Press "Enter" to skip to content

Category: R

Understanding the Poisson Distribution

Published 2022-06-23 by Kevin Feasel

Achim Zeileis shows off my favorite statistical distribution:

The Poisson distribution has many distinctive features, e.g., both its expectation and variance are equal and given by the parameter λλ. Thus, E(Y)=λE(Y)=λ and Var(Y)=λVar(Y)=λ. Moreover, the Poisson distribution is related to other basic probability distributions. Namely, it can be obtained as the limit of the binomial distribution when the number of attempts is high and the success probability low. Or the Poisson distribution can be approximated by a normal distribution when λλ is large. See Wikipedia (2002) for further properties and references.

Here, we leverage the distributions3 package (Hayes et al. 2022) to work with the Poisson distribution in R. In distributions3, Poisson distribution objects can be generated with the Poisson() function. Subsequently, methods for generic functions can be used print the objects; extract mean and variance; evaluate density, cumulative distribution, or quantile function; or simulate random samples.

Read on for a detailed tutorial. H/T R-bloggers.

Comments closed

Currency Conversion with priceR

Published 2022-06-20 by Kevin Feasel

Bryan Shalloway needs to make change for a trillion Zimbabwe dollars (prior to revaluation):

In this post I’ll walk through an example of how to convert between currencies. A challenge is that the conversion rate is constantly changing. If you have historical data you’ll want the conversion to be based on what the exchange rate was at the time. Hence the fields you need when doing currency conversion are:

1. Date of transaction

2. Start currency (what you’ll be converting from)

3. End currency (what you’ll be converting to)

4. Price (in units of starting currency)

Bryan also makes the smart move by memoizing the data first, as those API calls can get expensive otherwise.

Comments closed

Converting between Decimal and Binary

Published 2022-06-20 by Kevin Feasel

Tomaz Kastrun has run out of useless functions and has to create useful ones:

How does the conversion between decimal to binary or from binary to decimal behave? With another useless function, I have plotted the points (x = decimal number, y = converted binary number) on a scatter plot. Just to find out that the graph shows the binomial distribution function.

Read on for the conversion process and a fun analysis.

Comments closed

Building a Q&A Engine in R with httr and Shiny

Published 2022-06-13 by Kevin Feasel

Benjamin Smith builds an oracle but with R, not Delphi:

Knowing how to write API requests and handle their responses is a valuable skill that a developer, data
engineer or data analyst/scientist needs to know. In this short blog I share how its possible leverage DuckDuckGo’s instant answer API to create a oracle which can answer (some) of your questions using the httr package and Shiny.

Click through for a simple app which does the job.

Comments closed

Creating Reproducible Examples with CI

Published 2022-06-03 by Kevin Feasel

Colin Gillespie and Jack Walton tackle a common training problem:

As the number of courses we offer increased, so did the maintenance burden of our associated training materials (lecture notes, slides, exercises, and more). To ease this burden, and to assist in ensuring that our training materials build consistently, we developed an R package called {jrNotes2}. Amongst other things, this package ensures that all courses:

– have identical “template files”: .gitlab-ci.yml, .gitignore, Makefiles, index.Rmd, …;

– have the same directory structure, and

– pass a set of quality-assurance checks.

This is smart but read on to see why it’s still a challenge. This is especially true in the R and Python worlds, where breaking changes seem to be so common.

Comments closed

Web Accessibility and Shiny

Published 2022-05-20 by Kevin Feasel

Jamie Owen has a two-parter. First up, why web accessibility standards are important:

An accessible website is more than putting content online. Making a website accessible means ensuring that it can be used by as many people as possible. Accessibility standards such as the Web Content Accessibility Guidelines (WCAG) help to standardise the way in which a website can interact with assistive technologies. Allowing developers to incorporate instructions into their web applications which can be interpreted by technologies such as screen readers helps to maintain a consistent user experience for all.

Second, how Shiny apps tend to stack up:

The great thing about {shiny} is that it allows data practitioners a relatively simple, quick approach to providing an intuitive user interface to their R code via a web application. So effective is {shiny} at this job that it can be done with little to no traditional web development knowledge on the part of the developer. {shiny} and associated packages provide collections of R functions that return HTML, CSS and JavaScript which is then shipped to a browser. The variety of packages giving trivial access to styled front end components and widgets is already large and constantly growing. What this means is that R programmers can achieve a huge amount in the way of building complex, visually attractive web applications without needing to care very much about the underlying generated content that is interpreted by the browser.

As a quick spoiler, not so well. Read on for the full report.

Comments closed

Importing Data into R

Published 2022-05-12 by Kevin Feasel

Sebastian Sauer shows off several ways of loading data into R:

Importing data into R can cause headaches for newbies. For some, the concept of relative and absolute paths is new. That’s why I compiled here some recommendations on how to important data into R and on how to ditch the “what’s my path” problem.

Click through for some notes. This post focused on files rather than databases, though that’s a very common way of loading data as well.

Comments closed

Movie Color Swaps in R

Published 2022-05-09 by Kevin Feasel

Mark White does some coloration switcharoos:

I also love film, and I started thinking about ways I could generate color palettes from films that use color beautifully. There are a number of packages that can generate color palettes from images in R, but I wanted to try writing the code myself.

I also wanted to not just generate a color palette from an image, but then swapping it with a different color palette from a different film. This is similar to neural style transfer with TensorFlow, but much simpler. I’m one of those people that likes to joke how OLS is undefeated; I generally praise the use of simpler models over more complex ones. So instead of a neural network, I use k-means clustering to transfer a color palette of one still frame from a film onto another frame from a different movie.

There are some interesting outcomes in the post, including a mashup of 2001: A Space Odyssey’s color scheme onto Arrival, as well as Kill Bill and Dr. Strangelove. The latter reminds me of a still from the credits sequence to a 1970s movie. H/T R-Bloggers.

Comments closed