Category: R

Colin Gillespie shows how you can customize R via the .Rprofile file:

Every time R starts, it runs through a couple of R scripts. One of these scripts is the .Rprofile. This allows users to customise their particular set-up. However, some care has to be taken, as if this script is broken, this can cause R to break. If this happens, just delete the script!

Full details of how the .Rprofile works can be found in my book with Robin on Efficient R programming. However, roughly R will look for a file called .Rprofile first in your current working directory, then in your home area. Crucially, it will only load the first file found. This means you can have per project Rprofile.

Click through for a sample R profile which has a lot going on.

David Robinson has another fun puzzle:

Mathematician William Feller posed the following problem:

If you flip a coin times, what is the probability there are no streaks of heads in a row?

Note that while the number of heads in a sequence is governed by the binomial distribution, the presence of consecutive heads is a bit more complicated, because the presence of a streak at various points in the sequence isn’t independent

Click through for a solution in R.

David Robinson has fun with puzzle-solving:

Solving this puzzle in R is interesting enough, but it’s particularly challenging to do so in a computationally efficient way. As much as I love the tidyverse, this, like the “lost boarding pass” puzzle and Emily Robinson’s evaluation of the best Pokémon team, serves as a great example of using R’s matrix operations to work efficiently with data.

I’ve done a lot of puzzles recently, and I realized that showing the end result isn’t a representation of my thought process. I don’t show all the dead ends and bugs, or explain why I ended up choosing a particular path. So in the same spirit as my Tidy Tuesday screencasts, I recorded myself solving this puzzle (though not the process of turning it into a blog post).

Most of the post is analysis around the problem, but you do get a viable solution as well.

Tomaz Kastrun builds a validation function for Sudoku:

Function validater will validate for the sudoku board a particular solution at a particular position:

validater(sudoku, 1, c(1,4))

In matrix, at position x=1, y=4, where there is 0, it will test if number 1 is valid or not. If the number is valid, it returns TRUE (number) to outer function for finding complete solution.

This function iterates through all the possible 0-positions and iterates through solutions that are still available based on the rules:

Click through for that validation function.

Tomaz Kastrun takes us through calling CMD commands from R:

From time to time, when developing in R, working and wrangling data , preparing for machine learning projects, it comes the time, one would still need to access the operating system commands from/in R.

In this blog post, let’s take a look at some most useful cmd commands when using R.  Please note, that the cmd commands apply only to windows environment, for Linux/MacOS, the system commands should be slightly changed, but the wrapper R code should remains the same.

The need does come up, so it’s good to have that knowledge at hand.

Andrew Treadway shows how to use the Map() and mapply() functions in R:

An older post on this blog talked about several alternative base apply functions. This post will talk about how to apply a function across multiple vectors or lists with Map and mapply in R. These functions are generalizations of sapply and lapply, which allow you to more easily loop over multiple vectors or lists simultaneously.

The idea of Map in functional programming takes a bit of time to really wrap your head around, but once you do, it becomes extremely powerful. H/T R-bloggers