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Category: R

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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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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