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Category: R

New R Package: hstats

Published 2023-10-17 by Kevin Feasel

Michael Mayer has a new package:

The current version offers:

  • H statistics per feature, feature pair, and feature triple
  • multivariate predictions at no additional cost
  • a convenient API
  • other important tools from explainable ML:
    • performance calculations
    • permutation importance (e.g., to select features for calculating H-statistics)
    • partial dependence plots (including grouping, multivariate, multivariable)
    • individual conditional expectations (ICE)
  • Case-weights are available for all methods, which is important, e.g., in insurance applications.

Click through for an example of how it works, followed by some simple benchmarking to give you an idea of how it performs compared to similar tools.

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Creating Horizontal Legends in R

Published 2023-10-13 by Kevin Feasel

Steven Sanderson flattens the legend:

Creating a horizontal legend in base R can be a useful skill when you want to label multiple categories in a plot without taking up too much vertical space. In this blog post, we’ll explore various methods to create horizontal legends in R and provide examples with clear explanations.

Read on for two demos, one with a single legend and one which creates two legends. I’m not so sure about how valuable the latter is (because you’re splitting valuable information into two places, losing some of the glanceability of a chart along the way), but it is interesting that you can do it.

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Changing the Style of a Legend in R

Published 2023-10-11 by Kevin Feasel

Steven Sanderson is a legend:

Before diving into code examples, let’s understand the basics. In R, legends are essential for explaining the meaning of different elements in your plot, such as colors, lines, or shapes. Legends help your audience interpret the data effectively.

In most cases, R’s base plotting system provides you with control over the legend’s size. The key functions we’ll explore are legend() and guides(). We’ll also delve into how to modify legend size in popular plotting packages like ggplot2.

Click through for those demonstrations.

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Bionic Reading in R

Published 2023-10-10 by Kevin Feasel

Tomaz Kastrun says reading is fundamental:

Trick your brain into faster reading with the help of Bionic Reading. With the help of highlighting part of the words, it “guides your eyes over the text and the brain remembers previously learned words more quickly.” (source: br-about)

Here is a beautiful example of how text with the use of opacity, colours, size and many other elements can be quickly achieved for faster reading.

Click through for an example and how to implement it in R.

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Radar Charts in R

Published 2023-10-06 by Kevin Feasel

Steven Sanderson has radar love:

Radar charts, also known as spider, web, polar, or star plots, are a useful way to visualize multivariate data. In R, we can create radar charts using the fmsb library. Here are several examples of how to create radar charts in R using the fmsb library:

Radar charts are a guilty pleasure of mine. They are rarely the right choice, but when they are, I love it so much.

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Drawing Horizontal Box Plots in R

Published 2023-10-04 by Kevin Feasel

Steven Sanderson is not limited to one axis:

Boxplots are a great way to visualize the distribution of a numerical variable. They show the median, quartiles, and outliers of the data, and can be used to compare the distributions of multiple groups.

Horizontal boxplots are a variant of the traditional boxplot, where the x-axis is horizontal and the y-axis is vertical. This can be useful for visualizing data where the x-axis variable is categorical, such as species or treatment group.

Click through for an example using base R and ggplot2.

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Plotting Decision Trees in R

Published 2023-10-03 by Kevin Feasel

Steven Sanderson builds a tree:

Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to understand and interpret, and they can be used to build complex models without the need for feature engineering.

Once you have trained a decision tree model, you can use it to make predictions on new data. However, it can also be helpful to plot the decision tree to better understand how it works and to identify any potential problems.

In this blog post, we will show you how to plot decision trees in R using the rpart and rpart.plot packages. We will also provide an extensive example using the iris data set and explain the code blocks in simple to use terms.

Read on to see an example of how to do this.

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Overlaying Lines with Points in Base R

Published 2023-09-29 by Kevin Feasel

Steven Sanderson adds points to those lines:

In this blog post, we’ll explore how to overlay points or lines on a plot using Base R. We’ll use the plot() function to create the initial plot and then show how to overlay points with points() and lines with lines(). We’ll provide several examples, explaining each code block in simple terms, and encourage you to try them out on your own datasets.

Read on to see how. It’s also pretty easy to do in ggplot2 or other visualization libraries.

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Faceted Images in ggplot2

Published 2023-09-28 by Kevin Feasel

Steven Sanderson shows multiple plots on one image:

Data visualization is a crucial tool in the data scientist’s toolkit. It allows us to explore and communicate complex patterns and insights effectively. In the world of R programming, one of the most powerful and versatile packages for data visualization is ggplot2. Among its many features, ggplot2 offers the facet_grid() function, which enables you to create multiple plots arranged in a grid, making it easier to visualize different groups of data simultaneously.

In this blog post, we’ll dive into the fascinating world of facet_grid() using a practical example. We’ll generate some synthetic data, split it into multiple groups, and then use facet_grid() to create a visually appealing grid of plots.

Read on for the demo script. The text talks about facet_grid() and the demo is facet_wrap(). The two behave very similarly, though they have slightly different use cases.

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