A Fine Slice Of SQL Server
Steven Sanderson smooshes strings together:
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 different ways to concatenate strings using base R, the
stringr,stringi, andgluepackages. Let’s go!
Read on for examples using paste(), paste0(), str_c(), stri_c(), and glue().
Steven Sanderson counts the ways:
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
stringrorstringi, R provides efficient ways to accomplish this. In this post, we’ll explore how to do this using three different methods.
Read on for three separate examples.
Comments closedSteven Sanderson goes looking for patterns:
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. Whether you’re a beginner or an experienced R user, this guide will should be of some use and provide you with some practical examples.
Read on for those three examples.
Comments closedSteven Sanderson continues a series on sampling:
In this post, we will explore systematic sampling in R using base R functions. Systematic sampling is a technique where you select every (k^{th}) element from a list or dataset. This method is straightforward and useful when you want a representative sample without the complexity of more advanced sampling techniques.
Let’s dive into an example to understand how it works.
In very technical circles, this is also known as the “eenie-meenie-meiney-moe technique” and is very similar to the “duck-duck-goose” algorithm, though that has an additional stochastic input.
Comments closedSteven Sanderson shows us one sampling technique:
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 members from these selected clusters. This method can save time and resources compared to simple random sampling.
In this post, we’ll walk through how to perform cluster sampling in R. We’ll use a sample dataset and break down the code step-by-step. By the end, you’ll have a clear understanding of how to implement cluster sampling in your projects.
Read on for the scenario and sample code.
Comments closedSteven Sanderson builds a sample:
Stratified sampling is a technique used to ensure that different subgroups (strata) within a population are represented in a sample. This method is particularly useful when certain strata are underrepresented in a simple random sample. In this post, we’ll explore how to perform stratified sampling in R using both base R and the
dplyrpackage. We’ll walk through examples and explain the code, so you can try these techniques on your own data.
Click through to see how.
Comments closedSteven Sanderson performs a pattern match:
In R, finding patterns in text is a common task, and one of the most powerful functions to do this is
grep(). This function is used to search for patterns in strings, allowing you to locate elements that match a specific pattern. Today, we’ll explore how to use wildcard characters withgrep()to enhance your string searching capabilities. Let’s dive in!
Read on to learn more about how to use the grep() function.
Jerry Tuttle describes a paradox:
So you spent hours, or maybe days, cranking out thousands of numbers, you submit it to your boss just at the deadline, your boss quickly peruses your exhibit of numbers, points to a single number and says, “This number doesn’t look right.” Bosses have an uncanny ability to do this.
Your boss is pointing to something like this: Your company sells property insurance on both personal and commercial properties. The average personal property premium increased 10% in 2024. The average commercial property premium increased 10% in 2024. But you say the combined average property premium decreased 3% in 2024. You realize that negative 3% does not look right.
Although the blog post doesn’t explicitly mention Simpson’s paradox, I’d argue that this is a good example of the idea. H/T R-Bloggers.
Comments closedIn this post I show how groupScatterPlot(), function of the rnatoolbox R package can be used for plotting the individual values in several groups together with their mean (or other statistics). I think this is a useful function for plotting grouped data when some groups (or all groups) have few data points ! You may be wondering why to include such function in the rnatoolbox package ?! Well ! I happen to use it quit a bit for plotting expression values of different groups of genes/transcripts in a sample or expression levels of a specific gene/transcript in several sample groups.
Click through for the sample code and output. H/T R-Bloggers.
Comments closedSteven Sanderson is looking for two things:
Hello, fellow R programmers! Today, we’re looking at a practical topic that often comes up when dealing with text data: how to check if a string contains multiple substrings. We’ll cover how to do this in base R, as well as using the
stringrandstringipackages. Each approach has its own advantages, so let’s explore them together.
Read on for three separate examples.
Comments closed