticks), because some characters in the math expressions may be interpreted as Markdown syntax (e.g., a pair of underscores may make text italic). This function detects math expressions in Markdown (by heuristics), and wrap them in backticks.

Usage

protect_math(x, token = "", use_block = FALSE)

Arguments

Details

Expressions in pairs of dollar signs or double dollar signs are treated as math, if there are no spaces after the starting dollar sign, or before the ending dollar sign. There should be a space or ( before the starting dollar sign, unless the math expression starts from the very beginning of a line. For a pair of single dollar signs, the ending dollar sign should not be followed by a number, and the inner math expression should not be wrapped in backticks. With these assumptions, there should not be too many false positives when detecing math expressions.

Besides, LaTeX environments (⁠\begin{*}⁠ and ⁠\end{*}⁠) are also protected in backticks.

Value

A character vector with math expressions in backticks.

Note

If you are using Pandoc or the rmarkdown package, there is no need to use this function, because Pandoc's Markdown can recognize math expressions.

Examples

library(xfun)
protect_math(c("hi $a+b$", "hello $$\\alpha$$", "no math here: $x is $10 dollars"))
protect_math(c("hi $$", "\\begin{equation}", "x + y = z", "\\end{equation}"))
protect_math("$a+b$", "===")

Description

The function raw_string() assigns the class xfun_raw_string to the character vector, and the corresponding printing function print.xfun_raw_string() uses cat(x, sep = '\n') to write the character vector to the console, which will suppress the leading indices (such as ⁠[1]⁠) and double quotes, and it may be easier to read the characters in the raw form (especially when there are escape sequences).

Usage

raw_string(x)

## S3 method for class 'xfun_raw_string'
print(x, ...)

Arguments

Examples

library(xfun)
raw_string(head(LETTERS))
raw_string(c("a \"b\"", "hello\tworld!"))

Description

Read files one by one, and optionally add text before/after the content. Then combine all content into one character vector.

Usage

read_all(files, before = function(f, x) NULL, after = function(f, x) NULL)

Arguments

Value

A character vector.

Examples

# two files in this package
fs = system.file("scripts", c("call-fun.R", "child-pids.sh"), package = "xfun")
xfun::read_all(fs)

# add file paths before file content and an empty line after content
xfun::read_all(fs, before = function(f) paste("#-----", f, "-----"), after = "")

# add constants
xfun::read_all(fs, before = "/*", after = c("*/", ""))

Description

This is a wrapper function of readBin() with default arguments what = "raw" and ⁠n = ⁠file.size(file), which means it will read the full content of a binary file as a raw vector by default.

Usage

read_bin(file, what = "raw", n = file.info(file)$size, ...)

Arguments

Value

A vector returned from readBin().

Examples

f = tempfile()
cat("abc", file = f)
xfun::read_bin(f)
unlink(f)

Description

Read or write files, assuming they are encoded in UTF-8. read_utf8() is roughly readLines(encoding = 'UTF-8') (a warning will be issued if non-UTF8 lines are found), and write_utf8() calls writeLines(enc2utf8(text), useBytes = TRUE).

Usage

read_utf8(con, error = FALSE)

write_utf8(text, con, ...)

append_utf8(text, con, sort = TRUE)

append_unique(text, con, sort = function(x) base::sort(unique(x)))

Arguments

Details

The function append_utf8() appends UTF-8 content to a file or connection based on read_utf8() and write_utf8(), and optionally sort the content. The function append_unique() appends unique lines to a file or connection.

Value

read_utf8() returns a character vector of the file content; write_utf8() returns the con argument (invisibly).

Description

Run R code and capture various types of output, including text output, plots, messages, warnings, and errors.

Usage

record(
  code = NULL,
  dev = "png",
  dev.path = "xfun-record",
  dev.ext = dev_ext(dev),
  dev.args = list(),
  message = TRUE,
  warning = TRUE,
  error = NA,
  cache = list(),
  print = record_print,
  print.args = list(),
  verbose = getOption("xfun.record.verbose", 0),
  envir = parent.frame()
)

## S3 method for class 'xfun_record_results'
format(x, to = c("text", "html"), encode = FALSE, template = FALSE, ...)

## S3 method for class 'xfun_record_results'
print(
  x,
  browse = interactive(),
  to = if (browse) "html" else "text",
  template = TRUE,
  ...
)

Arguments

Value

record() returns a list of the class xfun_record_results that contains elements with these possible classes: record_source (source code), record_output (text output), record_plot (plot file paths), record_message (messages), record_warning (warnings), and record_error (errors, only when the argument error = TRUE).

