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Category: R

ggbrick in CRAN

Published 2024-03-22 by Kevin Feasel

Dan Oehm notes another brick in the wall:

If you’re looking for something a little different, ggbrick creates a ‘waffle’ style chart with the aesthetic of a brick wall. The usage is similar to geom_col where you supply counts as the height of the bar and a fill for a stacked bar. Each whole brick represents 1 unit. Two half bricks equal one whole brick.

It has been available on Git for a while, but recently I’ve made some changes and it now has CRAN’s tick of approval.

Click through to see how you can use it. This style of waffle chart, in the right scenario, can be quite useful, providing a high-level view and also giving you some idea of fine-grained magnitudes. H/T R-Bloggers.

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Using the cut() Function in R

Published 2024-03-21 by Kevin Feasel

Steven Sanderson is about to cut somebody:

In the realm of data analysis, understanding how to effectively segment your data is paramount. Whether you’re dealing with age groups, income brackets, or any other continuous variable, the ability to categorize your data can provide invaluable insights. In R, the cut() function is a powerful tool for precisely this purpose. In this guide, we’ll explore how to harness the full potential of cut() to slice and dice your data with ease.

Read on for examples of how to use the cut() function.

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Duplicating Rows in R

Published 2024-03-20 by Kevin Feasel

Steven Sanderson repeats the punch line a few times:

Are you working with a dataset where you need to duplicate certain rows multiple times? Perhaps you want to create synthetic data by replicating existing observations, or you need to handle imbalanced data by oversampling minority classes. Whatever the reason, replicating rows in a data frame is a handy skill to have in your R programming toolkit.

In this post, we’ll explore how to replicate rows in a data frame using base R functions. We’ll cover replicating each row the same number of times, as well as replicating rows a different number of times based on a specified pattern.

Click through to replicate data without copy-paste.

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Plotting Training and Testing Results with tidyAML

Published 2024-03-18 by Kevin Feasel

Steven Sanderson builds a plot:

In the realm of machine learning, visualizing model predictions is essential for understanding the performance and behavior of our algorithms. When it comes to regression tasks, plotting predictions alongside actual values provides valuable insights into how well our model is capturing the underlying patterns in the data. With the plot_regression_predictions() function in tidyAML, this process becomes seamless and informative.

Read on to see how the function works and the kind of result you can expect from it.

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tidyAML 0.0.5 Now Available

Published 2024-03-14 by Kevin Feasel

Steven Sanderson has an announcement:

I’m thrilled to announce the latest release of tidyAML, version 0.0.5, now available for download on CRAN or GitHub!

In this release, we’ve introduced some fantastic new features and made minor fixes and improvements to enhance your experience with tidyAML.

Click through to see what’s new in this version.

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Pulling Samples in R with sample()

Published 2024-03-13 by Kevin Feasel

Steven Sanderson takes a sample:

The sample() function in R is a powerful tool that allows you to generate random samples from a given dataset or vector. It’s an essential function for tasks such as data analysis, Monte Carlo simulations, and randomized experiments. In this blog post, we’ll explore the sample() function in detail and provide examples to help you understand how to use it effectively.

Read on to see what options are available with sample() and the different ways in which you can use the function.

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Subsetting Data Frames in R using Multiple Conditions

Published 2024-03-08 by Kevin Feasel

Steven Sanderson can’t stop at one filter:

In data analysis with R, subsetting data frames based on multiple conditions is a common task. It allows us to extract specific subsets of data that meet certain criteria. In this blog post, we will explore how to subset a data frame using three different methods: base R’s subset() function, dplyr’s filter() function, and the data.table package.

Click through for examples.

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Renaming Factor Levels in R

Published 2024-03-06 by Kevin Feasel

Steven Sanderson renames factor levels of a categorical variable:

Before we jump into renaming factor levels, let’s quickly recap what factors are and why they’re useful. Factors are used to represent categorical data in R. They store both the values of the categorical variables and their corresponding levels. Each level represents a unique category within the variable.

Click through for three methods you can use to pull this off.

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Setting Data Frame Columns as Indexes in R

Published 2024-03-04 by Kevin Feasel

Steven Sanderson explains and does:

Before we dive into the how, let’s briefly discuss why you might want to set a column as the index in your data frame. By doing so, you essentially designate that column as the unique identifier for each row in your data. This can be particularly useful when dealing with time-series data, categorical variables, or any other column that serves as a natural identifier.

Setting a column as the index offers several advantages:

Read on to see those advantages.

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