I’ve just set up a single page website (= online business card) for myself and my husband: https://pius.cloud/ . This post summarises what I did. If you’re looking to get started with something super quickly, then only the first two steps are essential (Creating a website and Serving a website).
Creating a website (using Nicepage) I’ve created websites using various tools such as straight up HTML, WordPress, Hugo+blogdown (this site – riinu.
With news of the lockdown in March came the dawning reality that we wouldn’t be able to deliver our usual HealthyR 2.5 day quick start course in May.
The course is always over-subscribed so we were keen to find a solution rather than cancelling altogether.
HealthyR teaches the Notebook format which is already an online tool hosted by RStudio Cloud – so we knew that bit would work online. But what to do about getting attendees and tutors online, delivering lectures and offering interactive support with coding? Could we recreate our usual classroom environment online?
Never a group to shy away from a technical challenge, and with expertise in online education, we set about researching what online tools could be used.
After trying various options we went with Blackboard Collaborate to provide an online classroom, together with our usual RStudio Cloud to provide the Notebooks interface. Collaborate has a really nice feature of ‘break-out rooms’ where small groups can be assigned a separate online room with a tutor to work through exercises. The tutor can provide support and answer questions, using the screen share option to see exactly what each person might be having difficulty with.
After a few rehearsals to work out what roles to assign all our moderators and attendees, how to send people to the break rooms and recall them back to the main room we were set!
Ahead of the course, attendees were emailed the usual pre-course materials and a log in for their RStudio Cloud accounts, together with an invite to a Collaborate session for each of the 3 days. We split the 20 attendees who had confirmed attendance into groups of 5 and assigned one of our fantastic tutors to each group.
We also set up a an extra break out room with a dedicated tutor which could be used for anyone needing specific one-to-one help.
After the ice-breaker, ‘What’s a new thing you’ve done since lockdown?’ – everything from macrame to margaritas plus tie-dying and a lot of baking – the course got underway with the first lecture.
One or two delegates had some problems with internet connections, and the assigning of breakout rooms took a bit of getting used, but Riinu soon worked out an efficient system and the first coding exercises were underway!
We were delighted that the course received really positive feedback overall – none of us were sure this would work, but it did! The live coding sessions and pop quizzes were particularly popular.
We’ll definitely run HealthyR online again if the lockdown continues. Even after the lockdown, moving online widens access and offers the possibility for our international collaborators to join a course without having to travel.
Thank you to all our attendees who quickly adapted to the online format and to our amazing tutors, Tom, Kenny, Derek, Peter, Katie, Stephen, Michael and Ewen, who provided 3 days of their time to run the course, led as ever, by Riinu.
Collaborate and RStudio Cloud worked very well for me. The breakout rooms were a nice touch to allow discussions.
Clear and easy instructions. Worked seamlessly!
This was a great course. I think in person would have allowed more interaction so I would still keep your original format available after this lockdown is over but well done on adapting and providing an excellent course.
NA – Not Available/Not applicable is R’s way of denoting empty or missing values. When doing comparisons – such as equal to, greater than, etc. – extra care and thought needs to go into how missing values (NAs) are handled. More explanations about this can be found in the Chapter 2: R basics of our book that is freely available at the HealthyR website
This post lists a couple of different ways of keeping or discarding rows based on how important the variables with missing values are to you.
If you’ve been watching the news or twitter over the past
week, you may have seen the appendicitis-related headlines about unnecessary operations
being performed. The RIFT collaborative and Dmitri Nepogodiev have really
spearheaded some cool work looking at who gets unnecessary operations, which
are all well worth a read:
So, when Dmitri asked if I could develop a web application
for risk scoring to help identify those at low risk of appendicitis, I was very
Having quite often used risk calculators in clinical
practice, I started to write a list of what makes a good calculator and how to
make one that can be used effectively. The most important were:
Easy to use
Works on any platform (as NHS IT has a wide
variety of browsers!) and on mobile (some hospitals have great Wi-Fi through
Can be quickly updated
Looks good and gives an intuitive result
Lightweight requiring minimal processing power,
so many users can use simultaneously
Now we use a lot of R in surgical informatics, but Shiny wasn’t going to be the one for this as it’s not that mobile friendly and doesn’t necessarily work on every browser that smoothly (sorry shiny!). Similarly, the computational footprint required to run shiny is too heavy for this. So, using codepen.io and a pug html compiler, I wrote a mobile friendly website (Still a couple of tweaks I’d like to make to make entirely mobile friendly!).
