Decision Mechanics

Insight. Applied.

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.

Jupyter Notebooks—love ’em or hate ’em?

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Jupyter Notebooks are popular with data scientists. Microsoft even offers a free, hosted, “no-install” service for Python, R and F#.

However, there are some downsides to notebooks—mostly to do with software engineering best practices.

Joel Grus gave a provocative talk at JupyterCon 2018 entitled “I Don’t Like Notebooks”. Yihui Xie then followed up with a response to Grus’ talk.

Both authors make a good case and have interesting points. As ever, the truth is that notebooks are good in some situations and not so good in others.

Personally, I use both. Notebooks for smaller, exploratory, data science projects and IDEs (Visual Studio Code, PyCharm and RStudio) for everything else.

Python tops programming language list

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Python has topped the IEEE Spectrum list of top programming languages again this year—extending its lead in the process.

The sources used to compiled the list cover

contexts that include social chatter, open-source code production, and job postings.

Obviously that list of sources isn’t an accurate reflection of what developers are doing day-to-day in organisations. Any list of top programming languages that puts R (#7) above JavaScript (#8) clearly has some methodological challenges. My belief is that the list reflects the current buzz around data science.

However, interest in Python clearly remains high. As it does in R—#7 is impressive for a domain-specific language.