The format() method returns a character vector of plain-text output or HTML code for displaying the results.

The print() method prints the results as plain text or HTML to the console or displays the HTML page.

Examples

code = c("# a warning test", "1:2 + 1:3", "par(mar = c(4, 4, 1, .2))",
    "barplot(5:1, col = 2:6, horiz = TRUE)", "head(iris)",
    "sunflowerplot(iris[, 3:4], seg.col = 'purple')",
    "if (TRUE) {\n  message('Hello, xfun::record()!')\n}",
    "# throw an error", "1 + 'a'")
res = xfun::record(code, dev.args = list(width = 9, height = 6.75),
    error = TRUE)
xfun::tree(res)
format(res)
# find and clean up plot files
plots = Filter(function(x) inherits(x, "record_plot"),
    res)
file.remove(unlist(plots))

Description

An S3 generic function to be called to print visible values in code when the code is recorded by record(). It is similar to knitr::knit_print(). By default, it captures the normal print() output and returns the result as a character vector. The knitr_kable method is for printing knitr::kable() output. Users and package authors can define other S3 methods to extend this function.

Usage

record_print(x, ...)

## Default S3 method:
record_print(x, ...)

## S3 method for class 'record_asis'
record_print(x, ...)

new_record(x, class)

Arguments

Value

A record_print() method should return a character vector or a list of character vectors. The original classes of the vector will be discarded, and the vector will be treated as console output by default (i.e., new_record(class = "output")). If it should be another type of output, wrap the vector in new_record() and specify a class name.

Description

Given a directory, return the relative path that is relative to this directory. For example, the path ‘foo/bar.txt’ relative to the directory ‘foo/’ is ‘bar.txt’, and the path ‘/a/b/c.txt’ relative to ‘/d/e/’ is ‘../../a/b/c.txt’.

Usage

relative_path(x, dir = ".", use.. = TRUE, error = TRUE)

Arguments

Value

A vector of relative paths if the conversion succeeded; otherwise the original paths when error = FALSE, and an error when error = TRUE.

Examples

xfun::relative_path("foo/bar.txt", "foo/")
xfun::relative_path("foo/bar/a.txt", "foo/haha/")
xfun::relative_path(getwd())

Description

Rename a series of files and add an incremental numeric prefix to the filenames. For example, files ‘a.txt’, ‘b.txt’, and ‘c.txt’ can be renamed to ‘1-a.txt’, ‘2-b.txt’, and ‘3-c.txt’.

Usage

rename_seq(
  pattern = "^[0-9]+-.+[.]Rmd$",
  format = "auto",
  replace = TRUE,
  start = 1,
  dry_run = TRUE
)

Arguments

Value

A named character vector. The names are original filenames, and the vector itself is the new filenames.

Examples

xfun::rename_seq()
xfun::rename_seq("[.](jpeg|png)$", format = "%04d")

Description

Read data from a REST API and optionally with an authorization token in the request header. The function rest_api_raw() returns the raw text of the response, and rest_api() will parse the response with jsonlite::fromJSON() (assuming that the response is in the JSON format).

Usage

rest_api(...)

rest_api_raw(root, endpoint, token = "", params = list(), headers = NULL)

github_api(
  endpoint,
  token = "",
  params = list(),
  headers = NULL,
  raw = !loadable("jsonlite")
)

Arguments

Details

These functions are simple wrappers based on url() and read_utf8(). Specifically, the headers argument is passed to url(), and read_utf8() will send a ‘⁠GET⁠’ request to the API server. This means these functions only support the ‘⁠GET⁠’ method. If you need to use other HTTP methods (such as ‘⁠POST⁠’), you have to use other packages such as curl and httr.

github_api() is a wrapper function based on rest_api_raw() to obtain data from the GitHub API: https://docs.github.com/en/rest. You can provide a personal access token (PAT) via the token argument, or via one of the environment variables GITHUB_PAT, GITHUB_TOKEN, GH_TOKEN. A PAT allows for a much higher rate limit in API calls. Without a token, you can only make 60 calls in an hour.

