Скачать или смотреть How to Create a Custom Function in R to Analyze Data Frames Efficiently

Discover how to create a custom function in R that adds a new column to your data frame, using specific criteria to identify screening needs. Perfect for both beginners and experienced users.
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This video is based on the question https://stackoverflow.com/q/68459306/ asked by the user 'Tseng' ( https://stackoverflow.com/u/11695673/ ) and on the answer https://stackoverflow.com/a/68462660/ provided by the user 'Ben' ( https://stackoverflow.com/u/3460670/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding the Need for Custom Functions in R

When working with data frames in R, there are often scenarios where you might need to categorize or classify your data based on existing columns. This becomes especially useful when you want to derive insights or create new data that depends on multiple other factors.

In today’s guide, we will tackle a situation where we need to add a new column called Action to a given data frame. This column will indicate whether individuals require a screening test based on the number of days between their screening and enrollment dates.

Problem Overview

You have a data frame with four key columns:

Name

screen_date

enroll_date

screen2enroll_days

You want to dynamically create a function that reads data from one or more of these columns and generates a new column (Action). This column will categorize individuals based on their screen2enroll_days value into:

Up-to-date

Requires Screening

No Screening Required

Here is a sample of the initial data frame:

Namescreen_dateenroll_datescreen2enroll_daysenrollment_typeJohn2020-08-202020-08-0114TypeXMike2020-08-202020-08-0114TypeYSam2020-10-202020-08-0565TypeYDan2020-11-052020-08-0590TypeXCreating the Custom Function

Step-by-Step Breakdown

Define the Function: Create a function that takes in the data frame and other necessary parameters. You'll want to ensure that you can dynamically reference the column names in the data frame.

Utilize the mutate() Function: Use mutate() from the dplyr package to add a new column. The case_when() function will help categorize the values of the Action column based on the logical conditions you establish.

Correct Usage of Arguments: Make sure to use proper naming for the arguments and ensure they are passed correctly when calling the function.

Example Code

Here’s how the function looks, along with how to call it:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

{{new_col}}: This is a convenient way to reference a new column name directly in the function.

case_when(): This function allows for multiple conditions. Each condition checks the screen2enroll_days value and assigns the appropriate action.

Dynamic Column Reference: The {{my_col}} and {{my_col2}} references allow the function to take any column specified when calling the function, providing flexibility.

Conclusion

Creating custom functions in R for manipulating data frames can significantly simplify your workflow when analyzing data. The above function allows for dynamic categorization based on various conditions, making your data analysis more efficient and scalable.

Now, you have the tools to not only categorize your data effectively but also to adjust and extend the function as your data analysis needs grow. Happy coding in R!