Welcome to the final lesson in our course focused on working with different files as data sources. In this lesson, you will learn how to write table data to a CSV file using R's built-in write.csv() function. Throughout this course, you have been introduced to various aspects of handling table data. We've covered parsing tables from text and CSV files and writing data to text files. Now, we'll bring our knowledge full circle by exploring how to export data to a CSV file, a common data format that is widely used for data exchange between different applications and platforms.

CSV (Comma-Separated Values) files allow for easy storage and sharing of structured data in a tabular format. R's write.csv() function simplifies working with CSV files, letting you store data efficiently. By the end of this lesson, you should be comfortable using the write.csv() function to write data to CSV files.

Before we can write data to a CSV file, we need to prepare it. You should organize your data in a data frame, where each column represents a variable and each row represents an observation.

Here's an example:

R
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1data <- data.frame(
2  Name = c("John", "Alice", "Bob"),
3  Age = c(28, 34, 23),
4  Occupation = c("Engineer", "Doctor", "Artist")
5)

To write data to a CSV file in R, use the write.csv() function directly. You do not need context managers as used in other programming languages. You can specify the output file path and then use the write.csv() function to export your data frame to a CSV file. The function allows you to specify whether or not to include row names in the output CSV, which is often preferable for cleaner data representation:

R
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1output_file_path <- "output.csv"
2write.csv(data, file = output_file_path, row.names = FALSE)
3cat(sprintf("Data written to %s as a CSV.\n", output_file_path))
4
5# Output:
6# Data written to output.csv as a CSV.

While the default delimiter for write.csv() is a comma, you can use write.table() in R to specify a different delimiter if necessary. This can be important depending on how your data consumer expects the CSV format.

Here's how you can specify a custom delimiter using write.table():

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1write.table(data, file = output_file_path, sep = ";", row.names = FALSE, col.names = TRUE)

In this example, a semicolon (;) is used as the delimiter instead of a comma.

Finally, let's understand the row.names and col.names parameters used in the function:

• row.names: Specifies whether to include the row names of the data frame in the output CSV file. By setting it to FALSE, you exclude row names from the CSV, which often results in a cleaner and simpler file structure. Including row names could otherwise add an unwanted initial column to your data.

• col.names: Indicates whether to include the column names in the output CSV file. Setting it to TRUE ensures that the header row with column names is present at the top of the CSV file, which is typically desirable for readability and understanding the data structure.

Let's see the complete example and its output.

R
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1# Creating a data frame
2data <- data.frame(
3  Name = c("John", "Alice", "Bob"),
4  Age = c(28, 34, 23),
5  Occupation = c("Engineer", "Doctor", "Artist")
6)
7
8# Writing the data to a CSV file
9output_file_path <- "output.csv"
10write.csv(data, file = output_file_path, row.names = FALSE)
11cat(sprintf("Data written to %s as a CSV.\n", output_file_path))
12
13# Output:
14# Data written to output.csv as a CSV.

By running this code, you will have generated a CSV file named output.csv in your current directory, containing your table data.

Congratulations on completing the course! You are now equipped with the skills to read, organize, and write table data across both text and CSV files using R. In this lesson, you learned how to utilize the write.csv() function to write structured data to a CSV file effectively, ensuring that your data is both accessible and shareable in a widely accepted format.

As you move forward, practice these skills with varied datasets to reinforce your understanding. Experiment with writing different types of structured data and observe how changes impact your CSV output. It's been a pleasure guiding you through this learning journey, and I'm confident these skills will significantly aid your data-handling capabilities in real-world applications. Happy coding!