Advanced R Training Overview
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.
Important Note: We have listed more topics here than could be covered in 4 days and we would tailor the selection of topics covered to your specific needs. Please contact us for a quote and to arrange a discussion with one of our senior R instructors about customizing this class to your experience and goals.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- 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.
Outline
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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:
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
Training Materials
All Advanced R training students receive comprehensive courseware.
Software Requirements
- A recent release of R 4.x
- IDE or text editor of your choice (RStudio recommended)