Value

A character vector (the raw JSON response) or an R object parsed from the JSON text.

Examples

# a normal GET request
xfun::rest_api("https://httpbin.org", "/get")
xfun::rest_api_raw("https://httpbin.org", "/get")

# send the request with an auth header
xfun::rest_api("https://httpbin.org", "/headers", "OPEN SESAME!")

# with query parameters
xfun::rest_api("https://httpbin.org", "/response-headers", params = list(foo = "bar"))

# get the rate limit info from GitHub
xfun::github_api("/rate_limit")

Description

If the function returns an error, retry it for the specified number of times, with a pause between attempts.

Usage

retry(fun, ..., .times = 3, .pause = 5)

Arguments

Details

One application of this function is to download a web resource. Since the download might fail sometimes, you may want to retry it for a few more times.

Examples

# read the GitHub releases info of the repo yihui/xfun
xfun::retry(xfun::github_releases, "yihui/xfun")

Description

Install the source package, figure out the reverse dependencies on CRAN, download all of their source packages, and run R CMD check on them in parallel.

Usage

rev_check(
  pkg,
  which = "all",
  recheck = NULL,
  ignore = NULL,
  update = TRUE,
  timeout = getOption("xfun.rev_check.timeout", 15 * 60),
  src = file.path(src_dir, pkg),
  src_dir = getOption("xfun.rev_check.src_dir")
)

compare_Rcheck(status_only = TRUE, output = "00check_diffs.md")

Arguments

Details

Everything occurs under the current working directory, and you are recommended to call this function under a designated directory, especially when the number of reverse dependencies is large, because all source packages will be downloaded to this directory, and all ‘*.Rcheck’ directories will be generated under this directory, too.

If a source tarball of the expected version has been downloaded before (under the ‘tarball’ directory), it will not be downloaded again (to save time and bandwidth).

After a package has been checked, the associated ‘*.Rcheck’ directory will be deleted if the check was successful (no warnings or errors or notes), which means if you see a ‘*.Rcheck’ directory, it means the check failed, and you need to take a look at the log files under that directory.

The time to finish the check is recorded for each package. As the check goes on, the total remaining time will be roughly estimated via n * mean(times), where n is the number of packages remaining to be checked, and times is a vector of elapsed time of packages that have been checked.

If a check on a reverse dependency failed, its ‘*.Rcheck’ directory will be renamed to ‘*.Rcheck2’, and another check will be run against the CRAN version of the package unless options(xfun.rev_check.compare = FALSE) is set. If the logs of the two checks are the same, it means no new problems were introduced in the package, and you can probably ignore this particular reverse dependency. The function compare_Rcheck() can be used to create a summary of all the differences in the check logs under ‘*.Rcheck’ and ‘*.Rcheck2’. This will be done automatically if options(xfun.rev_check.summary = TRUE) has been set.

A recommended workflow is to use a special directory to run rev_check(), set the global options() xfun.rev_check.src_dir and repos in the R startup (see ⁠?⁠Startup) profile file .Rprofile under this directory, and (optionally) set R_LIBS_USER in ‘.Renviron’ to use a special library path (so that your usual library will not be cluttered). Then run xfun::rev_check(pkg) once, investigate and fix the problems or (if you believe it was not your fault) ignore broken packages in the file ‘00ignore’, and run xfun::rev_check(pkg) again to recheck the failed packages. Repeat this process until all ‘*.Rcheck’ directories are gone.

As an example, I set options(repos = c(CRAN = 'https://cran.rstudio.com'), xfun.rev_check.src_dir = '~/Dropbox/repo') in ‘.Rprofile’, and ⁠R_LIBS_USER=~/R-tmp⁠ in ‘.Renviron’. Then I can run, for example, xfun::rev_check('knitr') repeatedly under a special directory ‘~/Downloads/revcheck’. Reverse dependencies and their dependencies will be installed to ‘~/R-tmp’, and knitr will be installed from ‘~/Dropbox/repo/kintr’.

