A Fine Slice Of SQL Server
Steven Sanderson looks for the pattern:
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, conducting text analysis,greplcan be your go-to tool. Let’s break down the syntax, offer a practical example, and guide you on a path to proficiency.
Read on for all of that.
Comments closedBen Johnston spins in circles:
Welcome back to my R for SEO series. We’re in the home stretch now, with part seven. Today, we’re going to be looking at different ways that we can run functions or commands over a series of elements using the various kinds of loops that exist in R.
If you’ve followed along so far, or you’ve tried some experimentation of your own, you’ve probably encountered loops and applys along the way. I know early on in my R journey, it very much seemed like pot luck as to which apply I should use, or whether a loop was easier, so hopefully today’s piece will start to clear that up for you a little.
I know that most programming courses cover these elements earlier, but for me, it really didn’t click until I’d learned more about the other areas we’ve covered in this series, so that’s why I’ve placed it here.
Read on for examples of For loops and While loops, as well as breaking conditions.
Ben also talks about loops versus using the apply() series of functions (or equivalent map() functions in the purrr library). I tend to lean heavily on using the mapping function approach when there are no side effects, and use for loops when there are. H/T R-Bloggers.
Wingspan is a great game even though I’ve only played it a few times. The mechanics are great, there are lots of bird varitions, and a bunch of different strategies to try. There are 170 birds, and I’ve probably only seen 30 of them. So, true to form, I’ve dabbled in a bit of data analysis to get a view of all the different types of cards in the game.
Open source wins again since the {wingspan} R package exists. It contains the details of each bird in the core, European, Oceania, and swift start sets. I’ll only be using the core set for this analysis since that’s the only one I’m semi familiar with.
Having not played the game before, Dan’s visuals drew me in. There’s also a regression analysis and discussion of the trade-off between in-game power versus victory points. H/T R-Bloggers.
Comments closedSteven Sanderson glues together some vectors:
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, andglue. We’ll use a practical example involving a data frame with names, job titles, and salaries. By the end of this post, you’ll feel confident using these tools to manipulate and combine strings in your own projects. Let’s get started!
Read on to see how to do this in several ways.
Comments closedSteven 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 closed