Discover and read the best of Twitter Threads about #Shiny
Most recents (24)
1/ shiny is a powerful R package for creating interactive web applications, but did you know that there are other packages that can take your Shiny apps to the next level? In this thread, I'll share some of my favorites! #rstats #datascience #shiny
2/ First up is shinydashboard. This package allows you to create beautiful, interactive dashboards with Shiny. With shinydashboard, you can add custom navigation menus, user authentication, and much more to your Shiny apps.
3/ Another great package to consider is shinythemes. This package provides a collection of Bootstrap themes that you can use to style your Shiny app. You can choose from a variety of themes, including flatly, cosmo, and lumen, to give your app a professional look.
What is one of the most exciting areas of chatgpt use in data science?
I’m most excited about web apps.
Here’s why. 🧵
#datascience #ChatGPT #rstats
I’m most excited about web apps.
Here’s why. 🧵
#datascience #ChatGPT #rstats
It’s no secret.
Most data scientists don’t know HTML, JavaScript or CSS.
This is why I’m a huge fanboy for #R #Shiny
Most data scientists don’t know HTML, JavaScript or CSS.
This is why I’m a huge fanboy for #R #Shiny
When Shiny came out 11 years ago, it was a game changer.
Data scientists could now put their algorithms inside of an app.
And Business People could use it!
Data scientists could now put their algorithms inside of an app.
And Business People could use it!
GAME CHANGER: This #r #shiny app was built using #chatgpt.
But the process is riddled with landmines that 90% of data scientists will screw up.
I want to help. Here's how... 🧵
#rstats #datascience
But the process is riddled with landmines that 90% of data scientists will screw up.
I want to help. Here's how... 🧵
#rstats #datascience
I'm not going to sugarcoat it...
Chatgpt got me 80% there.
Chatgpt got me 80% there.
The other 20% was a bit of a challenge...
There were mistakes you will make if you try to run raw chatgpt code.
And the best way to learn is to see someone do it live.
There were mistakes you will make if you try to run raw chatgpt code.
And the best way to learn is to see someone do it live.
Can #chatgpt make a #shiny app?
It can. And I wrote an article on how to do it. ❤️
Article: buff.ly/432VXb8
#rstats #datascience
It can. And I wrote an article on how to do it. ❤️
Article: buff.ly/432VXb8
#rstats #datascience
Follow and retweet to keep this project going.
It takes 2 seconds. And it helps us R folks stay strong. 💪
It takes 2 seconds. And it helps us R folks stay strong. 💪
This is a game-changer:
ChatGPT just made this #r #shiny app for me in under 15 minutes... 🧵
#rstats #datascience
ChatGPT just made this #r #shiny app for me in under 15 minutes... 🧵
#rstats #datascience
I asked chatgpt a simple question...
And here's what ChatGPT did:
1. It created the basic code for an XGBoost Machine Learning model for scoring the customer spend
(Note: I had to ask it to replace random forest with xgboost and caret with #tidymodels)
1. It created the basic code for an XGBoost Machine Learning model for scoring the customer spend
(Note: I had to ask it to replace random forest with xgboost and caret with #tidymodels)
#GPT4 is way better at generating code than previous versions!
See a #rstats #shiny app fully generated by GPT-4. It displays a ggplot of mtcars, uses {shiny.semantic} for UI and has some nice styling in CSS 🚀
This level was impossible to achieve in GPT3.
Here's the process🧵
See a #rstats #shiny app fully generated by GPT-4. It displays a ggplot of mtcars, uses {shiny.semantic} for UI and has some nice styling in CSS 🚀
This level was impossible to achieve in GPT3.
Here's the process🧵
I started with a simple prompt describing my expectations.
GPT-4 got much better at following instructions, but it made errors in functions it used.
GPT-4 got much better at following instructions, but it made errors in functions it used.
It was enough to copy-paste the error message (no need even to ask it for a fix) to get a corrected version.
This took a bit of back-and-forth, but after about 5 bugfixes I got a working app!
I also asked it to make some changes in what the dropdowns did.
This took a bit of back-and-forth, but after about 5 bugfixes I got a working app!
