Discover and read the best of Twitter Threads about #shiny

#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

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...

...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...

Read 11 tweets

Albert Rapp | @rappa753@fosstodon.org

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

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.

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.

Read 14 tweets

@rappa753

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

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.

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.

Read 16 tweets

Erik Nelson

@erik_sci

@10xGenomics

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/

(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.

I hate to say it but...

#Shiny is giving tableau a run for it's money.

Here's why...

#rstats

Tableau is a great tool. For descriptive analysis...

...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.

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

1. The Shiny website

The 1st place to go to learn shiny.

shiny.rstudio.com

2. Flexdashboard website

Flexdashboard combines Rmarkdown & Shiny to make quick apps.

pkgs.rstudio.com/flexdashboard/

Today, we'll discover how you can use the power of #rstats to create an interactive #shinyapp ✨

@ViktErik

💡 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, ...

@ViktErik

@ViktErik ... or the #SDG by @Shel_Kariuki, bit.ly/sdg-shiny), ...

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 😊

👩🏼‍💻 How to write them?

In #rstats, functions can be as simple as this:

@cosima_meyer

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👩🏼‍💻

@cosima_meyer

@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

@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.

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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

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.

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!!

Read 5 tweets

Jake Lawlor

@Jake_Lawlor1

@_bios2

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…

🧵:

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.

@rstudio

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!

Read 13 tweets

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

One important part of getting hired is getting your resume to be selected for a manual review.

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".

Péter Sólymos

@psolymos

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!

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

Runtime: shinyrmd - The doc is pre-rendered before deployment so that the HTML loads faster.

Add a rmarkdown::render() step in the Dockerfile