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R Programming Training: Introduction to R Programming

4.4 out of 5 ( 49 reviews)

Course Number: RPROG-100
Duration: 4 days
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R Programming Training Overview

Accelebrate's Introduction to R Programming training course teaches attendees ;how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations

Location and Pricing

Most Accelebrate courses are delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

R Programming Training Prerequisites

Students should have knowledge of basic statistics (t-test, chi-square-test, regression) and know the difference between descriptive and inferential statistics. No programming experience is needed.

Hands-on/Lecture Ratio

This R Programming training class is 60% hands-on, 40% lecture, with the longest lecture segments lasting for 15 minutes. Students "learn by doing," with immediate opportunities to apply the material they learn to real-world problems.

R Programming Training Materials

All R Programming training students receive a copy of Addison-Wesley's R for Everyone and related courseware.

Software Needed on Each Student PC

  • R 3.0 or later with console
  • IDE or text editor of your choice (RStudio recommended)

R Programming Training Objectives

All students will:

  • Master the use of the R interactive environment
  • Expand R by installing R packages
  • Explore and understand how to use the R documentation
  • Read Structured Data into R from various sources
  • Understand the different data types in R
  • Understand the different data structures in R
  • Understand how to use dates in R
  • Using R for mathematical operations
  • Use of vectorized calculations
  • Write user-defined R functions
  • How and when to use control statements
  • Looping constructs in R
  • Use Apply to iterate functions across data
  • Reshape data to support different analyses
  • Understand split-apply-combine (group-wise operations) in R
  • Deal with missing data
  • Manipulate strings in R
  • Understand basic regular expressions in R
  • Understand base R graphics
  • Focus on GGplot2 graphics for R
  • Be familiar with trellis (lattice) graphics
  • Use R for descriptive statistics
  • Use R for inferential statistics
  • Write multivariate models in R
  • Understand confounding and adjustment in multivariate models
  • Understand interaction in multivariate models
  • Predict/Score new data using models
  • Understand basic non-linear functions in models
  • Understand how to link data, statistical methods, and actionable questions

R Programming Training Outline

  • Overview
    • History of R
    • Advantages and disadvantages
    • Downloading and installing
    • How to find documentation
  • Introduction
    • Using the R console
    • Getting help
    • Learning about the environment
    • Writing and executing scripts
    • Saving your work
  • Installing Packages
    • Finding resources
    • Installing resources
  • Data Structures, Variables
    • Variables and assignment
    • Data types
    • Indexing, subsetting
    • Viewing data and summaries
    • Naming conventions
    • Objects
  • Getting Data into the R Environment
    • Built-in data
    • Reading data from structured text files
    • Reading data using ODBC
  • Control Flow
    • Truth testing
    • Branching
    • Looping
    • Vectorized calculations
  • Functions in Depth
    • Parameters
    • Return values
    • Variable scope
    • Exception handling
  • Handling Dates in R
    • Date and date-time classes in R
    • Formatting dates for modeling
  • Descriptive Statistics
    • Continuous data
    • Categorical data
  • Inferential Statistics
    • Bivariate correlation
    • T-test and non-parametric equivalents
    • Chi-squared test
    • Distribution testing
    • Power testing
  • Group By Calculations
    • Split apply combine strategy
  • Base Graphics
    • Base graphics system in R
    • Scatterplots, histograms, barcharts, box and whiskers, dotplots
    • Labels, legends, Titles, Axes
    • Exporting graphics to different formats
  • Advanced R Graphics: GGPlot2
    • Understanding the grammar of graphics
    • Quick plot function
    • Building graphics by pieces
  • Linear Regression
    • Linear models
    • Regression plots
    • Confounding / Interaction in regression
    • Scoring new data from models (prediction)
  • Conclusion
  • Optional topics that can be swapped for other units in the course or added:
    • Advanced Regular Expressions in R 
      • Using PERL style regular expressions in R
    • 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 so fast?
      • 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