I also asked it to make some changes in what the dropdowns did.
#Shiny is now available in Python!
But with 24 web frameworks in python (that I could find), why should you care?
Here's why.
#datascience #rstats #python
But with 24 web frameworks in python (that I could find), why should you care?
Here's why.
#datascience #rstats #python
Shiny - the ultra-popular R library for turning data science analysis into automated web applications - Was just released for Python.
And there's a big reason to start learning Shiny in Python...
And there's a big reason to start learning Shiny in Python...
...Because Posit (formerly Rstudio) has a team of engineers building the ecosystem
And they've already succeeded in making the best web app ecosystem in R.
And there's one more reason that 80% of R programmers are overlooking...
And they've already succeeded in making the best web app ecosystem in R.
And there's one more reason that 80% of R programmers are overlooking...
There are more than 10,685,000 people learning to work with data.
Yet most data science jobs now have over 1000+ applicants.
And only 5 actually make it to an interview.
Use these 4 skills to stand out (free to steal & use to advance your career).
#datascience #rstats
Yet most data science jobs now have over 1000+ applicants.
And only 5 actually make it to an interview.
Use these 4 skills to stand out (free to steal & use to advance your career).
#datascience #rstats
1. Time series analysis
Up to 90% of companies need better forecasting.
And less than 5% of data scientists are competent in time series analysis.
Up to 90% of companies need better forecasting.
And less than 5% of data scientists are competent in time series analysis.
So becoming good at time series is a massive advantage.
Demonstrate an application of time series analysis in the interview, and set yourself apart from the crowd.
Demonstrate an application of time series analysis in the interview, and set yourself apart from the crowd.
Analytics dashboards are a popular way to explore data or to communicate insights.
So it's good to know how to build one.
Here's how you do that with R in no time. 🧵 #rstats #shiny
So it's good to know how to build one.
Here's how you do that with R in no time. 🧵 #rstats #shiny
1 // Data
First, you need data.
It's always fun to work with your personal data, so I will use my the last three months of my Twitter analytics data. You can download yours at analytics.twitter.com
You could also download the public metrics with {rtweet} instead.
First, you need data.
It's always fun to work with your personal data, so I will use my the last three months of my Twitter analytics data. You can download yours at analytics.twitter.com
You could also download the public metrics with {rtweet} instead.
2 // Data cleaning
Next, do a bit of data cleaning. In my case, I just have to clean the column names and select a few columns.
{janitor} and {dplyr} make that easy.
Next, do a bit of data cleaning. In my case, I just have to clean the column names and select a few columns.
{janitor} and {dplyr} make that easy.
In my 1st tweetorial, I’m happy to share our preprint—my first, first author manuscript! We used @10xGenomics single-nucleus RNA-seq in a mouse model of electroconvulsive seizures (ECS) to assess activity-regulated gene expression in hippocampal #neurons. biorxiv.org/content/10.110…
Activity-regulated gene expression patterns in the #hippocampus regulate #plasticity, #learning, and #memory, and are linked to risk and treatment response for neuropsychiatric disorders. However, the cell type-specificity of these patterns is still not well characterized.
We sequenced a bunch of neuronal nuclei (15,990) from mouse hippocampus and used graph-based clustering and a priori marker genes to annotate these nuclei across all major hippocampal subregions and neuron types in both of our experimental conditions (Sham and ECS).
(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/
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.
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.
Tableau is a great tool. For descriptive analysis...
...but it's terrible at predictive analysis.
Enter Shiny.
...but it's terrible at predictive analysis.
Enter Shiny.
Shiny's big con is that it takes forever to build an app.
You still need to know HTML & CSS to make it look good.
UNTIL NOW.
You still need to know HTML & CSS to make it look good.
UNTIL NOW.
Shiny is a powerful tool that data scientists can use for web apps & production.
But most data scientists struggle.
Here are 7 resources on shiny that helped me.
#rstats #shiny #excel #python
But most data scientists struggle.
Here are 7 resources on shiny that helped me.
