R can make flow charts programmatically.

This is how. The R package is ggflowchart.

It makes it super easy to create flow charts programmatically in R:

Have you heard of Upset Plots?

It's a new plot type designed to understand intersections between many categories. Get it free in R (on Github here): github.com/const-ae/ggups…

This is mind-blowing (I only wish I knew it sooner).

Easy linear regression coefficient reports in R.

This is how: The function is ggstats::ggcoef_model().

It turns your Linear Regression into an easy-to-understand coefficient report.

I don't know how many times I've done this the hard way.

This new R package is wild.

It's called ggalign. Here's what it can do: 1. Gene expression matrix:

25 R packages you need to learn in 2024.

Let's dive in: 1. Janitor: Simplifying Data Cleaning
2. Skimr: Quick Data Summarization
3. bslib: Next-Gen UI for Shiny Apps
4. box: Modularize Your R Scripts
5. data.table & tidytable: High-Performance Data Manipulation

How to analyze your data faster with R: In the fast-paced world of data science, getting quick insights into your data is crucial.

It allows you to understand your data better, make informed decisions, and expedite the modeling process.

A/B Testing in R.

This is how: 1. A/B Testing Definition:

A/B testing is a statistical method for comparing 2 groups to determine if there is a statistically significant difference between the two groups.

It's often used in marketing analytics to gauge the effectiveness of marketing efforts.

What is the most difficult part of becoming a data scientist?

3 common mistakes (and tips that worked for me): 1. Not solving business problems

Solving business problems is what drives revenue for your company.

But it’s tough to figure out how to convert to a data problem.

The trick is building.

When you make things, you make mistakes. And you grow.

Stop using box plots.

Use this plot instead.

Here's why: I used to love Box Plots. Until I found out the harsh truth.

Boxplots fail when it comes to bimodal and multi-modal distributions.

Myth: R can't be used for production

Fact: R can with Vetiver.

Let me explain: Vetiver is an R package that provides tooling to:

- Version
- Deploy
- Monitor

Trained ML Models. Here's how:

It took me 5-years to feel confident in data science.

True story.🧵 This is coming from a person that has created two R packages that combine for 1.5 Million downloads.

Has trained elite data scientists at Apple, Walmart, Google.

And has built a career teaching students how to become data scientists.

Why did it take so long?

Bold prediction: The Birth Of The Business Scientist.

Here's what is about to happen. According to Glassdoor research, we already have:

1. Data Analyst, $71,298/yr
2. Business Analyst, $83,924/yr
3. Data Scientist, $124,680/yr

But noticeably missing is the "Business Scientist".

"You can't deploy in R."

BS...

You CAN with bslib. bslib is an R package that provides a modern UI toolkit for Shiny and R Markdown based on Bootstrap.

Building a SQL Data Visualization Copilot:

I'm building my new AI Bootcamp, and here's what's coming... Companies are excited about AI. But 90% have no clue where to use it.

Well, I'd use AI for data analytics automation.

This is how:

Python has some wild time series libraries.

Case in point: Bayesloop

Let's dive in: bayesloop is a python module that focuses on fitting time series models with time-varying parameters and model selection based on Bayesian inference.

R is insane for data analytics...

Case in point: mapgl

Let's dive in: mapgl is an R package that connects to:

1. Mapbox GL JavaScript Library
2. MapLibre GL JS Library

Why is that important? (Can't I just use Leaflet?)

This is why:

Companies are now hiring "Generative AI Data Scientists."

What does this mean? Let me explain: Over the last decade, Data Science and Machine Learning have changed Business Analytics. This has driven companies growth:

- Increased Revenue
- Building new products
- Reducing costs

90% of data scientists, analysts, and traders aren't using Python for macroeconomic insights effectively.

Let's fix that: 90% of data scientists, analysts, and traders aren't using Python for macroeconomic insights effectively.

1. They're overlooking critical indicators.
2. They're missing out on strategic trading opportunities.
3. And it's costing them big time in market moves.

Data Scientists: It's not how good you are at ChatGPT.

It's how good you are at combining your knowledge with ChatGPT.

Let me explain. A common misconception is that Data Scientists think ChatGPT will do their job for them.

In my experience, this is wrong.

Yes- ChatGPT is very helpful.

But it's not human-level intelligence.

Can ChatGPT be used for Time Series in R?

I've had success with using Chatgpt for about 80% of my time series code.

Here's how I got my projects to 100% with chatgpt. 🧵 ChatGPT is a game changer for time series. No question.

But is it perfect? Hardly.

Things I've experienced (and overcame):

Stop making R Shiny Web Apps from scratch.

Use ChatGPT instead.

This is how (Step 2 is the best). 🧵 R Shiny Web Apps take me days to build when I make them from scratch.

ChatGPT has been my secret weapon.

I use a special technique called Prompt Stacking, which is a simple idea.

Here's how Prompt Stacking works: