Category: R

Steven Sanderson grabs a name:

Hello, R users! Today, we’ll dive into a common text manipulation task: extracting strings before a space. This is a handy trick for dealing with names, addresses, or any text data where you need to isolate the first part of a string.

We’ll explore three approaches: using base R, stringr, and stringi. Each method offers its unique advantages, so you can choose the one that fits your style best.

Click through for the three examples. I will note that if you’re actually using this code to split names, well, names tend to be a lot trickier than we give them credit for. Keep in mind that people can have multi-part names (“Debbie Mae” or “van den Berg”), so unless you know the data all follows a specific pattern, don’t assume the data follows a specific pattern.

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Steven Sanderson builds a Windows task:

Today, let’s dive into a nifty R package called taskscheduleR that can automate running your R scripts. Whether you need to execute a task every hour or just once a day, taskscheduleR has you covered. This package leverages the Windows Task Scheduler, making it a breeze to schedule and automate repetitive tasks directly from R. Let’s walk through a couple of examples from my new book, “Extending Excel with Python and R”.

Click through for those examples. Also check out Steven’s new book, that came out at the end of April.

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Tomaz Kastrun adds dragons to the edge of the map:

The algorithm is a fractal curve of Hausdorff dimension 2. One starts with one segment. In each iteration the number of segments is doubled by taking each segment as the diagonal of a square and replacing it by half the square (90 degrees). Alternating and doing the left and right function / direction to complement in order to get the shape.

Clickt hrough for the sample code and how the plot looks.

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Steven Sanderson toes a bit of tag replacement:

Hello, R enthusiasts! Today, we’re jumping into a common text processing task: extracting strings between specific characters. This is a great skill for data cleaning and manipulation, especially when working with raw text data. I’m going to show you how to achieve this using base R, the stringr package, and the stringi package. Let’s go!

Read on for examples.

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