#rstats #shiny #excel #python
2. Flexdashboard website
Flexdashboard combines Rmarkdown & Shiny to make quick apps.
pkgs.rstudio.com/flexdashboard/
Flexdashboard combines Rmarkdown & Shiny to make quick apps.
pkgs.rstudio.com/flexdashboard/
💡 What is a ShinyApp?
Shiny is a framework that allows you to create web applications - ShinyApps ☺️ You can use them for multiple purposes - to visualize data 🎨 (for instance the Scottish Household Survey by @ViktErik, bit.ly/3TqZevY, ...
Shiny is a framework that allows you to create web applications - ShinyApps ☺️ You can use them for multiple purposes - to visualize data 🎨 (for instance the Scottish Household Survey by @ViktErik, bit.ly/3TqZevY, ...
The beauty of R is its versatility and of course the community 💜 you can use R (and different #CRAN packages) for literally anything (I use #shiny to build web applications, #blogdown to set up and maintain my website, #xaringan to create slide decks, ….).
All these great tools build upon one “little” (or not so little) thing: functions!
💡 What are functions?
A function is an inherent code block that performs a specific task, such as calculating a sum. And that's exactly what we are doing now 😊
💡 What are functions?
A function is an inherent code block that performs a specific task, such as calculating a sum. And that's exactly what we are doing now 😊
Hello everyone 👋 my name is Cosima and I usually tweet at @cosima_meyer.
In my professional life, I work as a data scientist in the field of AI. I love my job because it allows me to work with fantastic teams to solve puzzles👩🏼💻
In my professional life, I work as a data scientist in the field of AI. I love my job because it allows me to work with fantastic teams to solve puzzles👩🏼💻
@cosima_meyer Before working in the industry, I explored my passion for research in academia. I hold a PhD from the University of Mannheim in #politicalscience (my dissertation dealt with post-civil war stability 🕊).
@cosima_meyer While working at @MZESUniMannheim, I co-founded a #datascience blog “Methods Bites” (@mzes_ssdl) which provides several hands-on tutorials on #NLP, scraping #Twitter data, #dataviz, or how to publish your package on #CRAN.
My #Shiny Web Apps for #Consulting Training (Part 1) was INSANE.
AND I've just made it available for everyone.
Here's how to get it in under 10-seconds.
#rstats #datascience
AND I've just made it available for everyone.
Here's how to get it in under 10-seconds.
#rstats #datascience
On Wednesday, 750 data scientists joined me to learn about new R Shiny functionality that makes building apps for clients easer, faster AND better.
And, we talked about how to go from a $1,000 app to a $15,000 app with not a lot of work.
And, we talked about how to go from a $1,000 app to a $15,000 app with not a lot of work.
If you want to join in on the FUN, you can check the full training out FOR FREE. My free gift to you.
AND, I have a 2nd part which promises a 100X improvement in shiny app building coming on September 14th!!
AND, I have a 2nd part which promises a 100X improvement in shiny app building coming on September 14th!!
Do you want to learn #RStats, but aren't quite sure where to start?
Myself and some great colleagues from @_bios2 wrote down our best tips for learning R on your own, out today in @PLOSCompBiol 🔓
✨Ten Simple Rules for Teaching Yourself R✨
journals.plos.org/ploscompbiol/a…
🧵:
Myself and some great colleagues from @_bios2 wrote down our best tips for learning R on your own, out today in @PLOSCompBiol 🔓
✨Ten Simple Rules for Teaching Yourself R✨
journals.plos.org/ploscompbiol/a…
🧵:
RULE 1: Prepare for a steep learning curve 📈
#RStats is a language, and as such, won't be picked up in a day. When learning R, remember that progress will be slow and you may have some growing pains. As you get more practice, you'll become better at picking up new techniques.
#RStats is a language, and as such, won't be picked up in a day. When learning R, remember that progress will be slow and you may have some growing pains. As you get more practice, you'll become better at picking up new techniques.
RULE 2: Take the time to read a book 📚
No matter how general or specific an R skill you want to
learn, there is probably a great book on that skill!
One great place to look is the "Books" section on the
@rstudio website:
rstudio.com/resources/book…, but plenty more exist, too!
No matter how general or specific an R skill you want to
learn, there is probably a great book on that skill!
