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:
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.
fs functions always return ‘tidy’ paths. Tidy paths
/to delimit directories
Tidy paths are also coloured (if your terminal supports it) based on the file permissions and file type. This colouring can be customised 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
Directories and links are special types of files, so
file_ functions will generally also work when applied to a directory or link.
library(fs) # 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 dir_ls() #> DESCRIPTION LICENSE.md NAMESPACE #> NEWS.md R README.Rmd #> README.md _pkgdown.yml appveyor.yml #> codecov.yml cran-comments.md fs.Rcheck #> fs.Rproj fs_22.214.171.12401.tar.gz inst #> man man-roxygen revdep #> src tests vignettes # create a new directory tmp <- dir_create(file_temp()) tmp #> /tmp/filedd463d6d7e0f # create new files in that directory file_create(path(tmp, "my-file.txt")) dir_ls(tmp) #> /tmp/filedd463d6d7e0f/my-file.txt # remove files from the directory file_delete(path(tmp, "my-file.txt")) dir_ls(tmp) #> character(0) # remove the directory dir_delete(tmp)
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.
Filter files by type, permission and size
dir_info("src", recursive = FALSE) %>% filter(type == "file", permissions == "u+r", size > "10KB") %>% arrange(desc(size)) %>% select(path, permissions, size, modification_time) #> # A tibble: 2 x 4 #> path permissions size modification_time #> <fs::path> <fs::perms> <fs::bytes> <dttm> #> 1 src/RcppExports.cpp rw-rw-r-- 12.2K 2019-03-20 15:30:17 #> 2 src/file.cc rw-rw-r-- 10.1K 2019-03-20 15:30:17
Tabulate and display folder size.
dir_info("src", recursive = TRUE) %>% group_by(directory = path_dir(path)) %>% tally(wt = size, sort = TRUE) #> # A tibble: 53 x 2 #> directory n #> <fs::path> <fs::bytes> #> 1 src/libuv 2.46M #> 2 src/libuv/test 869.22K #> 3 src/libuv/src/win 683.14K #> 4 src/libuv/src/unix 526.87K #> 5 src/libuv/docs/src/static 332.05K #> 6 src/libuv/m4 319.95K #> 7 src/libuv/include 192.33K #> 8 src/libuv/docs/src/static/diagrams.key 191.74K #> 9 src/libuv/docs/src 163.7K #> 10 src/libuv/docs/code 112K #> # … with 43 more rows
Read a collection of files into one data frame.
# 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") iris_files #> 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 x 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 file_delete(iris_files)
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.