An Introduction to xfun

A Collection of Miscellaneous Functions

Yihui Xie


After writing about 20 R packages, I found I had accumulated several utility functions that I used across different packages, so I decided to extract them into a separate package. Previously I had been using the evil triple-colon ::: to access these internal utility functions. Now with xfun, these functions have been exported, and more importantly, documented. It should be better to use them under the sun instead of in the dark.

This page shows examples of a subset of functions in this package. For a full list of functions, see the help page help(package = 'xfun'). The source package is available on Github:

No more partial matching for lists!

I have been bitten many times by partial matching in lists, e.g., when I want x$a but the element a does not exist in the list x, it returns the value x$abc if abc exists in x. This is very annoying to me which is why I created strict lists. A strict list is a list for which the partial matching of the $ operator is disabled. The functions xfun::strict_list() and xfun::as_strict_list() are the equivalents to base::list() and base::as.list() respectively which always return as strict list, e.g.,

(z = strict_list(aaa = "I am aaa", b = 1:5))
## $aaa
## [1] "I am aaa"
## $b
## [1] 1 2 3 4 5
z$a  # NULL (strict matching)
z$aaa  # I am aaa
## [1] "I am aaa"
## [1] 1 2 3 4 5
z$c = "you can create a new element"

z2 = unclass(z)  # a normal list
z2$a  # partial matching
## [1] "I am aaa"
z3 = as_strict_list(z2)  # a strict list again
z3$a  # NULL (strict matching) again!

Similarly, the default partial matching in attr() can be annoying, too. The function xfun::attr() is simply a shorthand of attr(..., exact = TRUE).

I want it, or I do not want. There is no “I probably want”.

Output character vectors for human eyes

When R prints a character vector, your eyes may be distracted by the indices like [1], double quotes, and escape sequences. To see a character vector in its “raw” form, you can use cat(..., sep = '\n'). The function raw_string() marks a character vector as “raw”, and the corresponding printing function will call cat(sep = '\n') to print the character vector to the console.

(x = c("a \"b\"", "hello\tworld!"))
[1] "a \"b\""       "hello\tworld!"
raw_string(x)  # this is more likely to be what you want to see
a "b"
hello   world!

Search and replace strings in files

I can never remember how to properly use grep or sed to search and replace strings in multiple files. My favorite IDE, RStudio, has not provided this feature yet (you can only search and replace in the currently opened file). Therefore I did a quick and dirty implementation in R, including functions gsub_files(), gsub_dir(), and gsub_ext(), to search and replace strings in multiple files under a directory. Note that the files are assumed to be encoded in UTF-8. If you do not use UTF-8, we cannot be friends. Seriously.

All functions are based on gsub_file(), which performs searching and replacing in a single file, e.g.,

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

The function gsub_dir() is very flexible: you can limit the list of files by MIME types, or extensions. For example, if you want to do substitution in text files, you may use gsub_dir(..., mimetype = '^text/').

WARNING: Before using these functions, make sure that you have backed up your files, or version control your files. The files will be modified in-place. If you do not back up or use version control, there is no chance to regret.

Manipulate filename extensions

Functions file_ext() and sans_ext() are based on functions in tools. The function with_ext() adds or replaces extensions of filenames, and it is vectorized.

p = c("abc.doc", "def123.tex", "path/to/foo.Rmd")
## [1] "doc" "tex" "Rmd"
## [1] "abc"         "def123"      "path/to/foo"
with_ext(p, ".txt")
## [1] "abc.txt"         "def123.txt"      "path/to/foo.txt"
with_ext(p, c(".ppt", ".sty", ".Rnw"))
## [1] "abc.ppt"         "def123.sty"      "path/to/foo.Rnw"
with_ext(p, "html")
## [1] "abc.html"         "def123.html"      "path/to/foo.html"

Types of operating systems

The series of functions is_linux(), is_macos(), is_unix(), and is_windows() test the types of the OS, using the information from .Platform and, e.g.,

## [1] TRUE
## [1] TRUE
## [1] FALSE
## [1] FALSE

Loading and attaching packages

Oftentimes I see users attach a series of packages in the beginning of their scripts by repeating library() multiple times. This could be easily vectorized, and the function xfun::pkg_attach() does this job. For example,


is equivalent to

xfun::pkg_attach(c('testit', 'parallel', 'tinytex', 'mime'))

I also see scripts that contain code to install a package if it is not available, e.g.,

if (!requireNamespace('tinytex')) install.packages('tinytex')

This could be done via


The function pkg_attach2() is a shorthand of pkg_attach(..., install = TRUE), which means if a package is not available, install it. This function can also deal with multiple packages.

The function loadable() tests if a package is loadable.

Read/write files in UTF-8

Functions read_utf8() and write_utf8() can be used to read/write files in UTF-8. They are simple wrappers of readLines() and writeLines().

Convert numbers to English words

The function numbers_to_words() (or n2w() for short) converts numbers to English words.

n2w(0, cap = TRUE)
## [1] "Zero"
n2w(seq(0, 121, 11), and = TRUE)
##  [1] "zero"                       "eleven"                    
##  [3] "twenty-two"                 "thirty-three"              
##  [5] "forty-four"                 "fifty-five"                
##  [7] "sixty-six"                  "seventy-seven"             
##  [9] "eighty-eight"               "ninety-nine"               
## [11] "one hundred and ten"        "one hundred and twenty-one"
## [1] "one million"
n2w(1e+11 + 12345678)
## [1] "one hundred billion, twelve million, three hundred forty-five thousand, six hundred seventy-eight"
## [1] "minus nine hundred eighty-seven million, six hundred fifty-four thousand, three hundred twenty-one"
n2w(1e+15 - 1)
## [1] "nine hundred ninety-nine trillion, nine hundred ninety-nine billion, nine hundred ninety-nine million, nine hundred ninety-nine thousand, nine hundred ninety-nine"

Cache an R expression to an RDS file

The function cache_rds() provides a simple caching mechanism: the first time an expression is passed to it, it saves the result to an RDS file; the next time it will read the RDS file and return the value instead of evaluating the expression again. If you want to invalidate the cache, you can use the argument rerun = TRUE.

res = xfun::cache_rds({
  # pretend the computing here is a time-consuming

When the function is used in a code chunk in a knitr document, the RDS cache file is saved to the path determined by the chunk label (the filename) and the chunk option cache.path (usually the cache directory), so you do not have to provide the file and dir arguments of cache_rds().

This caching mechanism is much simpler than knitr’s caching. Cache invalidation is often tricky (see this post), so this function may be helpful if you want more transparency and control over when to invalidate the cache (for cache_rds(), the cache is invalidated only when the cache file is deleted, which can be achieved via the argument rerun = TRUE).

Check reverse dependencies of a package

Running R CMD check on the reverse dependencies of knitr and rmarkdown is my least favorite thing in developing R packages, because the numbers of their reverse dependencies are huge. The function rev_check() reflects some of my past experience in this process. I think I have automated it as much as possible, and made it as easy as possible to discover possible new problems introduced by the current version of the package (compared to the CRAN version). Finally I can just sit back and let it run.

Input a character vector into the RStudio source editor

The function rstudio_type() inputs characters in the RStudio source editor as if they were typed by a human. I came up with the idea when preparing my talk for rstudio::conf 2018 (see this post for more details).