Category: R

Sang-Heon Lee does some analysis:

Pairs trading literature use the Hurst exponent frequently since it gives an simple and intuitive indicator for the behavior of stock returns. Using S&P 500 returns, let’s learn how to estimate it using R code manually and then use R package conveniently.

Click through for those two examples, as well as a more detailed explanation of the math driving this. H/T R-Bloggers.

Tomaz Kastrun gives us a solution to the Traveling Salesman Problem:

Travelling Salesman Problem is an NP-complete problem and an old mathematical problem. For this useless function, we will look for the nearest city from the previous city (or starting point) and repeat until we visit all cities. The greedy solution is fairly simplified but one disadvantage; it might not give you the best path (optimal solution) and proving that the solution is correct is an additional issue 

As Tomaz notes, this is not guaranteed to be the best solution, just a solution. Considering that TSP is NP-hard, if Tomaz did have a globally optimal solution for us, he certainly wouldn’t be calling it ‘useless-useful’ but instead would be calling it “My prize-winning algorithm.”

Dominik Rafacz loves infix functions:

Custom infix functions are one of my favorite features in R. This article is my love letter to them. But first, a quick recap.

For those unfamiliar with the terminology, infix function is a function fun which is called using infix notation, e.g., x fun y instead of fun(x, y). Those functions are also called infix operators by base R, and I will use those terms and name infixes interchangeably. There are a lot of infix operators in base R used very frequently, i.e., arithmetic or logical operators. We use them so often that we usually forget that they are functions. And that we can call them just like regular functions.

Infix functions are something I tend to forget entirely about developing on my own but they can be extremely useful, as Dominik shows. H/T R-Bloggers.

Tomaz Kastrun shares some advice:

Tidyverse provides a handful of great functions for operating across multiple columns simultaneously. Across is a function, that makes it easy to apply the same transformation over numerous columns in summarise() and mutate() functions.

Across accepts two arguments; a) array of columns and b) function or list of functions to be applied to selected columns.

Check out eight tips for working with packages in R’s tidyverse.