One great place to look is the "Books" section on the
@rstudio website:
rstudio.com/resources/book…, but plenty more exist, too!
I spent about 1 year studying the #datascience job hiring process, and I found out what works to speed it up.
Here's 1 of 5 things I learned. 🧵
#rstats #shiny
Here's 1 of 5 things I learned. 🧵
#rstats #shiny
One important part of getting hired is getting your resume to be selected for a manual review.
What does this mean?
What does this mean?
Well, according to S&P Global (Fortune 500 Firm, Financial Sector), each data science position gets around 1,000 applicants.
That's too many to go through manually.
So they use automated systems called "ATS".
That's too many to go through manually.
So they use automated systems called "ATS".
Rmarkdown is a reproducible authoring format in #rstats supporting dozens of static and dynamic output formats.
Let's review why and how you should containerize Rmd files!
Let's review why and how you should containerize Rmd files!
Runtime: shiny - Add this to the document’s YAML header.
Now you can add Shiny widgets and #Shiny render functions to the file’s #rstats code chunks.
Use rmarkdown::run() as CMD in your Dockerfile
Now you can add Shiny widgets and #Shiny render functions to the file’s #rstats code chunks.
Use rmarkdown::run() as CMD in your Dockerfile
Runtime: shinyrmd - The doc is pre-rendered before deployment so that the HTML loads faster.
Add a rmarkdown::render() step in the Dockerfile
Add a rmarkdown::render() step in the Dockerfile
1/10 Since October @ftargument has been running a crowd forecasting project for #COVID19. First in Germany and Poland and now in all ECDC nations + the United Kingdom.
You can contribute your forecasts here: cmmid-lshtm.shinyapps.io/crowd-forecast/
You can contribute your forecasts here: cmmid-lshtm.shinyapps.io/crowd-forecast/
2/10 We submit an ensemble of these forecasts to the ECDC forecast hub where they are combined with many other models from other forecasters and evaluated.
covid19forecasthub.eu/visualisation
covid19forecasthub.eu/visualisation
3/10 This crowd forecasting method has been found to perform well compared to models submitted by multiple groups and to be one of the only methods to produce useful forecasts at longer time horizons.
medrxiv.org/content/10.110…
medrxiv.org/content/10.110…
There will be stories in the coming days of how the UK R rate is "creeping up". This information is
a) almost certainly true
b) hard to interpret
c) probably not as scary as it sounds
A short thread ⬇️
a) almost certainly true
b) hard to interpret
c) probably not as scary as it sounds
A short thread ⬇️
Quick note: I'm talking purely from cases data found on the government dashboard here: coronavirus.data.gov.uk
Other measures and leading indicators are available.
Other measures and leading indicators are available.
Daily numbers of new infections have been going down nicely since January, though at different rates across regions. For example, here I'm showing the nine regions of England and highlighting Yorkshire and the Humber (left) and London (right).
To *animal researchers*, a must-read on a sensitive topic rdcu.bs/cfsk6: statistical power of #animalresearch and of the number of animals used. A thread of why-do-I-like-this-paper or watch a 10-min video bit.ly/2M0EyNm @NatureNeuro go.nature.com/3dopwwl
Disclaimer: own production with amazing collaborators @sarabdjitsingh@MathiasVSchimdt@KorosiAniko@z_baram@JelleKnop@JessicaLBolton@joeribordes & twitterless others
Red is bad, green is good. At best 12.5% of animal experiments are estimated to be sufficiently prospectively powered. ‘At best’ because the estimate is before any subsequent experimental bias, p-hacking/fishing, multiple testing…
Just learned an amazing #rstats trick: You can embed #learnr apps using `knitr::include_app()` for live coding demos *inside* #xaringan slides! 🤯 This way you don't have to switch b/w slides and #rstudio IDE for live demos, reducing cognitive load. #rmarkdown knows no bounds! 🤩
Here's a little code snippet that shows how:
IMO live coding inside slides would be particularly useful for illustrating common beginner mistakes and "gotchas" (as demo'd above). If you have the #shiny apps publishing capacity, you could even have students type code inside slides along with you in real time
