How to Use read.delim in R?

In this article, we will learn how to use the read.delim() in the R Programming Language.

Example 1: Using read.delim() function to read a space-separated text file

The read.delim() function is used to read delimited text files in the R Language. It doesn’t need any external package to work. This function converts a delimited text file into a data frame and can be used to read a variety of space-separated files for example CSV. 

Syntax:

read.delim( file, header)

where:

• file: determines the file name to be read with full path.
• header: A Boolean that determines whether the first line represents the header of the table. Default is TRUE.

Under this example, we are reading a data frame from a space-separated text file using the read.delim() function in R language.

Text file in use:

Program:

R

# read the space separated dataframe
data_frame <- read.delim('sample.txt')

# view data frame
data_frame

Output:

       group  y           x
1  category1 55 -0.15703480
2  category1 63  0.63781188
3  category1 62 -1.59689179
4  category1 59 -0.61527367
5  category1 64  0.80799947
6  category1 73  1.03513951
7  category1 56  0.67577537
8  category1 66 -0.37485984
9  category1 73  0.14448351
10 category1 68 -0.53013492
11 category1 63  0.57979608
12 category1 74 -0.08396805
13 category1 67 -0.63099142
14 category1 50 -0.49751923

Example 2: Using read.delim() function to read manual symbol separated text file

To read a text file separated by a manual symbol, we use the sep parameter to determine the symbol that separates the data in the text file. In this way, we can read comma-separated-values, tab-separated values, etc.

Syntax:

read.delim( file, sep)

where:

• file: determines the file name to be read with full path.
• sep: determines table delimiter. Default is a tab (\t).

In this example, we are reading a data frame from a comma-separated text file using the read.delim() function with the sep parameter in the R language. 

Text file in use:

Program:

R

# read the tab separated dataframe
data_frame <- read.delim('my_data.txt', sep=',')

# view data frame
data_frame

Output:

       group  y           x
1  category1 63  0.95195245
2  category1 77 -1.68432491
3  category1 72  0.03062164
4  category1 67 -1.56885679
5  category1 69 -0.35835908
6  category1 53 -0.87003090
7  category1 73 -0.88877644
8  category1 64  0.67040206
9  category1 66 -0.20397715
10 category1 58 -0.29472917
11 category1 68 -1.47210730
12 category1 68 -1.40288930
13 category1 65 -0.14653898
14 category1 70  0.76216057
15 category1 71 -0.21718205
16 category1 64  0.72430687
17 category1 70 -0.24907560
18 category1 60 -1.24296149