R Read Text File to DataFrame

In today’s data-driven world, collecting data from multiple sources and turning it into a structured manner is a critical responsibility for data analysts and scientists. Text files are a prominent source of data, as they frequently include useful information in plain text format. To be used successfully, this data must be translated into a structured format, such as a DataFrame, which is a two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes.

Reading text files in R

Reading text files in R Programming Language is the process of taking data from plain text files and transforming it into a structured format that is easy to edit and analyze. Here are the types of text files available.

1. CSV (Comma-Separated Values)

• CSV files use commas to separate values in each row.
• Example: data.csv

2. TSV (Tab-Separated Values):

• TSV files use tabs as separators between values.
• Example: data.tsv

3. Space-Separated Values:

• Space-separated files use spaces to separate values in each row.
• Example: data.txt

4. Fixed-Width Files:

• Fixed-width files have columns aligned at specific positions, with no delimiters.
• Example: data.dat

Common Functions for Reading Text Files

There are three main methods :

1. Using read.csv() function
2. Using read.delim() function
3. Using read.table() function

Let’s take an example that you have a data frame df with student information loaded into a csv file.

The data contains three columns: “Name”, “Roll No”, and “Marks”.

1. Using read.csv() function

CSV files are commonly used to store tabular data. Here’s how to read CSV files into a DataFrame using R:

• Use the read.csv() method with the proper options, such as the file location and delimiter.
• Assign the results to a DataFrame variable.

For import your dataset you can take any dataset and replace the path in code.

R

# Read the CSV file into a data frame
df <- read.csv('C:\\Users\\GFG19565\\Downloads\\heart.csv')
# Print the contents of the data frame
head(df)

Output:

  age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
1  52   1  0      125  212   0       1     168     0     1.0     2  2    3      0
2  53   1  0      140  203   1       0     155     1     3.1     0  0    3      0
3  70   1  0      145  174   0       1     125     1     2.6     0  0    3      0
4  61   1  0      148  203   0       1     161     0     0.0     2  1    3      0
5  62   0  0      138  294   1       1     106     0     1.9     1  3    2      0
6  58   0  0      100  248   0       0     122     0     1.0     1  0    2      1

2.Using read.delim() function

The read.delim() method reads data from the file “data.tsv”. Values in TSV files are separated by tabs, and this function defaults to using the tab (\t) delimiter.

R

# Read the tsv file into a data frame
df <- read.delim('C:\\Users\\GFG19565\\Downloads\\heart.csv')
# Print the contents of the data frame
head(df)

Output:

  age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
1  52   1  0      125  212   0       1     168     0     1.0     2  2    3      0
2  53   1  0      140  203   1       0     155     1     3.1     0  0    3      0
3  70   1  0      145  174   0       1     125     1     2.6     0  0    3      0
4  61   1  0      148  203   0       1     161     0     0.0     2  1    3      0
5  62   0  0      138  294   1       1     106     0     1.9     1  3    2      0
6  58   0  0      100  248   0       0     122     0     1.0     1  0    2      1

3. Using read.table() function

Tabular files store data in rows and columns. How to read tabular files into a DataFrame in R:

• Use the read.table() function with appropriate parameters
• Copy the file path from the Students.txt file and paste it into the df data frame and then print the contents of the data frame.

R

# Read data from the text file
df <- read.table('C:\\Users\\GFG19565\\Downloads\\heart.csv', sep='\t', header=TRUE)

# Display the data frame
head(df)

Output:

  age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
1  52   1  0      125  212   0       1     168     0     1.0     2  2    3      0
2  53   1  0      140  203   1       0     155     1     3.1     0  0    3      0
3  70   1  0      145  174   0       1     125     1     2.6     0  0    3      0
4  61   1  0      148  203   0       1     161     0     0.0     2  1    3      0
5  62   0  0      138  294   1       1     106     0     1.9     1  3    2      0
6  58   0  0      100  248   0       0     122     0     1.0     1  0    2      1

Customizing the Reading Process

1. sep: Sets the separating character for reading.table().
2. header: Determines if the file has a header row.
3. na.strings: Specifies which strings should be treated as missing values.
4. quote: Sets the quoting character for values that contain separators.
5. Fill: Determines whether missing values should be filled with NA.

Handling Variations in Text Files

1. Missing Values

• Use the na.strings argument to define which strings should be handled as missing values.
• Example: read.csv(“data.csv”, na.strings = c(“”, “NA”).

2. Different Separators

• Specify the separator with the sep option in read.table().
• Example: read.table(“data.txt”, sep = “”)

3 .Inconsistent Data

• Use the quote argument to define the quoting character for values that contain separators.
• Example: read.csv(“data.csv”, quote = ‘”‘).

Conclusion

Reading text files into a DataFrame in R is an important step in the data analysis process. Analysts can efficiently extract, modify, and analyse data from a variety of sources using R functions and packages. Understanding various text file reading methods and proper data management procedures guarantees that R analysis findings are reliable and meaningful.