Solving the group_by Issue with the infer Package in R for Bootstrapping Statistics

Описание: Learn how to effectively use the `infer` package in R to generate confidence intervals through bootstrapping, even when facing `group_by` challenges.
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This video is based on the question https://stackoverflow.com/q/68429173/ asked by the user 'hachiko' ( https://stackoverflow.com/u/7147717/ ) and on the answer https://stackoverflow.com/a/68429628/ provided by the user 'Ronak Shah' ( https://stackoverflow.com/u/3962914/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Mastering Bootstrapping with the infer Package in R

Bootstrapping is a powerful statistical technique used to estimate the distribution of a statistic (like the mean) by resampling with replacement from the data. However, if you're working with R and the infer package, you might encounter some challenges when trying to use the group_by function effectively. This post addresses a specific issue when performing bootstrapping on grouped data frames and offers a solution that ensures you're able to generate accurate confidence intervals seamlessly.

What's the Problem?

In the scenario presented, the user attempted to group a dataset (in this case, the mtcars dataset) by a categorical variable and then perform bootstrapping to calculate confidence intervals for different measurements (e.g., weight, horsepower, etc.). However, despite using the group_by function, they found that only a single summary row was returned instead of separate results for each group. This led to confusion over whether the infer package was functioning correctly with grouped data.

Investigating the Issue

To understand the situation better, let's break down the steps that were taken before the group_by function was applied:

The mtcars dataset was modified to convert several numerical variables into factors.

The dataset was reshaped into a long format where numeric measurements were listed under a single values column while their corresponding variable names were listed under a names column.

Attempts to group this long-format dataset by names and calculate bootstrapped mean values resulted in incorrect outputs.

The author noted that when filtering by a specific name (like "wt"), the code worked as expected, indicating that the problem lay with the group_by function not recognizing the grouping attributes during the bootstrap process.

How to Solve the Problem

The solution to the issue lies in splitting the grouped data frame into smaller subsets, applying the bootstrap function on each subset individually, and then combining the results. Here’s how you can do that step-by-step:

Step 1: Load Required Libraries

Make sure you have the necessary libraries loaded:

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

Step 2: Split the Data Frame

Utilize the split function to separate the long-format mtcars data frame by the names variable. This creates a list of data frames, each corresponding to a different measurement:

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

Step 3: Apply the Bootstrapping Analysis

Use map_df from the purrr package to iterate over each data frame in the list. Apply the specify, generate, calculate, and get_ci functions to compute the confidence intervals based on the values response for each group:

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

Step 4: Collect the Results

After executing the above code, you will receive a neat table containing the following for each measured variable (e.g., disp, drat, hp, mpg, qsec, wt):

name: The variable name

lower_ci: The lower end of the confidence interval

upper_ci: The upper end of the confidence interval

Conclusion

The infer package does not natively handle grouping attributes in the same way as base R functions. By splitting your data frame and applying bootstrapping across each subset, you can successfully calculate confidence intervals for multiple variables efficiently. This approach not only resolves the problem of generating grouped summaries but also expands your understanding of using functional programming within R.

Giving your data the attention it needs while being mindful of the tools at your disposal is key. Happy bootstrapping!