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Introduction to R Programming

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...

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$3,020 USD
Duration 4 days
Available Formats Classroom

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.

Skills Gained

All students will:

  • Master the use of the R and RStudio 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 create and manipulate dates in R
  • Use the tidyverse collection of packages to manipulate dataframes
  • Write user-defined R functions
  • Use control statements
  • Write Loop constructs in R
  • Use the apply family of functions to iterate functions across data
  • Expand iteration and programming through the Purrr package
  • Reshape data from long to wide and back to support different analyses
  • Perform merge operations with R
  • Understand split-apply-combine (group-wise operations) in R
  • Identify and deal with missing data
  • Manipulate strings in R
  • Understand basic regular expressions in R
  • Understand base R graphics
  • Focus on GGplot2 graphics for R for generating charts
  • Use RMarkdown to programmatically generate reproducible reports
  • Use R for descriptive statistics
  • Use R for inferential statistics
  • Write multivariate models in R (general linear models)
  • 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


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.

Course Details

Software Requirements

  • A recent release of R 4.x
  • IDE or text editor of your choice (RStudio recommended)



  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation


  • Using the R console and RStudio
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Object oriented programming
  • Introduction to vectorized calculations
  • Introduction to data frames
  • Installing and loading packages
  • Working directory
  • Saving your work

Variable types and data structures

  • Variables and assignment
  • Data types
  • Numeric, character, boolean, and factors
  • Data structures
  • Vectors, matrices, arrays, dataframes, lists
  • Indexing, subsetting
  • Assigning new values
  • 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

Dataframe manipulation with dplyr

  • Renaming columns
  • Adding new columns
  • Binning data (continuous to categorical)
  • Combining categorical values
  • Transforming variables
  • Handling missing data
  • Long to wide and back
  • Merging datasets together
  • Stacking datasets together (concatenation)

Handling dates in R using lubridate

  • Date and date-time classes in R
  • Formatting dates for modeling

Exploratory data analysis (descriptive statistics)

  • Continuous data
  • Distributions
  • Quantiles, mean
  • Bi-modal distributions
  • Histograms, box-plots
  • Categorical data
  • Tables
  • Barplots
  • Group by calculations with dplyr
  • Split-apply-combine
  • Reshaping and pivoting data in R (long to wide with aggregation)
  • pivot_wider and _longer with tidyr

Working with text data

  • Finding and matching patterns in text
  • Stringr package for text manipulation
  • Introduction to regular expressions in R
  • Categorical data wrangling with forcats

Control flow

  • Truth testing
  • Branching
  • Looping

Functions in depth

  • Parameters
  • Return values
  • Variable scope
  • Exception handling

Applying functions across dimensions

  • Sapply, lapply, apply
  • Programming with map and purrr

Graphics in R Overview

  • 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 plots (qplot function)
  • Building graphics by pieces (ggplot function)
  • Understanding geoms (geometries)
  • Linking chart elements to variable values
  • Controlling legends and axes
  • Exporting graphics

Inferential Statistics

  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test

General Linear Regression Models in R

  • Understanding formulas
  • Linear and logistic regression models
  • Regression plots
  • Confounding / interaction in regression
  • Evaluating residuals
  • Scoring new data from models (prediction)
  • Useful plots from regression models