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Category: R

Appropriate Uses of Jitter in Graphs

Published 2023-09-07 by Kevin Feasel

Steven Sanderson shakes things up:

As an R programmer, one of the most useful functions to know is the jitter function. The jitter function is used to add random noise to a numeric vector, which can be helpful when visualizing data in a scatterplot. By using the jitter function, we can get a better picture of the true underlying relationship between two variables in a dataset.

Read on to get an idea of how to use jitter, though I recommend making it very clear to chart viewers that you are, in fact, using jitter, as it can be easy to misinterpret the jitter as actual value locations.

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Kernel Density Plots in R

Published 2023-09-06 by Kevin Feasel

Steven Sanderson explains one common type of plot in R:

Kernel Density Plots are a type of plot that displays the distribution of values in a dataset using one continuous curve. They are similar to histograms, but they are even better at displaying the shape of a distribution since they aren’t affected by the number of bins used in the histogram. In this blog post, we will discuss what Kernel Density Plots are in simple terms, what they are useful for, and show several examples using both base R and ggplot2.

Read on to learn more, including how to generate these in base R, ggplot2, and with the tidy_density package.

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Random Number Generation in R

Published 2023-09-06 by Kevin Feasel

Adrian Tam rolls the dice:

Whether working on a machine learning project, a simulation, or other models, you need to generate random numbers in your code. R as a programming language, has several functions for random number generation. In this post, you will learn about them and see how they can be used in a larger program. Specifically, you will learn

  • How to generate Gaussian random numbers into a vector
  • How to generate uniform random numbers
  • How to manipulate random vectors and random matrices

And, of course, these are pseudo-random numbers because we’re still dealing with computers and random seeds, after all.

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Interesting R Functions for Package Dependencies and File Analysis

Published 2023-08-31 by Kevin Feasel

Maelle Salmon shows off a few interesting functions:

How does this package depend on this other package? pak::pkg_deps_explain()

The pak package by Gábor Csárdi makes installing packages easier. If I need to start working on a package, I clone it, then run pak::pak() to install and update its dependencies. It’s a “convenience function” that is convenient for sure! Bye bye remotes::install_deps().

Read on for an example of this, as well as details on two other functions in different packages. H/T R-Bloggers.

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Building Correlation Heatmaps in R

Published 2023-08-31 by Kevin Feasel

Steven Sanderson shows two packages for building heatmaps in R:

Data visualization is a powerful tool for understanding the relationships between variables in a dataset. One of the most common and insightful ways to visualize correlations is through heatmaps. In this blog post, we’ll dive into the world of correlation heatmaps using R, using the mtcars and iris datasets as examples. By the end of this post, you’ll be equipped to create informative correlation heatmaps on your own.

Read on to see how to build heatmaps with the corrplot and ggcorrplot packages.

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Returning Matrix Elements in Spiral Order in R

Published 2023-08-29 by Kevin Feasel

Tomaz Kastrun forgot to remove The Club from his REPL:

Another one from the Leetcode challenge. This time, get the elements (single values) from the matrix in a spiral order with a starting position of [1,1].

So, the basic idea is to retrieve a vector of elements from a matrix in the following order:

Probably not something you’d use with any frequency, but it’s a fun way to learn how to operate within matrices.

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Built-In R Datasets

Published 2023-08-29 by Kevin Feasel

Adrian Tam continues a series on getting started in R:

The ecosystem in R contains not only the function libraries to help you perform statistical analysis but also the data library that gives you some famous datasets to test out your program. There are a lot of built-in datasets in R. In this post, you will:

  • Learn some of the built-in datasets
  • Know how to use these datasets

Let’s get started.

Most of these built-in sets are fairly small and able to help you illustrate a specific point.

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Plotting Multiple Histograms in R

Published 2023-08-28 by Kevin Feasel

Steven Sanderson shows us two libraries to plot two histograms:

Histograms are a powerful tool for visualizing the distribution of numerical data. They allow us to quickly understand the frequency distribution of values within a dataset. In this tutorial, we’ll explore how to create multiple histograms using two popular R packages: base R and ggplot2. By the end of this guide, you’ll be able to confidently display multiple histograms on a single graph using both methods.

Click through for more than two examples.

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