mkearney/learnRvideos

📼 Videos for learning about R

learnRvideos

A repository of useful links to videos for learning about R

Interfaces

Rstudio

• Intro to RStudio
• Creating projects in Rstudio
• Using console in Rstudio
• Code completion in Rstudio
• Creating new R script in Rstudio
• Importing data into Rstudio
• Introducing notebooks

Data wrangling

Tidyverse

• Exploratory data analysis with tidyverse
• Intro to data wrangling and the tidyverse pt 1
• Intro to data wrangling and the tidyverse pt 2
• Intro to data wrangling and the tidyverse pt 3
• Data wrangling
• Tidy data and tidyr
• Working with two data sets: binds and joins

sf

• Tidy geometries with {sf}

glue

• Overview of {glue} package by Jim Hester

broom

• The {broom} package

Machine learning

TensorFlow

• Machine learning and TensorFlow

Basics

• Intro to machine learning with {caret}
• {caret} package webinar
• Modeling with gbm, glmnet, and caret
• xgboost with {caret}

Front ends

Rmarkdown

• Intro to bookdown
• Using Spark with Rmarkdown

Shiny

• Interactive graphics with Shiny
• Intro to flex dashboards
• Shiny gadgets

Websites

• Building a new Hugo site

Big data

Databases

• Interacting with databases

Spark

• Using Spark with Rmarkdown
• Understanding sparklyr deployment modes
• Advanced features of sparklyr
• Extending Spark using sparklyr

Web data

• Importing web data pt 1
• Importing web data pt 2

Advanced R

Generating random data

• Random variables

Development

• Profiling tools for faster code
• Covr test coverage

Programming

• The R6 class

Distributions

• Distributions

Confidence intervals

• Confidence intervals

R-centric statistics

The linear model (regression)

• Elementary OLS
• Example 1
• Example 2
• Example 3
• Multiple regression 2
• Multiple regression 3
• Standardized Betas
• Interactions- categorical
• Interactions- continuous

Non-linear and generalized (maximum likelihood) models

• Non-parametric models
• Logistic regression

Missing data

• Missing data