Скачать или смотреть How to Convert Character Strings to Executable Code in R

Discover how to convert character strings into executable R code efficiently and safely without using eval(parse()).
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This video is based on the question https://stackoverflow.com/q/64499445/ asked by the user 'Mel' ( https://stackoverflow.com/u/7685062/ ) and on the answer https://stackoverflow.com/a/64499808/ provided by the user 'Allan Cameron' ( https://stackoverflow.com/u/12500315/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Convert Character Strings to Executable Code in R: A Practical Guide

Converting character strings to executable code is a common challenge in R programming, especially when manipulating data frames and applying functions dynamically. If you've ever found yourself writing complex scripts to parse and execute code contained in strings, you're not alone. Today, we’ll explore a better method to accomplish this safely and efficiently.

The Problem at Hand

Imagine you have a dataframe and various numeric columns from which you want to create new columns that reflect changes relative to baseline values. You took the right approach of constructing a character string that contains your desired operations, but when executing the string with eval(parse()), you encounter difficulties.

The Data Frame

Consider the following example data frame created with random values:

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You aim to create new columns that show the differences between each numeric column and its corresponding baseline column (e.g., var1_change = var1 - var1_baseline). However, relying solely on eval(parse(...)) has proven problematic.

The Solution: A Better Approach Without Eval and Parse

While eval(parse()) can technically achieve your goal, it's generally discouraged due to potential security issues and inefficiency. Instead, we’ll utilize base R and function application techniques to directly compute the required values by leveraging mapply(). Here’s how to do it step by step:

Step 1: Identify Baseline and Non-baseline Columns

First, extract the names of the columns that end with _baseline and construct corresponding names for the non-baseline columns.

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Step 2: Calculate Differences

Next, we can calculate the desired change values in a single line using mapply() function, which allows vectorized operations across two sets of data:

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

Step 3: Alternatively Utilize a Single Pipe

If you prefer to maintain everything in a single pipeline expression, here’s a compact version using dplyr and pipes:

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

Conclusion

By employing these techniques, you can efficiently transform character strings into executable code in R without relying on the unsafe eval(parse()) method. This not only helps maintain efficiency in your code but also enhances its overall safety and readability.

Remember that while dynamic execution of code can be powerful, always consider whether there are simpler, more straightforward methods to achieve your data manipulation goals. Happy coding!