R tips and tricks – Faster Loops

Insert or bind?

This is the first in a series of planned posts, sharing some R tips and tricks. I hope to cover topics which are not easily found elsewhere. This post has to do with loops in R. There are two ways to save values when looping:
1. You can predefine a vector and fill it, or
2. you can recursively bind the values.

Which one is faster?

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Good coding practices – part 2

Introduction

In part 1 of Good coding practices we considered how best to code for someone else, may it be a colleague who is coming from Excel environment and is unfamiliar with scripting, a collaborator, a client or the future-you, the you few months from now. In this second part, I give some of my thoughts on how best to write functions, the do’s and dont’s.

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Good coding practices – part 1

Introduction

At work, I recently spent a lot of time coding for someone else, and like anything else you do, there is much to learn from it. It also got me thinking about scripting, and how best to go about it. To me it seems that the new working generation mostly tries to escape from working with Excel, but “let’s not kid ourselves: the most widely used piece of software for statistics is Excel” (Brian D. Ripley). this quote is 15 years old almost, but Excel still has a strong hold on the industry.

Here I discuss few good coding practices. Coding for someone else is not to be taken literally here. ‘Someone else’ is not necessarily a colleague, it could just as easily be the “future you”, the you reading your code six months from now (if you are lucky to get responsive referees). Did it never happened to you that your past-self was unduly cruel to your future-self? that you went back to some old code snippets and dearly regretted not adding few comments here and there? Of course it did.

Unlike the usual metric on which “good” is usually measured by when it comes to coding: good = efficient, here the metric would be different: good = friendly. They call this literate programming. There is a fairly deep discussion about this paradigm by John D. cook (follow what he has to say if you are not yet doing it, there is something for everyone).

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Forecast averaging example

Especially in economics/econometrics, modellers do not believe their models reflect reality as it is. No, the yield curve does NOT follow a three factor Nelson-Siegel model, the relation between a stock and its underlying factors is NOT linear, and volatility does NOT follow a Garch(1,1) process, nor Garch(?,?) for that matter. We simply look at the world, and try to find an apt description of what we see.

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Live volatility monitor

In April this year, Rstudio notified early users of shiny that Glimmer and Spark servers which host interactive-applications would be decommissioned. Basically, the company is moving forward to generate revenues from this great interactive application service. For us aspirants who use the service strictly as a hobby, that means, in a word: pay.

Basic subscription now costs around 40$ per month. Keeping your applications free of charge is possible BUT, as long as it is not used for more than 25 hours per month. So if your site generate some traffic, most users would simply not be able to access the app. Apart from that, you are subject to some built-in Rstudio’s logo which can’t be removed without having a paid subscription. That is a shame, but a company’s gotta eat right? I am using Rstudio’s services from their very beginning, and the company definitely deserve to eat! only I wish there would be another step between the monthly 0$ option which provides too slim capabilities, and the monthly 40$ option which is, in my admittedly biased opinion, too pricey for a ‘sometimes’ hobby.

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R and Dropbox

When you woRk, you probably have a set of useful functions/packages you constantly use. For example, I often use the excellent quantmod package, and the nice multi.sapply function. You want your tools loaded when R session fires.

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Intraday volatility measures

In the last few decades there has been tremendous progress in the realm of volatility estimation. A major step is the additional use of intraday price path. It has been shown that estimates which consider intraday information are more accurate. Which is to say they converge faster to the real unobserved value of the true volatility.

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