Value

A named numeric vector with the names being package names of reverse dependencies; 0 indicates check success, 1 indicates failure, and 2 indicates that a package was not checked due to global timeout.

See Also

devtools::revdep_check() is more sophisticated, but currently has a few major issues that affect me: (1) It always deletes the ‘*.Rcheck’ directories (https://github.com/r-lib/devtools/issues/1395), which makes it difficult to know more information about the failures; (2) It does not fully install the source package before checking its reverse dependencies (https://github.com/r-lib/devtools/pull/1397); (3) I feel it is fairly difficult to iterate the check (ignore the successful packages and only check the failed packages); by comparison, xfun::rev_check() only requires you to run a short command repeatedly (failed packages are indicated by the existing ‘*.Rcheck’ directories, and automatically checked again the next time).

xfun::rev_check() borrowed a very nice feature from devtools::revdep_check(): estimating and displaying the remaining time. This is particularly useful for packages with huge numbers of reverse dependencies.

Description

Wrapper functions to run the commands Rscript and R CMD.

Usage

Rscript(args, ...)

Rcmd(args, ...)

Arguments

Value

A value returned by system2().

Examples

library(xfun)
Rscript(c("-e", "1+1"))
Rcmd(c("build", "--help"))

Description

Save the argument values of a function in a temporary RDS file, open a new R session via Rscript(), read the argument values, call the function, and read the returned value back to the current R session.

Usage

Rscript_call(
  fun,
  args = list(),
  options = NULL,
  ...,
  wait = TRUE,
  fail = sprintf("Failed to run '%s' in a new R session", deparse(substitute(fun))[1])
)

Arguments

Value

If wait = TRUE, the returned value of the function in the new R session. If wait = FALSE, three file paths will be returned: the first one stores fun and args (as a list), the second one is supposed to store the returned value of the function, and the third one stores the possible error message.

Examples

factorial(10)
# should return the same value
xfun::Rscript_call("factorial", list(10))

# the first argument can be either a character string or a function
xfun::Rscript_call(factorial, list(10))

# Run Rscript starting a vanilla R session
xfun::Rscript_call(factorial, list(10), options = c("--vanilla"))

Description

Use the rstudioapi package to insert characters one by one into the RStudio source editor, as if they were typed by a human.

Usage

rstudio_type(x, pause = function() 0.1, mistake = 0, save = 0)

Arguments

Examples

library(xfun)
if (loadable("rstudioapi") && rstudioapi::isAvailable()) {
    rstudio_type("Hello, RStudio! xfun::rstudio_type() looks pretty cool!",
        pause = function() runif(1, 0, 0.5), mistake = 0.1)
}

Description

Compare two paths after normalizing them with the same separator (/).

Usage

same_path(p1, p2, ...)

Arguments

Examples

library(xfun)
same_path("~/foo", file.path(Sys.getenv("HOME"), "foo"))

Description

This function tweaks the output of sessionInfo(): (1) It adds the RStudio version information if running in the RStudio IDE; (2) It removes the information about matrix products, BLAS, and LAPACK; (3) It removes the names of base R packages; (4) It prints out package versions in a single group, and does not differentiate between loaded and attached packages.

Usage

session_info(packages = NULL, dependencies = TRUE)

Arguments

Details

It also allows you to only print out the versions of specified packages (via the packages argument) and optionally their recursive dependencies. For these specified packages (if provided), if a function xfun_session_info() exists in a package, it will be called and expected to return a character vector to be appended to the output of session_info(). This provides a mechanism for other packages to inject more information into the session_info output. For example, rmarkdown (>= 1.20.2) has a function xfun_session_info() that returns the version of Pandoc, which can be very useful information for diagnostics.

Value

A character vector of the session information marked as raw_string().

Examples

xfun::session_info()
if (xfun::loadable("MASS")) xfun::session_info("MASS")

Description

Set environment variables from a named character vector, and return the old values of the variables, so they could be restored later.

Usage

set_envvar(vars)

Arguments

Details

The motivation of this function is that Sys.setenv() does not return the old values of the environment variables, so it is not straightforward to restore the variables later.

Value

Old values of the variables (if not set, NA).

