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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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$3,020 USD
Course Code ACCEL-R-ADV
Duration 4 days
Available Formats Classroom

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.

Skills Gained

  • 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

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)

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