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Category: R

Row Re-Ordering in Shiny Apps

Published 2024-02-02 by Kevin Feasel

Stephane Laurent does a bit of work:

The ‘RowReorder’ extension of datatables is available in the DT package. This extension allows to reorder the rows of a DT table by dragging and dropping. However, if you enable this extension in a Shiny app for a table using the server-side processing (option server=TRUE in renderDT), that won’t work: each time the rows are reordered, they will jump back to their original locations.

Read on to see what you need to do in that case, as well as an example of how to do it. H/T R-Bloggers.

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Where the Bayesian and Frequentist Approaches Meet

Published 2024-02-02 by Kevin Feasel

Sebastian Sauer bridges the gap:

However, a disadvantage of Bayes analysis, at least at its current state, is that it has higher technical and computational demands. For beginners in particular, this may present a substantial (entry) burden. Teaching statistics, I have found that students (and many colleagues) have had difficulties installing Stan (particularly the C++ compiler needed in order to run Stan); Stan is the probabilistic programming language which many front-end Bayes engines use such as brms in R.

Thus, the installation process being not so user-friendly, a burden is placed for beginners which may prevent using Bayes methods.

In that light, this post explores the numerical simarilities of Bayes regression models and Frequentis models. The idea is to use a Frequentist regression model as a proxi for a full Bayesian analysis. The value added is the quick computation and the simple technical setup.

Click through for the conditions where you’ll find very similar results, as well as a few examples of it in action.

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Extracting the Month from a Date with R

Published 2024-02-01 by Kevin Feasel

Steven Sanderson asks what month it is:

Greetings fellow R enthusiasts! Today, we’re diving into a fundamental task: extracting the month from a date in R. Whether you’re new to R or a seasoned pro, understanding how to manipulate dates is essential. We’ll explore two popular methods: using base R and the powerful lubridate package. So, let’s roll up our sleeves and get started!

Read on for several examples across two solution spaces.

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Preserving Non-Occurring Levels in R

Published 2024-01-31 by Kevin Feasel

Sebastian Sauer saves the levels:

The summary table does not show the level TRUE, as it is not occuring in the data. This can be problematic: If the data is unknown before summarizing and you would expect that both/all levels (TRUE, FALSE) occur. Just imagine that a subsequent function will count the level TRUE and the level FALSE. If one level is missing, your system may brake down.

Click through for a solution, where, even if your dataset is missing a particular level (value of a categorical variable), you will still see it in the final output. That way, if you train a model on this data and the new level shows up in your test dataset or in the wild, it won’t cause an error.

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Finding the Cake Dataset’s Original Source

Published 2024-01-29 by Kevin Feasel

Rasmus Baath has done a good deed for all:

In statistics, there are a number of classic datasets that pop up in examples, tutorials, etc. There’s the infamous iris dataset (just type iris in your nearest R prompt), the Palmer penguins (the modern iris replacement), the titanic dataset(s) (I hope you’re not a guy in 3rd class!), etc. While looking for a dataset to illustrate a simple hierarchical model I stumbled upon another one: The cake dataset in the lme4 package which is described as containing “data on the breakage angle of chocolate cakes made with three different recipes and baked at six different temperatures [as] presented in Cook (1938)1”. For me, this raised a lot of questions: Why measure the breakage angle of chocolate cakes? Why was this data collected? And what were the recipes?

Read on as Rasmus unravels the mysteries of the cake dataset with the help of several others. H/T R-Bloggers.

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Finding the Earliest Date in R

Published 2024-01-29 by Kevin Feasel

Steven Sanderson puts on the archaeologist’s fedora and bullwhip:

Greetings, fellow data enthusiasts! Today, we embark on a quest to uncover the earliest date lurking within a column of dates using the power of R. Whether you’re a seasoned R programmer or a curious newcomer, fear not, for we shall navigate through this journey step by step, unraveling the mysteries of date manipulation along the way.

Imagine you have a dataset filled with dates, and you’re tasked with finding the earliest one among them. How would you tackle this challenge? Fear not, for R comes to our rescue with its arsenal of functions and packages.

Click through to see how, keeping those pernicious missing values in mind.

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Calculating Date Differences in Month with R

Published 2024-01-25 by Kevin Feasel

Steven Sanderson has ways to track months:

Greetings fellow R enthusiasts! Today, let’s dive into the fascinating world of date calculations. Whether you’re a data scientist, analyst, or just someone who loves coding in R, understanding how to calculate the number of months between dates is a valuable skill. In this blog post, we’ll explore two approaches using both base R and the lubridate package, ensuring you have the tools to tackle any date-related challenge that comes your way.

Read on to see how to do this in base R as well as the lubridate package.

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Bootstrapping in TidyDensity

Published 2024-01-23 by Kevin Feasel

Steven Sanderson pulls us up by the bootstraps:

Imagine this: You have a dataset, say, car mileage (MPG) from the classic mtcars dataset. You want to understand the average MPG, but what if that average is just a mirage? What if it’s skewed by a few outliers or doesn’t capture the full story?

Enter bootstrapping, a statistical technique that’s like taking your data on a wild ride. It creates multiple copies of your data, each with a slight twist, and then calculates the statistic you’re interested in (e.g., average MPG) for each copy. This gives you a distribution of possible averages, revealing the variability and potential biases lurking beneath the surface.

Read on to learn more about bootstrapping in general and how to use the bootstrap_stat_plot() function in TidyDensity.

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Data Reading and Writing with arrow

Published 2024-01-19 by Kevin Feasel

Colin Gillespie performs two of the three R’s:

Apache Arrow is a cross-language development platform for in-memory data. As it’s in-memory (as opposed to data stored on disk), it provides additional speed boosts. It’s designed for efficient analytic operations, and uses a standardised language-independent columnar memory format for flat and hierarchical data. The {arrow} R package provides an interface to the ‘Arrow C++’ library – an efficient package for analytic operations on modern hardware.

There are many great tutorials on using {arrow} (see the links at the bottom of the post for example). The purpose of this blog post isn’t to simply reproduce a few examples, but to understand some of what’s happening behind the scenes. In this particular post, we’re interested in understanding the reading/writing aspects of {arrow}.

Read on to see it in action in R.

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tidyAML Updates

Published 2024-01-19 by Kevin Feasel

Steven Sanderson has been busy. First up, a post on tidyAML updates:

One of the standout features in this release is the addition of extract_regression_residuals(). This function empowers users to delve deeper into regression models, providing a valuable tool for analyzing and understanding residuals. Whether you’re fine-tuning your models or gaining insights into data patterns, this enhancement adds a crucial layer to your analytical arsenal.

Then, Steven goes into detail on .drap_na:

In the newest release of tidyAML there has been an addition of a new parameter to the functions fast_classification() and fast_regression(). The parameter is .drop_na and it is a logical value that defaults to TRUE. This parameter is used to determine if the function should drop rows with missing values from the output if a model cannot be built for some reason. Let’s take a look at the function and it’s arguments.

After that, we get to see an updated function:

In response to user feedback, we’ve enhanced the internal_make_wflw_predictions() function to provide a comprehensive set of predictions. Now, when you make a call to this function, it includes:

  1. The Actual Data: This is the real-world data that your model aims to predict. Having access to this information helps you assess how well your model is performing on unseen instances.
  2. Training Predictions: Predictions made on the training dataset. This is essential for understanding how well your model generalizes to the data it was trained on.
  3. Testing Predictions: Predictions made on the testing dataset. This is crucial for evaluating the model’s performance on data it hasn’t seen during the training phase.

You can also check out the package’s GitHub repository and see more.

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