Introduction The agrep() function in base R is used for approximate string matching, also known as fuzzy matching. Here’s how to use it effectively: Basic syntax The basic syntax of agrep() is as follows: agrep( pattern, x, max.distan... [Read more...]

Understanding grep() in R The grep() function is a powerful tool in base R for pattern matching and searching within strings. It’s part of R’s base package, making it readily available without additional installations. grep() is versatile, but ... [Read more...]

Introduction Comments play a critical role in programming, serving as notes within the source code that explain what the code does, why certain decisions were made, or how a particular function or module works. In C programming, comments are pa... [Read more...]

Introduction Are you looking to boost your data manipulation skills? Look no further than the powerful sapply() function! In this comprehensive guide, we’ll explore how to leverage sapply() effectively, especially when working with multiple arg... [Read more...]

Introduction As you go into the world of Linux, you’re bound to encounter the term “shell.” If you’re following along with The Linux Command Line: A Complete Introduction by William Shotts, you’re already on the right path. This post will intro... [Read more...]

Introduction If you’re just starting with C programming, you’ve probably noticed that almost every C program begins with a main() function. But have you ever wondered why this function is so crucial? In this blog post, we’ll dive into what the ... [Read more...]

Introduction lapply vs. sapply in R: What’s the Difference? In the world of R programming, understanding the difference between lapply() and sapply() can make your coding life much easier. These two functions are part of R’s powerful app... [Read more...]

Introduction Hey there, useR’s! Today, we’re going to talk about two super useful functions in R: grep() and grepl(). These functions might sound similar, but they have some key differences that are important to understand. Let’s break it down ... [Read more...]

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Introduction Hello, fellow useRs! Today, we’re going to expand on previous uses of the grepl() function where we looked for a single pattern and move onto to a search for multiple patterns within strings. Whether you’re cleaning data, conductin... [Read more...]

Introduction Hello, fellow useRs! Today, we’re going to discuss the art of concatenating matrices in R. Concatenating matrices is all about combining smaller pieces into a larger whole, and in R, the functions rbind() and cbind() are your go-to... [Read more...]

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Introduction Welcome to another exciting R programming tutorial! Today, we will explore how to concatenate vectors of strings using different methods in R: base R, stringr, stringi, and glue. We’ll use a practical example involving a data frame... [Read more...]

Introduction String concatenation is a fundamental operation in data manipulation and cleaning. If you are working in R, mastering string concatenation will significantly enhance your data processing capabilities. This blog post will cover diff... [Read more...]

Introduction Counting the occurrences of a specific character within a string is a common task in data processing and text manipulation. Whether you’re working with base R or leveraging the power of packages like stringr or stringi, R provides ... [Read more...]

Introduction Welcome to another exciting blog post where we walk into the world of R programming. Today, we’re going to explore how to check if a string contains specific characters using three different approaches: base R, stringr, and stringi... [Read more...]

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Introduction Greetings, useR! Today, we’re exploring a handy function from base R that will help with string manipulation: toupper(). This little function is the complement to tolower() which I have previously written about. Let’s take a look! ... [Read more...]

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Introduction Cluster sampling is a useful technique when dealing with large datasets spread across different groups or clusters. It involves dividing the population into clusters, randomly selecting some clusters, and then sampling all or some ... [Read more...]