list.files() in R: list files in a directory

Learn list.files() in r: list files in a directory with this comprehensive R tutorial. Includes practical examples and code snippets.

Introduction

The list.files() function in R allows you to programmatically retrieve file names from directories on your computer. This is essential for automating data workflows, batch processing multiple files, or simply exploring what files exist in a specific location.

Getting Started

library(tidyverse)

Example 1: Basic Usage

The Problem

You need to see what files exist in your current working directory and understand the basic syntax of list.files().

Step 1: List all files in current directory

Check what’s in your current working directory.

# Get all files in current directory
current_files <- list.files()
print(current_files)

This returns a character vector containing all file names in your working directory.

Step 2: List files in a specific directory

Target a specific folder path instead of the current directory.

# List files in a specific directory
# Replace with an actual path on your system
folder_files <- list.files(path = "data/")
head(folder_files)

The path argument lets you specify exactly which directory to examine.

Step 3: Include full file paths

Get complete file paths instead of just file names.

# Get full paths instead of just names
full_paths <- list.files(path = "data/", full.names = TRUE)
head(full_paths, 3)

Setting full.names = TRUE returns complete file paths, which is useful for reading files programmatically.

Example 2: Practical Application

The Problem

You have a folder containing multiple CSV files with penguin data from different years, and you want to read all of them into R automatically. This is a common scenario when dealing with data stored across multiple files.

Step 1: Filter for specific file types

Find only CSV files in your directory using pattern matching.

# Find only CSV files
csv_files <- list.files(
  path = "data/", 
  pattern = "\\.csv$", 
  full.names = TRUE
)
print(csv_files)

The pattern argument uses regular expressions - \\.csv$ means “files ending with .csv”.

Step 2: Include subdirectories in search

Search recursively through all subdirectories for CSV files.

# Search recursively through subdirectories
all_csvs <- list.files(
  path = "data/", 
  pattern = "\\.csv$", 
  recursive = TRUE,
  full.names = TRUE
)
print(all_csvs)

The recursive = TRUE option searches through all nested folders within the specified directory.

Step 3: Read multiple files automatically

Use the file list to read all CSV files into a single data frame.

# Read all CSV files and combine them
combined_data <- all_csvs |>
  map_dfr(read_csv, .id = "file_source") |>
  mutate(file_name = basename(all_csvs[file_source]))

glimpse(combined_data)

This creates a master dataset with all files combined, including a column tracking which file each row came from.

Step 4: Filter files by date or name pattern

Find files with specific naming conventions or date patterns.

# Find files with "penguin" in the name from 2023
recent_penguin_files <- list.files(
  path = "data/",
  pattern = "penguin.*2023.*\\.csv$",
  full.names = TRUE
)
print(recent_penguin_files)

This pattern matches files containing “penguin” followed by “2023” and ending in “.csv”.

Step 5: Get additional file information

Retrieve file details like size and modification date.

# Get file info along with names
file_details <- list.files(
  path = "data/", 
  pattern = "\\.csv$",
  full.names = TRUE
) |>
  map_dfr(~ data.frame(
    file = .x,
    size = file.size(.x),
    modified = file.mtime(.x)
  ))

head(file_details)

This creates a data frame with file names, sizes in bytes, and last modification times.

Summary

• list.files() retrieves file names from directories programmatically
• Use path to specify directory location and pattern for file filtering with regular expressions
  
• Set full.names = TRUE to get complete file paths for reading files
• Enable recursive = TRUE to search through subdirectories automatically

• Combine with map_dfr() to efficiently read and merge multiple files into one dataset

Related Posts

• List of Built in Datasets in R
• How to Convert a List to a dataframe in R
• ls() in R: list objects/variables in your environment
• How to Split a Dataframe into a list of Dataframes by groups in R
• How to convert a list to a dataframe