Similarly, I get asked why not an app? Well app development requires developing on multiple platforms (Apple, Android, Blackberry) and can’t be used on those pesky NHS PCs. Furthermore, if something goes out of date or needs to be updated quickly – repairing it will take ages as updates sometimes have to be vetted by app stores etc.
My codepen.io for the calculator:
Codepen.io is a great development tool and allows you to
combine and get inspired by other people’s work too!
I then set up a micro instance on google cloud, installed the pug compiler and apache2, selected a fixed IP and opened the HTTP port to the world and all done! (this set up is a little more involved than this but was straightforward!). The micro instance is very very cheap so it’s not expensive to run. The Birmingham crew then bought a lovely domain appy-risk.org for me to attach it to.
Here’s the obligatory increase in CPU usage since publication (slightly higher but as you can tell – it’s quite light:
TLDR: You can teach R on people’s own laptops without having them install anything or require an internet connection.
Members of the Surgical Informatics team in Ghana, 2019. More information: surgicalinformatics.org
Running R programming courses on people’s own laptops is a pain, especially as we use a lot of very useful extensions that actually make learning and using R much easier and more fun. But long installation instructions can be very off-putting for complete beginners, and people can be discouraged to learn programming if installation hurdles invoke their imposter syndrome.
We almost always run our courses in places with a good internet connection (it does not have to be super fast or flawless), so we get our students all set up on RStudio Server (hosted by us) or https://rstudio.cloud (a free service provided by RStudio!).
You connect to either of these options using a web browser, and even very old computers can handle this. That’s because the actual computations happen on the server and not on the student’s computer. So the computer just serves as a window to the training instance used.
Now, these options work really well as long as you have a stable internet connection. But for teaching R offline and on people’s own laptops, you either have to:
make sure everyone installs everything correctly before they attend the course
Download all the software and extensions, put them on USB sticks and try to install them together at the start
start serving RStudio from a your computer using Local Area Network (LAN) created by a router
Now, we already discussed why the first option is problematic (gatekeeper for complete beginners). The second option – installing everything at the start together – means that you start the course with the most boring part. And since everyone’s computers are different (both by operating systems as well as different versions of the operating systems), this can take quite a while to sort. Therefore, queue in option c) – an RStudio Server LAN party.
A computer with more than 4GB of RAM. macOS alone uses around 2-3GB just to keep going, and running the RStudio Server docker container was using another 3-4 GB, so you’ll definitely need more than 4GB in total.
A network router. For a small number of participants, the same one you already have at home will work. Had to specify “network” here, as apparently, even my Google search for “router” suggests the power tool before network routers.
Docker – free software, dead easy to install on macOS (search the internet for “download Docker”). Looks like installation on the Windows Home operating system might be trickier. If you are a Windows Home user who is using Docker, please do post a link to your favourite instructions in the comments below.
Internet connection for setting up – to download RStudio’s docker image and install your extra packages.
My MacBook Pro serving RStudio to 10 other computers in Ghana, November 2019.
Running RStudio using Docker is so simple you won’t believe me. It honestly is just a single-liner to be entered into your Terminal (Command Prompt on Windows):
This will automatically download a Docker image put together by RStudio. The one called verse includes all the tidyverse packages as well as publishing-related ones (R Markdown, Shiny, etc.). You can find a list of the difference ones here: https://github.com/rocker-org/rocker
Then open a browser and go to localhost:8787 and you should be greeted with an RStudio Server login! (Localhost only works on a Mac or Linux, if using Windows, take a note of your IP address and use that instead of localhost.) More information and instructions can be found here: https://github.com/rocker-org/rocker/wiki/Using-the-RStudio-image
Tip: RStudio suggests port 8787, which is what I used for consistency, but if you set it up on 80 you can omit the :80 as that’s the default anyway. So you can just go to localhost (or something like 127.0.0.0 if using Windows).
