Yeah pretty sure ease of access / updating and portability was behind this. If I remember correctly, the day I demoted this I had to make some changes (like, an hour beforehand) and being able to just tweak the yaml and run it was much easier that rooting through individual files.
Would do it again
This kind of thing?
github.com/johnmackinto...
I stored server and table names in yaml config file, then used those to loop through the database tables, return data and populate shiny app.
It's in the generate_app_data script (I think).
📦 medicalcoder v0.8.0 is now on CRAN.
medicalcoder is a unified and longitudinally aware framework for ICD-based comorbidity assessment in #rstats
CRAN: cran.r-project.org/package=medi...
GitHub: github.com/dewittpe/med...
Website: peteredewitt.com/medicalcoder/
Was your prompt "get this package to a state I'm happy with, in just a few lines of code"?
Awesome, I was actually looking at this on GitHub a week or so ago and noticed some activity. Will be brilliant to have this on CRAN as I have absolutely had it with annotating plots via trial & error
I always understood it as not being safe in a script you might share with others, as running that code may remove objects that they require for their analysis.
If you don't share code, then do what you want, and enjoy shouting at yourself if things go wrong 😁
I've just accidentally scanned this in Tesco. Cost me £2.99
Wait - are these not there for you?
Really nice
I've been working with population pyramids too. Waiting for some updated projections. The shape of the pyramids is wildly different.
I've used a similar approach in the past - looping to create separate images then stitched together to make a gif. I used {gifski} which was pretty fast.
music.youtube.com/watch?v=7idy...
One of the best live rock bands right now, this will have you tapping your foot and singing along (badly, in my case)
It seems that it's very difficult to write about tidy evaluation without a bunch of extra, increasingly confusing theory, and has been the case since it was introduced.
"Oh you're drowning in the meta programming waters..would you like some MORE SEA?"
No, get me to land, with a towel and dry socks
Would you consider adding these 2 tweets or a summary of them to your blog post as a TL/DR?
Would be immensely helpful for future reference
Haven't seen {reactablefmtr} mentioned, but you can create some beautiful reactive tables with it.
Other than that, flextable is my day to day choice (due partly to its excellent MS Word support)
Glad to be of help @kurtstat.bsky.social and amazing to see that you will be discussing this at EdinbR later this month 👍
You can interact with this at
johnmackintosh.github.io/small-area-shiny/
Yes, I should have thought of a better name
#rstats
Thanks @brodriguesco.bsky.social
I will share later
I hope y"all spotted the reverse psychology:
"Not going to win any awards.."
and I fully expect to be nominated for everything, including, but not limited to, chart of the year, plot of the decade, and graphic of the century.
Screenshot of a shiny app with a population pyramid. The app allows selection of one of thirteen geographical boundaries. There are dark blue bars on the left of the pyramid representing counts of males, in five year age bands from 0 to 90+ Light blue bars represent counts of females.
Not going to win any awards but I'm quite pleased with this.
Firstly, it's my first dabble with shinylive.
Secondly, apart from shiny itself, it's all base R, no other packages.
I do need to brush up on my shiny layouts though..
#rstats
Facets might prove useful also.
I think I played around with transparency at the time but , as you discovered, it doesn't work..
Delighted to make it in among the highlights of the always excellent RWeekly and to hear my work being discussed on the podcast too.
Thanks guys @rpodcast.bsky.social
At least you got rid of the unnecessary gridlines...(one of my pet peeves is when I see any form of process control chart with extraneous gridlines on it)
grey / amber / blue ?
Brilliant, thanks.
#rstats
Anyone know of a way to download *all* your GitHub gists (including private ones) using the GitHub API or (?)R package?
Saw an old blog post that had a method bit it maxed out at 30 gists, and I have way more than that
www.manning.com/books/beyond...
I really like this book, fills in a lot of gaps that aren't addressed elsewhere, including R4DS.
You might like to look at {capsule).
renv was a headache for me on a Windows network share, although it is fine (relatively) on local drive.
Depending on the scale of the project, capsule might be worth your time
github.com/MilesMcBain/...
Thanks Christian. I think / hope I've been usurped since then 😄
I've built pretty much every main chart type in PowerBI, including xmr-s, and Laney corrected U-charts, but not in a position to share any of that just yet. There are some good add-ins for PBI already though.
Just checked that link to xmrit, and find links to my work in Qlik (and my spccharter R package, which is still a bit of WIP)
These things must come and go in cycles. When I learned about them, it was "nothing new" & healthcare was seen as lagging behind industry. Now many resources are from h/c
Imagine you have a lot of data that is related, and should be in a database / data warehouse, but isn't, and likely won't ever be (for whatever reason)
Create your own database locally with duckdb. Bonus, write dplyr code for querying it (via duckplyr)
I remember being OK with ggplot2, but struggling with data manipulation on a daily basis (I was still up to my neck in Excel and also learning SQL at the same time.
When dplyr first came out, it was like someone finally turned the lights on.
group_by() behaviour in dplyr has changed a few times over the years. I wanted to piece together a history of what changed and when, and the feedback that got it to its current position (I remember "peeling off of layers" causing some angst).
I never got round to it, but would be interesting..
