This R package supports the use of standardized folder names in R projects. The idea is to provide some functions to allow you to avoid using hardcoded paths and setwd() in your R scripts.

Instead, you can use variables like folders$data to refer to folder paths. These paths can be standardized between projects. The folders can be created for you under the parent folder of your R project.

Using the defaults, or some other standardized list of folder names, all of your projects can have the same general folder structure. This can help you write cleaner, more portable, and more reproducible code.

What does this package do?

Without this package, you could include some code like this in your scripts:

# Create some standard folders in my project
folders <- list(data = "data", figures = "figures", results = "results")
folders <- lapply(folders, here)
result <- lapply(folders, dir.create, showWarnings = FALSE, recursive = TRUE)

# Save some data to a file in the "data" folder
write.csv(iris, file = here(folders$data, "iris.csv"))

# Cleanup unused folders
result <- lapply(folders, function(x) {
  if (length(dir(x)) == 0) unlink(x, recursive = TRUE)

# Confirm that the data file still exists
file.exists(here(folders$data, "iris.csv"))

There is no persistence of the standard folder names, however, and if you needed to use an alternate folder path, you would need to modify this script and any other that used that alternate path.

However, using folders, you can specify a project-wide configuration file which will store these standard folder names. Any customization can be made to that file, and the other scripts in your project can use that file with no extra changes needed to them. Collaborators can have their own custom folder paths without modifying the shared scripts and affecting the other collaborators. Plus there is less code needed in each script to make use of these standard or customized folder paths, as the folders package takes care of the details.

Default Folders

The package defaults provide “code”, “conf”, “data”, “doc”, “figures” and “results” folders. You can specify alternatives in a YAML configuration file, which this package will read and use instead. See “Configuration file” below for more details.

You will note there is a “code” folder. If your scripts are in the “code” folder, your code will still be able to find the other folders, thanks to the here package.

RStudio Projects

This package is intended to be used with RStudio Projects.

A benefit of using RStudio Projects is, once you open the project in RStudio, you will be placed in the parent folder of your project (aka. “project root”). All of your work in the project will be relative to that location, especially if your project only uses files and subfolders within that parent folder. This the most portable way to work. Further, if you are working with a git repository, you will most likely want to clone this repository into an RStudio Project.

Other Supported Environments

This package will also work outside of RStudio Projects. For example, if you are working in a folder tracked by git, then the top level of the git repository will be identified as the “project root” folder. This behavior is determined by the here package.

If you are neither working in an RStudio project, nor in a folder tracked by a version control system (git or Subversion), nor an R package development folder, then the current working directory at the time the here package was loaded will be treated as the “project root” folder.

Or you can force a folder to be the “project root” with a .here file. You can create one with the here::set_here() function. See the here package documentation for more information. However, if your goal is to write more reproducible code and follow best practices, you should really ask yourself why you are not using RStudio Projects or version control.


You can install the stable version from CRAN with:


You can install the development version from GitHub with:

# install.packages("devtools")

Or, if you prefer using pacman:

if (!requireNamespace("pacman", quietly = TRUE)) install.packages('pacman')


When you install this package, the following dependencies should be installed for you: config, here, yaml.

You will need to load the here package with your scripts to make the most use of the folders package, as seen in the Basic Usage examples below.

Basic Usage

The following code chunk can be used at the beginning of your scripts to make use of standardized folders in your projects.

# Load packages, installing as needed
if (!requireNamespace("pacman", quietly = TRUE)) install.packages('pacman')
pacman::p_load(here, folders)

# Get the list of standard folders and create any folders which are missing
conf_file <- here('conf', 'folders.yml')
folders <- get_folders(conf_file)
result <- create_folders(folders)

Then, later in your scripts, you can refer to folders like this:

## [1] TRUE

Or you can add to the standard folder paths like this:

file_path <- here(folders$data, "data.csv")

Basic Usage Scenario

Here is an example of a script which will initialize the folders and then write a data file to the folders$data folder. Aside from setting the path to the configuration file, there are no hardcoded paths and there is no setwd().