For those of you who have never seen or used RStudio Server, this is what it looks like:
Rstudio Server is almost identical to RStudio Desktop. Main difference is the “Upload” button in the Files pane. This one is running in a Docker container, served at port 8787, and accessed using Safari (but any web browser will work).
The Docker single-liner above will create a single user with sudo rights (since I’ve included -e ROOT=TRUE). After logging into the instance, you can then add other users and copy the course materials to everyone using these scripts: https://github.com/einarpius/create_rstudio_users Note that the instance is running Debian, so you’ll need very basic familiarity with managing file permissions on the command line. For example, you’ll need to make the scripts executable with chmod 700 create_users.sh.
Then connect to the same router you’ll be using for your LAN party, go to router settings and assign yourself a fixed IP address, e.g., 18.104.22.168. Once other people connect to the network created by this router (either by WiFi or cable), they need to type 22.214.171.124:8787 into any browser and can just start using RStudio. This will work as long as your computer is running Docker and you are all connected to the same router.
I had 10 people connected to my laptop and, most of the time, the strain on my CPU was negligible – around 10-20%. That’s because it was a course for complete beginners and they were mostly reading the instructions (included in the training Notebooks they were running R code in). So they weren’t actually hitting Run at the same time, and the tasks weren’t computationally heavy. When we did ask everyone to hit the “Knit to PDF” button all at the same time, it got a bit slower and my CPU was apparently working at 200%. But nothing crashed and everyone got their PDFs made.
Why are you calling it a LAN party?
My friends and I having a LAN party in Estonia, 2010. We would mostly play StarCraft or Civilization, or as pictured here – racing games to wind down at the end.
LAN stands for Local Area Network and in most cases means “devices connected to the same WiFi*”. You’ve probably used LANs lots in your life without even realising. One common example is printers: you know when a printer asks you to connect to the same network to be able to print your files? This usually means your computer and the printer will be in a LAN. If your printed accepted files via any internet connection, rather than just the same local network, then people around the world could submit stuff for your printer. Furthermore, if you have any smart devices in your home, they’ll be having a constant LAN party with each other.
The term “LAN party” means people coming together to play multiplayer computer games – as it will allow people to play in the same “world”, to either build things together or fight with each other. Good internet access has made LAN parties practically obsolete – people and their computers no longer have to physically be in the same location to play multiplayer games together. I use the term very loosely to refer to anything fun happening on the same network. And being able to use RStudio is definitely a party in my books anyway.
But it is for security reasons (e.g., the printer example), or sharing resources in places without excellent internet connection where LAN parties are still very much relevant.
* Overall, most existing LANs operate via Ethernet cables (or “internet cables” as most people, including myself refer to them). WiFi LAN or WLAN is a type of LAN. Have a look at your home router, it will probably have different lights for “internet” and “WLAN”/“wireless”. A LAN can also be connected to the internet – if the router itself is connected to the internet. That’s the main purpose of a router – to take the internet coming into your house via a single Ethernet cable, and share it with all your other devices. A LAN is usually just a nice side-effect of that.
Docker, containers, images
Docker image – a file bundling an operating system + programs and files Docker container – a running image (it may be paused or stopped)
List of all your containers: docker ps -a (just docker ps will list running containers, so the ones not stopped or paused)
When you run rstudio/verse for the first time it will be downloaded into your images. The next time it will be taken directly from there, rather than downloaded. So you’ll only need internet access once.
