Creating a Histogram with Frequency and Density Axes Simultaneously in R

In this article, we will explore how to create a histogram that combines both frequency and density axes. We’ll dive into the world of R programming language and cover various aspects of creating such a plot.

Introduction to Histograms

A histogram is a graphical representation of the distribution of numerical data. It’s a useful tool for understanding the shape, center, and spread of a dataset. In this article, we will focus on creating histograms with both frequency and density axes.

Understanding Frequency and Density Axes

The frequency axis represents the count or number of occurrences of each value in the dataset, while the density axis represents the relative frequency of each value. The density axis is useful for understanding the shape and distribution of the data.

Creating a Histogram with Both Axes

To create a histogram that combines both frequency and density axes, we can use the hist() function in R, which creates a standard histogram. However, this function does not allow us to add a density plot directly. We need to use a combination of functions to achieve this.

One approach is to use the lines() function along with the density() function from the stats package. This will create a separate density plot alongside the frequency plot.

However, as mentioned in the provided Stack Overflow question, adding both axes at the same time can be challenging. The obvious difficulty is either the density or the frequency won’t be pretty/round numbers (if you want them to both be round then the ticks will likely be at different places). We’ll explore a more sophisticated approach using R’s hist() function.

Approach 1: Using Base R

As mentioned in the question, we can create a histogram with both frequency and density axes using base R. The obvious difficulty is defining the axes correctly.

One way to achieve this is by using the par() function, which allows us to set various plot parameters, including margins and axis labels.

To create the frequency plot, we use the hist() function in combination with the plot=FALSE argument. This tells R not to display the histogram directly but rather returns a list of data that can be used to plot other things later on.

We then calculate the density using the density() function from the stats package.

Next, we create the frequency plot by calling hist() again with prob=TRUE, which adds up to make the entire area under the curve equal 1. We also set yaxt="n", which tells R not to display any y-axis labels until they’re explicitly added later on. Finally, we calculate the limits of the y-axis using the ylim argument.

To add the density plot, we create a new histogram with prob=TRUE and then use the lines() function to add the density curve over it.

Using Base R Function: histfreq()

As mentioned in the question, one possible solution is to create a custom function called histfreq(). This function takes an input vector X, along with some optional arguments like lines=TRUE.

Inside this function, we calculate the histogram and density of X using the respective functions. We also add labels and tick marks on both axes.

The key difference between this approach and the previous one is that we don’t have to manually define the axes. The histfreq() function does all the hard work for us.

Usage and Example

To use the histfreq() function, simply call it with an input vector like so:

histfreq(rnorm(57), lines=TRUE)

This creates a histogram plot that combines both frequency and density axes. You can customize various aspects of this plot by passing different arguments to the histfreq() function.

Conclusion

In this article, we explored how to create a histogram that combines both frequency and density axes in R. We covered the basics of histograms and then delved into more advanced techniques using base R programming language. Specifically, we discussed two approaches: creating a custom histogram with manual axis labels and another approach using the histfreq() function.

We hope this article has provided you with insights and knowledge on how to create informative plots that showcase your data’s distribution and density.

Last modified on 2024-09-28