# Load packages, installing as needed
if (!requireNamespace("pacman", quietly = TRUE)) install.packages('pacman')
pacman::p_load(here, folders)

# Get the list of standard folders and create any folders which are missing
conf_file <- here('conf', 'folders.yml')
folders <- get_folders(conf_file)
result <- create_folders(folders)

# Check to see that the data folder has been created
## [1] TRUE
# Create a dataset to use for writing a CSV file to the data folder
df <- data.frame(x = letters[1:3], y = 1:3)

# Confirm that the CSV file does not yet exist
file_path <- here(folders$data, "data.csv")
## [1] FALSE
# Write the CSV file
write.csv(df, file_path, row.names = FALSE)

# Verify that the file was written
## [1] TRUE
# Cleanup unused (empty) folders (Optional, as you may prefer to keep them)
result <- cleanup_folders(folders)

# Verify that the data folder and CSV file still exist after cleanup
## [1] TRUE
# Verify that the configuration file still exists after cleanup
## [1] TRUE

Working with subfolders

You can refer to subfolders relative to the paths in your folders list using here(). For example, if you had a folder called “raw” under your data folder, just refer to that folder with here(folders$data, "raw"):

conf_file <- here('conf', 'folders.yml')
folders <- get_folders(conf_file)
raw_df <- here(folders$data, "raw", "file.csv")

If you want to create a subfolder hierarchy under all of your main folders, you can use lapply() or purrr::map() to create that hierarchy. For example, we can create a “phase” folder under each folder in folders and then a “01” folder under each “phase” folder:

conf_file <- here('conf', 'folders.yml')
folders <- lapply(get_folders(conf_file), here, "phase", "01")
res <- create_folders(folders) 

You can place that near the top of each of your scripts, adjusting for the project phase the script is used for, then you can then use folders$data to refer to a path like data/phase/01 within the parent folder. This way, your scripts can always refer to the appropriate data, results, etc., folder for that project phase using the same variables, e.g., folders$data, folders$results, etc.

df <- read.csv(here(folders$data, "data.csv"))

If you want to remove empty folders recursively, you can include this code at the end of your script:

# Cleanup empty folders recursively
dir_lst <- sort(list.dirs(unlist(lapply(folders, here))), decreasing = TRUE)
result <- sapply(dir_lst, cleanup_folders, conf_file = conf_file)

Or, if you are using the latest development version of the package from GitHub, then it’s as simple as:

# Cleanup empty folders recursively (using development version of package)
result <- cleanup_folders(folders, recursive = TRUE)

Configuration file

The configuration file, if not already present, will be written by get_folders() to a YAML file with a path and filename that you provide. Usually this would be named something like folders.yml, as in the examples above, and usually you will want this file stored in either the parent folder or the “conf” subfolder of your R project. This file will be read by config::get() on subsequent executions of get_folders(). This behavior can be modified by function parameters.

The default configuration file looks like:

  code: code
  conf: conf
  data: data
  doc: doc
  figures: figures
  results: results

Once this file has been created, you can edit it to modify the default folder paths. However, we advise you to stick to the defaults to maintain maximum consistency between your projects. If you do wish to edit it, you can do so with any text editor or with R as shown below.

# Load packages, installing as needed
if (!requireNamespace("pacman", quietly = TRUE)) install.packages('pacman')
pacman::p_load(here, yaml, folders)

# Get the list of standard folders, creating the configuration file if missing
conf_file <- here('conf', 'folders.yml')
folders <- get_folders(conf_file)

# Replace a default with a custom folder path
folders$data <- "data_folder"

# Edit the default configuration file to save the modification
write_yaml(list(default = folders), file = conf_file)

Handling platform-dependent paths

You can add other configuration names besides “default”. For example, if you had a path that would be operating system dependent, you could edit your configuration file like this (abbreviated here to show only the data path):

  data: data
  data: //server/path/to/data
  data: /path/to/data
  data: /Volumes/path/to/data

And then you can read in the appropriate paths for the system you are using:

conf_file <- here('conf', 'folders.yml')
folders <- get_folders(conf_file, conf_name =[['sysname']])
data_folder <- folders$data

… so that your script can still be platform independent even though the data path is not. If your specified conf_name does not exist in the configuration file, then the defaults are used instead.