Stop an active container: docker stop container-name
Start it up again: docker start container-name
Save a container as an image (for versioning or passing on to other people):
docker commit container-name pository:tag
For example: docker commit rstudio-server rstudio/riinu:test1
Rename container (by default it will get a random label, I’d change it to rstudio-server):
docker rename happy_hippo rstudio-server
You can then start your container with: docker start rstudio-server
These past two days are new frontier for the HealthyR course, taking the number of continents we’ve run it in up to 2.After the NIHR Unit on Global Surgery meeting, we travelled to Tamale, Ghana’s third largest city. The Wellcome Trust have kindly funded the development of the innovative, open-source HealthyR notebooks course. Spearheaded by Dr Riinu Ots, this course aims to provide an easy way for anyone in the world to learn R.This is particularly powerful where resources are limited and there are plenty of questions that need to be answered. Enter Stephen Tabiri, professor of Surgery at the University for Development Studies in Tamale. Stephen is as surgeon and has a large team of junior surgeons in training, nurses and other clinicians.In an innovative twist, it was held on a mix of laptops, from the data centre and on delegates own machines. Riinu had a brilliant solution, that served an offline R studio instance to delegates computers.Day 1 quickly introduced some key concepts to the delegates who quickly worked through the materials! After lunch a global surgery showcase event was held, which showcased the wide range of tools available to analyse data in R!Day 2 kicked off nicely, completing the basics session and then straight into everyone’s favourite session – Plotting! Here there were a lot of pleased delegates as they made complicated and colourful ggplots! People were making a lot of progress, in what can sometimes be a challenging language to learn!We finally closed on a logistic regression session delivered by Ewen Harrison, where people built their own models!Throughout the course there were numerous people bringing laptops to install RStudio software on their own desktops. A very enthusiastic and keen bunch of data scientists!Excitingly, members of the Ghana R community also attended, to offer support and discuss how best to provide a sustainable future for data science in Ghana.
The Surgical Informations groups are delighted to be visiting Tamale in Ghana to deliver our flagship HealthyR Notebooks course as part of our Wellcome Trust grant, ‘HealthyR Notebooks: Democratising open and reproducible data analysis in resource-poor environments’.
We’re being made extremely welcome by our hosts Professor Stephen Tabiri and Benard Ofori Appiah from the NIHR Global Health Research Unit on Global Surgery hub in Ghana.
Over the next few days we’ll be establishing a data centre in Ghana with the provision of 15 laptops and training 20 local delegates to use R for healthcare data analysis. This will build capacity for future data driven research in partnership with the NIHR Global Surgery Unit in Ghana.
Something for the more advanced R user! We’ll be back to our more exciting programming shortly (I hope!).
rlang? I already speak R
Quite right. rlang is part of the tidyverse side of things, so is probably more useful if you’re an advanced R user. It’s certainly not for the faint-hearted and needs a comprehensive understanding of how R ‘sees’ the code you write.
rlang is a low-level programming API for R which the tidyverse uses (meaning it speaks to R in as R like way as possible, rather than a ‘high-level’ – high level is more user orientated and interpretable). It enables you to extend what the tidyverse can do and adapt it for your own uses. It’s particularly good to use if you’re doing lots of more ‘programming’ type R work, for example, building a package, making a complex shiny app or writing functions. It might also be handy if you’re doing lots of big data manipulation and want to manipulate different datasets in the same way, for example.
Here’s an example of dynamically naming variables
In this example, say we have a tibble of variables, but we want to apply dynamic changes to it (so we feed R a variable, that can change, either using another function like purr::map or in a ShinyApp). In this instance, specifying each variable and each different possible consequence using different logical functions would take forever and be very clunky. So we can use rlang to simply put a dynamic variable/object through the same function.
We make use of the curly curlys too, which allow us to avoid using bulky enquo() – !! syntax
#Make data - here nonsense numbers on deaths per 100k from green spacemen say
Well, what a great 3 days this has been! Again today, we gained extra people to join in HealthyR Notebooks – A formidable achievement for a statistics course!
We kicked off with a brilliant session by Ewen Harrison about survival analysis and time to event data, introducing new concepts and the R survival package. Then went into an amazing session by Riinu Ots, who showcased how to plot your data with real world, practical examples. This session really was brilliant, living up to Riinus catch-phrase ‘always plot your data’. This was followed by a short pop quiz, which all participants did brilliantly!
After a tasty lunch, we then continued into a new session, how to work with your data. This session is aimed at translating the learning of HealthyR, straight to a reallife dataset of the participants choosing. Participants were guided through the practical application of R to their own data, giving them a springboard to produce some cool analyses after the course.
Following the final session we departed to Tallinn for flights back home tomorrow.
All in all, HealthyR notebooks were a success and very fun to teach. Estonia was well worth the trip and showed we could teach R to an international audience (even having fun at the same time!). Looking forward to developing the course further when the team go to Ghana later this year. Big thanks to Julius for organising the course and to the Welcome Trust!