Decision Mechanics

Insight. Applied.

R now has native pipes

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The latest release of R introduces an F#-style pipe operator.

Pipes have been available in R for a while, via the magrittr package. However, with the release of R 4.1.0, pipes are supported in base R.

The syntax has changed from that used in magrittr. The magrittr pipe operator is %>%. The new base R pipe operator is |>.

So, instead of

df %>% head()

we have

df |> head()

R 4.1.0 also introduces a lightweight, lambda-style function syntax, which can be usefully combined with the new pipe operator when piping to an argument other than the first one.

iris |> {\(x) lm(Sepal.Width ~ Sepal.Length, data = x)}()

COVID-19 epidemiology with R

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Interesting, and timely, article on using R to analyse COVID-19 incidence data collated by John Hopkins University.

It makes use of two epidemiology packages.

  • earlyR for estimation of infectiousness, as measured by the reproduction number (R), in the early stages of an outbreak.

  • EpiEstim for estimating the time varying instantaneous reproduction number during epidemics

These packages, and many more, are part of the R Epidemics Consortium (RECON). They

[…] gather experts in data science, modelling methodology, public health, and software development to create the next generation of analytics tools for informing the response to disease outbreaks, health emergencies and humanitarian crises, using the R software and other free, open-source resources.

Is it easier to learn R or Python?

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Andy Kirk at Visualising Data ran a Twitter poll about the relative accessibility of R and Python to non-developers.

59% said that R was more accessible.

Obviously, the poll is far from scientific, but the comments he received reflect my own experiences of teaching both languages—such as the significance of the RStudio IDE and the tidyverse packages in getting people off the ground.