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Category: R

The Value of the keyring Package

Published 2024-02-29 by Kevin Feasel

Maelle Salmon looks at a good package in R

Does your package need the user to provide secrets, like API tokens, to work? Have you considered telling your package users about the keyring package, or even forcing them to use it?

The keyring package maintained by Gábor Csárdi is a package that accesses the system credential store from R: each operating system has a special place for storing secrets securely, that keyring knows how to interact with. The credential store can hold several keyrings, each keyring can be protected by a specific password and can hold several keys which are the secrets.

Read on for several advantages of using the keyring package.

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Melting Datasets in R

Published 2024-02-28 by Kevin Feasel

Steven Sanderson performs a melt():

The melt() function in the data.table package is an extremely useful tool for reshaping datasets in R. However, for beginners, understanding how to use melt() can be tricky. In this post, I’ll walk through several examples to demonstrate how to use melt() to move from wide to long data formats.

“Melting,” by the way, is the R term for unpivoting data.

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Filtering data.tables and data.frames in R

Published 2024-02-27 by Kevin Feasel

Steven Sanderson doesn’t need all of the data:

Ah, data! The lifeblood of many an analysis, but sometimes it can feel like you’re lost in a tangled jungle. Thankfully, R offers powerful tools to navigate this data wilderness, and filtering is one of the most essential skills in your arsenal. Today, we’ll explore how to filter both data.tables and data.frames, making your data exploration a breeze!

Click through for ways to filter two popular constructs in R.

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An Overview of Data Types in R

Published 2024-02-26 by Kevin Feasel

Steven Sanderson talks data types:

Imagine your data as a diverse collection of individuals. Some might be numbers (like age or weight), while others might be text (like names or addresses). These different categories are called data types, and R recognizes several key ones:

Click through for that list. It’s a bit different from what you’d expect if you come at this from a SQL or C-based programming language background. But they all make good sense when you remember that R is a domain-specific language for statistics, so it’s going to emphasize the things that make the most sense for statisticians and data scientists.

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Adding Superscripts and Subscripts to Axis Labels in R

Published 2024-02-22 by Kevin Feasel

Steven Sanderson changes the script:

Before we dive into the code, let’s quickly review what superscripts and subscripts are.

  • Superscripts: These are smaller-sized characters or numbers that appear above the baseline of the text. They are often used to denote exponents or indices.
  • Subscripts: On the other hand, subscripts are smaller-sized characters or numbers that appear below the baseline of the text. They are commonly used in mathematical expressions or chemical formulas.

Read on to see how you can generate these in R visuals.

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The apply() Functions in R

Published 2024-02-19 by Kevin Feasel

Steven Sanderson talks about a series of functions:

Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations with R’s “apply” family: apply(), lapply(), sapply(), and tapply(). These functions are your secret weapons for efficiency and elegance, so buckle up and prepare to be amazed!

But first, the “why”: Loops are great, but for repetitive tasks on data structures, vectorization reigns supreme. It’s faster, cleaner, and lets you focus on the “what” instead of the “how” of your analysis. Enter the apply family, each member offering a unique twist on applying functions to your data.

The trickiest part about the apply() series is remembering which one does what. This is where purrr’s map() function does a better job, I think.

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Working with Date Sequences in R

Published 2024-02-15 by Kevin Feasel

Steven Sanderson isn’t satisfied with a single date:

In the world of data analysis and manipulation, working with dates is a common and crucial task. Whether you’re analyzing financial data, tracking trends over time, or forecasting future events, understanding how to generate date sequences efficiently is essential. In this blog post, we’ll explore three powerful R packages—lubridate, timetk, and base R—that make working with dates a breeze. By the end of this guide, you’ll be equipped with the knowledge to generate date sequences effortlessly and efficiently in R.

Click through for several ways to generate date sequences, including weekly sequences.

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Checking for Date Columns in R

Published 2024-02-12 by Kevin Feasel

Steven Sanderson is looking for a date:

As an R programmer, you may often encounter datasets where you need to determine whether a column contains date values. This task is crucial for data cleaning, manipulation, and analysis. In this blog post, we’ll explore various methods to check if a column is a date in R, with a focus on using the lubridate package and the ts_is_date_class() function from the healthyR.ts package.

Click through to see how, using lubridate and healthyR.

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Finding the Week Number in R

Published 2024-02-08 by Kevin Feasel

Steven Sanderson checks the week:

When working with dates in R, you may need to extract the week number for any given date. This can be useful for doing time series analysis or visualizations by week.

In this post, I’ll demonstrate how to get the week number from dates in R using both base R and the lubridate package. I’ll provide simple examples so you can try it yourself.

Steven also makes a good point about ISO weeks (which are common in Europe) versus calendar weeks.

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