not_the_nsa's comments

I was first in, best dressed to write the R API wrapper for an open source server (ODK Central). Proceeded to go through open source peer review at rOpenSci and made it a robust R package that is now used by a good few people.

The package is of course nothing HN worthy, but I'm proud of having contributed something back to FOSS.

The crunchy bit was parsing form data by introspecting a form schema (then XML, now JSON) which initially nearly made me lose my mind, hence the package name "ruODK"?

Australia here. When we're not on fire or under water (which is also where scooters perform rather badly), it often seems to be too hot to wear safety gear.

Add the sense of entitlement of large 4WDs to complete non-enforcement of road rules protecting riders of both motor- and acoustic bikes by the Police, and you've got a deadly mix going.

My grandfather, a teacher in peace times and Wehrmacht Lt in WWII, reportedly used steno to write notes on impossibly small pieces of paper. I guess the savings in paper suited his extremely frugal mindset, sharpened by the two world wars and two post-war periods he had to live through.

I've experienced this from the opposite side (positive feedback) and wanted to share that the tone and structure of the rOpenSci community seem to foster positive feedback and community interactions.

I maintain an R package, an API wrapper for a popular electronic data capture framework, which was peer-reviewed by rOpenSci (https://ropensci.org/). They put extreme care into making software as accessible and inclusive as possible, which I like a lot. Especially the rOpenSci community (see their CoC (https://ropensci.org/code-of-conduct/)) is full of considerate, caring human beings. One could call them the Anti-SO.

Through the peer review process, community requests and GH issues, there's probably a year's worth of spit-polishing added on top of the "works for me" level. This is my first publicly used software package, so I'm taking this as a learning curve and I'm bending over backwards to making this package as inviting and usable as possible. I get away with this as I'm a public servant and paid to deliver value to the general public. And I must say, every improvement over my initial "good enough" version has paid off against the main project I'm using this package for.

So my experience with this tiny, niche, package is overwhelmingly positive. I blame the audience - R programmers, rOpenSci members, and members of the community of the software I'm API-wrapping, which all are stressed out researchers grateful for help. These communities have also a good code of conduct. Maybe also issue templates with friendly guidance to give me very precise feedback (and the advice to take common sense over my guidance) help. Maybe they also repel low effort "could you please do my homework for me" queries.

Thanks for sharing this. Right now I'm burnt out beyond words, having delivered a phase of a huge project, followed by upper management actions that make me feel extremely devalued and dehumanized. I've already written a list of personal development tasks for the three weeks' leave ahead - I need to up-skill, then find a new employer - which I'll restructure into little wins.

A discovery by the ASKAP (I work across the road from their data centre, the Pawsey SC Centre), code on GH, data in CKAN. Nice.

I work in a state govt department. Very simple infrastructure additions have paid out majorly for us. It's my job to innovate around research infrastructure, so the following should be read as outcomes of my role, not bragging.

I migrated my division from Word docs on shared drives to Atlassian Confluence Wikis, which made knowledge more discoverable amd accessible.

For a work group, I set up a CKAN data catalogue. Soon after, our division adopted it, and after demonstrating it at a GovHack hackathon, I got seconded to our sister agency to build the first production version of https://data.wa.gov.au.

For a statewide turtle monitoring program, I switched out paper-based data capture with electronic data capture using OpenDataKit. We now have real-time data analysis and reporting. Many colleagues have found a similar need for electronic data capture. Claim to fame: wrote an R package `ruODK` to facilitate data access from ODK to R.

Not mentioned are the fantastic user communities, especially the culture of inclusiveness and openness fostered by RStudio ([code of conduct](https://github.com/tidyverse/dev-day-2019/blob/master/CODE_O...) and [rOpenSci](https://ropensci.org/community/). Basically the inverse of SnarkOverflow.

Especially rOpenSci's peer review process ([more here](https://devguide.ropensci.org/softwarereviewintro.html)) for R packages is fantastic.

I do most data engineering in R (RMarkdown workbooks), and most software engineering in Python/Django. It took three separate, dedicated attempts to get warm with R (pre-tidyverse, showing my age), now I'm interrupting work on an RShiny app to write this comment. The ecosystem around the tidyverse helps immensely to convert my colleague's workflows from Excel to R. Clarity and simplicity wins over purity here (you may now light your pitchforks). And NSE still breaks my brain.

For context, this dude went on to create software that is used for electronic data capture all over the world, and build one of the most inclusive, diverse, and friendly communities around it I've had the pleasure of participating in. The drive behind his PhD, and what came out of it, made my life and that of many researchers and volunteers in our neck of the woods easier.

This webcast sheds some light on his motivation and perspective: https://www.geekwire.com/2013/geekwire-podcast-windows-8-ash...

The ODK ecosystem (build, aggregate, collect) has been in use for our Western Australian flatback turtle conservation program since 2016, now being used by Dept Biodiversity, Conservation and Attractions staff, community programs and Volunteers across the Kimberley and Pilbara regions. We've captured about 12k turtle tracks/nests in ca 2k hours of logged survey work. Data is ingested into a custom data warehouse, comes out via a RESTful API, is analysed in RMarkdown workbooks with common steps documented in a custom R package.

Widgets for notoriously error-prone data like location and date/time as well as replacing free text with dropdown options where possible made the difference for us.

ODK works really well for us, and the fantastic developer community has been greatly supportive addressing feature requests and bugs (quickest fix: Clint Tseng fixed an ODK Build bug reported mid-workshop within 2h). I'd like to see the industry heavyweights like ESRI being that responsive :-)

Cheers to the ODK community!

Have you got info what you'll going to build? Can you prepare a starter project to hit the ground running?

If you have to start from scratch, having built (and documented) a working example helps greatly to get going quickly.

Last, don't forget to take care of yourself, hydrate, bring some food, painkillers, earplugs and so on. Situations like those are stressful enough, easy to rack up a migraine due to dehydration.

Good luck!