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Category: R

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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Visualizing Data in R with ggplot2

Published 2023-09-27 by Kevin Feasel

Adrian Tam continues a series on R:

One of the most popular plotting libraries in R is not the plotting function in R base, but the ggplot2 library. People use that because it is flexible. This library also works using the philosophy of “grammar of graphics”, which is not to generate a visualization upon a function call, but to define what should be in the plot, and you can refine it further before setting it into a picture. In this post, you will learn about ggplot2 and see some examples. In particular, you will learn:

  • How to make use of ggplot2 to create a plot from a dataset
  • How to create various charts and graphics with multiple facades using ggplot2

It takes a little while to understand the grammar of graphics approach that ggplot2 takes, but once you do, you realize just how good this library is for generating static images.

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Data Exploration in R with dplyr

Published 2023-09-26 by Kevin Feasel

Adrian Tam continues a series on R:

When you are working on a data science project, the data is often tabular structured. You can use the built-in data table to handle such data in R. You can also use the famous library dplyr instead to benefit from its rich toolset. In this post, you will learn how dplyr can help you explore and manipulate tabular data. In particular, you will learn:

  • How to handle a data frame
  • How to perform some common operations on a data frame

I like dplyr a lot for its “functional flow”—you pipe outputs of one function to be inputs of the next function, so the chain makes a lot of sense. If you want high performance, though, it’s often not the best choice—that’s usually data.table.

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ggplot2 in Python Notebooks

Published 2023-09-26 by Kevin Feasel

John Mount runs R in Python with rpy2:

For an article on A/B testing that I am preparing, I asked my partner Dr. Nina Zumel if she could do me a favor and write some code to produce the diagrams. She prepared an excellent parameterized diagram generator. However being the author of the book Practical Data Science with R, she built it in R using ggplot2. This would be great, except the A/B testing article is being developed in Python, as it targets programmers familiar with Python.

As the production of the diagrams is not part of the proposed article, I decided to use the rpy2 package to integrate the R diagrams directly into the new worksheet. Alternatively, I could translate her code into Python using one of: Seaborn objects, plotnine, ggpy, or others. The large number of options is evidence of how influential Leland Wilkinson’s grammar of graphics (gg) is.

Click through to see how you can execute R code within the context of Python, similar to how you can use the reticulate package to execute Python code in the context of R.

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