Examples

vars = xfun::set_envvar(c(FOO = "1234"))
Sys.getenv("FOO")
xfun::set_envvar(vars)
Sys.getenv("FOO")

Description

Use magick::image_resize() to shrink an image if its width is larger than the value specified by the argument width, and optionally call tinify() to compress it.

Usage

shrink_images(
  width = 800,
  dir = ".",
  files = all_files("[.](png|jpe?g|webp)$", dir),
  tinify = FALSE
)

Arguments

Examples

f = xfun:::all_files("[.](png|jpe?g)$", R.home("doc"))
file.copy(f, tempdir())
f = file.path(tempdir(), basename(f))
magick::image_info(magick::image_read(f))  # some widths are larger than 300
xfun::shrink_images(300, files = f)
magick::image_info(magick::image_read(f))  # all widths <= 300 now
file.remove(f)

Description

Call unlist(strsplit(x, '\n')) on the character vector x and make sure it works in a few edge cases: split_lines('') returns '' instead of character(0) (which is the returned value of strsplit('', '\n')); split_lines('a\n') returns c('a', '') instead of c('a') (which is the returned value of strsplit('a\n', '\n').

Usage

split_lines(x)

Arguments

Value

All elements of the character vector are split by '\n' into lines.

Examples

xfun::split_lines(c("a", "b\nc"))

Description

Parse the lines of code one by one to find complete expressions in the code, and put them in a list.

Usage

split_source(x, merge_comments = FALSE, line_number = FALSE)

Arguments

Value

A list of character vectors, and each vector contains a complete R expression, with an attribute lines indicating the starting and ending line numbers of the expression if the argument line_number = TRUE.

Examples

code = c("# comment 1", "# comment 2", "if (TRUE) {", "1 + 1", "}", "print(1:5)")
xfun::split_source(code)
xfun::split_source(code, merge_comments = TRUE)

Description

A strict list is essentially a normal list() but it does not allow partial matching with $.

Usage

strict_list(...)

as_strict_list(x)

## S3 method for class 'xfun_strict_list'
x$name

## S3 method for class 'xfun_strict_list'
print(x, ...)

Arguments

Details

To me, partial matching is often more annoying and surprising than convenient. It can lead to bugs that are very hard to discover, and I have been bitten by it many times. When I write x$name, I always mean precisely name. You should use a modern code editor to autocomplete the name if it is too long to type, instead of using partial names.

Value

Both strict_list() and as_strict_list() return a list with the class xfun_strict_list. Whereas as_strict_list() attempts to coerce its argument x to a list if necessary, strict_list() just wraps its argument ... in a list, i.e., it will add another list level regardless if ... already is of type list.

Examples

library(xfun)
(z = strict_list(aaa = "I am aaa", b = 1:5))
z$a  # NULL!
z$aaa  # I am aaa
z$b
z$c = "create a new element"

z2 = unclass(z)  # a normal list
z2$a  # partial matching

z3 = as_strict_list(z2)  # a strict list again
z3$a  # NULL again!

Description

Remove HTML tags and comments from text.

Usage

strip_html(x)

Arguments

Value

A character vector with HTML tags and comments stripped off.

Examples

xfun::strip_html("<a href=\"#\">Hello <!-- comment -->world!</a>")

Description

Build a source package and submit it to CRAN with the curl package.

Usage

submit_cran(file = pkg_build(), comment = "")

Arguments

See Also

devtools::submit_cran() does the same job, with a few more dependencies in addition to curl (such as cli); xfun::submit_cran() only depends on curl.

Description

This is a wrapper function based on system2(). If system2() returns character output (e.g., with the argument stdout = TRUE), check if the output is encoded in UTF-8. If it is, mark it with UTF-8 explicitly.

Usage

system3(...)

Arguments

Value

The value returned by system2().

Examples

a = shQuote(c("-e", "print(intToUtf8(c(20320, 22909)))"))
x2 = system2("Rscript", a, stdout = TRUE)
Encoding(x2)  # unknown

x3 = xfun::system3("Rscript", a, stdout = TRUE)
# encoding of x3 should be UTF-8 if the current locale is UTF-8
!l10n_info()[["UTF-8"]] || Encoding(x3) == "UTF-8"  # should be TRUE

Description

Compress PNG/JPEG images with ‘⁠api.tinify.com⁠’, and download the compressed images. These functions require R packages curl and jsonlite. tinify_dir() is a wrapper function of tinify() to compress images under a directory.

