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Category: R

What is Parquet and Why Use It?

Published 2021-09-29 by Kevin Feasel

The folks at Jumping Rivers explain what the Parquet file format is and how you can use it in R:

Apache Parquet is a popular column storage file format used by Hadoop systems, such as Pig, Spark, and Hive. The file format is language independent and has a binary representation. Parquet is used to efficiently store large data sets and has the extension .parquet. This blog post aims to understand how parquet works and the tricks it uses to efficiently store data.

Read on for that explanation and plenty of sample code.

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Rolling Means with MazamaRollUtils

Published 2021-09-28 by Kevin Feasel

Jonathan Callahan has an interesting R package for us:

The initial release of MazmaRollUtils provides all the basic rolling functions with features like alignment and missing value removal along with additional capabilities for smoothing, damping and outlier detection — all common activities in time series analysis.

Click through for an explanation of the process, and then check out the package itself on GitHub. H/T R-Bloggers.

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Analog and Digital Clocks in R

Published 2021-09-17 by Kevin Feasel

Tomaz Kastrun reminds me of xclock:

It is all about measuring time using useless clocks. Script takes a system time and displays any given clock in a rather “static” way. You can choose between analog, small digital and big digital clock. And when playing with the time, you can also learn something new.

Click through to see how to make an analog clock plot in R, and then try it again with a digital clock.

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magrittr’s Four Pipes

Published 2021-09-09 by Kevin Feasel

Gregory Janesch shows off the various pipes in the magrittr R package:

The magrittr package is a part of the extended tidyverse – i.e., not one of the ones normally loaded. It is the one that supplies the pipe operator (%>%), but it turns out that the package actually contains four pipe operators in total. All are intended to streamline and improve the readability of code, though the three non-basic ones are a bit more situational, and I’ve rarely seen them used, so I thought I would go into them a bit.

The CRAN page for magrittr is here; much of this post is based off of the package’s vignettes and documentation.

Click through for demonstrations of each. I’ve only seen the basic pipe in use as well, but the others look quite interesting and I can see use cases where knowing about them would be helpful. Also, note in the comments about the secret 5th pipe. H/T R-Bloggers

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Subgroup Analysis via Bayesian Hierarchical Modeling

Published 2021-09-03 by Kevin Feasel

Keith Goldfield ponders subgroup analysis:

Which got me thinking, of course, about subgroup analyses. In the context of a null hypothesis significance testing framework, it is well known that conducting numerous post hoc analyses carries the risk of dramatically inflating the probability of a Type 1 error – concluding there is some sort of effect when in fact there is none. So, if there is no overall effect, and you decide to look at a subgroup of the sample (say patients over 50), you may find that the treatment has an effect in that group. But, if you failed to adjust for multiple tests, than that conclusion may not be warranted. And if that second subgroup analysis was not pre-specified or planned ahead of time, that conclusion may be even more dubious.

If we use a Bayesian approach, we might be able to avoid this problem, and there might be no need to adjust for multiple tests. I have started to explore this a bit using simulated data under different data generation processes and prior distribution assumptions. It might all be a bit too much for a single post, so I am planning on spreading it out a bit.

Read on for two separate Bayesian model approaches to the problem. H/T R-Bloggers.

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Using tsoutliers() to Detect Time Series Outliers

Published 2021-09-02 by Kevin Feasel

Rob J. Hyndman shows off a function in the forecast package in R:

The tsoutliers() function in the forecast package for R is useful for identifying anomalies in a time series. However, it is not properly documented anywhere. This post is intended to fill that gap.

The function began as an answer on CrossValidated and was later added to the forecast package because I thought it might be useful to other people. It has since been updated and made more reliable.

Read on to see how it works. This is one of the reasons I like the R programming language so much for data analysis and statistics: usually, somebody smarter than me has already built a solution to the problem and it’s just a matter of finding the right function. H/T R-Bloggers

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Estimating the Likelihood of an Underdog Winning at Soccer

Published 2021-08-27 by Kevin Feasel

Holger von Jouanne-Diedrich lays out the math for us:

The Bundesliga is Germany’s primary football league. It is one of the most important football leagues in the world, broadcast on television in over 200 countries.

If you want to get your hands on a tool to forecast the result of any game (and perform some more statistical analyses), read on!

What I would like is a tool which has SC Freiburg utterly dominating Bayern. Said tool may be more mythological than scientific (or at least a copy of Football Manager and a little bit of save scumming…), but I’ll take it.

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From API Call to ML Services Prediction

Published 2021-08-20 by Kevin Feasel

Tomaz Kastrun continues a series:

From the previous two blog posts:

– Creating REST API for reading data from Microsoft SQL Server in web browser

– Writing Data to Microsoft SQL Server from web browser using REST API and node.js

We have looked into the installation process of Node.js, setup of Microsoft SQL Server and made couple of examples on reading the data from database through REST API and how to insert data back to database.

In this post, we will be looking the R predictions using API calls against a sample dataset.

Click through to see it in action.

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A Learning Path for Data Science with R

Published 2021-08-19 by Kevin Feasel

Holger von Jouanne-Diedrich has a greatest hits album:

Over the course of the last two and a half years, I have written over one hundred posts for my blog “Learning Machines” on the topics of data science, i.e. statistics, artificial intelligence, machine learning, and deep learning.

I use many of those in my university classes and in this post, I will give you the first part of a learning path for the knowledge that has accumulated on this blog over the years to become a well-rounded data scientist, so read on!

Read on for links to dozens of posts on interesting topics.

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