Category: R

James Rowland-Jones has an update for us:

The importance of R was first recognized by the SQL Server team back in 2016 with the launch of SQL ML Services and R Server. Over the years we have added Python to SQL ML Services in 2017 and Java support through our language extensions in 2019. Earlier this year we also announced the general availability of SQL ML Services into Azure SQL Managed Instance. SparkR, sparklyr, and PySpark are also available as part of SQL Server Big Data Clusters. We remain committed to R.

With that said, much has changed in the world of data science and analytics since 2016. Microsoft’s approach to open-source software has undergone a similar transformation in the same period. It is therefore time for us to share how we, in Azure SQL and SQL Server, are changing to meet the needs of our users and the R community moving forward.

I never used ML Server (but have used SQL Server ML Services a lot), so that part of the announcement doesn’t affect me and I’m not sure how many organizations it does affect. Switching to CRAN R is a good idea and I appreciate that they’re open-sourcing the RevoScaleR and revoscalepy code bases. The one thing I’d really like to see in vNext’s Machine Learning Services is an easy way to update the version of R

Holger von Jouanne-Diedrich shows how many ways there are to solve a problem of squares:

This time we want to solve the following simple task with R: Take the numbers 1 to 100, square them, and add all the even numbers while subtracting the odd ones!

If you want to see how to do that in at least seven different ways in R, read on!

There are many different solutions possible, making use of several aspects of the R language. So this blog post can be seen as a fun exercise to recap some of the concepts explained in our introduction to R: Learning R: The Ultimate Introduction (incl. Machine Learning!).

Give it a try and then check out the variety of solutions.

Sang-Heon Lee looks at reinvestment risk:

From this post, we can learn the reinvestment risk of coupon bond. It is worth noting that 1) YTM is attainable when roll rate is the same as YTM and 2) The argument that coupon rate is equal to YTM at issuance (par yield) is only applied to standard coupon bond with in arrears interest payment schedule. Unlike standard coupon bond, coupon bond with in advance interest payment has a higher YTM than coupon rate at an issuance.

Click through for the explanation as well as the R code used. H/T R-Bloggers.

Holger von Jouanne-Diedrich builds out payoff diagrams:

Not many people understand the financial alchemy of modern financial investment vehicles, like hedge funds, that often use sophisticated trading strategies. But everybody understands the meaning of rising and falling markets. Why not simply translate one into the other?

If you want to get your hands on a simple R script that creates an easy-to-understand plot (a profit & loss profile or payoff diagram) out of any price series, read on!

Click through for several examples of code and financial instruments.

Bernardo Lares takes us through the lares library’s caching functionality:

If you’ve never heard of cache (/kaSH/) before, Google it and you’ll quickly find that it is “a collection of items of the same type stored in a hidden or inaccessible place”. Basically, you have “something” stored “somewhere” so you can fetch it “sometime” later. If it sounds basic, it (can be) is! This simple technic can come quite handy when you are coding functions that take some time to gather and/or process the data you’re working with. In other words, think of those processes that take some time to run and there’s really no need to re-run it “every time” because the outcome will be exactly the same. Also, you are unnecessarily spending time, computer power, and real energy when you re-process cache-able stuff.

Today I’ll show you how I use cache in R to accelerate results, avoid re-processing, and improve UX for my users using the lareslares library. Let’s see a couple of functions that actually leverage cache usage and how can you start using them.

Read on for a walkthrough of the process.

Peter Solymos takes us through configuring a Shiny application which runs via systemd:

Lots of resources describe how you can host Shiny apps with Docker, Shiny Server, or via other means. But we also know Shiny apps can be launched locally. What makes your local setup different from these other options is that your local machine does not usually have a static internet protocol (IPv4) address. Without a static IPv4, it is really hard to share the app with other people because the address keeps changing unpredictably, and you might sometimes power off your machine.

Shiny uses the httpuv R package under the hood which is an HTTP and websocket server library. Could you just run Shiny directly on a remote server? This post explores this topic using systemd the system and service manager for most modern Linux distributions. All I am trying to do in this post is to make a point that the shiny R package is really self-sufficient and in the simplest case, it does not need any other layer for sharing an app.

Click through for the process. H/T R-Bloggers.

Holger von Jouanne-Diedrich wins a truth-telling contest two towns over:

A short one for today: in this post we will learn how to easily create truth tables with R and will contribute our code to the growing repository of Rosetta code. I hope that you will learn a few tricks along the way, so read on!

Read on for a short function which generates truth tables based on an expression. Very cool.