Course Code: 25384

R Programming Fundamentals

Class Dates:
3 Days
Class Time:
Instructor-Led Training, Virtual Instructor-Led Training


  • Course Overview
  • Learn how to program in R and use it for effective data analysis. This three-day course gives an overview of R, and covers setup, data types, data reshaping, different file types, regression, and more.
  • Audience
  • Those who want to start their study of programming in R, especially those with no prior programming experience. If you do have some programming experience and want to learn R


  • Prior programming experience with an object-oriented language. Familiarity with basic statistics concepts is necessary for the third day of the course.

Course Details

  • Introduction to R
  • Introduction to R, Executing Basic Functions in the R Console
  • Setting up a New Project, Installing Packages
  • Using R and RStudio, and Installing Useful Packages
  • Variable Types and Data Structures, Data Structures, Vectors, Lists, Dataframes, Matrices
  • Basic Flow Control, Building Basic Loops,
  • Data Import and Export, Excel Spreadsheets
  • Getting Help with R
  • Package Documentation and Vignettes
  • Exploring the Introduction to dplyr Vignette
  • Rstudio Community, Stack Overflow
  • Data Visualization and Graphics
  • Data Visualization and Graphics
  • Creating Base Plots, Model Objects
  • ggplot2, Histogram, Bar Chart, Scatterplot, Boxplot
  • Interactive Plots, Plotly, Shiny, Exploring Shiny and Plotly
  • Data Management
  • Data Management
  • Factor Variables, Creating Factor Variable in a Dataset, using ifelse() Statements
  • Using the recode () Function
  • Examining and Changing the Levels of Pre-existing Factor Variables
  • Creating an Ordered Factor Variable
  • Splitting, Combining, Merging, and Joining Datasets
  • Summarizing Data, Tables in R
  • Creating Different Tables Using the table () Function
  • Using dplyr methods to Create Summary Tables
  • Splitting, Combining, Merging, and Joining Datasets
  • Splitting and Un-splitting Data with Base R and dplyr Methods
  • Combining Data & matrices of Objects into Dataframes, Merging & Joining Data
  • Solutions
  • Exercises
  • .