Sharing a quick tip that I’ve found useful while building with Shiny, recently: It’s handy to be able to save off the current state of a data set for bringing over to a scratch file. I made the download link appear only when running in my local environment. This way I can easily snapshot my in process data set for experimenting with visualization in my scratch file.
In ui.R:
if(interactive()) {
downloadLink("downloadData", "💾️ Download data set")
}
And in server.R:
output$downloadData <- downloadHandler(
filename = function() {
paste("shiny-export_", Sys.Date(), ".csv", sep="")
},
content = function(file) {
write.csv(globaldata$armor, file)
}
)
I had a good time this weekend coming up with a new way to visualize armor stat distribution in my Destiny 2 profiler tool. 🎮
I’ve had a nice afternoon working on my hobby R/Shiny project, a loadout finder for the game Destiny 2. These improvements make it a lot more flexible and informative: It can now optionally include armor that would otherwise be filtered out of configurations by the minimum stat threshold, and it will show current mods used in displayed loadouts.
This revision of ArmoreR is really turning into something I’m happy with and proud of. I’ve learned so much since the very first iteration of this about a year ago. It’s really fun and rewarding to come back to it with a bunch of new expertise and make it much better in all ways, including a new approach to dealing with such a large amount of information.
(best Letterkenny voice)
Sundays are for fixin' Shiny!
Last fall I wrote a bit about a Destiny 2 power level tracking tool I built using R. I’ve now converted it to a full-on Shiny app and solved some issues with the oauth2 flow that stumped me in my intermittent tinkering with it. I’m super satisfied to have been able to get this to work! Now that I have the authentication process figured out, I’m eager to also convert my armor profiling tool to use it. Look out!
You can check it out here: traveleR
Okay, one final (probably) revision of tuneR for today, adding a bit of color and sizing to the plot of tags by artist, to show variation in tag rank within each artist.
Today’s small revision of tuneR adds a scrollable table of user-assigned tags to your artists, and a plot of artists by tag sorted to show the most used tags across your top 25 artists this year. Coming soon – possibly some track-level data!
Inspired by all the enthusiasm for Spotify’s end-of-year Wrapped summaries, tuneR is my extremely rudimentary first start at my own version, done in R/Shiny. I’d love to hear if it works for you and what else would make it more fun!