Usage

tinify(
  input,
  output,
  quiet = FALSE,
  force = FALSE,
  key = env_option("xfun.tinify.key"),
  history = env_option("xfun.tinify.history")
)

tinify_dir(dir = ".", ...)

Arguments

Details

You are recommended to set the API key in ‘.Rprofile’ or ‘.Renviron’. After that, the only required argument of this function is input. If the original images can be overwritten by the compressed images, you may either use output = identity, or set the value of the history argument in ‘.Rprofile’ or ‘.Renviron’.

Value

The output file paths.

References

Tinify API: https://tinypng.com/developers.

See Also

The tinieR package (https://github.com/jmablog/tinieR/) is a more comprehensive implementation of the Tinify API, whereas xfun::tinify() has only implemented the feature of shrinking images.

Examples

f = xfun:::R_logo("jpg$")
xfun::tinify(f)  # remember to set the API key before trying this

Description

A JSON serializer that only works on a limited types of R data (NULL, lists, logical scalars, character/numeric vectors). A character string of the class JS_EVAL is treated as raw JavaScript, so will not be quoted. The function json_vector() converts an atomic R vector to JSON.

Usage

tojson(x)

json_vector(x, to_array = FALSE, quote = TRUE)

Arguments

Value

A character string.

See Also

The jsonlite package provides a full JSON serializer.

Examples

library(xfun)
tojson(NULL)
tojson(1:10)
tojson(TRUE)
tojson(FALSE)
cat(tojson(list(a = 1, b = list(c = 1:3, d = "abc"))))
cat(tojson(list(c("a", "b"), 1:5, TRUE)))

# the class JS_EVAL is originally from htmlwidgets::JS()
JS = function(x) structure(x, class = "JS_EVAL")
cat(tojson(list(a = 1:5, b = JS("function() {return true;}"))))

Turn the output of str() into a tree diagram

Description

The super useful function str() uses ⁠..⁠ to indicate the level of sub-elements of an object, which may be difficult to read. This function uses vertical pipes to connect all sub-elements on the same level, so it is clearer which elements belong to the same parent element in an object with a nested structure (such as a nested list).

Usage

tree(...)

Arguments

Value

A character string as a raw_string().

Examples

fit = lsfit(1:9, 1:9)
str(fit)
xfun::tree(fit)

fit = lm(dist ~ speed, data = cars)
str(fit)
xfun::tree(fit)

# some trivial examples
xfun::tree(1:10)
xfun::tree(iris)

Description

Use tryCatch() to check if an expression throws an error.

Usage

try_error(expr)

Arguments

Value

TRUE (error) or FALSE (success).

Examples

xfun::try_error(stop("foo"))  # TRUE
xfun::try_error(1:10)  # FALSE

Description

An abbreviation of try(silent = TRUE).

Usage

try_silent(expr)

Arguments

Examples

library(xfun)
z = try_silent(stop("Wrong!"))
inherits(z, "try-error")

Description

The function upload_ftp() runs the command curl -T file server to upload a file to an FTP server if the system command curl is available, otherwise it uses the R package curl. The function upload_win_builder() uses upload_ftp() to upload packages to the win-builder server.

Usage

upload_ftp(file, server, dir = "")

upload_win_builder(
  file = pkg_build(),
  version = c("R-devel", "R-release", "R-oldrelease"),
  server = c("ftp", "https"),
  solaris = pkg_available("rhub")
)

Arguments

Details

These functions were written mainly to save package developers the trouble of going to the win-builder web page and uploading packages there manually.

Value

Status code returned from system2() or curl::curl_fetch_memory().

Description

This function uses the curl package or the system command curl (whichever is available) to upload a image to https://imgur.com.

