Скачать или смотреть Unlocking the Density Bin Indices in R Histograms

Discover how to retrieve index values of density bin entries in histograms using R. This guide breaks down the process to enhance your data visualization techniques.
---
This video is based on the question https://stackoverflow.com/q/68010824/ asked by the user 'jack kelly' ( https://stackoverflow.com/u/15758438/ ) and on the answer https://stackoverflow.com/a/68011229/ provided by the user 'thelatemail' ( https://stackoverflow.com/u/496803/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Get index of density bin values of histogram in hist() R

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Density Bin Indices in R Histograms

When working with data analysis in R, visualizing datasets through histograms is crucial for understanding the distribution of your data. However, a common question arises: How can I get the index values of the bins in a histogram created using hist()? In this post, we'll break down the problem and provide a comprehensive solution to this question.

The Problem

You're interested in obtaining the index values corresponding to the 'y' values in a histogram generated by the hist() function. For instance, when you use the following code:

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

This generates a histogram, and you can access testhist$density to get the density values for each bin. What you want to do is find out which bin index corresponds to each of those density values.

The Clarifications

Before diving into the solution, it's essential to clarify a few points regarding the use of the hist() function:

Correct Parameters: The hist() function accepts a breaks argument, not n. Although it may seem that specifying n=5000 works, it's a coincidence resulting from the underlying use of the pretty() function to determine break points.

Effective Breaks: Specifying breaks=5000 does not guarantee you will get exactly 5000 bins. The function uses the concept of "pretty" numbers which may alter how break points are established.

Understanding Elements:

testhist$density gives the density for each bin.

Each bin's amount of breaks is one more than the number of bins (that is, n+ 1).

Step-by-Step Solution

Retrieving Bin Indices

To obtain the bin indices corresponding to each value in your density output, you can employ the following approach:

Creating Your Histogram: Generate your histogram using the specified breaks:

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

Matching Original Values to Bins: To find out which index corresponds to the original values of x, use the cut() function:

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

Review Your Results: You can check how many of the original values fall into each bin:

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

Example Output

You might get a table output showing how many values fall into each bin. Each integer in the table represents the bin index for that specific range.

Conclusion

Understanding how to retrieve bin indices for your histogram data not only fosters a deeper analysis of your dataset but also enhances your ability to present data visually in R. Having clarity on how the hist() function works, alongside the use of accompanying functions like cut(), makes the task straightforward.

If you're diving deeper into data visualization and analysis with R, mastering these techniques will greatly improve your skills!

For more tips and tricks on R programming and data analysis, stay tuned for our next posts!