R Read Text File to DataFrame

R base package provides several functions to load or read a single text file (TXT) and multiple text files into R DataFrame. Text file with extension .txt is a human-readable format that is sometimes used to store scientific and analytical data. When storing data in text files the fields are usually separated by a tab delimiter.

In my previous article, I explained how to import a CSV file into Data Frame and import an Excel file into Data Frame. In this article, I will explain how to read a text file by using read.table() into Data Frame with examples? To export to Text File use wirte.table()

1. Quick Examples of Read Text File

Following are quick examples of how to read a text file to DataFrame in R.


# Quick Examples

# Read text file
df = read.table('/Users/admin/file.txt',sep='\t')

# Read multiple text files
list_files = list('/Users/admin/file.txt', '/Users/admin/file2.txt')
df = read.table(list_files,sep='\t')

# Read text file with header
df = read.table('/Users/admin/file.txt',sep='\t', header=TRUE)

# Read text file with custom columns
col_names= c('id_col','name_col','dob_col','gender_col')
df = read.table('/Users/admin/file.txt',sep='\t', header=TRUE,col.names = col_names)

# Skip first 2 rows
df = read.table('/Users/admin/file.txt',sep='\t', skip = 2)

2. Read TEXT File in R using read.table()

read.table() is a function from the R base package which is used to read text files where fields are separated by any delimiter. If you have a comma-separated CSV file use read.csv() function.

2.1 Syntax of read.table()

Following is the syntax of the read.table() function.


# Synyax of read.table()
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = default.stringsAsFactors(),
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

2.2 Read TEXT File Example

I have a text file with a tab delimiter and I will use sep='\t' argument with read.table() function to read it into DataFrame.


# Read text file
df = read.table('/Users/admin/file.txt',sep='\t')
print(df)

2.3 Read Multiple Text Files

In order to read multiple text files in R, create a list with the file names and pass it as an argument to this function.


# Read multiple text files
list_files = list('/Users/admin/file.txt', '/Users/admin/file2.txt')
df = read.table(list_files,sep='\t')
print(df)

2.4 Read File with Header

If you have a text file with a header then you have to use header=TRUE argument, Not specifying this will consider the header row as a data record.


# Read text file with header
df = read.table('/Users/admin/file.txt',sep='\t', header=TRUE)
print(df)

2.5 Assign new Columns

When you don't want the column names from the file header and wanted to use your own column names use col.names argument which accepts a Vector, use c() to create a Vector with the column names you desire.


# Read text file with custom column names
col_names= c('id_col','name_col','dob_col','gender_col')
df = read.table('/Users/admin/file.txt',sep='\t', header=TRUE,col.names = col_names)
print(df)

Alternatively, you can also rename columns in DataFrame right after creating the data frame.

2.6 Skip Rows of a TXT file

Sometimes you may need to skip a few rows while reading the text file to R DataFrame. You can do this by using the skip argument.


# Skip first 2 rows
df = read.table('/Users/admin/file.txt',sep='\t', skip = 2)
df

3. Read TEXT File using read.delim()

You can also use read.delim() to read a text file into DataFrame.


# using read.delim()
df = read.delim('/Users/admin/file.txt',header = TRUE, sep = "\t")
df

4. Use read_tsv() to Read Tab Delimiter Text File

If you are working with larger files, you should use the read_tsv() function from readr package. Since readr is a third-party library, you'll need to install it first with install.packages('readr'). After the installation is complete, load the readr library using library("readr") to utilize the read_csv() function.


# Load readr
library("readr")

# Read CSV into DataFrame
read_csv = read_tsv('/Users/admin/file.txt')
print(read_csv)

5. Conclusion

In this article, you have learned how to read or import data from a single text file (txt) and multiple text files into a DataFrame by using read.table() and read.delim() and read_tsv() from the readr package with examples.

References

https://readr.tidyverse.org/