fs

fs provides a cross-platform, uniform interface to file system operations. It is built on top of the libuv C library. The JavaScript community uses libuv extensively, as it is the back-end component of nodejs. libuv is therefore rigorously tested across a diverse set of systems. The name, and some of the interface, is partially inspired by Rust’s fs module.

Installation

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

install.packages("fs")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("r-lib/fs")

Comparison vs base equivalents

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

• 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 and typically return error codes which need to be manually checked.

• 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.

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

• 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()).

Usage

fs functions are divided into four main categories: manipulating paths (path_), files (file_), directories (dir_), and links (link_). Directories and links are special types of files, so file_ functions will generally also work when applied to a directory or link.

library(fs)

# list files in the current directory
dir_ls()
#> DESCRIPTION  LICENSE.md   NAMESPACE    R            README.Rmd   
#> README.md    _pkgdown.yml appveyor.yml codecov.yml  doc          
#> docs         fs.Rproj     man          man-roxygen  script.R     
#> src          tests        tools

# 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, although because it is a minimal-dependency infrastructure package it doesn’t provide the pipe itself. You will need to attach magrittr or similar.

library(magrittr)

paths <- file_temp() %>%
  dir_create() %>%
  path(letters[1:5]) %>%
  file_create()
paths
#> /tmp/filedd464dbb3467/a /tmp/filedd464dbb3467/b /tmp/filedd464dbb3467/c 
#> /tmp/filedd464dbb3467/d /tmp/filedd464dbb3467/e

paths %>% file_delete()

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

suppressMessages(
  library(tidyverse))

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: 9 x 4
#>   path                permissions        size modification_time  
#>   <fs::path>          <fs::perms> <fs::bytes> <dttm>             
#> 1 src/RcppExports.o   rw-r--r--        641.5K 2018-01-11 09:53:17
#> 2 src/dir.o           rw-r--r--        434.9K 2018-01-11 09:53:17
#> 3 src/fs.so           rwxr-xr-x        415.3K 2018-01-11 09:53:36
#> 4 src/id.o            rw-r--r--        388.5K 2018-01-11 09:53:17
#> 5 src/file.o          rw-r--r--        309.8K 2018-01-11 09:53:17
#> 6 src/path.o          rw-r--r--        244.8K 2018-01-11 09:53:17
#> 7 src/link.o          rw-r--r--        219.6K 2018-01-11 09:53:17
#> 8 src/error.o         rw-r--r--         17.3K 2018-01-11 09:53:17
#> 9 src/RcppExports.cpp rw-r--r--         10.5K 2018-01-10 22:10:06

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                                          2.65M
#>  2 src/libuv                                    2.53M
#>  3 src/libuv/autom4te.cache                     2.13M
#>  4 src/libuv/src/unix                           1.08M
#>  5 src/libuv/test                             865.36K
#>  6 src/libuv/src/win                          683.14K
#>  7 src/libuv/m4                               334.61K
#>  8 src/libuv/docs/src/static                  328.32K
#>  9 src/libuv/include                          192.33K
#> 10 src/libuv/docs/src/static/diagrams.key     184.04K
#> # ... with 43 more rows

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")
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             5.10        3.50         1.40       0.200
#> 2 setosa.tsv             4.90        3.00         1.40       0.200
#> 3 versicolor.tsv         7.00        3.20         4.70       1.40 
#> 4 versicolor.tsv         6.40        3.20         4.50       1.50 
#> 5 virginica.tsv          6.30        3.30         6.00       2.50 
#> 6 virginica.tsv          5.80        2.70         5.10       1.90

file_delete(iris_files)