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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
Sep 17, 2022 • 10 tweets • 6 min read • Read on X
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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/
3. Shiny Widgets gallery

See dozens of example reactive widget input / outputs for shiny

shiny.rstudio.com/gallery/widget…
4. shinyWidgets by dreamrs

Advanced & customizable reactive widgets that can really take your shiny apps to the next level

dreamrs.github.io/shinyWidgets/i…
5. HTML Widgets

Interactive visuals for shiny apps

htmlwidgets.org/showcase_leafl…
6. Shiny JS

Makes it easy to add JavaScript actions to your shiny apps.

deanattali.com/shinyjs/
7. Bslib

Upgrade shiny From Bootstrap 3 to 4 or 5 and makes it easy to make custom themes.

rstudio.github.io/bslib/
And if you want all of these 7 R packages plus 93 more in one consolidated #cheatsheet, download my ultimate #R cheat sheet.

business-science.io/r-cheatsheet.h…
One last resource.

If you've been struggling to learn R, I’d like to help.

I put together a free R webinar that consolidates the 10 secrets that helped me in my career.

learn.business-science.io/free-rtrack-ma…

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Dec 12
Some people really like Data Cleaning. I don't.

It takes me away from analyzing data. The fun part.

So I made an AI Agent to help. Image
The AI Agent contains 4 steps:

1. Create data cleaner code
2. Execute data cleaner code
3. If problem, fix code
4. Once fixed, explain code

Here's how it works:
1. In the first step, the AI analyzes an incoming data set.

Based on the features and statistics of the dataset, it creates code to perform a custom data cleaning.

Key skill: Python Code Generation Image
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 16
Python has powerful time series libraries.

Case in point: skforecast

Let me explain: Image
Skforecast is a Python library for time series forecasting using machine learning models.

Skforecast works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others. Image
skforecast shines with probabilistic forecasting.

When trying to anticipate future values, most forecasting models try to predict what will be the most likely value.

This is called point forecasting.
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 15
My favorite R package for ultra-fast exploratory analysis: Image
The R package is called correlationfunnel.

Yes, I built it.
What correlationfunnel does is:

* Speeds Up Exploratory Data Analysis

* Improves Feature Selection

* Gets You To Business Insights Faster Image
Read 6 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 14
The best beginner book on time series?

FPP: Forecasting Principles and Practice

Let's dive in: Image
Some may consider FPP the bible of time series.

I agree.

Start with FPP Version 2 or 3, and you won't go wrong.

This is what I like:
1. Time Series Visualizations

There's no better intuition for time series than a visualization.

- Time Plots
- Seasonal Plots
- ACF Plots

All are absolutely critical to understanding time series patterns. Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 12
BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition in R

Let's explore: Image
1. What is BEAST?

BEAST stands for Bayesian Estimator of Abrupt change, Seasonality, and Trend.

But what does that mean?
According to the authors, BEAST is a fast, generic Bayesian model averaging algorithm to decompose time series or 1D sequential data into individual components, such as abrupt changes, trends, and periodic/seasonal variations
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 11
I used to struggle with working with Time Series.

After 10 years, I mastered it.

Then I spent 3 years making this R package so you can too: Image
The R package is timetk. I built it to make your life easier when working with Time Series:

- Plotting (Visualization)
- Data Wrangling
- Correlation
- Seasonality
- Imputation
- Outliers (Anomalies)
- Feature engineering
- Cross Validation
I'll focus on Time Series Plotting today.

1. Plotting Time Series: Use plot_time_series() Image
Read 12 tweets

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