(1/n) All about Shiny. My go to approach for building shiny apps is finding an example I like, going to the github to review the code, and retrofitting it for my use case (w/ credits!).

shiny.rstudio.com/gallery/
(2/n) I learned this approach via an online course where we deployed models via Shiny to show performance. It was daunting at first and my first foray into building an application.

If you are just getting into Shiny and feel overwhelmed, you've got this & keep at it.
(3/n) Version control & Peer review: Something I like about code based dashboards (vs. low/no code solutions) is that it version control becomes possible via git, and dashboards that require complicated calculations are more visible/transparent in a single script.
(4/n) This is my personal opinion, so I’d love to hear everyone’s thoughts on this.
(5/n) The shiny apps I’ve built have been on the scrappy side, but open source frameworks exist for more production focused apps (I learned about golem + rhino at rstudio::conf this summer, but have not used personally)
(6/n) I had a bit of a learning curve with Shiny, which is why I was particularly excited by the announcement of the Shiny UI editor, being developed by @NicholasStrayer currently in alpha (so not prod ready just yet, but you can use +give feedback).
rstudio.github.io/shinyuieditor/
@NicholasStrayer (7/n) Deployment: My personal experience on the personal side has been to deploy using the free tier of shinyapps.io (though paid tiers also exist with adding functionality such as authentication).
@NicholasStrayer (8/n) For professional use, your employer may have a framework for deployment of applications, so it might be a good idea to check with others on your team (or engineering/IT) to see if applications exist.
@NicholasStrayer (9/n) There was a question about deploying on Azure - I have only used AWS/GCP. It looks like with an internet search for ‘deploy shiny on azure’ there are many tutorials available. I’d love to hear more about your deployment experiences with Shiny!
@NicholasStrayer (10/10) To what extent have y’all used Shiny? What was your experience like?

#rstats #shiny #rshiny #deployment #application #rladies

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More from @WeAreRLadies

Oct 10
(1/n) I love telling stories with data and I have recently discovered #gganimate as a way to take my #dataviz to the next level.

gganimate.com
Here is some sample code to generate a random walk, create a line plot, and layer in the animation. I love how it's simple to layer within the #ggplot2 framework. Image
(3/n) Here is the resulting plot! using the `transition_reveal()` it keeps the previous points on the graph, but it is highly configurable. For example, I could use `transition_time()` or `transition_state()` to only show the point being added.
Read 7 tweets
Oct 7
(1/n) I'm sure that there are tons of #DataViz & #rmarkdown tips & tricks out there. So in this thread, I would just like to share a bit of my coding adventures. 🤓💻
(2/n) Before discovering the wonderfulness of ggplot2, my figures were confined to the "limits" of MS Excel. While it is definitely possible to create coherent plots in Excel, it does get kinda clunky when you have more complex data. And what about overlaying my plots?? 🤨
(3/n) Cue ggplot2! Its super intuitive, customizable & compatible with tons of extensions that "enhances" one's plotting capabilities.

I've also been using @CedScherer AMAZING guide, which takes your plots to a whole other level. 🤩
cedricscherer.com/2019/08/05/a-g…
Read 6 tweets
Oct 6
(1/n) Let's touch a bit on analysing social media data with R. I've dabbled in this a little, mostly for Twitter data.

Thanks to @TwitterDev, you can apply for Academic Research Access (developer.twitter.com/en/products/tw…) to gather data through their API endpoints. How neat is that!
@TwitterDev (2/n) There are a couple of packages for downloading and working with Twitter data.

So far, I've been using academictwitteR, which has super useful vignettes and straight forward functions!

cran.r-project.org/web/packages/a…
@TwitterDev (3/n) Here's a simple demonstration! After setting your tokens (see cran.r-project.org/web/packages/a…), use get_all_tweets() to obtain tweets based on your queries.

Here I'm getting tweets from @WeAreRLadies in December 2021 😊 R codes for downloading and viewing tweets with the academic
Read 4 tweets
Oct 6
(1/n) Why do we do literature reviews? Apart from it forming a PhD chapter (as most of us would kinda have to do), literature reviews give us an overview of our research topic(s). Talking about the INs and OUTs of Topic XYZ~ 📚📖📑

Cartoon by @twisteddoodles
@twisteddoodles (2/n) Well, instead of manually downloading and trawling through every single paper (really??), did you know that you can use R for literature reviews?

An example is litsearchr by @ElizaGrames. Check out it's amazing versatilities here! elizagrames.github.io/litsearchr/
@twisteddoodles @ElizaGrames (3/n) There are many more packages too. Here are a couple that I've come across:

More by @ElizaGrames
synthesisr
topictagger
metaverse

other packages:
bibliometrix
revtools
Adjutant
metagear

More links:
elizagrames.github.io/litsearchr/lit…
bibliometrix.org/vignettes/Intr…
scientificallysound.org/2022/03/22/met…
Read 4 tweets
Oct 5
(1/n) It's time for some dung beetle "juicy-ness" again~

My first attempt at using R for spatial data is to map the occurrences of dung beetles in Sabah, Malaysia and Singapore! Image showing the maps of Singapore and Sabah, Malaysia. It
(2/n) If you would like some background as to what motivated this, check out our cool GBIF-funded project here! 😎gbif.org/project/BIFA6_…
(3/n) My ambitious goal was to make the occurrence maps interactive! With some googling, I found many resources using Leaflet, an open-source JavaScript library for interactive mapping. Best of all, the leaflet R package is super versatile and allows for easy map customizability.
Read 6 tweets
Oct 5
(1/n) Today's topic is working with spatial data in R! TBH I'm pretty new to this and my spatial data experience has mostly been with using GIS software like ArcGIS, QGIS.

It was only this year where I really started dabbling in using R for spatial data mapping. 🗺️📌 Image of a woman thinking about spatial maps and spatial dat
(2/n) While prepping for today's topic, I came across this amazing guide (geobgu.xyz/presentations/…).

What really caught my attention is the history of spatial analysis in R and that R's capabilities in handling spatial data is increasingly improving! 🤩
(3/n) What I really like about using R for spatial data is that its a single environment for data processing, analysing and mapping

I can also use other packages for data manipulation etc before translating them into spatial objects with more specific spatial packages. 🤓
Read 5 tweets

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