Effective COVID-19 dashboard showing UK cases—from Public Health England.
It makes use of two epidemiology packages.
earlyRfor estimation of infectiousness, as measured by the reproduction number (R), in the early stages of an outbreak.
EpiEstimfor 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.
As companies start to take data more seriously we are seeing more of them publishing data design guidelines. Three comprehensive examples are
Consider adopting one of these if your organisation is just starting to get into data visualisation—or if your existing charts aren’t up to scratch.
Pandas is a Python package for working with tabular data—as in a spreadsheet or database table. It provides similar functionality to R’s data frames.
As pandas is rich in features it can be difficult to remember all its operations and syntax, so Enthought have produced a visual guide to the package in 8 handy pages.
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.