R Packages Download Stats

One big advantage of using open-source tools is the fantastic ecosystems that typically accompany them. Being able to tap into a massive open-source community, by way of downloading freely available code is decidedly useful. But, yes, there are downsides to downloads.

For one, there are too many packages out there. There are imperfect duplicates. You can easily end up downloading inferior code/package/module compared to existing other. Second, there is a matter of security. I myself try to refrain from downloading relatively new code, not yet tried-and-true. How do we know if a package is solid?

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Publication in Significance – code

Couple of months ago I published a paper in Significance – couple of pages describing the essence of deep learning algorithms, and why they are so popular. I got a few requests for the code which generated the figures in that paper. This weekend I reviewed my code and was content to see that I used a pseudorandom numbers, with a seed (as oppose to completely random numbers; without a seed). So now the figures are exactly reproducible. The actual code to produce the figures, and the figures themselves (e.g. for teaching purposes) are provided below.

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R tips and tricks – shell.exec

When you startup your machine, the first thing you do is to open the various programs you work with. Examples: your note-taking program, the pdf file that you need to read, the ppt file you were last working on, and of course your strongest link with the outside world nowadays; your email box. This post shows how to automate this process. Windows machines notoriously need restarting for every little (un)install. I trust you will find this startup automation advice handy.

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R tips and tricks – readClipboard

Here is a small utility function to save you some boring work.

Say you have a file to read into R. The file path is C:\Users\folder1\folder2\folder3\mydata.csv. So what do you do? you copy the path, paste it to the editor, and start reversing the backslash into a forward slash so that R can read your file.

With the help of the rstudioapi package, the readClipboard function and the following function:

You can
1. Simply copy the path C:\Users\folder1\folder2\folder3\mydata.csv
2. execute pathh <- get_path()
3. use pathh which is now R-ready.

No more reversing or escaping backslash.

R tips and tricks – Timing and profiling code

Modern statistical methods use simulations; generating different scenarios and repeating those thousands of times over. Therefore, even trivial operations burden computational speed.

In the words of my favorite statistician Bradley Efron:

“There is some sort of law working here, whereby statistical methodology always expands to strain the current limits of computation.”

In addition to the need for faster computation, the richness of open-source ecosystem means that you often encounter different functions doing the same thing, sometimes even under the same name. This post explains how to measure the computational efficacy of a function so you know which one to use, with a couple of actual examples for reducing computational time.

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R Journal publication

The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.

Christoph Weiss, Gernot Roetzer and myself have joined forces to write an R package and the accompanied paper: Forecast Combinations in R using the ForecastComb Package, which is now published in the R journal. Below you can find a few of my thoughts about the journey towards publication in the R journal, and a few words about working with a small team of three, from three different locations.

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R tips and tricks – higher-order functions

A higher-order function is a function that takes one or more functions as arguments, and\or returns a function as its result. This can be super handy in programming when you want to tilt your code towards readability and still keep it concise.

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R tips and tricks – the assign() function

The R language has some quirks compared to other languages. One thing which you need to constantly watch for when moving to- or from R, is that R starts its indexing at one, while almost all other languages start indexing at zero, which takes some getting used to. Another quirk is the explicit need for clarity when modifying a variable, compared with other languages.

Take python for example, but I think it looks the same in most common languages:

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R tips and tricks – the pipe operator

The R language has improved over the years. Amidst numerous splendid augmentations, the magrittr package by Stefan Milton Bache allows us to write more readable code. It uses an ingenious piping convention which will be explained shortly. This post talks about when to use those pipes, and when to avoid using pipes in your code. I am all about that bass readability, but I am also about speed. Use the pipe operator, but watch the tradeoff.

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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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