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Jumpstart With R 2.0

50 R Hacks

Migrating A Business Process From Excel To R

Here's What The R Starter Pack Is For

Hello!

My name is Matt Dancho and I’m so glad you’re considering picking up the R Starter Pack.

I created the R Starter Pack after teaching R to thousands of people around the U.S.

(As well as training big companies like Apple & S&P Global.)

When I was learning R, I was working as a Business Analyst at a company called Bonney Forge.

What Bonney Forge does is create pipes and valves for LARGE industrial practices like factories… oil rigs… etc.

And I spent a TON of time doing things like, trying to do calculations… making reports… slideshows… and helping higher ups make decisions.

But after doing it for 4 years- I had reached my full potential with Excel.

In other words, there was a gap between what I wanted to do... and what I could do.

Even worse, my career wasn’t going anywhere and I knew it.

So I started to explore different ways to streamline my job and learn a new skill I liked.

Originally I tried to learn Python.

However it was too much like “coding” and not really like Excel.

Then I found R and it works like Excel- but on steroids.

What Used To Take Me Days With Excel Took Minutes With R

With R I graduated from a "normal" business analyst...

... to becoming the director of sales & forecasting.

All while making $150,000 in a year (plus bonuses!)

Why? Because I was so good at predicting sales.

Now here’s the downside: it took me 3 years to automate my job and move into a new position. Why?

Because I not only had to learn R, but I also had to figure out what to learn.

If I would have had The R Starter Pack, I would have cut down my learning time by 90%.

So that’s the opportunity you have in front of you.

Let me show you how to automate your job and then join the exciting world of sales predictions… forecasting… and machine learning.

What's Inside "Jumpstart With R 2.0"

Jumpstart with R isn't just a "beginner's course".

It takes you from complete 0, to being able to model SQL databases.

Not only are you going to learn basic functions, but you're going to have an in-depth understanding of:

How R works,
How to use R efficiently, and
You'll get a foundation of knowledge for the 50 R Hacks course

Jumpstart is fantastic for both people who are absolute beginners and intermediate R users.

What's Inside "50 R Hacks"

50 R Hacks is a compendium of my best custom R Hacks.

These are work arounds I've discovered using R for the last 8 years.

So when you want to do advanced commands, computations, and forecasts, these hacks are especially useful.

Even better, I give you the data sets to practice with, the complete code base AND a video tutorial of each hack.

That way you can follow me step-by-step and learn over my shoulder.

What's Inside "Migrating A Business Process From Excel To R"

So businesses used to hire me as a consultant to move their business processes from excel to R all the time.

The reason for this is simple.

R is far more efficient than excel.

R can handle 100,000,000's of rows whereas excel can only handle about 1,000,000.

R can join & separate data and you can combine R datasets for advanced computation.

Plus everything is automated. Once you have the script, all you do is plug in your data sets (which takes about 30 seconds) and hit enter and you're done.

I used to watch youtube videos at work while R did all the computing for me.

And so instead of taking days to do computations and make reports, I'd have it done in about 10 minutes.

In this workshop, I'm going to show you how to replicate ANYTHING you do in excel, and make it a script in R.

Jumpstart With R 2.0

This course takes you from zero to hero with R.

Not only are you learning the basics, but I'm going to show you how to do everything you could do in excel with R & more.

Here's A Preview Of A Little Bit Of What's Inside:

Installing R

How To Import & Join Data

How To Model SQL Databases

How To Handle Different Types Of Data

50 R Hacks

50 R Hacks is the ultimate treasure trove of actionable commands you can execute in R.

These are all of my advanced features that I've either discovered- or invented in one convenient place for you

50 R Hacks In All:

Hack 01: How to Read Multiple CSV Files (6:00)
Hack 02: How to Scrape Word (.docx) Files in R (9:08)
Hack 03: How to Automate PowerPoint Slide Decks from R (9:32)
Hack 04: How to Write Multiple CSV Files (8:30)
Hack 05: How to Automate Excel Spreadsheets from R (7:22)
Hack 06: How to Create 3D Pivot Tables in R (9:41)
Hack 07: How to Automate PDF Reports from R (9:25)
Hack 08: Testing out the NEW Tidyverse Relocate Function (8:34)
Hack 09: Testing out the NEW Tidyverse Across Function (8:43)
Hack 10: Testing out the NEW Pivot Wider & Pivot Longer Functions (10:15)
Hack 11: Group Splitting (with Linear Regression Models) (10:51)
Hack 12: Nested Modeling (with ARIMA) (26:42)
Hack 13: Making Maps with R (Geospatial Intro) (12:57)
Hack 14: Principal Component Analysis (PCA) in 7 minutes (15:17)
Hack 15: Logistic Regression in 7-minutes (12:21)
Hack 16: Time series plots in 5-minutes + Cyberpunk Theme Bonus (10:18)
Hack 17: The 6 Rstudio shortcuts I use most frequently (7:28)

Hack 18: Experimenting with rayshader for 3D plotting (8:43)
Hack 19: Testing out imputation methods with simputation (10:10)
Hack 20: How to write SQL from R (dbplyr) (7:44)
Hack 21: 4 ways for making data frames in R (10:10)
Hack 22: Create accurate statistical plots with ggstatplot (7:51)
Hack 23: Exploratory data analysis: ppscore vs correlationfunnel (11:49)
Hack 24: How to add Shiny to Rmarkdown documents (6:35)
Hack 25: Fast exploratory data analysis with DataExplorer (9:10)
Hack 26: Quick data profiling with skimr (6:58)
Hack 27: Shiny in 10-minutes (10:53)
Hack 28: Tableau-like drag-and-drop visualizations with esquisse (9:57)
Hack 29: A walkthrough of the RMarkdown Visual Editor (7:58)
Hack 30: Automating R Slide Decks (xaringan) (11:56)
Hack 31: Big data with dtplyr (data.table backend) (6:20)
Hack 32: Copy-and-paste HTML tables into R (datapasta) (7:02)
Hack 33: Better correlation plots in R (corrmorrant) (7:49)

Hack 34: Making tables using mmtable2 (ggplot2 for tables) (8:12)
Hack 35: Patch together ggplots with patchwork (9:28)
Hack 36: Experimenting with side-plots (ggside) (8:53)
Hack 37: Interactive data editing in R (DataEditR) (4:36)
Hack 38: Testing out half-plots (gghalves) (8:15)
Hack 39: Using grafify to make quick ggplots that look great! (6:51)
Hack 40: Modeltime in 10-minutes (my time series forecasting package) (11:15)
Hack 41: Make model performance plots (easystats) (8:03)
Hack 42: Raincloud plots using ggdist (8:06)
Hack 43: Cluster plots using ggforce (7:30)
Hack 44: Dumbell plots using ggalt (6:17)
Hack 45: Lollipop plots using ggalt (6:05)
Hack 46: Parallel processing with purrr + furrr (6:21)
Hack 47: Heatmaps in R using ggplot2 (10:14)
Hack 48: New R package for Business Analytics (Radiant) (9:39)
Hack 49: Explaining machine learning models (modelStudio) (9:46)
Hack 50: Visualizing many ggplots (trelliscopejs) (5:29)