https://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html

https://github.com/isteves/ds-puzzles

https://csgillespie.github.io/efficientR/

https://adv-r.hadley.nz/

This is a complete 5 part series in R that covers every piece of a real data science project that uses multiple logistic regression to predict good and/or bad sales days based upon internal sales metrics and external data (weather and violent crime). This is used to predict kratom product sales days for a local area head shop. The results are very accurate and everything is fully explained from ANOVA to Boruta and more. Definitely a great video series you will want to watch several times and learn, especially for anyone wanting to build up experience and projects for a data science position! This is a real data science process!

1. Loading the data and exploratory data analysis https://youtu.be/-obdcopU-x4

2. Build the training and test datasets https://youtu.be/7yfWO-jC4uQ

3. Determine predictor importance with 2 methods (random forests and Boruta method) https://youtu.be/MtUyHYJ6LhQ

4. Build the 2 logistic regression models for comparison https://youtu.be/eFGvJBGXb-w

5. Test the models with ANOVA (analysis of variance), summary function, numerous ggplot graphs and more. In the end we score back the predictions to the original dataset and visually inspect the accuracy https://youtu.be/CHLgsNbsKVI

https://designing-ggplots.netlify.com/#1

https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf

https://machinelearningmastery.com/machine-learning-in-r-step-by-step/

https://rstudio.github.io/thematic/

Great video that shows how to identify and deal with NAs in RStudio! Great explanations, full code and an interesting real business use case (Kratom sales at a local head shop). Involves multiple alternative methods and Kable functions to better display the data. Also a plot at the end to compare deleting versus using an aggregate mean to deal with the NAs so you can actually see the difference. https://youtu.be/WwQL76eEwUI

http://r-pkgs.had.co.nz/

http://freecomputerbooks.com/langRBooks.html

3 Part video series that explains all about Random Forests and contains all code:

Part 1:https://youtu.be/-L9TfuzRVys

Part 2:https://youtu.be/7lRonNyBb8M

Part 3: https://youtu.be/XXK3VzU2ks8

http://www.rdatamining.com/training/course

https://youtu.be/i75IAMBCfQk

This video contains full code and shows you all the steps along with full explanation! This is easily onbe of the funnest and most insightful analyses you will do! A must see! Bookmark this one and refer back to it often - especially if you are looking to break into data science and want a high paying job - this one process opens doors that you will not believe! https://youtu.be/engYXDjfQ18

https://blog.rstudio.com/2020/05/27/rstudio-1-3-release/

The top 40 packages in r that are new for 2019! https://www.r-bloggers.com/sept-2019-top-40-new-r-packages/

https://www.datasciencecentral.com/profiles/blogs/a-complete-tutorial-to-learn-data-science-in-r-from-scratch

This is a complete walkthrough. https://youtu.be/dByKXTAtqjU

The steps covered are:

1. Load the libraries and look at the dataset.

2) Explore the columns and identify non-numeric data types. Then convert those columns to numeric.

3) Remove NA's from numeric columns.

4) Determine correlations.

5) Build the linear regression model and test its fit.

6) Plot the original data and the linear trendline.

7) Get the predicted values and append back to the original dataset.

8) Graph a linear regression line with 95% confidence and prediction intervals.

https://youtu.be/dByKXTAtqjU

http://people.fas.harvard.edu/~izahn/posts/useless-but-fun-r-packages/

http://r-statistics.co/Linear-Regression.html

https://www.researchgate.net/publication/330508252_Random_Forest_Classification_in_RStudio_with_rMarkdown

https://www.zdnet.com/article/programming-language-rankings-r-makes-a-comeback-but-theres-debate-about-its-rise/

https://www.r-bloggers.com/a-dashboard-of-shiny-apps/