Advanced R Programming

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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.

1650 € HT 3 jours DB-LRA

Programme

  • Advanced Regular Expressions in R
  • Using Perl-Style Regular Expressions in R
  • Machine Learning Approaches in R
  • Pre-processing Data
  • Feature Selection
  • Supervised Learning:
  • 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
  • Parametric Survival Models
  • 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
  • Bayesian Inference Using R