I originally posted this to twitter, but figured a more permanent home might be useful.
A quick post on managing remote teams for anyone new to it amid the fast-evolving Coronavirus/Covid-19 situation:
Be intentional. You can’t rely on ad-hoc office chitchat to stay on top things. Have regular check-ins and catch-ups with your team.
Cameras on. It feels really weird at first, but being able to see people helps to maintain social bonds and provides useful clues for when people are about to speak and so on.
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I’ve been asked a few times recently about the prompt I have in my terminal.
It looks like this…
The people who’ve asked me about it have wondered what it shows, how it was done and how they can do it for themselves, so I thought I’d write it up for anyone that might be interested.
What does it show? Each of these things automatically updates (as required) each time you run a command in the terminal.
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I don’t generally write much code where R’s performance is a bottleneck, it’s just not what I do. The way I use R is different to many other people and as such I’ve never had to spend much time thinking about the performance of the language.
A few days ago, while reading an article in Quanta, I became interested in the Collatz Conjecture and I wrote up a little function to test how the conjecture plays out.
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Imagine you want to use data to really understand something about your product, users, market or the world. Along comes the infinitely re-programmable sales droid, promising that their tool or service will solve all your problems.
Whatever that silver bullet solution is probably uses a unique combination of cloud computing and cutting edge artificial intelligence algorithms in a neural network designed to stop you from losing ground against the competition.
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A new version of the R source was released today and so, as is customary, I download and install it on my personal Linux servers. My main server runs Ubuntu and the other run CentOS.
To download, build and install R I use the below script. It relies on you having ‘sudo’ enabled for your account as well as already having the build dependencies installed (see this post from RStudio for more info).
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In my last post, “Lifting the lid on CRAN”, we took a look at how R and CRAN interact to enable R users to install packages. In this post we’re going to dig a little deeper by building our own CRAN-like repo that we can install packages from.
Enterprise R package management Before we get started, I just want to stress that what we’ll learn about here is no substitute for using a product like RStudio Package Manager.
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CRAN is one of the many things that makes R such a great language. For those that don’t know, it’s where R users get the vast majority of the add-on packages that they use with the core language. CRAN also hosts downloads of the language itself, source code, tools and so on, but it’s most well known among users as the place where all the packages come from.
The “CRAN team” are responsible for the ongoing maintenance of CRAN, handle new and updated package submissions and generally ensure everything is running smoothly.
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For the last 6 months I’ve been working from home. My job, like that of most other knowledge workers, isn’t really location dependent and can essentially be done from anywhere with an internet connection. About 6 months ago my project commitments started to ramp up and I arranged to work from home to better manage my time. I can honestly say it’s been transformational.
To start with, I appreciate that not everyone is able to work from home, or even wants to.
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I was recently interviewed for James Le’s excellent Datacast podcast.
I talked to James about my career: from how I got started all the way up to the present day, as well as my passion for data and helping organisations professionalise their data science capability. You can’t even tell how cold I was during the interview - the heating was out in my office!
The podcast is only on it’s 9th episode, but it’s off to a great start and I’d encourage you to subscribe to the show.
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If you’re a regular reader of this blog you may have noticed the distinct lack of charts.
I’ve written about my journey with R before, but not too much about what I do actually use R for. In short, I use it mainly for software engineering type work in order to automate other things in R, or to support the work of data scientists. There’s perhaps a bit more to it than that, but that’s the main thrust of it.
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