Скачать или смотреть How to Programmatically Read CSV Files into R Objects from a Character Vector

Learn how to efficiently read CSV files in R using a character vector and create objects in your global environment.
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This video is based on the question https://stackoverflow.com/q/67468634/ asked by the user 's_davidsson' ( https://stackoverflow.com/u/14838518/ ) and on the answer https://stackoverflow.com/a/67501376/ provided by the user 'Anoushiravan R' ( https://stackoverflow.com/u/14314520/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Have you ever found yourself with a list of CSV filenames stored in a character vector in R and wondered how to efficiently read them into your environment as objects? This is a common task for data analysts and programmers working with R, particularly in data science projects. In this guide, we will explore how to read in files programmatically using R and store them as separate objects seamlessly.

Understanding the Problem

You have a character vector called filenames, which contains CSV file names, such as:

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These files exist in your working directory, and you want to read them in as data frames and create objects for each in your global environment. Thankfully, there is a straightforward solution using R's powerful functions.

Step-by-Step Solution

Let’s break down how to achieve this using a combination of R functions, including lapply, list.files, and list2env.

1. Create a Character Vector of File Names

To start, you'll want to make sure you have a character vector containing the names of the CSV files you wish to read. You can either create one manually as shown before or use the list.files function to automatically grab files with a .csv extension in your working directory.

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2. Read the CSV Files

Next, we will use the lapply function to apply the read.csv operation over each filename found in the vector tmp. This step will result in a list where each element represents a data frame corresponding to each CSV file.

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Breaking Down the Code:

lapply(tmp, read.csv): Reads each CSV file defined in tmp.

setNames(gsub(".csv", "", tmp)): Renames the list elements by stripping the .csv extension so that the resulting list has meaningful names.

3. Split the List

To make the data frames easily distinguishable, we can split the list based on the names of the elements. This prepares them for storage as individual objects.

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4. Save as Objects in the Global Environment

Finally, use list2env to save the data frames from the list into your global environment, making them accessible as individual objects.

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Conclusion

By following these steps, you can programmatically read in CSV files and create corresponding objects in your R global environment. This technique not only saves time but also allows for smoother workflows when handling multiple files.

For a more thorough dive into R and its file-handling capabilities, consider visiting additional resources that cover data manipulation and file management in R.

Now that you have a systematic approach to handle CSV files using R, you can focus on analyzing your data rather than spending time managing file imports!