coding | ConservationBytes.com

Free resources for learning (and getting better with) R

15 11 2021

While I’m currently in Github mode (see previous post), I thought I’d share a list of resources I started putting together for learning and upskilling in the R programming language.

If you don’t know what R is, this probably won’t be of much use to you. But if you are a novice user, want to improve your skills, or just have access to a kick-arse list of cheatsheets, then this Github repository should be useful.

I started putting this list together for members of the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage, but I see no reason why it should be limited to that particular group of people.

I don’t claim that this list is exhaustive, nor do I vouch for the quality of any of the listed resources. Some of them are deprecated and fairly old too, so be warned.

The first section includes online resources such as short courses, reference guides, analysis demos, tips for more-efficient programming, better plotting guidelines, as well as some R-related mini-universes like markdown, ggplot, Shiny, and tidyverse.

The section following is a list of popular online communities, list-servers, and blogs that help R users track down advice for solving niggly coding and statistical problems.

The next section is a whopping-great archive of R cheatsheets, covering everything from the basics, plotting, cartography, databasing, applications, time series analysis, machine learning, time & date, building packages, parallel computing, resampling methods, markdown, and more.

Read the rest of this entry »

Comments : 2 Comments »
Tags: coding, Github, help, learning, Python, resources, statistics
Categories : modelling, statistics

The ε-index app: a fairer way to rank researchers with citation data

9 11 2020

Back in April I blogged about an idea I had to provide a more discipline-, gender-, and career stage-balanced way of ranking researchers using citation data.

Most of you are of course aware of the ubiquitous h-index, and its experience-corrected variant, the m-quotient (h-index ÷ years publishing), but I expect that you haven’t heard of the battery of other citation-based indices on offer that attempt to correct various flaws in the h-index. While many of them are major improvements, almost no one uses them.

Why aren’t they used? Most likely because they aren’t easy to calculate, or require trawling through both open-access and/or subscription-based databases to get the information necessary to calculate them.

Hence, the h-index still rules, despite its many flaws, like under-emphasising a researcher’s entire body of work, gender biases, and weighting towards people who have been at it longer. The h-index is also provided free of charge by Google Scholar, so it’s the easiest metric to default to.

So, how does one correct for at least some of these biases while still being able to calculate an index quickly? I think we have the answer.

Since that blog post back in April, a team of seven scientists and I from eight different science disciplines (archaeology, chemistry, ecology, evolution & development, geology, microbiology, ophthalmology, and palaeontology) refined the technique I reported back then, and have submitted a paper describing how what we call the ‘ε-index’ (epsilon index) performs.

Read the rest of this entry »

Comments : 4 Comments »
Tags: bibliometrics, citations, code, coding, epsilon index, Github, h-index, ε-index, m-index, performance, ranking, RStudio, Shiny, Shiny app
Categories : gender, mathematics, scientific publishing

Ecological Network Analysis Workshop

8 04 2019

network-transformation-optimizednfv-16x9.jpg.rendition.intel.web.480.270 We are most fortunate that Dr Giovanni Strona of the EU Joint Research Centrein Ispra, Italy, will be visiting Flinders University for most of April. As a recipient of the prestigious International Visitor Fellowship, Dr Strona has kindly agreed to give a day-long (and hands-on) workshop in network modelling.

What is network analysis? Well, anything that is connected to other things is ostensibly a ‘network’ — think social-media users, neurones, electric elements in a circuit, or species in an ecological community. It doesn’t really matter what the ‘nodes’ of a network actually represent, because all networks more or less share the same properties. The analysis of network structure and behaviour is what Dr Strona will focus on for the workshop.

Being ecologists, we will of course be primarily interested in ecological networks, but maths and coding is essentially the same for all types of networks. Interested in attending this free and rare opportunity? If so, please register your interest here.

The workshop will be held at the Bedford Park Campus of Flinders University from 09:00-17:00 on 29 April 2019. The outline of the workshop is described in more detail below. Read the rest of this entry »

Comments : 1 Comment »
Tags: coding, data visualisation, ecological networks, edge directionality, k-core, network analysis, networks, nodes, R programming language
Categories : community ecology, conservation, ecology, modelling

Science + music = productivity

17 05 2018

da2a4c4015f37dcd15015a2bfcef2a2d A take on a small section of my recent book, The Effective Scientist, about the importance of music in science.

—

I don’t know any scientists who don’t love music, and I will go out on a limb by stating that most of us probably combine our science activities with music during the quieter times in front of the computer.

One tool that can effectively mask distractions when writing or coding, especially noisy ones, is music. I consider my earphones to be an essential tool of the science trade, for they allow me to ‘tune out’ as I ‘tune in’ to my favourite mood music.

However, a little caution is required here. If the music is set to loud to mask the ambient noises that you are presently finding annoying, you might discover that your capacity to concentrate is reduced. The style of music is also important. When I am writing actual text, anything that could induce the slightest foot tapping or head banging tends to send me off into space; I prefer something light and instrumental in these circumstances, like Vivaldi, Mozart, or Miles Davis.

On the contrary, if I am merely transcribing data, coding, analysing, or creating display items, then I tend to go more for heavy metal or electronica to set an intense pace. While this is absolutely a personal choice, you might do well inevitably to find some combination of music styles that works best for you.

I’m going to use this occasion though to list my top-10 metal/hard-core tracks that I find particularly good for coding. Somehow for me, heavy metal and coding go together like Vegemite and toast (but the combination doesn’t work for writing papers, although at this very moment I’m listen to some of the tracks listed below). This list is also a little window into my own frustration with the Anthropocene and the political inertia about limiting the damage we humans are doing to our own life-support system.

In no particular order, here are my top-10 heavy-metal/coding/angst/frustration tunes (listen to the lyrics — they help): Read the rest of this entry »