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fs provides a cross-platform, uniform interface to file system operations. It shares the same back-end component as nodejs, the libuv C library, which brings the benefit of extensive real-world use and rigorous cross-platform testing. The name, and some of the interface, is partially inspired by Rust’s fs module.


You can install the released version of fs from CRAN with:


And the development version from GitHub with:

# install.packages("devtools")

Comparison vs base equivalents

fs functions smooth over some of the idiosyncrasies of file handling with base R functions:

  • Vectorization. All fs functions are vectorized, accepting multiple paths as input. Base functions are inconsistently vectorized.

  • Predictable return values that always convey a path. All fs functions return a character vector of paths, a named integer or a logical vector, where the names give the paths. Base return values are more varied: they are often logical or contain error codes which require downstream processing.

  • Explicit failure. If fs operations fail, they throw an error. Base functions tend to generate a warning and a system dependent error code. This makes it easy to miss a failure.

  • UTF-8 all the things. fs functions always convert input paths to UTF-8 and return results as UTF-8. This gives you path encoding consistency across OSes. Base functions rely on the native system encoding.

  • Naming convention. fs functions use a consistent naming convention. Because base R’s functions were gradually added over time there are a number of different conventions used (e.g. path.expand() vs normalizePath(); Sys.chmod() vs file.access()).

Tidy paths

fs functions always return ‘tidy’ paths. Tidy paths

  • Always use / to delimit directories
  • never have multiple / or trailing /

Tidy paths are also coloured (if your terminal supports it) based on the file permissions and file type. This colouring can be customized or extended by setting the LS_COLORS environment variable, in the same output format as GNU dircolors.


fs functions are divided into four main categories:

  • path_ for manipulating and constructing paths
  • file_ for files
  • dir_ for directories
  • link_ for links

Directories and links are special types of files, so file_ functions will generally also work when applied to a directory or link.


# Construct a path to a file with `path()`
path("foo", "bar", letters[1:3], ext = "txt")
#> foo/bar/a.txt foo/bar/b.txt foo/bar/c.txt

# list files in the current directory
#> NAMESPACE          R                README.Rmd       
#>        _pkgdown.yml     codecov.yml 
#> fs.Rproj         inst             man              man-roxygen      
#> revdep           src              tests            vignettes

# create a new directory
tmp <- dir_create(file_temp())
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file553519e91baa

# create new files in that directory
file_create(path(tmp, "my-file.txt"))
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file553519e91baa/my-file.txt

# remove files from the directory
file_delete(path(tmp, "my-file.txt"))
#> character(0)

# remove the directory

fs is designed to work well with the pipe, though because it is a minimal-dependency infrastructure package it doesn’t provide the pipe itself. You will need to attach magrittr or similar.


paths <- file_temp() %>%
  dir_create() %>%
  path(letters[1:5]) %>%
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file5535783c1027/a
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file5535783c1027/b
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file5535783c1027/c
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file5535783c1027/d
#> /var/folders/9x/_8jnmxwj3rq1t90mlr6_0k1w0000gn/T/Rtmp8qNYF8/file5535783c1027/e

paths %>% file_delete()

fs functions also work well in conjunction with other tidyverse packages, like dplyr and purrr.

Some examples…


Filter files by type, permission and size

dir_info("src", recurse = FALSE) %>%
  filter(type == "file", permissions == "u+r", size > "10KB") %>%
  arrange(desc(size)) %>%
  select(path, permissions, size, modification_time)
#> # A tibble: 12 × 4
#>    path          permissions        size modification_time  
#>    <fs::path>    <fs::perms> <fs::bytes> <dttm>             
#>  1 src/     rwxr-xr-x        267.7K 2021-11-29 17:54:31
#>  2 src/id.o      rw-r--r--        159.2K 2021-11-29 17:54:10
#>  3 src/dir.o     rw-r--r--         96.7K 2021-11-29 17:54:10
#>  4 src/path.o    rw-r--r--         94.2K 2021-11-29 17:54:10
#>  5 src/link.o    rw-r--r--           76K 2021-11-29 17:54:10
#>  6 src/utils.o   rw-r--r--         75.1K 2021-11-29 17:54:10
#>  7 src/getmode.o rw-r--r--           67K 2021-11-29 17:54:10
#>  8 src/file.o    rw-r--r--         61.5K 2021-11-29 17:54:10
#>  9 src/error.o   rw-r--r--         19.8K 2021-11-29 17:54:10
#> 10 src/init.o    rw-r--r--         17.3K 2021-11-29 17:54:10
#> 11 src/fs.o      rw-r--r--         12.1K 2021-11-29 17:54:10
#> 12 src/   rw-r--r--         11.5K 2021-11-29 17:54:06

Tabulate and display folder size.

dir_info("src", recurse = TRUE) %>%
  group_by(directory = path_dir(path)) %>%
  tally(wt = size, sort = TRUE)
#> # A tibble: 11 × 2
#>    directory                             n
#>    <chr>                       <fs::bytes>
#>  1 src/libuv-1.38.1                  2.67M
#>  2 src/libuv-1.38.1/src/unix         1.37M
#>  3 src                              986.5K
#>  4 src/libuv-1.38.1/src/win        729.66K
#>  5 src/libuv-1.38.1/src            329.28K
#>  6 src/libuv-1.38.1/include/uv     138.27K
#>  7 src/libuv-1.38.1/include         64.03K
#>  8 src/unix                         63.43K
#>  9 src/bsd                          20.02K
#> 10 src/windows                       4.73K
#> 11 src/libuv-1.38.1/test                64

Read a collection of files into one data frame.

dir_ls() returns a named vector, so it can be used directly with purrr::map_df(.id).

# Create separate files for each species
iris %>%
  split(.$Species) %>%
  map(select, -Species) %>%
  iwalk(~ write_tsv(.x, paste0(.y, ".tsv")))

# Show the files
iris_files <- dir_ls(glob = "*.tsv")
#> setosa.tsv     versicolor.tsv virginica.tsv

# Read the data into a single table, including the filenames
iris_files %>%
  map_df(read_tsv, .id = "file", col_types = cols(), n_max = 2)
#> # A tibble: 6 × 5
#>   file           Sepal.Length Sepal.Width Petal.Length Petal.Width
#>   <chr>                 <dbl>       <dbl>        <dbl>       <dbl>
#> 1 setosa.tsv              5.1         3.5          1.4         0.2
#> 2 setosa.tsv              4.9         3            1.4         0.2
#> 3 versicolor.tsv          7           3.2          4.7         1.4
#> 4 versicolor.tsv          6.4         3.2          4.5         1.5
#> 5 virginica.tsv           6.3         3.3          6           2.5
#> 6 virginica.tsv           5.8         2.7          5.1         1.9


Feedback wanted!

We hope fs is a useful tool for both analysis scripts and packages. Please open GitHub issues for any feature requests or bugs.

In particular, we have found non-ASCII filenames in non-English locales on Windows to be especially tricky to reproduce and handle correctly. Feedback from users who use commonly have this situation is greatly appreciated.

Code of Conduct

Please note that the fs project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.