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Category: R

Converting Odds to Probabilities with R

Published 2020-03-02 by Kevin Feasel

Jonas Christoffer Lindstrom has a new package:

Now you might think that converting decimal odds to probabilities should be easy, you can just use the definition above and take the inverse of the odds to recover the probability. But it is not that simple, since in practice using this simple formula will give you improper probabilities. They will not sum to 1, as they should, but be slightly larger. This gives the bookmakers an edge and the probabilities (which aren’t real probabilities) can not be considered fair, and so different methods for correcting this exists.

Read on to learn more about the problem and a few solutions. H/T R-Bloggers.

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Pivoting Data in R

Published 2020-02-26 by Kevin Feasel

Dave Mason shows how you can pivot SQL Server data using Machine Learning Services and R:

Pivoting data in SQL Server is something that comes up infrequently for me. Whenever the need arises, I have to pause and ask myself “What is it I’m trying to do again?”. Next I go to the documentation for the T-SQL PIVOT syntax (which I’ll never commit to memory) and realize for the umpteenth time the pivoted values have to be hard coded. Then I ponder using dynamic T-SQL because I won’t always know the values to pivot at query design time.

If T-SQL isn’t a good hammer to PIVOT’s nail, perhaps R is. There are different packages that make summarizing and transposing data frames somewhat easy. It can even dynamically pivot unknown values at runtime. But there is a catch, which I’ll get to in a bit.

This excerpt ends on a cliffhanger, so you’ll have to read Dave’s post to learn about the catch.

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Publishable Adverse Event Tables in R

Published 2020-02-13 by Kevin Feasel

Inge Christoffer Olsen shows how to clean up tables in R for publication:

The summary of Adverse Events is a nice table just summing up the adverse events in the trial. Note the “[N] n (%)”-format which is the number of events, number of patients with events and percentage of patients with event.

This particular example is about adverse events, but the key concepts in the code apply to many kinds of tables you want to make look a bit nicer. H/T R-Bloggers

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Monitoring for Distribution Changes

Published 2020-02-13 by Kevin Feasel

Nina Zumel explains how we can track if something has changed by monitoring its distribution:

A client recently came to us with a question: what’s a good way to monitor data or model output for changes? That is, how can you tell if new data is distributed differently from previous data, or if the distribution of scores returned by a model have changed? This client, like many others who have faced the same problem, simply checked whether the mean and standard deviation of the data had changed more than some amount, where the threshold value they checked against was selected in a more or less ad-hoc manner. But they were curious whether there was some other, perhaps more principled way, to check for a change in distribution.

The answer is, of course, that there is. Click through to see a few of the techniques.

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Changes in the R foreach Package

Published 2020-02-12 by Kevin Feasel

Hong Ooi announces some changes to the foreach package in R:

This post is to announce some new and upcoming changes in the foreach package.

First, foreach can now be found on GitHub! The repository is at https://github.com/RevolutionAnalytics/foreach, replacing its old home on R-Forge. Right now the repo hosts both the foreach and iterators packages, but that may change later.

There are also some changes to the package itself, so read on for those.

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Pulling R Packages from Fedora

Published 2020-02-11 by Kevin Feasel

Inaki Ucar has an interesting project:

Bringing R packages to Fedora (in fact, to any distro) is an Herculean task, especially considering the rate at which CRAN grows nowadays. So I am happy to announce the cran2copr project, which is an attempt to maintain binary RPM repos for most of CRAN (~15k packages as of Feb. 2020) in an automated way using Fedora Copr.

Click through for installation instructions if you’re using an RPM-based Linux distribution like Fedora or CentOS. H/T R-Bloggers.

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Calculating Distances in R

Published 2020-02-10 by Kevin Feasel

Chris Brown gives us three ways to calculate distance in R:

Calculating a distance on a map sounds straightforward, but it can be confusing how many different ways there are to do this in R.

This complexity arises because there are different ways of defining ‘distance’ on the Earth’s surface.

The Earth is spherical. So do you want to calculate distances around the sphere (‘great circle distances’) or distances on a map (‘Euclidean distances’).

Then there are barriers. For example, for distances in the ocean, we often want to know the nearest distance around islands.

Then there is the added complexity of the different spatial data types. Here we will just look at points, but these same concepts apply to other data types, like shapes.

Read on to learn these three separate techniques. H/T R-Bloggers.

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