Reading data from ASCII files into R is a common task in data analysis and statistical computing. ASCII files, known for their simplicity and wide compatibility, often contain text data that can be easily processed in R. Here we read multiple ASCII files into R Programming Language.

What are ASCII Files?

ASCII (American Standard Code for Information Interchange) files are plain text files that store data in a human-readable format. Each character in an ASCII file is represented by a unique code. ASCII files can come in various formats, such as .txt, .csv, and .tsv, and are commonly used to store various types of data including text, tabular data, logs, and configuration settings.

• .txt: Plain text files that can contain any type of text data.
• .csv: Comma-separated values files where data is organized in rows and columns, separated by commas.
• .tsv: Tab-separated values files where data is organized in rows and columns, separated by tabs.

Preparing the Files

Before reading ASCII files into R, it’s crucial to ensure that the files are properly prepared.

• Ensure all files follow the same structure if they are supposed to be combined.
• Use consistent and descriptive file names for easy identification.
• Store all files in a single directory for easy access.

Read Many ASCII Files into R

Implement of Read Many ASCII Files into R

Now we will discuss step by step implementation of How to Read Many ASCII Files into R Programming Language.

Step 1: Installing and Loading Packages

First we will install and load the required package.

• readr: Provides fast and friendly functions to read rectangular data.
• dplyr: A grammar of data manipulation, providing a consistent set of verbs to help us to solve the most common data manipulation challenges.
• purrr: Enhances R’s functional programming capabilities, making it easy to work with lists and functions.

# Install packages
install.packages(c("readr", "dplyr", "purrr"))

# Load packages
library(readr)
library(dplyr)
library(purrr)

Step 2: Reading a Single ASCII File

To read a single ASCII file, we can use functions like read_delim() from the readr package.

# Read a single ASCII file
file_path <- "C:/Users/Tonmoy/Downloads/ASCII/tag.txt"
data <- read_delim(file_path, delim = "\t", col_names = FALSE)
print(data)

Output:

# A tibble: 1 × 1
  X1               
  <chr>            
1 #GfgKarloHoJayega

Step 3: Reading and Combined Multiple ASCII Files

Now we will read and Combined Multiple ASCII Files in R.

• To read multiple ASCII files, you can use a loop or the map() function from the purrr package.
• Once the files are read into R, they can be combined into a single data frame for further analysis. This can be done using the bind_rows() function from the dplyr package
• Finally, we combine all the contents into a data frame combined_data and display it.

# Define the directory containing the ASCII files
directory_path <- "C:/Users/Tonmoy/Downloads/ASCII/"

# Get a list of all .txt files in the directory
file_list <- list.files(directory_path, pattern = "\\.txt$", full.names = TRUE)

# Read each file into a list of data frames
data_list <- map(file_list, ~ read_delim(.x, delim = "\t", col_names = FALSE))

# Combine all data frames into one
combined_data <- bind_rows(data_list)

# Display the combined data
print(combined_data)

Output:

# A tibble: 2 × 1
  X1                           
  <chr>                        
1 #GfgKarloHoJayega            
2 Hi , Welcome to GeeksforGeeks

• Define the directory path: Specify where the ASCII files are located.
• List all .txt files: Generate a list of all .txt files in the directory.
• Read files into data frames: Read each file into a data frame and store them in a list.
• Combine data frames: Merge all data frames into a single data frame.
• Display combined data: Print the combined data frame to the console.

This workflow is efficient for handling multiple files and combining their data for further analysis.

Conclusion

Reading multiple ASCII files into R is a straightforward process that can be efficiently managed with the right functions and methods. By preparing the files correctly and using R’s powerful data manipulation capabilities, we can easily read, combine, and analyze data from numerous ASCII files.