Usage

upload_imgur(
  file,
  key = env_option("xfun.upload_imgur.key", "9f3460e67f308f6"),
  use_curl = loadable("curl"),
  include_xml = FALSE
)

Arguments

Details

One application is to upload local image files to Imgur when knitting a document with knitr: you can set the ⁠knitr::opts_knit$set(upload.fun = xfun::upload_imgur⁠, so the output document does not need local image files any more, and it is ready to be published online.

Value

A character string of the link to the image. If include_xml = TRUE, this string carries an attribute named XML, which is the XML response from Imgur (it will be parsed by xml2 if available). See Imgur API in the references.

Note

Please register your own Imgur application to get your client ID; you can certainly use mine, but this ID is in the public domain so everyone has access to all images associated to it.

Author(s)

Yihui Xie, adapted from the imguR package by Aaron Statham

References

A demo: https://yihui.org/knitr/demo/upload/

Examples

## Not run: 
f = tempfile(fileext = ".png")
png(f)
plot(rnorm(100), main = R.version.string)
dev.off()

res = imgur_upload(f, include_xml = TRUE)
res  # link to original URL of the image
attr(res, "XML")  # all information
if (interactive())
    browseURL(res)

# to use your own key
options(xfun.upload_imgur.key = "your imgur key")

## End(Not run)

Description

Try to send a HEAD request to a URL using curlGetHeaders() or the curl package, and see if it returns a successful status code.

Usage

url_accessible(x, use_curl = !capabilities("libcurl"), ...)

Arguments

Value

TRUE or FALSE.

Examples

xfun::url_accessible("https://yihui.org")

Description

Get the base names of URLs via basename(), and remove the possible query parameters or hash from the names.

Usage

url_filename(x, default = "index.html")

Arguments

Value

A character vector of filenames at the end of URLs.

Examples

xfun::url_filename("https://yihui.org/images/logo.png")
xfun::url_filename("https://yihui.org/index.html")
xfun::url_filename("https://yihui.org/index.html?foo=bar")
xfun::url_filename("https://yihui.org/index.html#about")
xfun::url_filename("https://yihui.org")
xfun::url_filename("https://yihui.org/")

Description

Try to parse() the code and see if an error occurs.

Usage

valid_syntax(code, silent = TRUE)

Arguments

Value

TRUE if the code could be parsed, otherwise FALSE.

Examples

xfun::valid_syntax("1+1")
xfun::valid_syntax("1+")
xfun::valid_syntax(c("if(T){1+1}", "else {2+2}"), silent = FALSE)

Description

Split a document into the YAML metadata (which starts with ⁠---⁠ in the beginning of the document) and the body. The YAML metadata will be parsed.

Usage

yaml_body(x, ...)

Arguments

Value

A list of components yaml (the parsed YAML data), lines (starting and ending line numbers of YAML), and body (a character vector of the body text). If YAML metadata does not exist in the document, the components yaml and lines will be missing.

Examples

xfun::yaml_body(c("---", "title: Hello", "output: litedown::html_format", "---",
    "", "Content."))

Description

If the yaml package is installed, use yaml::yaml.load() to read the data. If not, use a simple parser instead, which only supports a limited number of data types (see “Examples”). In particular, it does not support values that span across multiple lines (such as multi-line text).

Usage

yaml_load(
  x,
  ...,
  handlers = NULL,
  envir = parent.frame(),
  use_yaml = loadable("yaml")
)

Arguments

Value

An R object (typically a list).

Note

R expressions in YAML will be returned as expressions when they are not evaluated. This is different with yaml::yaml.load(), which returns character strings for expressions.

Examples

# test the simple parser without using the yaml package
read_yaml = function(...) xfun::yaml_load(..., use_yaml = FALSE)
read_yaml("a: 1")
read_yaml("a: 1\nb: \"foo\"\nc: null")
read_yaml("a:\n  b: false\n  c: true\n  d: 1.234\ne: bar")
read_yaml("a: !expr paste(1:10, collapse = \", \")")
read_yaml("a: [1, 3, 4, 2]")
read_yaml("a: [1, \"abc\", 4, 2]")
read_yaml("a: [\"foo\", \"bar\"]")
read_yaml("a: [true, false, true]")
# the other form of array is not supported
read_yaml("a:\n  - b\n  - c")
# and you must use the yaml package
if (loadable("yaml")) yaml_load("a:\n  - b\n  - c")