Rython tips and tricks – Clipboard

For whatever reason, clipboard functionalities from Rython are under-utilized. One utility function for reversing backslashes is found here. This post demonstrates how you can use the clipboard to circumvent saving and loading files. It’s convenient for when you just want the quick insight or visual, rather than a full-blown replicable process.

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Rython tips and tricks – Snippets

R or Python? who cares! Which editor? now that’s a different story.

I like Rstudio for many reasons. Outside the personal, Rstudio allows you to write both R + Python = Rython in the same script. Apart from that, the editor’s level of complexity is well-balanced, not functionality-overkill like some, nor too simplistic like some others. This post shares how to save time with snippets (easy in Rstudio). Snippets save time by reducing the amount of typing required, it’s the most convenient way to program copy-pasting into the machine’s memory.

In addition to the useful built-ins snippets provided by Rstudio like lib or fun for R and imp or def for Python, you can write your own snippets. Below are a couple I wrote myself that you might find helpful. But first we start with how to use snippets.

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R tips and tricks – get the gist

In scientific programming speed is important. Functions written for general public use have a lot of control-flow checks which are not necessary if you are confident enough with your code.To quicken your code execution I suggest to strip run-of-the-mill functions to their bare bones. You can save serious wall-clock time by using only the laborers code lines. Below is a walk-through example of what I mean.

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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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Most popular posts – 2021

Kind of sad, but the same intro which served last year, befits this year also.

Littered with Corona, this year was not easy. But looking around me, I feel grateful. The following quote by Socrates comes to mind:

“If all our misfortunes were laid in one common heap whence everyone must take an equal portion, most people would be content to take their own and depart.”

On topic, as with previous years I checked my website traffic-analytics. Without further ado here are the three most popular posts for 2021.

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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 – 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 + Python = Rython

Enough! Enough with that pointless R versus Python debate. I find it almost as pointless as the Bayesian vs Frequentist “dispute”. I advocate here what I advocated there (“..don’t be a Bayesian, nor be a Frequenist, be opportunist“).

Nowadays even marginally tedious computation is being sent to faster, minimum-overhead languages like C++. So it’s mainly syntax administration we insist to insist on. What does it matter if we have this:

Or that

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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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