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Category: R

The Basics of Autoregressive Models

Holger von Jouanne-Diedrich explains some of the principels of autoregressive models through a demonstration:

Well, this seems to be good news for the sales team: rising sales! Yet, how does this model arrive at those numbers? To understand what is going on we will now rebuild the model. Basically, everything is in the name already: auto-regressive, i.e. a (linear) regression on (a delayed copy of) itself (auto from Ancient Greek self)!

So, what we are going to do is create a delayed copy of the time series and run a linear regression on it. We will use the lm() function from base R for that (see also Learning Data Science: Modelling Basics).

Read on for some additional understanding.

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Using Specific R Package Versions in Docker Images

Roman Lustrik shares how to fix package versions in Docker images:

Using package in R is easy. You install from CRAN using install.packages("packagename"), it resolves dependencies and you’re good to go. What R natively doesn’t handle so well is installing a particular package version without jumping through hoops. Technically you need the source file of the package version you want to install AND all source files of the dependencies (in the correct version, of course). This has been made almost seamless with packages packrat and recently, renv.

This comes handy when you are constructing a Docker file to run in production. Usually you want to run this defensively and do not want things to change from one image build to another. To get there, you can save all your package names and version into a file (renv.lock) and use that to reconstruct the now defined package structure with predictable versions (see renv vignette here).

This is quite useful as R package developers tend not to covet backwards compatibility, and one of the key benefits of containers is to have the option to keep the same code base and configuration in all environments.

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Using INLA for Spatial Regression in R

Lionel Hertzog continues a series on spatial regression:

INLA is a package that allows to fit a broad range of model, it uses Laplace approximation to fit Bayesian models much, much faster than algorithms such as MCMC. INLA allows for fitting geostatistical models via stochastic partial differential equation (SPDE), a good place for more background informations on this are these two gitbooks: spde-gitbook and inla-gitbook.

This is not the gentlest introduction, so if you’re new to the concept go back and read part 1.

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Data Visualization in R

Dan Fitton provides an introductory overview to several visualization tools in R:

The other way to communicate data with R is to produce an interactive dashboard or web application within R using Shiny. Whereas Markdown reports are most useful for explanatory analysis; Shiny, in my opinion, is useful for exploratory data analysis. This is when you want to display information for investigative purposes, allowing the user to gain greater familiarity by having the ability to interact with data, filter it, and dig deeper into the underlying details.

Shiny is incredibly flexible, providing the user the capability of turning their R code and objects, including tables, plots, and analysis, into a comprehensive and interactive web page or app, without requiring a fully-fledged web development skillset. Although there is a steep learning curve, the freedom and precision Shiny brings means that for the most part you are limited only by your skillset rather than the tool itself.

I’ve seen some really useful Shiny dashboards. Dan is right that there can be a lot of work put into getting them right, but if you do, the results can be outstanding.

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Text Customization with ggtext

Abdul Majed Raja shows an example of using the ggtext library:

ggplot2 is go-to R package for anyone who wants to make beautiful static visualizations in R. But most ggplot2 gplots look almost the same and little many data analysts or data scientists care about customizing it, primarily because it’s not very intuitive to do so. That’s where ggplot2 extensions come in very handy. ggtext is an R package (by Claus O. Wilke) that helps in customizing the text present in ggplot2 plots. It could be the text outside the plot canvas or the text (annotation) within the plot canvas.

Click through for the code sample and video. H/T R-Bloggers.

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The Basics of A/B Testing with R

Holger von Jouanne-Diedrich walks us through a simple example of A/B testing and analysis using R:

The bad news is, that you have to understand a little bit about statistical hypothesis testing, the good news is that if you read the following post, you have everything you need (plus, as an added bonus R has all the tools you need already at hand!): From Coin Tosses to p-Hacking: Make Statistics Significant Again! (ok, reading it would make it over one minute…).

Check out that article and the example in the blog post as well. R makes it really easy to perform this sort of analysis.

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Obfuscating Data in SQL Server

Dave Mason has a data obfuscator:

In a previous post, I explored an option for generating fake data in sql server using Machine Learning services and the R language. I’ve expanded on that by creating some stored procedures that can be used for both generating data sets of fake data, and for obfuscating existing SQL Server data with fake data.

The code is available in a Github repository. For now, it consists of ten stored procedures. 

Unlike something like Dynamic Data Masking, this is a permanent update to the table. That makes it quite helpful for getting production distributions and use cases into non-production environments.

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Vectorized R I/O in Apache Spark 3.0

Hyukjin Kwon gives us a preview of SparkR improvements in Apache Spark 3.0:

When SparkR does not require interaction with the R process, the performance is virtually identical to other language APIs such as Scala, Java and Python. However, significant performance degradation happens when SparkR jobs interact with native R functions or data types.

Databricks Runtime introduced vectorization in SparkR to improve the performance of data I/O between Spark and R. We are excited to announce that using the R APIs from Apache Arrow 0.15.1, the vectorization is now available in the upcoming Apache Spark 3.0 with the substantial performance improvements.

This blog post outlines Spark and R interaction inside SparkR, the current native implementation and the vectorized implementation in SparkR with benchmark results.

Certain operations get ridiculously faster with this change.

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