Category: R

Steven Sanderson asks, ketchup or catsup:

The OR operator is a fundamental component in R programming that enables you to evaluate multiple conditions simultaneously. This guide will walk you through everything from basic syntax to advanced applications, helping you master logical operations in R for effective data manipulation and analysis.

Click through for several examples.

Steven Sanderson makes a list but doesn’t check it twice:

Lists are fundamental data structures in R programming that allow you to store multiple elements of different types in a single object. This comprehensive guide will walk you through everything you need to know about creating and working with lists in R.

Click through for quite a few examples.

Steven Sanderson builds a loop:

Looping through column names in R is a crucial technique for data manipulation, especially for beginners. This article will guide you through various methods to loop through column names in R, providing practical examples and insights to enhance your data analysis skills.

Read on for examples with for loops, the dynamic duo of lapply() and sapply(), and the map() function in the purrr library.

Steven Sanderson renames a column or three:

When working with data frames in R, you might find yourself needing to modify column names to include additional information, such as a suffix. This can be particularly useful when merging datasets or when you want to ensure that column names are unique and descriptive.

Read on to see how.

Sebastian Sauer makes a list and checks it twice:

In this post, I want to show you how to work with list columns in R. List columns are a powerful feature of the tidyverse that allow you to store multiple objects in a single column of a data frame. This can be useful when you have a list of objects that you want to keep together, such as a list of data frames or a list of models.

Click through for a demo.

Steven Sanderson does a bit of merging:

Combining data frames is a fundamental task in data analysis, especially when dealing with datasets that have different structures. In R, there are several ways to achieve this, using base R functions, the dplyr package, and the data.table package. This guide will walk you through each method, providing examples and explanations suitable for beginner R programmers. This article will explore three primary methods in R: base R functions, dplyr, and data.table. Each method has its advantages, and understanding them will enhance your data manipulation skills.

There are quite a few examples here, depending on whether you intend to join the datasets or perform a set operation such as union or intersect.