Advanced R Programming

Accelebrate's Advanced R course teaches students more sophisticated R skills, including using advanced regular expressions, machine learning, random effects modeling, Bayesian Inference, advanced R...

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Course Code ACCEL-R-ADV
Duration 4 days
Available Formats Classroom
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Accelebrate's Advanced R course teaches students more sophisticated R skills, including using advanced regular expressions, machine learning, random effects modeling, Bayesian Inference, advanced R time series, and much more.

Skills Gained

All students will learn how to:

  • Use advanced regular expressions in R
  • Apply advanced missing data techniques
  • Work with advanced R time series
  • Use data.table for big data
  • Work with linear models
  • Extend R to time to event and survival analyses
  • Work with Bayesian Inference using R

Prerequisites

All students should have attended Accelebrate's Introduction to R Programming course, or have equivalent knowledge.

Course Details

Software Requirements

  • R 3.0 or later with console
  • IDE or text editor of your choice (RStudio recommended)
  • Related lab files that Accelebrate provides

Outline

Advanced Regular Expressions in R

  • Using Perl-Style Regular Expressions in R

Machine Learning Approaches in R

  • Pre-processing Data
  • Feature Selection
  • Supervised Learning:
  • Classification Models
  • Regression Models
  • Unsupervised Learning:
  • Clustering

Advanced Missing Data Techniques

  • Understanding the different types of Missing Data
  • Implications for Analysis
  • The AMELIA package
  • Multiple Imputation

Advanced R Time Series

  • The ts class
  • The zoo package
  • The xts class
  • Lubridate for advanced date handling
  • Autocorrelation Plots
  • Seasonal, trend, and noise plots
  • Financial Charting with R

Using data.table for Big Data

  • Why do we need data.table?
  • Why is it
  • The i and the j arguments in data.table
  • Merging data with data.table
  • Group-by functions with data.table
  • Using data.table in functions

Generalized Linear Models

  • Logistic Regression
  • Poisson Regression
  • Gamma Regression

Extend R to Time to Event or Survival Analyses

  • Visualizing Hazards Across Time
  • Understanding the Log Rank Test
  • Cox Proportional Hazards Modeling
  • Understand Time Varying Covariates
  • Understand Time Dependent Covariates
  • Understanding the Hazard Ratio
  • Implement Frailty Models for Clustered Data
  • Parametric Survival Models
  • Weibull Model
  • Exponential Model
  • Predicting Failure Times

Random Effects Modeling in Linear Regression

  • Random effects introduction
  • Covariance structures
  • Interpreting random effects in models
  • Longitudinal Data
  • Clustered Data
  • Prediction in Random Effects

Extension: Random Effects Modeling in Logistic Regression

  • Random effects introduction
  • Covariance structures
  • Interpreting random effects in models
  • Marginal versus Conditional Models
  • Stratified Logistic regression
  • GEE Models in Logistic Regression

Bayesian Inference Using R

  • Linear model
  • Logistic Model
  • Random Effects / Fixed effects model
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