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Category: R

Uniform Random Number Generation in R

Published 2023-11-01 by Kevin Feasel

Steven Sanderson digs into the uniform distribution:

Randomness is an essential part of many statistical and machine learning tasks. In R, there are a number of functions that can be used to generate random numbers, but the runif() function is the most commonly used.

Something mildly embarrassing for me is that it took me a while to figure out why they call the command runif(). That’s because, at first, I didn’t pronounce it r unif but rather run if.

In reality, *unif() means “uniform distribution” and r stands for “random number.” There are several other functions based on the uniform distribution and Steven looks at those as well in this post.

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An Analysis of Goal Line Runs out of Shotgun

Published 2023-10-31 by Kevin Feasel

I decided to test a common narrative:

A common theme among Buffalo Bills fans is the idea that the Bills run too many plays out of shotgun near the opposing team’s goal line, and this is hampering their ability to score points. Instead, these fans argue, they should run from under center, either a direct handoff or a quarterback sneak. If you were to press fans on this, I believe you’d also hear that the Bills are unique, or at least uniquely bad, at running such plays.

I’m going to use the nflfastR package to analyze play-by-play data and see just how well this bit of fan wisdom holds up.

Spoiler alert: it doesn’t.

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Log-Log Plots in R

Published 2023-10-31 by Kevin Feasel

Steven Sanderson thinks in percentages:

A log-log plot is a type of graph where both the x-axis and y-axis are in logarithmic scales. This is particularly useful when dealing with data that spans several orders of magnitude. By taking the logarithm of the data, we can compress large values and reveal patterns that might be hidden on a linear scale.

Let’s start with a simple example using base R.

Read on to see how you can create these plots and what you can do to customize them.

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Plotting Logistic Regression in R

Published 2023-10-30 by Kevin Feasel

Steven Sanderson performs a logistic regression:

Logistic regression is a statistical method used for predicting the probability of a binary outcome. It’s a fundamental tool in machine learning and statistics, often employed in various fields such as healthcare, finance, and marketing. We use logistic regression when we want to understand the relationship between one or more independent variables and a binary outcome, which can be “yes/no,” “1/0,” or any two-class distinction.

Click through to learn how to do this.

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Enabling Python and R Support for VS Code Polyglot Notebooks

Published 2023-10-30 by Kevin Feasel

Joy George Kunjikkur enables a preview option:

Obviously, we should have Polyglot notebooks up and running. The first step to enable Python preview is that we need to install Jupyter on the machine and make sure the Python kernel spec is available. Run the below command to make sure it is there.

It looks like what the preview is doing is shelling out to Jupyter notebooks, so I’d imagine variables won’t cross over between languages.

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Building a Bland-Altman Plot in R

Published 2023-10-27 by Kevin Feasel

Steven Sanderson performs a comparison:

Before we dive into the code, let’s briefly understand what a Bland-Altman plot is. It’s a graphical method to visualize the agreement between two measurement techniques, often used in fields like medicine or any domain with comparative measurements. The plot displays the differences between two measurements (Y-axis) against their means (X-axis).

Click through to see how this works and how you can interpret the results.

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Functional Programming and R

Published 2023-10-26 by Kevin Feasel

Anirban Shaw ties functional programming to R:

Functional Programming‘s relevance in the R programming language, a language primarily known for its prowess in data analysis and statistical computing, is particularly noteworthy. By leveraging functional programming, organizations can improve operational efficiency and gain a competitive edge. 

R’s ecosystem is enriched by functional programming paradigms, which enable developers and data scientists to write concise and expressive code for tasks such as data manipulation, transformation, and visualization.

In this article, we take a deep dive into the fundamental characteristics of R, the advantages of adopting functional programming within it and the essential concepts ingrained in the core of R. 

Read on to see how the two fit together. H/T R-Bloggers.

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Scree Plots in R

Published 2023-10-25 by Kevin Feasel

Steven Sanderson builds a scree plot:

A scree plot is a line plot that shows the eigenvalues or variance explained by each principal component (PC) in a Principal Component Analysis (PCA). It is a useful tool for determining the number of PCs to retain in a PCA model.

In this blog post, we will show you how to create a scree plot in base R. We will use the iris dataset as an example.

Read on to learn more about the plot, as well as examples of how to create scree plots.

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Bubble Charts in ggplot2

Published 2023-10-24 by Kevin Feasel

Steven Sanderson creates a bubble chart:

Bubble charts are a great way to visualize data with three dimensions. The size of the bubbles represents a third variable, which can be used to show the importance of that variable or to identify relationships between the three variables.

To create a bubble chart in R using ggplot2, you will need to use the geom_point() function. This function will plot points on your chart, and you can use the size aesthetic to control the size of the points.

Click through for two examples, one which is a pretty good outcome for using a bubble chart, and one which exposes the key weakness of bubble charts.

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Several Useful R Functions

Published 2023-10-23 by Kevin Feasel

Maelle Salmon shows off four useful R functions:

Recently I caught myself using which(grepl(...)),

animals <- c("cat", "bird", "dog", "fish")
which(grepl("i", animals))
#> [1] 2 4

when the simpler alternative is

animals <- c("cat", "bird", "dog", "fish")
grep("i", animals)
#> [1] 2 4

Read on for another example of using grep() instead of grepl(), as well as three other functions you might want to keep in mind. H/T R-Bloggers.

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